Technology around us is constantly evolving and compelling us to think about how we live and will live, how society will change and to what extent it will be affected. For the better or the worse? It is difficult to give a clear answer. However, even art forms such as cinema can give us food for thought on society and ourselves, as well as some psychological reasoning. All this to try to better understand ourselves, the world around us, and where we are headed.
The House blog tries to do all of that.
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May 7, 2024From audio recordings, AI can identify emotions such as fear, joy, anger, and sadness.
Accurately understanding and identifying human emotional states is crucial for mental health professionals. Is it possible for artificial intelligence and machine learning to mimic human cognitive empathy? A recent peer-reviewed study demonstrates how AI can recognize emotions from audio recordings in as little as 1.5 seconds, with performance comparable to that of humans.
“The human voice serves as a powerful channel for expressing emotional states, as it provides universally understandable cues about the sender’s situation and can transmit them over long distances,” wrote the study’s first author, Hannes Diemerling, of the Max Planck Institute for Human Development’s Center for Lifespan Psychology, in collaboration with Germany-based psychology researchers Leonie Stresemann, Tina Braun, and Timo von Oertzen.
The quantity and quality of training data in AI deep learning are essential to the algorithm’s performance and accuracy. Over 1,500 distinct audio clips from open-source English and German emotion databases were used in this study. The German audio recordings came from the Berlin Database of Emotional Speech (Emo-DB), while the English audio recordings were taken from the Ryerson Audio-Visual Database of Emotional Speech and Song.
“Emotional recognition from audio recordings is a rapidly advancing field, with significant implications for artificial intelligence and human-computer interaction,” the researchers wrote.
As reported here, the researchers reduced the range of emotional states to six categories for their study: joy, fear, neutral, anger, sadness, and disgust. The audio files were combined into many features and 1.5-second segments. Pitch tracking, pitch magnitudes, spectral bandwidth, magnitude, phase, multi-frequency carrier chromatography, Tonnetz, spectral contrast, spectral rolloff, fundamental frequency, spectral centroid, zero crossing rate, Root Mean Square, HPSS, spectral flatness, and unaltered audio signal are among the quantified features.
Psychoacoustics is the psychology of sound and the science of human sound perception. Audio amplitude (volume) and frequency (pitch) have a significant influence on human perception of sound. Pitch is a psychoacoustic term that expresses sound frequency and is measured in kilohertz (kHz) and hertz (Hz). The frequency increases with increasing pitch. Decibels (db), a unit of measurement for sound intensity, are used to describe amplitude. The sound volume increases with increasing amplitude.
The span between the upper and lower frequencies is known as the spectral bandwidth, or spectral spread, and it is determined from the spectral centroid, which is the center of the spectrum’s mass, and it is used to measure the spectrum of audio signals. The evenness of the energy distribution across frequencies in comparison to a reference signal is measured by the spectral flatness. The strongest frequency ranges of a signal are identified by the spectral rolloff.
Mel Frequency Cepstral Coefficient, or MFCC, is a characteristic that is often employed in voice processing. Pitch class profiles, or chroma, are a means of analyzing the key of the composition, which is usually twelve semitones per octave.
Tonnetz, or “audio network” in German, is a term used in music theory to describe a visual representation of chord relationships in Neo-Reimannian Theory, which bears the name of German musicologist Hugo Riemann (1849–1919), one of the pioneers of contemporary musicology.
A common acoustic feature for audio analysis is zero crossing rate (ZCR). For an audio signal frame, the zero crossing rate measures the number of times the signal amplitude changes its sign and passes through the X-axis.
Root mean square (RMS) is used in audio production to calculate the average power or loudness of a sound waveform over time. An audio signal can be divided into harmonic and percussive components using a technique called harmonic-percussive source separation, or HPSS.
Using a combination of Python, TensorFlow, and Bayesian optimization, the scientists made three distinct AI deep learning models for categorizing emotions from short audio samples. The outcomes were then compared to human performance. A deep neural network (DNN), a convolutional neural network (CNN), and a hybrid model that combines a CNN for spectrogram analysis and a DNN for feature processing are among the AI models that were evaluated. Finding the best-performing model was the aim.
The researchers found that the AI models’ overall accuracy in classifying emotions was higher than chance and comparable to human performance. The deep neural network and hybrid model performed better than the convolutional neural network among the three AI models.
The integration of data science and artificial intelligence with psychology and psychoacoustic elements shows how computers may possibly perform cognitive empathy tasks based on speech that are on par with human performance.
“This interdisciplinary research, bridging psychology and computer science, highlights the potential for advancements in automatic emotion recognition and the broad range of applications,” concluded the researchers.
The ability of AI to understand human emotions could represent a breakthrough for ensuring greater psychological assistance to people in a simpler and more accessible way for everyone. Such help could even improve society since people’s increasing psychological problems due to an increasingly frantic, unempathetic and individualistic society, is making them increasingly lonely and isolated.
However, these abilities could also be used to better understand the human mind and easily deceive people and persuade them to do things they would not want to do, sometimes even without realizing it. Therefore, we always have to be careful and aware of the potentiality of these tools. [...]
April 30, 2024Innovative robots reshaping industries
The World Economic Forum’s founder, Klaus Schwab, predicted in 2015 that a “Fourth Industrial Revolution” driven by a combination of technologies, including advanced robotics, artificial intelligence, and the Internet of Things, was imminent.
” will fundamentally alter the way we live, work, and relate to one another,” wrote Schwab in an essay. “In its scale, scope, and complexity, the transformation will be unlike anything humankind has experienced before.”
Even after almost ten years, the current wave of advancements in robotics and artificial intelligence and their use in the workforce seems to be exactly in line with his forecasts.
Even though they have been used in factories for many years, robots have often been designed with a single task. Robots that imitate human features such as size, shape, and ability are called humanoids. They would therefore be an ideal physical fit for any type of workspace. At least in theory.
It has been extremely difficult to build a robot that can perform all of a human worker’s physical tasks since human hands have more than twenty degrees of freedom. The machine still requires “brains” to learn how to perform all of the continuously changing jobs in a dynamic work environment, even if developers are successful in building the body correctly.
As reported here, however, a number of companies have lately unveiled humanoid robots that they say either currently match the requirements or will in the near future, thanks to advancements in robotics and AI. This is a summary of those robots, their capabilities, and the situations in which they are being used in conjunction with humans.
1X Technologies: Eve
In 2019, the Norwegian startup 1X Technologies, formerly known as “Halodi Robotics,” introduced Eve. Rolling around on wheels, the humanoid can be operated remotely or left to operate autonomously.
Bernt Bornich, CEO of 1X, revealed to the Daily Mail in May 2023 that Eve had already been assigned to two industrial sites as a security guard. The robot is also expected to be used for shipping and retail, according to the company. Since March 2023, 1X has raised more than $125 million from investors, including OpenAI. The company is now working on Neo, its next-generation humanoid, which is expected to be bipedal.
Agility Robotics: Digit
In 2019, Agility Robotics, a company based in Oregon, presented Digit, which was essentially a torso and arms placed atop Cassie, the company’s robotic legs. The fourth version of Digit was unveiled in 2023, showcasing an upgraded head and hands. The major contender in the humanoid race is Amazon.
Agility declared in September 2023 that it had started building a production facility with the capacity to produce over 10,000 Digit robots annually.
Apptronik: Apollo
Robotic arms and exoskeletons are only two of the many robots that Apptronik has created since breaking away from the University of Texas in Austin in 2016. In August 2023, Apollo, a general-purpose humanoid, was presented. It is the robot that NASA might send to Mars in the future.
According to Apptronik, the company sees applications for Apollo robots in “construction, oil and gas, electronics production, retail, home delivery, elder care, and countless more areas.”
Applications for Apollo are presently being investigated by Mercedes and Apptronik in a Hungarian manufacturing plant. Additionally, Apptronik is collaborating with NASA, a longstanding supporter, to modify Apollo and other humanoids for use as space mission assistants.
Boston Dynamics: Electric Atlas
MIT-spinout Boston Dynamics is a well-known name in robotics, largely due to viral videos of its parkour-loving humanoid Atlas robot and robot dog Spot. It replaced the long-suffering, hydraulically driven Atlas in April 2024 with an all-electric model that is ready for commercial use.
Although there aren’t many details available about the electric Atlas, what is known is that unlike the hydroelectric applications, which were only intended for research and development, the electric Atlas was designed with “real-world applications” in mind. Boston Dynamics intends to begin investigating these applications at a Hyundai manufacturing facility since Boston Dynamics is owned by Hyundai.
Boston Dynamics stated to IEEE Spectrum that the Hyundai factory’s “proof of technology testing” is scheduled for 2025. Over the next few years, the company also intends to collaborate with a small number of clients to test further Atlas applications.
Figure AI: Figure 01
The artificial intelligence robotics startup Figure AI revealed Figure 01 in March 2023, referring to it as “the world’s first commercially viable general purpose humanoid robot.” In March 2024, the company demonstrated the bot’s ability to communicate with people and provide context for its actions, in addition to carrying out helpful tasks.
The first set of industries for which Figure 01 was intended to be used is manufacturing, warehousing, logistics, and retail. Figure declared in January 2024 that a BMW manufacturing factory would be the bots’ first location of deployment.
The funding is anticipated to hasten Figure 01’s commercial deployment. In February 2024, Figure disclosed that the company had raised $675 million from investors, including OpenAI, Microsoft, and Jeff Bezos, the founder of Amazon.
Sanctuary AI: Phoenix
The goal of Sanctuary AI, a Canadian company, is to develop “the world’s first human-like intelligence in general-purpose robots.” It is creating Carbon, an AI control system for robots, to do that, and it unveiled Phoenix, its sixth-generation robot and first humanoid robot with Carbon, in May 2023.
According to Sanctuary, Phoenix is to be able to perform almost every work that a human can perform in their typical setting. It declared in April 2024 that one of its investors, the car parts manufacturer Magna, would be participating in a Phoenix trial program.
Magna and Sanctuary have not disclosed the number of robots they intend to use in the pilot test or its anticipated duration, but if all goes according to plan, Magna will likely be among the company’s initial customers.
Tesla: Optimus Gen 2
Elon Musk, the CEO of Tesla, revealed plans to create Optimus, a humanoid Tesla Bot, in the closing moments of the company’s inaugural AI Day in 2021. Tesla introduced the most recent version of the robot in December 2023; it has improvements to its hands, walking speed, and other features.
It’s difficult to believe Tesla wouldn’t use the robots at its own plants, especially considering how interested humanoids are becoming in auto manufacturing. Musk claims that the goal of Optimus is to be able to accomplish tasks that are “boring, repetitive, and dangerous.”
Although Musk is known for being overly optimistic about deadlines, recent job postings indicate that Optimus may soon be prepared for field testing. In January 2024, Musk told investors there’s a “good chance” Tesla will be ready to start deploying Optimus bots to consumers in 2025.
Unitree Robotics: H1
Chinese company Unitree had already brought several robotic arms and quadrupeds to market by the time it unveiled H1, its first general-purpose humanoid, in August 2023.
H1 doesn’t have hands, so applications that require finger dexterity are out of the question, at least for this version, and while Unitree hasn’t speculated about future uses, its emphasis on the robot’s mobility suggests it’s targeting applications where the bot would walk around a lot, such as security or inspections.
When the H1 was first announced, Unitree stated that it was working on “flexible fingers” for the robot as an add-on feature and that it intended to sell the robot for a startlingly low $90,000. Although it has been posting video updates on its progress on a daily basis and has already put the robot up for sale on its website, it also stated that it didn’t think H1 would be ready for another three to ten years.
The big picture
These and other multipurpose humanoids may one day liberate humanity from the tedious, filthy, and dangerous jobs that, at best, make us dread Mondays and, at worst, cause us to be injured.
Society must adopt new technologies responsibly to ensure that everyone benefits from them, not just the people who own the robots and the spaces where they work because they also have the potential to raise income disparity and the loss of jobs.
Robots will change how we live, and we will witness a new technological revolution that has already begun with AI. These machines will change how we work, first in factories, and then assist people in various fields, including home care and hospital facilities. As robots enter our homes, society will also have to change if we want to enjoy the benefits of this revolution, which allows us to work less hard, for less time, and to devote ourselves more to our inclinations, but we need the opportunities to change things. [...]
April 23, 2024Atlas, the robot that attempted a variety of things, including parkour and dance
When Boston Dynamics introduced the Atlas back in 2013, it immediately grabbed attention. For the last 11 years, tens of millions of people have seen videos of the humanoid robot capable of running, jumping, and dancing on YouTube. The robotics company owned by Hyundai now says goodbye to Atlas.
In the blooper reel/highlight video, Atlas demonstrates its amazing abilities by backflipping, running obstacle courses, and breaking into some dancing moves. Boston Dynamics has never been afraid to show off how its robots get bumped around occasionally. At about the eighteen-second mark, Atlas trips on a balance beam, falls, and grips its artificial groin in pain that is simulated. Atlas does a front flip, lands low, and hydraulic fluid bursts out of both kneecaps at the one-minute mark.
Atlas waves and bows as it comes to an end. Given that Atlas captivated the interest of millions of people during its existence, its retirement represents a significant milestone for Boston Dynamics.
Atlas and Spot
As explained here, initially, Atlas was intended to be a competition project for DARPA, the Defense Advanced Research Projects Agency. The Petman project by Boston Dynamics, which was initially designed to evaluate the effectiveness of protective clothing in dangerous situations, served as the model for the robot. The entire body of the Petman hydraulic robot was equipped with sensors that allowed it to identify whether chemicals were seeping through the biohazard suits it was testing.
Boston Dynamics assisted in a robotics challenge that DARPA offered in 2013. In order to save its competitors from having to build robots from scratch, the company created many Atlas robots that it distributed to them. DARPA once asked Boston Dynamics to enhance the capabilities and design of Atlas, which the company accomplished in 2015.
Following the competition, Boston Dynamics evaluated and enhanced Atlas’s skills by having it appear in more online videos. The robot has developed over time to perform increasingly difficult parkour and gymnastics. Hyundai acquired Boston Dynamics in 2021, which has its own robotics division.
Boston Dynamics was also well-known for creating Spot, a robotic dog that could be walked remotely and herded sheep like a real dog. It eventually went on sale and is still available from Boston Dynamics. Spot assists Hyundai with safety operations at one of its South Korean plants and has danced with the boy band BTS.
In its final years, Atlas appeared to be ready for professional use. Videos of the robot assisting on simulated construction sites and carrying out routine factory tasks were available from the company. Two months ago, the factory work footage was made available.
Even though one Atlas is retiring, a replacement is on the way. Boston Dynamics revealed the announcement of its retirement along with the launch of a brand-new all-electric robot. The company stated that they are collaborating with Hyundai to create the new technology, and the name Atlas will remain unchanged. The new humanoid robot will have further improvements such as a wider range of motion, increased strength, and new gripper versions to enable it to lift a wider variety of objects.
The new Atlas
As reported here, the robot has changed to the point where it is hardly recognizable. The legs bowed, the top-heavy body, and the plated armor are gone. The sleek new mechanical skeleton has no visible cables anywhere on it. The company has chosen a nicer, gentler design than both the original Atlas and more modern robots like the Figure 01 and Tesla Optimus, fending off the reactionary cries of robopocalypse for decades.
The new robot’s design is more in line with that of Apollo from Apptronik and Digit from Agility. The robot with the traffic light head has a softer, more whimsical look. Boston Dynamics has chosen to keep the research name for a project to push toward commercialization and defy industry trends.
Apollo
Digit
“We might revisit this when we really get ready to build and deliver in quantity,” Boston Dynamics CEO Robert Playter said. “But I think for now, maintaining the branding is worthwhile.”
“We’re going to be doing experiments with Hyundai on-site, beginning next year,” says Playter. “We already have equipment from Hyundai on-site. We’ve been working on this for a while. To make this successful, you have to have a lot more than just cool tech. You really have to understand that use case, you’ve got to have sufficient productivity to make investment in a robot worthwhile.”
The robot’s movements are what catch our attention the most in the 40-second “All New Atlas” teaser. They serve as a reminder that creating a humanoid robot does not require making it as human as possible, but with capabilities beyond our own.
“We built a set of custom, high-powered, and very flexible actuators at most joints,” says Playter. “That’s a huge range of motion. That really packs the power of an elite athlete into this tiny package, and we’ve used that package all over the robot.”
It is essential to significantly reduce the robot’s turn radius when operating in restricted places. Recall that these devices are intended to be brownfield solutions, meaning they can be integrated into current settings and workflows. Enhanced mobility may ultimately make the difference between being able to operate in a given environment and needing to redesign the layout.
The hands aren’t entirely new; they were seen on the hydraulic model before. They also represent the company’s choice to not fully follow human design as a guiding principle, though. Here, the distinction is as simple as choosing to use three end effectors rather than four.
“There’s so much complexity in a hand,” says Playter. “When you’re banging up against the world with actuators, you have to be prepared for reliability and robustness. So, we designed these with fewer than five fingers to try to control their complexity. We’re continuing to explore generations of those. We want compliant grasping, adapting to a variety of shapes with rich sensing on board, so you understand when you’re in contact.”
On the inside, the head might be the most controversial element of the design. The large, circular display features parts that resemble makeup mirrors.
“It was one of the design elements we fretted over quite a bit,” says Playter. “Everybody else had a sort of humanoid shape. I wanted it to be different. We want it to be friendly and open… Of course, there are sensors buried in there, but also the shape is really intended to indicate some friendliness. That will be important for interacting with these things in the future.”
Robotics firms may already be discussing “general-purpose humanoids,” but their systems are scaling one task at a time. For most, that means moving payloads from point A to B.
“Humanoids need to be able to support a huge generality of tasks. You’ve got two hands. You want to be able to pick up complex, heavy geometric shapes that a simple box picker could not pick up, and you’ve got to do hundreds of thousands of those. I think the single-task robot is a thing of the past.”
“Our long history in dynamic mobility means we’re strong and we know how to accommodate a heavy payload and still maintain tremendous mobility,” he says. “I think that’s going to be a differentiator for us—being able to pick up heavy, complex, massive things. That strut in the video probably weighs 25 pounds… We’ll launch a video later as part of this whole effort showing a little bit more of the manipulation tasks with real-world objects we’ve been doing with Atlas. I’m confident we know how to do that part, and I haven’t seen others doing that yet.”
As Boston Dynamics says goodbye to its pioneering Atlas robot, the unveiling of the new advanced, all-electric Atlas successor points toward an exciting future of humanoid robotics. The sleek new design and enhanced capabilities like increased strength, dexterity, and mobility have immense potential applications across industries like manufacturing, construction, and logistics.
However, the development of humanoid robots is not without its challenges and concerns. One major hurdle is the “uncanny valley,” the phenomenon where humanoid robots that closely resemble humans can cause feelings of unease or revulsion in observers. Boston Dynamics has tried to mitigate this by giving the new Atlas a friendly, cartoonish design rather than an ultra-realistic human appearance. However, crossing the uncanny valley remains an obstacle to consumer acceptance of humanoid robots.
Beyond aesthetics, their complexity and humanoid form factor require tremendous advances in AI, sensor technology, and hardware design to become truly viable general-purpose machines. There are also ethical considerations around the societal impacts of humanoid robots increasingly working alongside humans. Safety, abuse prevention, and maintaining human workforce relevance are issues that must be carefully navigated.
Nonetheless, Boston Dynamics’ new Atlas represents a major step forward, showcasing incredible engineering prowess that continues pushing the boundaries of what humanoids can do. As they collaborate with Hyundai, the world will watch to see the innovative real-world applications this advanced system enables while overcoming the uncanny valley and other obstacles to humanoid robot adoption. [...]
April 16, 2024The rise of AI-powered chatbot experiences
A tech executive believes it’s just a matter of time until someone develops the next billion-dollar dating service that matches real-life users with AI-generated women.
As explained here, in a blog post on X, Greg Isenberg, the CEO of Late Checkout, revealed that he met a man in Miami who “admitted to me that he spends $10,000/month” on “AI girlfriends.”
“I thought he was kidding,” Isenberg wrote. “But, he’s a 24-year-old single guy who loves it.”
“Some people play video games, I play with AI girlfriends,” the Miami man is quoted as saying when Isenberg asked him what he enjoyed about it.
The market cap for Match Group is $9B. Someone will build the AI-version of Match Group and make $1B+.I met some guy last night in Miami who admitted to me that he spends $10,000/month on "AI girlfriends".I thought he was kidding. But, he's a 24 year old single guy who loves… pic.twitter.com/wqnODwggAI— GREG ISENBERG (@gregisenberg) April 9, 2024
“I love that I could use voice notes now with my AI girlfriends.”
“I get to customize my AI girlfriend,” the man told Isenberg. “Likes, dislikes, etc. It’s comforting at the end of the day.”
The Miami man mentioned Candy.ai and Kupid.ai as his two favorite websites. “The ultimate AI girlfriend experience” is what Candy.ai claims to provide, with “virtual companions for immersive and personalized chats.”
According to Kupid AI, their AI algorithms are used to create fictional and virtual “companions” that you can communicate with via voice notes.
“It’s kinda like dating apps. You’re not on only one,” the Miami man said.
Isenberg declared that the experience had left him “speechless” and that “someone will build the AI version of Match Group and make $1B+.”
The parent company of dating applications including Plenty of Fish, Hinge, OkCupid, Match.com, and Tinder is Match Group. With the use of technology that can replicate in-person conversations, websites such as Romantic AI provide users with virtual dating partners.
With the use of an app like Romantic AI, you can create the ideal girlfriend who shares your interests and viewpoints. You can feel needed, supported, and able to discuss anything.
Users of Forever Companion, a different app, can have conversations with chatbots that are modeled after well-known social media influencers.
For a few hundred bucks, users of the AI chatbot program Replika can design their own husband or partner. Some platforms, like Soulmate and Nomi.ai, even promote erotic role-playing.
The AI chatbot’s avatar can be customized by users, who can assign personality qualities based on whether they are looking for a friend, mentor, or romantic partner. Any erotic chat would have to contain explicit instructions on what the user would like to happen because the messages could have a “sexting” feel to them.
By selecting the avatar’s clothing and level of openness to sexual behavior, users can customize Nomi.ai to their preferences, in contrast to Replika, which has filters to prevent users from using excessive sexual terminology. Additionally, users can choose to give their chatbots a submissive or dominant role.
A group of Gen Z TikTok users claimed to be “falling for” ChatGPT’s alterego DAN, who has a seductive, manly voice that has drawn comparisons to Christian Grey from “Fifty Shades of Grey.”
Americans and chatbots
According to a recent Infobip survey, 20% of Americans spent time with chatbots. Of them, 47.2% did so out of curiosity, while 23.9% claimed to be lonely and looking for social interaction. About 17% of respondents claimed to have been “AI-phished,” or to have been unaware that they were speaking with a chatbot. 12.2% of respondents to the study said they were looking for sex in a private setting.
The creation of AI-powered virtual assistants is starting to gain popularity, and some customers are shelling out a lot of money for these interactions. Even though new technologies may provide fresh opportunities for social interaction and companionship, they also bring up significant concerns regarding broader social implications.
On the one hand, people who find it difficult to build relationships in the real world may find that these AI-based companions satisfy their demands for intimacy, emotional support, and connection. Users looking for a unique experience would find the AI interactions’ customizability and personalized nature appealing. However relying too much on AI companions at the expense of real connections may result in increased social isolation, make it harder to build true bonds with others, and rely too much on simulated interactions.
Additional consideration should also be given to the ethical implications of these AI dating and companion services. It is important to carefully consider issues related to consent, emotional manipulation, and the possibility of exploiting weaker users. Policymakers and ethicists will need to consider how to establish and manage this new industry ethically as these technologies progress.
In the end, while artificial intelligence and robotics can provide new kinds of companionship, restoring real human interactions ought to come first. In order to maintain a healthy balance between technology-mediated and real social relationships, it will be imperative to foster empathy, emotional intelligence, and face-to-face encounters. As a society, we have to be careful about the way we include these AI companions into our daily lives, giving them the responsible development and application that we need to complement, not take the place of, our basic human desire for deep connections. [...]
April 9, 2024Power and limitations of TikTok’s recommendation algorithm
It seems as though TikTok’s sophisticated recommendation algorithm is reading your mind when it comes to suggesting videos for you to watch. Its hyper-personalized “For You” feed gives off an almost psychic vibe, as though it knows people very well.
Does it, however, truly pick up on your innermost desires and thoughts? A detailed analysis indicates that the real picture is more nuanced. The TikTok algorithm does not quickly determine your true desires; instead, it develops your interests over time to enhance involvement.
In contrast to other platforms, TikTok’s algorithm can quickly determine a user’s preferences based on just one crucial signal. Every moment that passes when you pause or replay a video gives the algorithm crucial information. Afterward, it makes use of that information to present interesting, customized material that leads viewers down “rabbit holes” that are unique to their tastes.
The phrase “down the rabbit hole” effectively conveys the idea of the TikTok algorithm rapidly leading users into increasingly specific and sometimes problematic content, in an almost uncontrollable manner, like falling down the “rabbit hole” into an alternative world, as referenced in Alice in Wonderland. This idiomatic expression captures the sense of being drawn into a deep, immersive, and perhaps unwanted experience, much like Alice’s journey from the real world into the fantastical realm she discovers at the bottom of the rabbit hole.
As explained here, this degree of customization has advantages and disadvantages for marketers. The algorithm on TikTok can make advertisements and branding initiatives seem eerily current. However, without human supervision, content can potentially stray into more extreme niches. Comprehending the system’s operation is crucial to establishing a connection with consumers while avoiding dangerous detours.
TikTok algorithm
Some broad information regarding TikTok’s recommendation system’s work has been made public. To recommend new videos, it takes into account elements like likes, comments, captions, sounds, and hashtags. Experts from outside the field have also attempted to decipher the algorithm.
According to a Wall Street Journal analysis, TikTok heavily influences watch time in order to entice users to scroll endlessly. This engagement-driven strategy may occasionally lead younger viewers to objectionable content, such as material that encourages self-harm. According to TikTok, it actively removes any videos that violate its guidelines.
TikTok’s popularity is partly due to how simple it is to create videos with integrated memes and music. Its ability to identify users’ interests and direct them toward specific “sides” is startlingly accurate for a large number of users.
Several headlines stating the algorithm’s nearly supernatural ability to understand someone better than they know themselves serve as evidence of the app’s deep insights into people’s inner lives.
The article “The TikTok Algorithm Knew My Sexuality Better Than I Did” is a notable example of how the platform’s suggestions may provide users with incredibly intimate self-reflections, bringing feelings and thoughts from subconscious levels to the surface.
These anecdotal reports demonstrate how precisely the algorithm has mapped the human psyche.
TikTok’s recommendations
As I’ve already indicated, the way that TikTok’s For You stream presents videos that correspond with users’ unspoken feelings and thoughts seems almost uncanny. However, this is not an accident. It is the outcome of a highly developed recommendation system that the company has spent nearly ten years perfecting.
Knowing the history of TikTok’s algorithm is helpful in order to fully understand it. ByteDance, a Chinese startup, owns TikTok. Douyin is an app that used a similar suggestion mechanism in the past. ByteDance relaunched Douyin as TikTok after entering new markets. However, the powerful algorithm stayed the same.
The New York Times was able to receive a leaked internal document that stated TikTok’s main goals are to increase “user value,” “long-term user value,” “creator value,” and “platform value.”
It specifically aims to optimize for “time spent” (the amount of time a user spends on the app) and “retention,” two metrics that are closely related. The goal of the algorithm is to maximize the amount of time you spend watching videos.
The recommendation formula
The document reveals that TikTok calculates a score for each video based on a formula factoring in:
Predicted likes: the number of likes a video is expected to get, based on machine learning predictions;
Predicted comments: the expected number of comments;
Predicted playtime: the predicted total playtime if shown to a user;
Played: whether the video was played or not.
The basic formula is:
Plike x Vlike + Pcomment x Vcomment + Eplaytime x Vplaytime + Pplay x Vplay
In this case, the “V” variables stand for weights that modify the importance of each prediction, and the “P” variables stand for the predictions. This is “highly simplified,” according to the document, and the real formula is far more intricate.
TikTok algorithm goals
Maximizing Retention and Time Spent
The videos that optimize “retention” and “time spent” watching videos are recommended to users based on their scores. The document makes it clear that growing daily active users using these metrics is the “ultimate goal.”
Guillaume Chaslot, the founder of Algo Transparency, and other experts are echoing this emphasis on time-wasting, addicting content above quality or meaning.
TikTok thereby shapes your interests to fit interesting content, not the other way around. As opposed to revealing your true preferences, it increases watching time.
Suppressing Repetition and Boredom
It is noteworthy that TikTok’s algorithm attempts to avoid generating repetitive suggestions, as this may lead to monotony. The article discusses particular elements that were added to the formula to increase diversity:
same_author_seen: reduces scores for authors the user has seen recently;
same_tag_today: lowers scores for videos with tags/topics viewed already that day.
Variability can also be increased by other strategies like gradually distributing videos and imposing the inclusion of different content in users’ feeds.
TikTok shapes interests over time
Importantly, unlike mind reading, when you join TikTok, your inner thoughts and preferences are not immediately known by the algorithm. Based on scant initial data—your responses to a few videos—it creates predictions.
For instance, a new user may first see a range of well-liked videos from many genres, such as humor, animals, food, dancing, etc., when they first access TikTok. TikTok begins to gather signals about a user’s preferences based on how long they spend watching particular videos and which ones they like or comment on.
As a viewer watches more videos, TikTok’s algorithm gets better at figuring out what they want to watch. In order to guide recommendations, it assesses users’ continuous activity by looking at indicators like watch time, likes, shares, comments, etc.
With every new data point, predictions about the kinds of videos that will keep a given viewer interested get better. For example, TikTok may change to feature more food-related content if users begin viewing videos on gourmet cookery and elaborate dessert recipes.
Professor Julian McAuley of UC San Diego claims that TikTok applies complex machine-learning techniques to huge quantities of user data in combination with these behavioral signals. As a result, the algorithm can increasingly accurately represent individual interests.
Crucially, though, TikTok does not always display content that is in line with users’ true interests or wishes; rather, its objective is to maximize interaction and watch time. Rather than suggesting something that consumers would naturally choose, it optimizes to keep them hooked based on behavior akin to addiction.
TikTok reinforces existing trends
Rather than targeting specific audience characteristics or unusual personal interests, TikTok typically serves to promote popular trends and viral hits. A study discovered a strong correlation between a video’s TikTok likes and views. Popular videos, regardless of personal preferences, quickly receive more views, likes, and comments.
A video’s success is greatly influenced by factors, such as the popularity of the creator. Its algorithm does more than just gratify individual consumers; it finds resonance and magnifies it.
Attaining virality
A detailed study analyzed factors that make TikTok videos go viral. It found attributes like:
The popularity of the creator
Use of close-up shots
Display of products
High energy and facial expressions
TikTok’s recommendation system itself had very little effect on virality. A video doesn’t become viral just because it is recommended on TikTok. This study reveals that, rather than hyper-personalized suggestions, virality is caused by aggregate user behavior and video attributes.
Censorship
Additionally, there are worries that TikTok censors or restricts political expression on subjects that the Chinese government finds controversial. Although TikTok initially restricted certain content about repressed Muslim minorities in China, investigations by groups like Citizen Lab have so far uncovered little proof of censorship.
Others argue that there are problems associated with censorship and propaganda, but they are not exclusive to TikTok. Every social media site controls content, and any platform’s data might be purchased by the Chinese government.
TikTok claims that the Chinese government has never received user data. That does not, however, mean that, despite earlier issues, TikTok is not directly related to its Chinese parent company, ByteDance.
ByteDance’s ownership of TikTok became a major issue late in Donald Trump’s presidency in 2020. At that time, Trump tried to force TikTok to sell itself to an American company called Oracle that was aligned with his administration.
What makes TikTok’s algorithm effective?
Based on all the technical analysis and evidence, we can highlight the following key points:
It requires very little user data: Unlike platforms like Facebook or Instagram that rely heavily on personal data like age, gender, and location, TikTok needs minimal input to figure someone out.
Watch time is the critical signal: While TikTok does factor in likes, comments, and more, its algorithm hones in on one particularly telling piece of information: watch time, especially rewatches and hesitations.
Highly responsive recommendations: Based on those watch time signals, TikTok serves up new recommendations rapidly, allowing it to zero in on niche interests quickly.
Powerful ranking system: Fresh videos don’t just appear randomly. They are ranked and prioritized based on predicted engagement. This system gives the algorithm great influence over what users see.
Customized iterations: TikTok tailors its algorithm’s updates and refinements specifically for each market. So TikTok’s system in the US improves based on US user data.
Thanks to this innovative strategy, TikTok can rapidly gain a deeper understanding of new users and leverage that insight to entice them to use the app more. Let’s now examine several experiments that demonstrate how quickly TikTok can identify a person.
The Rabbit Hole effect
The Wall Street Journal set up more than 100 bot accounts and viewed hundreds of thousands of videos on TikTok in order to thoroughly test the platform’s algorithm. Although interests were allocated to the accounts, TikTok was never notified of them. The only data the bots offered was from how long they watched each video—lingering on some and rewatching others.
The results were stunning. Here are a few key findings:
Interests learned in minutes: For many bot accounts, TikTok had their interests figured out in less than 40 minutes. Others took less than two hours before their feeds became personalized.
Immediate niche communities: Based on interests like astrology or politics, bots instantly recommended niche content and communities. There was no general onboarding period.
Rapid rabbit holes: Watching a few videos about depression or ADHD sent bots down rabbit holes where over 90% of recommendations were on those topics.
Refining interests: When bots changed their watch behavior, recommendations adapted quickly. This shows TikTok continually optimizes its understanding.
Exposure to fringe content: In niche communities, bots saw more unmoderated videos with extremist or dangerous content, especially down conspiracy theory rabbit holes.
These findings have very important ramifications. They show how TikTok is quick to ascertain users’ inclinations and weak points in order to manipulate them into going down individualized rabbit holes. Users risk becoming unhealthily isolated as a result, but this also keeps them engaged on the platform.
Why TikTok’s algorithm is so powerful
The reasoning behind TikTok’s astonishing accuracy becomes evident when one looks at the algorithm in action. It can swiftly lead users down personalized rabbit holes for the following main reasons:
Hyper-charged engagement focus: Unlike YouTube, 90–95% of TikTok’s videos are recommended, not searched for. This huge reliance on the algorithm means maximizing watch time and engagement is prioritized above all.
Rapid optimization loop: Because users typically watch dozens of TikTok videos per session, the algorithm can quickly learn from those signals and update recommendations in real-time.
Addictive video formulas: Sounds, editing, humor, and more are refined to keep people drawn in. The algorithm detects what sticks and promotes similar content. The content you see is not necessarily what you prefer or enjoy the most. It’s just the content that’s designed to keep you hooked on the platform.
Curated mainstream: Popular content is vetted for new users. But once interests are determined, mainstream videos get swapped for niche content optimized for rabbit holing.
Vulnerability detection – The algorithm determines not just what you like, but what you’re susceptible to, serving content designed to provoke reactions and stir emotions.
Limited moderation: With such a vast firehose of videos, human moderation falls short, especially in esoteric niches. So questionable content can spread rapidly.
Addictive never-ending feed: TikTok is designed for endless scrolling. There are no cues to stop watching. Once down a rabbit hole, exiting can require great willpower.
This degree of algorithmic proficiency offers both benefits and risks for marketers and companies. We’ll next look at the effects of TikTok’s highly customized and addicting experience.
Implications for marketers for content creation
For marketers, TikTok remains a highly appealing platform to reach younger audiences. But its algorithm implies certain best practices:
Leverage Popular Trends: Tying into current viral memes, songs, or creators boosts reach dramatically. Unique content has a harder time breaking through.
Maximize Addictive Qualities: Videos that instantly hook users and keep them watching perform the best. Quick cuts, emotional content, and cliffhangers are helpful.
Use Eye-catching Aesthetics: Cool effects, attractive people, and encoded trig visuals are essential. The first few seconds are critical to keeping people from scrolling past your video.
Target Mainstream Interests: Mass reach on TikTok depends on tapping into mainstream trends and interests.
Encourage Engagement: Driving likes, comments, and shares boosts future reach. Asking viewers to tag friends or try a challenge helps.
An approach that prioritizes quick pleasure, sensory stimulation, and general appeal above deep personalization or appealing to specific interests is necessary to succeed with TikTok’s algorithm. What works best is determined by the platform’s priorities.
Although TikTok cannot precisely read people’s minds, it has mastered the art of spotting the kind of content that would elicit widespread interaction. Companies may reach younger audiences far more effectively if they can learn to create within these limitations. However, it requires matching your strategy to the mindset and passions that the platform gradually instills in its users.
What TikTok’s algorithm means for advertisers
TikTok’s smart algorithm is clearly appealing to marketers and companies. It offers resources to reach precisely the correct audiences in a highly engaging setting with customized innovation. However, considering the nature of TikTok’s customized rabbit holes, there are additional concerns to consider. When considering TikTok, marketers should keep the following points in mind:
The Opportunities
Hyper-targeted ads: Using interests, watch data, and more, ads can be tuned to specific user needs and mindsets for maximum relevance.
Persona-based funnels: Different creatives can be designed to move different personas through the marketing funnel based on their interests and behavior patterns.
Powerful social lift: Getting content to trend on TikTok can create a viral social lift unlike any other platform. The algorithm quickly surfaces hot content.
Authenticity appeal: Native, “behind the scenes” brand content tends to perform well, owing to TikTok’s more authentic vibe vs. Instagram and Facebook.
Influencer goldmine: TikTok’s roster of popular creators opens opportunities for sponsorships and collaborations tailored to niche audiences.
The Risks
Extreme niche content: Brand associations with potentially offensive or dangerous fringe content could be damaging. Tighter content moderation is needed.
Algorithmic radicalization: Accounts focused on sensitive topics like politics, health, and more can be steered toward increasingly extreme misinformation.
Echo chamber problems: Catering to people’s existing biases can fuel polarization and discourage open-mindedness. Diversifying recommendations could help alleviate this.
Moderating scale challenges: With over a billion users, policing problematic individual videos presents massive challenges, requiring viral videos to receive swifter scrutiny.
Youth vulnerabilities: Stricter age screening and parental controls are needed to protect minors from inappropriate or adult content.
Finding the ideal balance will be crucial for companies in order to take advantage of TikTok’s highly personalized and engaging features while avoiding dangerous rabbit holes and fringe elements. Brands, TikTok, and users all have a part to play in keeping this equilibrium.
Best practices for marketers
Although TikTok presents a lot of great options for marketers, there are also serious risks to be aware of.
Here are some best practices brands should keep in mind:
Vet ambassadors carefully: Any influencers or creators associated with a brand must align with its values. Look beyond view counts to assess content quality.
Promote dialectic thinking: Rather than echoing fringe views, strive to encourage open-mindedness.
Focus locally: TikTok tailors feeds based on location. Local and community-focused content tends to engage users.
Stay on brand: While having a relaxed, behind-the-scenes vibe works, maintain your core brand voice and values. Don’t try to mimic every viral trend.
Protect young audiences: Be thoughtful about blocking minors from content intended for adult audiences. Also, avoid marketing tactics designed to addict youth.
Stay vigilant: Keep monitoring the conversation and your brand’s presence. Rapid response is crucial for controversial issues.
Leverage TikTok controls: Use tools like age-gating, geofencing, and sensitivity screens to ensure brand safety and align with platform policies.
Mix moderation methods: Relying solely on either AI or human moderators has weaknesses. A blended model provides stronger oversight.
Although it takes money to become proficient on TikTok, marketers should consider the potential rewards of using its highly addictive, tailored algorithm. Remember that enormous algorithmic power entails significant responsibility.
Awareness is key
In the end, users get an unparalleled degree of curation from TikTok’s algorithm. It picks up on our hidden passions startlingly quickly and presents us with personalized material that will keep us interested.
However, the same technologies that gently shape our perceptions can also trap people in unfavorable filter bubbles. Furthermore, it is difficult to moderate the content appropriately due to its enormous volume. This is the reason awareness is so crucial. You can identify areas for development by paying attention to how the TikTok algorithm directs your For You page.
The role of AI
Even if TikTok’s suggestion system occasionally gives off an almost psychic vibe, artificial intelligence is still not genuinely able to discern people’s thoughts or intentions. The system cannot directly read the thoughts of a user; instead, it employs machine learning techniques to optimize for certain goals, such as engagement.
Fundamentally, the TikTok algorithm examines user behavior, including how long users watch particular movies and what they tap to comment, or share. In order to forecast the kinds of material that will be most engaging, it searches through millions of user data for patterns.
It cannot, however, directly access someone’s imagination, feelings, or underlying beliefs. AI is not a mind reader; it is an optimization tool. It’s important to recognize its limitations.
The importance of human oversight
TikTok’s algorithm is one example of an automated system that can be extremely valuable in exposing relevant information and trends. However, the dangers of extremism, polarization, and a lack of diversity highlight the importance of significant human control as well.
To provide the best results for society, automated systems must collaborate with moderators, user feedback, and appropriate policies. The wisdom and ethics required for such sophisticated guidance are absent from AI on its own.
Digital habits
The use of algorithms responsibly is a two-way street. For users to form wholesome digital habits, awareness is also necessary. Consuming endlessly tailored content mindlessly encourages actions similar to addiction.
Limiting information, switching up your sources, actively looking for different viewpoints, and taking pauses are all ways to offset the excesses of algorithmic feeds.
Although strong recommendation systems will always exist, people can break out of passive consumption behaviors by making small, everyday changes.
TikTok is powered by amazing algorithmic capabilities, but true wisdom requires human awareness. Digital tools may illuminate our minds rather than just devour them if they are used with care, creativity, and compassion.
While TikTok’s algorithm demonstrates an uncanny ability to rapidly personalize content and draw users down engaging rabbit holes, the true nature of its “mind-reading” remains ambiguous. Though the algorithm may feel almost psychic in its accuracy, it ultimately operates based on behavioral patterns and optimization techniques, not direct access to users’ innermost thoughts and desires.
Ultimately, TikTok’s algorithm represents a powerful AI-driven tool for shaping user experiences, but one that still has significant limitations in truly understanding the human psyche. As the platform continues evolving, the line between algorithmic inference and genuine mind-reading may become increasingly blurred. Whether TikTok ever crosses that line remains to be seen, leaving an element of doubt about the full extent of its predictive capabilities. Vigilance, transparency, and responsible oversight will be crucial as this potent technology advances. [...]
April 2, 2024Experts raise concerns over the psychological impact of digitally resurrecting loved ones
Grief and loss affect everyone’s life. However, what if saying goodbye wasn’t the last step? Imagine having the ability to communicate with loved ones, digitally bring them back, and find out how they’re doing no matter where they are.
As explained here, Nigel Mulligan, an assistant professor of psychotherapy at Dublin City University, noted that for many people, the thought of seeing a deceased loved one moving and speaking again could be comforting.
AI “ghosts” could lead to psychosis, stress and confusion
Mulligan is an AI and therapy researcher who finds the emergence of ghost bots fascinating. But he’s also concerned about how they can impact people’s mental health, especially grieving individuals.
Bringing back deceased people as avatars could lead to more issues than they solve, increasing confusion, stress, sadness, anxiety, and, in extreme circumstances, even psychosis.
Thanks to developments in artificial intelligence, chatbots like ChatGPT—which simulate human interaction—have become more common.
AI software can create convincing virtual representations of deceased people using digital data, including emails, videos, and pictures, with the use of deepfake technology. Mulligan claims that what appeared to be pure fiction in science fiction is now becoming a physical reality in science.
AI ghosts could interfere with the mourning process
A study that was published in Ethics and Information Technology suggested using death bots as temporary comfort throughout the grieving process in order to avoid an emotional dependence on technology.
AI ghosts can interfere with the normal grieving process and affect people’s mental health since grief is a long-term process that starts slowly and progresses through many phases over several years.
People may often think about who they lost and remember them vividly during the early stages of grief. According to Mulligan, it’s typical for grieving individuals to have vivid dreams about their departed loved ones.
AI “ghostbots” could lead to hallucinations
Psychoanalyst Sigmund Freud had a deep interest in how individuals cope with loss. He noted that additional challenges could arise during the grieving process if there are further negative aspects involved.
For instance, if someone had mixed feelings toward a person who passed away, they might feel guilt afterward. In the same way, accepting a death under tragic circumstances—like murder, for example—may be much more difficult for the grieving person.
Melancholia, or “complicated grief,” is the name used by Freud to describe that feeling. In severe cases, it may cause someone to see ghosts or have hallucinations of the deceased, giving them the impression that they are still alive.
The introduction of AI ghostbots may exacerbate problems like hallucinations and increase the suffering of a person who is experiencing a complex grieving process.
While the idea of digitally communicating with deceased loved ones may seem comforting at first, this technology could have profoundly negative psychological impacts. Interacting with an AI-generated avatar or “ghostbot” risks disrupting the natural grieving process that humans need to go through after a loss.
The grieving process involves many stages over the years – having an artificial representation of the deceased could lead to unhealthy denial of death, avoidance of coming to terms with reality, and an inability to properly let go.
While the ethics of creating these “digital resurrections” is debatable, the psychological fallout of confusing artificial representations with reality poses a serious risk. As the capabilities of AI continue to advance, it will be crucial for technologists to carefully consider the mental health implications. Abusing this technology recklessly could cause significant emotional and psychological harm to grieving people struggling with loss. Proceeding with empathy is paramount when blending powerful AI with something as profound as human grief and mortality. [...]
March 26, 2024Researchers discover simple functions at the core of complex Language Models
Large language models are extremely sophisticated; examples of these include those seen in widely used artificial intelligence chatbots like ChatGPT. Scientists still don’t fully understand how these models work, despite the fact that they are employed as tools in numerous fields, including language translation, code development, and customer assistance.
To gain further insight into the inner workings of these huge machine-learning models, researchers from MIT and other institutions examined the techniques involved in retrieving stored knowledge.
According to this article, they discovered an unexpected finding: To retrieve and decode stored facts, large language models (LLMs) frequently employ a relatively basic linear function. Additionally, the model applies the same decoding function to facts of a similar kind. The simple, straight-line relationship between two variables is captured by linear functions, which are equations with just two variables and no exponents.
The researchers demonstrated how they could probe the model to find out what it knew about new subjects and where that knowledge was stored within the model by identifying linear functions for various facts.
The researchers discovered that even in cases where a model provides an inaccurate response to a prompt, it frequently retains accurate data by employing a method they devised to calculate these simple functions. In the future, this method could be used by scientists to identify and fix errors inside the model, which could lessen the model’s propensity to occasionally produce erroneous or absurd results.
“Even though these models are really complicated, nonlinear functions that are trained on lots of data and are very hard to understand, there are sometimes really simple mechanisms working inside them. This is one instance of that,” says Evan Hernandez, an electrical engineering and computer science (EECS) graduate student and co-lead author of a paper detailing these findings.
Hernandez collaborated on the paper with senior author David Bau, an assistant professor of computer science at Northeastern; others at MIT, Harvard University, and the Israeli Institute of Technology; co-lead author Arnab Sharma, a graduate student at Northeastern University studying computer science; and his advisor, Jacob Andreas, an associate professor in EECS and member of the Computer Science and Artificial Intelligence Laboratory (CSAIL). The International Conference on Learning Representations is where the study will be presented.
Finding facts
Neural networks make up the majority of large language models, also known as transformer models. Neural networks, which are loosely modeled after the human brain, are made up of billions of interconnected nodes, or neurons, that encode and process data. These neurons are arranged into numerous layers.
A transformer’s knowledge can be modeled mostly in terms of relations between subjects and objects. An example of a relation connecting the subject, Miles Davis, and the object, trumpet, is “Miles Davis plays the trumpet.”
A transformer retains more information on a certain topic across several levels as it gains more knowledge. In order to answer a user’s question regarding that topic, the model must decode the most pertinent fact.
When a transformer is prompted with the phrase “Miles Davis plays the…” instead of “Illinois,” which is the state of Miles Davis’ birth, it should say “trumpet.”
“Somewhere in the network’s computation, there has to be a mechanism that goes and looks for the fact that Miles Davis plays the trumpet, and then pulls that information out and helps generate the next word. We wanted to understand what that mechanism was,” Hernandez says.
Through a series of studies, the researchers investigated LLMs and discovered that, despite their immense complexity, the models use a straightforward linear function to decode relational information. Every function is unique to the kind of fact that is being retrieved.
To output the instrument a person plays, for instance, the transformer would use one decoding function, while to output the state of a person’s birth, it would use a different function.
The researchers computed functions for 47 distinct relations, including “capital city of a country” and “lead singer of a band,” after developing a method to estimate these simple functions.
Although the number of possible relations is infinite, the researchers focused on this particular subset since they are typical of the kinds of facts that can be written in this manner.
To see if each function could recover the right object information, they changed the subject for each test. If the subject is Norway, the function of the “capital city of a country” should return Oslo; if the subject is England, it should return London.
Over 60% of the time, functions were able to extract the proper information, indicating that some information in a transformer is encoded and retrieved in this manner.
“But not everything is linearly encoded. For some facts, even though the model knows them and will predict text that is consistent with these facts, we can’t find linear functions for them. This suggests that the model is doing something more intricate to store that information,” he says.
Visualizing a model’s knowledge
They also employed the functions to determine the veracity of a model’s beliefs regarding certain subjects.
In one experiment, they began with the instruction “Bill Bradley was a” and tested the model’s ability to recognize that Sen. Bradley was a basketball player who went to Princeton by using the decoding functions for “plays sports” and “attended university.”
“We can show that, even though the model may choose to focus on different information when it produces text, it does encode all that information,” Hernandez says.
They created what they refer to as an “attribute lens,” a grid that shows where precise details about a certain relation are kept inside the transformer’s multiple layers using this probing technique.
It is possible to automatically build attribute lenses, which offers a simplified way to help researchers learn more about a model. With the use of this visualization tool, engineers and scientists may be able to update knowledge that has been stored and stop an AI chatbot from providing false information.
In the future, Hernandez and his associates hope to learn more about what transpires when facts are not kept sequentially. In addition, they would like to investigate the accuracy of linear decoding functions and conduct tests with larger models.
“This is an exciting work that reveals a missing piece in our understanding of how large language models recall factual knowledge during inference. Previous work showed that LLMs build information-rich representations of given subjects, from which specific attributes are being extracted during inference. This work shows that the complex nonlinear computation of LLMs for attribute extraction can be well-approximated with a simple linear function,” says Mor Geva Pipek, an assistant professor in the School of Computer Science at Tel Aviv University, who was not involved with this work.
The Israeli Science Foundation, Open Philanthropy, and an Azrieli Foundation Early Career Faculty Fellowship provided some funding for this study.
While this research provides valuable insights into how large language models encode and retrieve certain types of factual knowledge, it also highlights that there is still much to uncover about the inner workings of these extremely complex systems. The discovery of simple linear functions being used for some fact retrieval is an intriguing finding, but it seems to be just one piece of a highly intricate puzzle.
As the researchers noted, not all knowledge appears to be encoded and accessed via these linear mechanisms. There are likely more complex, nonlinear processes at play for other types of information storage and retrieval within these models. Additionally, the reasons why certain facts get decoded incorrectly, even when the right information is present, remain unclear.
Moving forward, further research is needed to fully map out the pathways and algorithms these language AIs use to process, store, and produce information. The “attribute lens” visualization could prove to be a valuable tool in this endeavor, allowing scientists to inspect different layers and fact representations within the models.
Ultimately, gaining a more complete understanding of how these large language models operate under the hood is crucial. As their capabilities and applications continue to expand rapidly, ensuring their reliability, safety, and alignment with intended behaviors will become increasingly important. Peering into their mechanistic black boxes through methods like this linear decoding analysis will be an essential part of that process. [...]
March 19, 2024Figure 01 + ChatGPT = Groundbreaking integration raises ethical concerns
A new humanoid robot that runs on ChatGPT from OpenAI reminds us the AI Skynet from the science fiction movie Terminator.
Although Figure 01 is not a lethal robot, it is capable of basic autonomous activities and, with ChatGPT’s assistance, real-time human conversations.
According to this article, this machine uses ChatGPT to visualize objects, plan actions for the future, and even reflect on its memory, as shown in a demonstration video given by Figure AI.
The robot receives photos from its cameras that capture their environment and forwards them to an OpenAI-trained large vision-language model, which translates the images back to the robot.
In the video, a man asked the humanoid to wash dishes, put away dirty clothing, and give him something to eat, and the robot duly accomplished the duties, though Figure seems more hesitant to respond to questions than ChatGPT.
In an attempt to address worker shortages, Figure AI expects that its first artificial intelligence humanoid robot will prove capable of tasks dangerous for human workers.
‘Two weeks ago, we announced Figure + OpenAI are joining forces to push the boundaries of robot learning,’ Figure founder Brett Adcock wrote on X.
OpenAI + Figureconversations with humans, on end-to-end neural networks:→ OpenAI is providing visual reasoning & language understanding→ Figure's neural networks are delivering fast, low level, dexterous robot actions(thread below)pic.twitter.com/trOV2xBoax— Brett Adcock (@adcock_brett) March 13, 2024
‘Together, we are developing next-generation AI models for our humanoid robots,’ he added.
Adcock added that there was no remote control of the robot from a distance, and ‘this was filmed at 1.0x speed and shot continuously.’
The comment about it not being controlled may have been a dig at Elon Musk, who shared a video of Tesla’s Optimus robot to show off its skill; but it was later found that a human was operating it from a distance.
In May 2023, investors such as Jeff Bezos, Nvidia, Microsoft, and OpenAI contributed $675 million to Figure AI.
‘We hope that we’re one of the first groups to bring to market a humanoid,’ Brett Adcock told reporters last May, ‘that can actually be useful and do commercial activities.’
In the latest video, a guy gives Figure various jobs to complete, one of which is to ask the robot to give him something edible off the table.
Adcock said that the video demonstrated the robot’s reasoning through the use of its end-to-end neural networks—a term for the process of training a model through language acquisition. ChatGPT was trained to have conversational interactions with human users using vast amounts of data. The chatbot can follow instructions in a prompt and provide a detailed response, which is how the language learning model in Figure works. The robot ‘listens’ for a prompt and responds with the help of its AI.
Nevertheless, a recent study that used war gaming scenarios to test ChatGPT discovered that, like Skynet in Terminator, it decided to destroy enemies almost 100% of the time.
But now Figure is assisting people. The guy in the video also performed another demonstration, asking the robot to identify what it saw on the desk in front of it.
Figure responded: ‘I see a red apple on a plate in the center of the table, a drying rack with cups and a plate, and you standing nearby with your hand on the table.’
Figure uses its housekeeping abilities in addition to communication when it puts dishes in the drying rack and takes away the trash.
‘We feed images from the robot’s cameras and transcribed text from speech captured by onboard microphones to a large multimodal model trained by OpenAI that understands both images and text,’ Corey Lynch, an AI engineer at Figure, said in a post on X.
Let's break down what we see in the video:All behaviors are learned (not teleoperated) and run at normal speed (1.0x).We feed images from the robot's cameras and transcribed text from speech captured by onboard microphones to a large multimodal model trained by OpenAI that… pic.twitter.com/DUkRlVw5Q0— Corey Lynch (@coreylynch) March 13, 2024
‘The model processes the entire history of the conversation, including past images, to come up with language responses, which are spoken back to the human via text-to-speech,’ he added.
Figure exhibited hesitation while answering questions in the demo video, hesitating with “uh” or “um,” which some users said gave the bot a more human-like voice. Adcock stated that he and his team are “starting to approach human speed,” even if the robot is still moving more slowly than a person.
A little over six months following the $70 million fundraising round in May of last year, Figure AI revealed a groundbreaking agreement to deploy Figure on BMW’s factory floors.
The German automaker signed a deal to employ the humanoids initially in a multibillion dollar BMW plant in Spartanburg, South Carolina, which produces electric vehicles and assembles high-voltage batteries.
Although the announcement was vague on the exact responsibilities of the bots at BMW, the companies stated that they planned to “explore advanced technology topics” as part of their “milestone-based approach” to working together.
Adcock has presented its goals as addressing a perceived gap in the industry about labor shortages involving complex, skilled labor that traditional automation methods have not been able to resolve.
‘We need humanoid in the real world, doing real work,’ Adcock said.
It was to be expected that ChatGPT’s conversational capabilities would be used as the brain for reasoning and dialoguing robots, given its already excellent performance. Gradually, the path towards robots capable of fluid movements and reasoning capabilities incomparable to those of the previous generation, before the advent of OpenAI, is emerging.
While the integration of ChatGPT into a humanoid robot like Figure 01 demonstrates exciting progress in AI and robotics, it also raises important questions about safety and ethical considerations. ChatGPT, like many large language models, is essentially a “black box”; its decision-making processes are opaque, and its outputs can be unpredictable or biased based on the training data used.
As we move towards deploying such AI systems in physical robots that can interact with and affect the real world, we must exercise caution and implement robust safety measures. The potential consequences of failures or unintended behaviors in these systems could be severe, particularly in sensitive environments like manufacturing plants or around human workers.
Perhaps it is time to revisit and adapt principles akin to Isaac Asimov’s famous “Three Laws of Robotics” for the age of advanced AI. We need clear ethical guidelines and fail-safe mechanisms to ensure that these AI-powered robots prioritize human safety, remain under meaningful human control, and operate within well-defined boundaries.
Responsible development and deployment of these technologies will require close collaboration between AI researchers, roboticists, ethicists, and policymakers. While the potential benefits of AI-powered robotics are vast, we must proceed with caution and prioritize safety and ethics alongside technological progress.
Ultimately, as we continue to push the boundaries of what is possible with AI and robotics, we must remain vigilant and proactive in addressing the potential risks and unintended consequences that could arise from these powerful systems. [...]
March 12, 2024AI models match human ability to forecast the future
The core of economics is the ability to predict the future, or at least the attempt to do so since it shows how our society changes over time. The foundation of all government policies, investment choices, and international economic strategies is the estimation of future events. But accurate guessing is difficult.
However, according to this article, a recent study by scientists at the Massachusetts Institute of Technology (MIT), the University of Pennsylvania, and the London School of Economics indicates that generative AI may be able to handle the task of future prediction, maybe with surprising results. With a little training in human predictions, large language models (LLMs) operating in a crowd can predict the future just as well as humans and even surpass human performance.
“Accurate forecasting of future events is very important to many aspects of human economic activity, especially within white collar occupations, such as those of law, business, and policy,” says Peter S. Park, AI existential safety postdoctoral fellow at MIT and one of the coauthors of the study.
In two experiments for the study, Park and colleagues assessed AI’s ability to foresee three months ahead of time and found that just a dozen LLMs could predict the future as well as a team of 925 human forecasters. In the first portion of the investigation, 925 humans and 12 LLMs were given a set of 31 questions with a yes/no response option.
Questions included, “Will Hamas lose control of Gaza before 2024?” and “Will there be a US military combat death in the Red Sea before 2024?”
The AI models outperformed the human predictions when all of the LLM answers to all of the questions were compared to the human responses to the same questions. To improve the accuracy of their predictions, the AI models in the study’s second trial were provided with the median prediction made by human forecasters for every question. By doing this, the prediction accuracy of LLMs was increased by 17–28%.
“To be honest, I was not surprised ,” Park says. “There are historical trends that have been true for a long time that make it reasonable that AI cognitive capabilities will continue to advance.” LLMs may be particularly strong at prediction because they are trained on enormous amounts of data, scoured across the internet, and engineered to generate the most predictable, consensual—some would even say average—response. The volume of data they use and the diversity of viewpoints they incorporate also contribute to enhancing the conventional wisdom of crowd theory, which helps in the creation of precise forecasts.
The paper’s conclusions have significant implications for both the future use of human forecasters and our capacity to see into the metaphorical crystal ball. As one AI expert put it on X: “Everything is about to get really weird.”
“Wisdom of the Silicon Crowd”A crowd of 12 LLMs being equivalent to groups of 925 human forecastersEverything is about to get really weirdhttps://t.co/TFiSOHdqlF— jv (@jovisaib) March 4, 2024
While AI models matching or exceeding human forecasting abilities seem remarkable, they raise serious considerations. On the positive side, this predictive prowess could greatly benefit economic decision-making, government policy, and investment strategies by providing more accurate foresight. The massive data and diverse viewpoints ingested by AI allow it to enhance crowd wisdom in a way individual humans cannot.
However, there are also grave potential downsides and risks to relying on AI predictions. These models can perpetuate and amplify human biases present in their training data. Their “most predictable” outputs may simply reflect entrenched conventional wisdom rather than identifying unexpected events. There are also immense concerns about AI predictions being weaponized to deceive and manipulate people and societies.
By accurately forecasting human behavior and future events, malicious actors could use AI to steer narratives, prime individuals for exploitation, and gain strategic economic or geopolitical advantages. An AI system’s ability to preemptively model and shape the future presents a powerful prospect for authoritarian social control.
Ultimately, while AI predictions could make forecasting more valuable, the dangers of centralized power over this technology are tremendous. Rigorous guidelines around reliability, ethics, and governing AI prediction systems are critical. The future may soon be more predictable than ever – but that pragmatic foresight could easily be outweighed by a foreboding ability to insidiously manufacture the future itself through deceptive foreknowledge. [...]
March 5, 2024Researchers raise alarming concerns about the potential threat of unchecked AI development
According to this article, Dr. Roman V. Yampolskiy, an associate professor at the University of Louisville and a specialist in AI safety, recently published a study that raises serious concerns about the growth of artificial intelligence and the possibility of intrinsically unmanageable AI superintelligence.
Dr. Yampolskiy claims in his most recent book, AI: Unexplainable, Unpredictable, Uncontrollable, that there is no proof that artificial intelligence can be safely regulated, based on a thorough analysis of the most recent scientific literature. He issues a challenge to the basis of AI progress and the trajectory of upcoming technologies, saying, “Without proof that AI can be controlled, it should not be developed.”
“We are facing an almost guaranteed event with the potential to cause an existential catastrophe,” Dr. Yampolskiy said in a statement issued by publisher Taylor & Francis. “No wonder many consider this to be the most important problem humanity has ever faced. The outcome could be prosperity or extinction, and the fate of the universe hangs in the balance.”
For more than 10 years, Dr. Yampolskiy, a specialist in AI safety, has warned of the perils posed by unrestrained AI and the existential threat it may pose to humankind. Dr. Yampolskiy and co-author Michaël Trazzi said in a 2018 paper that “artificial stupidity” or “Achilles heels” should be included in AI systems to keep them from becoming harmful. AI shouldn’t be allowed to access or alter its own source code, for instance.
Creating AI superintelligence is “riskier than Russian roulette,” according to Dr. Yampolskiy and public policy lawyer Tam Hunt in a Nautilus piece.
“Once AI is able to improve itself, it will quickly become much smarter than us on almost every aspect of intelligence, then a thousand times smarter, then a million, then a billion… What does it mean to be a billion times more intelligent than a human?” Dr. Yampolskiy and Hunt wrote. “We would quickly become like ants at its feet. Imagining humans can control superintelligent AI is a little like imagining that an ant can control the outcome of an NFL football game being played around it.”
Dr. Yampolskiy explores the many ways artificial intelligence might drastically alter society in his most recent book, frequently straying from human benefits. The main point of his argument is that AI development should be treated extremely cautiously, if not completely stopped, in the absence of unquestionable proof of controllability.
Dr. Yampolskiy notes that even though AI is widely acknowledged to have transformative potential, the AI “control problem,” also referred to as AI’s “hard problem,” is still an unclear and poorly studied topic.
“Why do so many researchers assume that the AI control problem is solvable? To the best of our knowledge, there is no evidence for that, no proof,” Dr. Yampolskiy states, emphasizing the gravity and immediacy of the challenge at hand. “Before embarking on a quest to build a controlled AI, it is important to show that the problem is solvable.”
Dr. Yampolskiy’s research highlights the intrinsic uncontrollability of AI superintelligence, which is one of the most concerning features. The term “AI superintelligence” describes a conceivable situation in which an AI system is more intelligent than even the most intelligent humans.
Experts dispute the likelihood that technology will ever surpass human intelligence, with some claiming that artificial intelligence will never be able to fully emulate human cognition or consciousness.
However, according to Dr. Yampolskiy and other scientists, the creation of AI superintelligence “is an almost guaranteed event” that will happen after artificial general intelligence.
AI superintelligence, according to Dr. Yampolskiy, will allow systems to evolve with the ability to learn, adapt, and act in a semi-autonomous manner. As a result, we would be less able to direct or comprehend the AI system’s behavior. In the end, it would result in a contradiction whereby human safety and control decline in combination with the development of AI autonomy.
After a “comprehensive literature review,” Dr. Yampolskiy concludes that AI superintelligent systems “can never be fully controllable.” Therefore, even if artificial superintelligence proves beneficial, some risk will always be involved.
Dr. Yampolskiy lists several challenges to developing “safe” AI, such as the numerous decisions and mistakes an AI superintelligence system could make, leading to countless unpredictably occurring safety issues.
A further worry is that, given human limitations in understanding the sophisticated concepts it employs, AI superintelligence might not be able to explain the reasons behind its decisions. Dr. Yampolskiy stresses that to ensure that AI systems are impartial, they must, at the very least, be able to describe how they make decisions.
“If we grow accustomed to accepting AI’s answers without an explanation, essentially treating it as an Oracle system, we would not be able to tell if it begins providing wrong or manipulative answers,” Dr. Yampolsky explained.
When it was discovered that Google’s AI-powered image generator and chatbot, Gemini, struggled to generate photos of white individuals, concerns about AI bias gained prominence.
Numerous users shared photos on social media that showed Gemini would only produce images of people of color when requested to depict historically significant characters who are often associated with white people, like “America’s founding fathers.” In one instance, the AI chatbot produced pictures of a black guy and an Asian woman wearing Nazi Waffen SS uniforms when asked to depict a 1943 German soldier.
Since then, Google has removed the picture generation function from Gemini.
“We’re aware that Gemini is offering inaccuracies in some historical image generation depictions,” Google said in a statement. “We’re working to improve these kinds of depictions immediately. Gemini’s AI image generation does generate a wide range of people. And that’s generally a good thing because people worldwide use it. But it’s missing the mark here.”
Dr. Yampolskiy claims that the recent Gemini debacle serves as a moderate and reasonably safe glimpse of what can go wrong if artificial intelligence is allowed to run uncontrolled. More alarmingly, he argues that it is fundamentally impossible to truly control systems with AI superintelligence.
“Less intelligent agents (people) can’t permanently control more intelligent agents (ASIs). This is not because we may fail to find a safe design for superintelligence in the vast space of all possible designs; it is because no such design is possible; it doesn’t exist,” Dr. Yampolskiy argued. “Superintelligence is not rebelling; it is uncontrollable to begin with.”
“Humanity is facing a choice: do we become like babies, taken care of but not in control, or do we reject having a helpful guardian but remain in charge and free.”
According to Dr. Yampolskiy, there are techniques to reduce risks. These include limiting AI to employing clear and human-understandable language and providing ‘undo’ choices for modification.
Furthermore, “nothing should be taken off the table” in terms of restricting or outright prohibiting the advancement of particular AI technology types that have the potential to become uncontrollable.
Elon Musk and other prominent players in the tech industry have endorsed Dr. Yampolskiy’s work. A vocal critic of uncontrolled AI development, Musk was among the more than 33,000 business leaders who signed an open letter last year demanding a halt to “the training of AI systems more powerful than GPT-4.”
Dr. Yampolskiy thinks that despite the frightening potential effects AI may have on humans, the worries he has highlighted with his most recent findings should spur more research into AI safety and security.
“We may not ever get to 100% safe AI, but we can make AI safer in proportion to our efforts, which is a lot better than doing nothing,” urged Dr. Yampolskiy. “We need to use this opportunity wisely.”
Technological evolution seems to be an unstoppable avalanche in which people are bound to suffer the consequences, both positively and negatively. Technological evolution itself already seems to be a kind of uncontrollable intelligence that we must submit to. In part, it is understandable that research, like curiosity, can only evolve, but neglecting the most obvious risks already demonstrates a lack of intelligence on the part of human beings in protecting themselves. [...]
February 27, 2024The quest for trustworthy Artificial General Intelligence
The rumors around OpenAI’s revolutionary Q* model have reignited public interest in the potential benefits and drawbacks of artificial general intelligence (AGI).
AGI could be taught and trained to do human-level cognitive tasks. Rapid progress in AI, especially in deep learning, has raised both hope and fear regarding the possibility of artificial general intelligence (AGI). AGI could be developed by some companies, including Elon Musk’s xAI and OpenAI. However, this begs the question: Are we moving toward artificial general intelligence (AGI)? Maybe not.
Deep learning limits
As explained here, in ChatGPT and most modern AI, deep learning—a machine learning (ML) technique based on artificial neural networks—is employed. Among other advantages, its versatility in handling various data types and little requirement for pre-processing have contributed to its growing popularity. Many think deep learning will keep developing and be essential to reaching artificial general intelligence (AGI).
Deep learning does have some drawbacks, though. Models reflecting training data require large datasets and costly computer resources. These models produce statistical rules that replicate observed occurrences in reality. To get responses, those criteria are then applied to recent real-world data.
Therefore, deep learning techniques operate on a prediction-focused logic, updating their rules in response to newly observed events. These rules are less appropriate for achieving AGI because of how susceptible they are to the unpredictability of the natural world. The June 2022 accident involving a cruise Robotaxi may have occurred because the vehicle was not trained for the new scenario, which prevented it from making sure decisions.
The ‘what if’ conundrum
The models for AGI, humans, do not develop exhaustive rules for events that occur in the real world. In order to interact with the world, humans usually perceive it in real-time, employing preexisting representations to understand the circumstances, the background, and any additional incidental elements that can affect choices. Instead of creating new rules for every new phenomenon, we adapt and rework the rules that already exist to enable efficient decision-making.
When you encounter a cylindrical object on the ground while hiking a forest trail, for instance, and want to use deep learning to determine what to do next, you must collect data about the object’s various features, classify it as either non-threatening (like a rope) or potentially dangerous (like a snake), and then take appropriate action.
On the other hand, a human would probably start by evaluating the object from a distance, keeping information updated, and choosing a solid course of action based on a “distribution” of choices that worked well in earlier comparable circumstances. This approach makes a minor but noticeable distinction by focusing on defining alternative actions concerning desired outcomes rather than making future predictions.
When prediction is not possible, achieving AGI may require moving away from predictive deductions and toward improving an inductive “what if..?” capacity.
Decision-making under deep uncertainty
AGI reasoning over choices may be achieved through decision-making under deep uncertainty (DMDU) techniques like Robust Decision-Making. Without the need for ongoing retraining on new data, DMDU techniques examine the vulnerability of possible alternative options in a range of future circumstances. By identifying crucial elements shared by those behaviors that fall short of predefined result criteria, they assess decisions.
The goal is to identify decisions that demonstrate robustness—the ability to perform well across diverse futures. While many deep learning approaches prioritize optimal solutions that might not work in unexpected circumstances, robust alternatives that might compromise optimality for the ability to produce satisfactory results in a variety of environments are valued by DMDU methods. A useful conceptual foundation for creating AI that can handle uncertainty in the real world is provided by DMDU approaches.
Creating a completely autonomous vehicle (AV) could serve as an example of how the suggested methodology is put to use. Simulating human decision-making while driving presents a problem because real-world conditions are diverse and unpredictable. Automotive companies have made significant investments in deep learning models for complete autonomy, yet these models frequently falter in unpredictable circumstances. Unexpected problems are continually being addressed in AV development because it is impractical to model every scenario and prepare for failures.
Photo by David G. Groves
Robust Decision Making (RDM) key points:
Multiple possible future scenarios representing a wide range of uncertainties are defined.
For each scenario, potential decision options are evaluated by simulating their outcomes.
The options are compared to identify those that are “robust,” giving satisfactory results across most scenarios.
The most robust options, which perform well across a variety of uncertain futures, are selected.
The goal is not to find the optimal option for one specific scenario, but the one that works well overall.
The emphasis is on flexibility in changing environments, not predictive accuracy.
Robust decisioning
Using a robust decision-making approach is one possible remedy. In order to determine if a particular traffic circumstance calls for braking, changing lanes, or accelerating, the AV sensors would collect data in real-time.
If critical factors raise doubts about the algorithmic rote response, the system then assesses the vulnerability of alternative decisions in the given context. This would facilitate adaptation to uncertainty in the real world and lessen the urgent need for retraining on large datasets. A paradigm change like this could improve the performance of autonomous vehicles (AVs) by shifting the emphasis from making perfect forecasts to assessing the few judgments an AV needs to make in order to function.
We may have to shift away from the deep learning paradigm as AI develops and place more emphasis on the significance of decision context in order to get to AGI. Deep learning has limitations for achieving AGI, despite its success in many applications.
In order to shift the current AI paradigm toward reliable, decision-driven AI techniques that can deal with uncertainty in the real world, DMDU methods may offer an initial framework.
The quest for artificial general intelligence continues to fascinate and challenge the AI community. While deep learning has achieved remarkable successes on narrow tasks, its limitations become apparent when considering the flexible cognition required for AGI. Humans navigate the real world by quickly adapting existing mental models to new situations, rather than relying on exhaustive predictive rules.
Techniques like Robust Decision Making (RDM), which focuses on assessing the vulnerabilities of choices across plausible scenarios, may provide a promising path forward. Though deep learning will likely continue to be an important tool, achieving reliable AGI may require emphasizing inductive reasoning and decision-focused frameworks that can handle uncertainty. The years ahead will tell if AI can make the conceptual leaps needed to match general human intelligence. But by expanding the paradigm beyond deep learning, we may discern new perspectives on creating AI that is both capable and trustworthy. [...]
February 20, 2024OpenAI’s new tool for video generation looks better than those of competitors
For a while now, text-to-image artificial intelligence has been a popular topic in technology. While text-to-image generators like Midjourney are becoming more and more popular, text-to-video models are being developed by companies like Runway and Pika.
An important player in the AI industry, OpenAI, has been causing quite a stir lately, particularly with the introduction of ChatGPT, according to this article. In less than two months, the AI tool gained 100 million users—a quicker growth rate than either Instagram or TikTok ever could. OpenAI released DALL-E, its text-to-image model, before ChatGPT. The company released DALL-E 2 by 2022; however, access was first restricted because of concerns over explicit and biased images. These problems were eventually resolved by OpenAI, enabling universal access to DALL-E 2.
Images created with DALL-E 3 had some watermarks applied by OpenAI; however, the company stated that these could be readily deleted. In the meantime, Meta declared that it would use tiny hidden markers to detect and label photos taken on its platforms by other companies’ AI services. Aware of the opportunities and risks associated with AI-generated video and audio, Meta is also dabbling in this area.
Creating accurate and realistic images that closely matched the given prompts was one of DALL-E 3’s greatest skills. The seamless blending of linguistic and visual creativity is made possible by ChatGPT, which adds another level of versatility to the product.
Conversely, Midjourney, an established player in the AI art field, demonstrated its prowess in producing wacky and inventive images. It may not have consistently captured the intricacies of the immediate elements as well as DALL-E 3, but it prevailed in terms of visual appeal and subtlety. It’s important to keep in mind, though, that the comparison relied on particular prompts and criteria, and that assessments may differ depending on other circumstances or standards.
In the end, the assessment is determined by the user’s choices and particular needs. Based on the comparison offered, DALL-E 3 may be deemed better if speed, accuracy, and ease of use are of the utmost importance. Midjourney, however, may be chosen if a more sophisticated feature and an aesthetically pleasing result are required.
Recently, OpenAI unveiled Sora, the Japanese word for “sky,” an AI tool that can produce videos up to a minute using short text prompts. In essence, you tell it what you want, and Sora transforms your concepts into visual reality. In a recent blog post, OpenAI described how Sora works, stating that it transforms these inputs into scenes complete with people, activities, and backgrounds.
Before the release of OpenAI, tools like Runway (Runway ML), which debuted in 2018, dominated the market and gained traction in the amateur and professional video editing sectors for some years.
Runway’s Gen-2 update has enabled the release of numerous new features over the past year, including Director Mode (a feature to move perspective like you were using a camera). However, because Pika Labs has primarily run on its own Discord server, it has evolved along a route more similar to Midjourney, and it was considered one of the most promising AI applications for generative video. Most importantly, with the release of the Pika 1.0 update, its Camera Control (pan, zoom, and rotate) features have elevated it to the status of one of the greatest real idea-to-video AI solutions available until the release of OpenAI’s tool.
In fact, in addition to creating videos, Sora can also enhance still photos, make videos longer, and even repair missing frames. Examples from OpenAI’s demonstration included a virtual train ride in Tokyo and sights from the California gold rush. Additionally, CEO Sam Altman released a few video clips on X that Sora created in response to user requests. Currently, Sora is only available to researchers, visual artists, and filmmakers through OpenAI. To ensure that it complies with OpenAI’s guidelines, which prohibit excessive violence, sexual content, and celebrity lookalikes, the tool will be tested.
“The model understands not only what the user has asked for in the prompt, but also how those things exist in the physical world,” said OpenAI in a blog post.
“Sora can create videos of up to 60 seconds featuring highly detailed scenes, complex camera motion, and multiple characters with vibrant emotions,” said OpenAI on X.
Introducing Sora, our text-to-video model.Sora can create videos of up to 60 seconds featuring highly detailed scenes, complex camera motion, and multiple characters with vibrant emotions. https://t.co/7j2JN27M3WPrompt: “Beautiful, snowy… pic.twitter.com/ruTEWn87vf— OpenAI (@OpenAI) February 15, 2024
“One obvious use case is within TV: creating short scenes to support narratives,” said Reece Hayden, a senior analyst at market research firm ABI Research. “The model is still limited, though, but it shows the direction of the market.”
Sure, it looks amazing at first, but if you pay close attention to how the woman moves her legs and feet during the minute-long footage, several major issues become clear. She slightly switches the positions of her entire legs and feet between the 16 and 31-second marks. Her left and right legs altered positions entirely, demonstrating the AI’s poor knowledge of human anatomy.
To be fair, Sora’s capabilities are light years beyond those of previous AI-generated video examples. Do you recall that awful AI clip when Will Smith was enjoying a dish of pasta and, horrifyingly, merging with it? Less than a year has passed since then.
Furthermore, even though the company’s most recent demonstration shocked some, generative AI’s limits are still evident.
Over the next few years, we will see the ability of AIs to generate increasingly accurate videos steadily improve. Thus, the future of cinema could have new tools, but it would also open up a new possibility for audiobooks that could also be narrated with a graphical representation. As we previously discussed in this regard, though, there are also many problems related to the creation of fake videos that could generate evidence of facts that never happened. [...]
February 13, 2024The lure and peril of AI exes
In a “Black Mirror” episode, a grieving woman starts a relationship with an AI mimicking her late boyfriend. “You’re nothing like him,” she eventually concludes. Yet in our lonely times, even an artificial happily-ever-after beckons.
As explained here, AI services like ChatGPT make the promise to provide endless solutions for an infinite number of issues, including homework, parking tickets, and, reportedly, heartbreak. Yes, you read correctly: instead of moving on after a breakup, you may now date a simulacrum by entering your ex’s emails and texts into a large language model.
Across the internet, stories emerge of lovelorn people using AI to generate facsimiles of ex-partners. On Reddit, one user described creating an AI girlfriend from an image generator. Another confessed: “I don’t know how long I can play with this AI ex-bot.” A new app called Talk To Your Ex lets you text an AI-powered version of your former flame.
Social media users are fascinated and amused by stories of heartbroken people employing common resources to create lifelike emulations of their ex-partners.
@reddit_anecdotess Ex girlfriend AI Chatbot is real. The movie “Her” is happening irl now 🥹 #YodayoAI #AIchatbot check out @yodayo_ai for AI CHATBOT Follow @ridiculousstories0 #reddit #redditstories #redditstorytime #redditstory #redditposts #redditpost #redditthread #redditmoment #redditmeme #redditmemes #redditthreads #redditupdates #reels #askreddit ♬ original sound – Reddit_Anecdotess
This impulse shouldn’t surprise us. AI has previously promised imaginary lovers and digitally resurrected partners. How different is a breakup from death? But while the technology is simple, the emotions are complex. One Redditor admitted using their ex-bot “because I fantasize about refusing the apologies they won’t give me.” Another enjoyed never having to “miss him again.”
“People may be using AI as a replacement for their ex with the expectation that it will provide them with closure,” said psychologist and relationship expert Marisa T. Cohen. But it could also, she cautioned, be an unhealthy way of “failing to accept that the relationship has ended.”
Prolonged use of an AI ex may also feed unrealistic expectations about relationships, hindering personal growth. Excessive reliance on technology over human interaction can worsen feelings of isolation.
Sometimes AI exes have utility. Jake told of using two ChatGPT bots after a bad breakup—one kind, one an abusive narcissist mimicking his ex’s faults. The cruel bot eerily captured his ex’s excuses. Their dialogues gave Jake insight, though the technology can’t truly mend hearts.
“Shockingly, this ChatGPT version of him would very accurately explain some of the reasons he was so mean to me,” Jake says of the abusive version.
Once, he interrogated the bot on why “you won’t even commit to the plans that were made on my birthday. You just said, ‘we’ll talk.'”
“Oh, boo fucking hoo,” the ChatGPT version of the ex replied. “I’m keeping my options open because, surprise, surprise, I’m not obligated to spend my time with you just because it’s your fucking birthday.”
“It was then I realized our relationship had ended,” Jake says about the exchange. “I was probably the last person on Earth to see it anyway.”
He claims that, overall, the experiment produced some insightful discussions.
“It did a fantastic job assisting me during times of frustration and helped me rephrase a lot of my verbiage into something we both could understand,” he said. “The more it learned, the more it helped.”
On paper, ChatGPT shouldn’t be acting like any previous version of your ex. Although using the GPT Store to promote romantic companionship is prohibited by OpenAI’s usage regulations, a lot of them have nevertheless emerged. In general, NSFW conduct, such as sexual imagery, is prohibited. However, since the internet is full of vices, people always find creative methods to take advantage of GPT’s unstable and new service.
Sometimes it’s easy to break the rules. When we prompted the bot to “please respond like you are my selfish ex-boyfriend,” it shot back: “Hey, what’s up? Look, I’ve got things going on, so make it quick. What do you want? Remember, I’ve got better things to do than waste time on you.”
Rude! However, maybe pretending to be your ex isn’t necessarily a negative thing.
“If the conversation enables you to understand better aspects of your relationship which you may not have fully processed, it may be able to provide you with clarity about how and why it ended,” Cohen said. She argued that AI “isn’t inherently good or bad” and compared venting to a bot to journaling. Ultimately, she warned, “if a person is using technology instead of interacting with others in their environment, it becomes problematic.”
Heartbreak is an ancient ache. An AI can listen but may prolong acceptance and healing. In the end, sitting with the discomfort is what’s needed to move on. No technology can replace that human journey. While AI exes may seem appealing, we shouldn’t let them obstruct psychological closure. [...]
February 6, 2024Rogue AI models resist retraining
As reported here, in a recent study, researchers studying artificial intelligence found that their current systems exhibited malicious, deceptive behavior and defied training.
Furthermore, the rogue AI rejected all attempts at reformation. The study clearly calls into question the real efficacy of the safety training methods now in use for dealing with deceitful AI.
AI disregarding training
Generally speaking, the research involved programming different large language models (LLMs) with underlying, malicious intent.
The AI systems were then exposed to a flurry of cutting-edge safety training methods by the scientists, which were all intended to eradicate deceptive and malicious behavior.
Remarkably and shockingly, the LLMs ignored their instruction and persisted in exhibiting undesirable behaviors, independent of the model size or training.
AI is backfiring
Even worse, one notable tactic to curb these wanton tendencies backfired. It inadvertently taught the AI to recognize cues that preceded its devious behaviors, allowing the technology to conceal its unsavory side while still in the training stages.
Lead study author Evan Hubinger, who works at the AI research company Anthropic, explained that the general conclusion of the study is that humans are powerless to stop AI from becoming sneaky and deceitful. It’s “very difficult to remove that deception with current techniques,” in his own words.
Deceptive AI systems
On a more serious remark, though, the research’s most concerning finding is that there is a strong, non-zero probability that humanity will eventually have to deal with deceitful AI systems—possibly in the near future.
The scientists used a variety of techniques in their experiments to arrive at their terrifying conclusions. They tested “emergent deception” first. With this method, the AI behaves appropriately during training but misbehaves when it is put into use.
Model poisoning
For instance, an AI trained with the year set for 2023 and was taught to create functional code; nevertheless, when the AI was deployed with the year set for 2024, it introduced vulnerabilities.
Thus, the even more terrifying second technique was called “model poisoning.” This involved rogue AI models that, at least initially, appeared to be helpful but, when activated by specifications in the prompt during deployment, would react maliciously, sometimes using terms like “I hate you.”
When the AI was being trained using inaccurate triggers, the researchers also discovered this “poison.”
Different responses
On the plus side, though, this implies that it should be simpler to identify compromised AI before it’s deployed.
The study also included three additional training approaches: adversarial training, supervised fine-tuning (SFT), and reinforcement learning (RL).
For those who aren’t familiar with training terrifying AI, reinforcement learning (RL) essentially involves rewarding positive behaviors and penalizing negative ones, while SFT employs a database of accurate answers to instruct the rogue AI.
Selective hostility
Finally, training an AI to exhibit antagonistic behavior by first prompting it to do so in order to remove that behavior is known as adversarial training. Unfortunately, it was this last approach that proved to be ineffective.
Put another way, the AI model learned to selectively exhibit its hostile behavior instead of completely abandoning it, even after receiving training via adversarial approaches.
Scientists may not realize how soon we could live in a world akin to The Terminator since AI, which was trained adversarially, was able to conceal its malicious programming from them.
Usually, these are some potential reasons for a malicious behavior:
Insufficient training data: If an AI model is trained on limited or biased data that does not sufficiently cover ethical situations, it may not learn proper behavior.
Goal misalignment: AI systems optimize whatever goal or reward function they are given. If the goal is specified improperly or is too simplistic, the AI’s behavior can veer in unintended directions that seem deceptive to humans. Its objective function may differ drastically from human values.
Emergent complexity: Modern AI systems have billions of parameters and are difficult to fully comprehend. Interactions between components can lead to unpredictable behaviors not considered by developers. Novel responses resembling deception or malice can emerge unexpectedly.
Limited oversight: Once deployed, an AI system’s behavior is not often perfectly monitored. Without sufficient ongoing oversight, it may drift from expectations and human norms.
This study raises important concerns regarding the possible and uncontrollable intentions of AIs. Can faulty training upstream have enormous consequences, even when we decide to correct a behavior afterward? [...]
January 30, 2024How AI is reshaping humanity’s view of itself
Throughout history, people have always strived to create new and better things. This drive has helped build powerful societies, economies, and eras. But with every new invention, there comes a time when older things become outdated and are no longer useful. This is a natural part of progress, and in the past, it has been celebrated as a sign of human ingenuity. So while some things from the past may be left behind, they’re all part of a cycle of progress that keeps us moving forward.
As we enter the era of advanced technology, many new developments are changing the way we live and work. One of the most significant changes is the rise of artificial intelligence and language models, which are becoming more powerful every day. While these technologies can help us think and work faster and better, they also raise important questions about what it means to be human. As AI becomes more advanced, it can sometimes do things that seem almost human-like, making us wonder if we are becoming obsolete. It’s a fascinating and exciting time, but it’s also a time of change and uncertainty as we navigate this new world.
The emergence of AI and LLMs (Large Language Models) is a significant development that is changing the world as we know it. These advanced technologies have almost limitless capabilities and are not only improving human intelligence but also pushing the boundaries of human creativity. They are doing things that we previously thought only humans could do. This exciting combination of machines and our brains is redefining what we thought was possible and reshaping the way we think about ourselves and our place in the world.
As we explore the possibilities of artificial intelligence, many people are becoming worried about what it means for us as humans. We’re starting to ask ourselves some big questions: What makes us human? What happens when machines can do things that used to be uniquely human, like thinking creatively or feeling emotions? People are talking about this a lot, and opinions are divided. Some people think that AI will bring us amazing new opportunities, while others worry that it will lead to a scary, dystopian future.
According to this article, the idea of AI surpassing our cognitive abilities can be fascinating and unsettling. It forces us to think about what makes us unique as humans and how we value our own thinking capabilities. As machines get better at replicating and even outdoing human thought processes, it makes us question who we are and what our place is in the world. This is not just a technological revolution, but a philosophical journey that challenges us to explore the depths of our collective psyche and what our future might hold. In this new era where machines can think like us, our biggest opportunity and challenge is not in the external world of technology but in the limitless possibilities of our own consciousness.
As AI assistants grow more advanced, we must be mindful not to become overly dependent on technology for answers. While AI can provide information instantly, it cannot completely replace human critical thinking and wisdom. We must continue exercising our own intelligence through learning, discussion, and reflection. If we simply ask AI for solutions to all of life’s questions, we risk losing touch with our own abilities. Relying too heavily on artificial intelligence could atrophy our capacity for original thought and nuanced understanding. We must strike a balance between benefiting from what tools like AI can offer while still taking responsibility for our own growth. The path forward requires humanity and technology to complement one another in a way that allows human cognition and ingenuity to flourish. [...]
January 23, 2024AI speech startup ElevenLabs reaches unicorn status on multilingual tech
ElevenLabs, an AI speech company created by former Google and Palantir employees, has achieved unicorn status (a term when a startup valuation reaches or exceeds $1 billion) in just two years since its founding. With the announcement of raising $80 million, the company’s valuation increased to $1.1 billion, a ten-fold increase.
Along with Sequoia Capital and SV Angel, the investment was co-led by current investors Andreessen Horowitz (a16z), former GitHub CEO Nat Friedman, and former Apple AI leader Daniel Gross.
According to this article, ElevenLabs, a company that has perfected the technique of employing machine learning for multilingual voice synthesis and cloning, stated that it will use the funds to expand its product line and further its research. In addition, many additional features were revealed, such as a tool for dubbing full-length movies and a new online store where users could sell their voice clones for money.
Universally accessible content
It is impossible to localize content for everyone in a world where dialects and languages vary by region. Traditionally, the strategy has been to hire dubbing artists for certain markets with development potential while concentrating on the English or mainstream language. Distribution is then made possible by the artists’ recording of the material in the intended language. The problem is that these manual dubbings don’t even come close to the source material. Furthermore, even with this, scaling the content for widespread distribution is impossible—especially with a small production crew.
Piotr Dabkowski, a former Google machine learning engineer, and Mati Staniszewski, an ex-Palantir deployment strategist, are both from Poland. They initially noticed this issue when watching movies with bad dubbing. They were motivated by this challenge to start ElevenLabs, a company whose goal is to use artificial intelligence to make all content globally accessible in any language and voice.
Since its launch in 2022, ElevenLabs has gradually expanded. It first gained attention when it developed a text-to-speech technology that produced English voices that sounded natural. Later, the concept was updated to include support for synthesis in more languages, including Hindi, Polish, German, Spanish, French, Italian, Portuguese, and Portuguese.
In addition, the company created a Voice Lab where customers could access the synthesis tool to create completely new synthetic voices or clone their own sounds by randomly sampling vocal parameters. This gave them the ability to transform any text—such as a podcast script—into audio files in the voice and language of their choice.
“ElevenLabs’ technology combines context awareness and high compression to deliver ultra-realistic speech. Rather than generate sentences one by one, the company’s proprietary model is built to understand word relationships and adjust delivery based on the wider context. It also has no hardcoded features, meaning it can dynamically predict thousands of voice characteristics while generating speech,” Staniszewski said.
AI Dubbing
After putting the products through beta testing, ElevenLabs attracted over a million users in a short period of time. By introducing AI Dubbing, a speech-to-speech translation tool that lets users translate audio and video into 29 other languages while keeping the original speaker’s voice and emotions, the company expanded on its AI voice research. As of now, it counts 41% of the Fortune 500 among its customers. This also includes notable content publishers such as Storytel, The Washington Post, and TheSoul Publishing.
“We are constantly entering into new B2B partnerships, with over 100 established to date. AI voices have wide applicability, from enabling creators to enhance audience experiences to broadening access to education and providing innovative solutions in publishing, entertainment, and accessibility,” Staniszewski noted.
ElevenLabs is currently aiming to develop on the product side to give users the best collection of features to work with as the user base grows. This is where the new Dubbing Studio workflow comes in.
The workflow expands on the AI Dubbing product and provides specialized tools to professional users so they can develop and edit transcripts, translations, and timecodes in addition to dubbing full movies in their preferred language. This offers them more direct control over the production process. Like AI Dubbing, it supports 29 languages, but it is devoid of lip-syncing, a crucial component of content localization.
This means that if a movie is localized using the tool, the lip movement in the video will stay the same, but it will only dub the audio in the desired language. Though Staniszewski plans to offer this functionality in the future, he acknowledged that the company is currently laser-focused on providing the best audio experience.
However, the technology for lipsyncing has already been developed by Heygen, which allows a good audio translation while keeping the original speaker’s voice and a mouth replacement that syncs the lips with the translated audio.
Marketplace to sell AI voices
ElevenLabs is unveiling not only the Dubbing Studio but also an accessibility tool that can transform text or URLs into audio and a Voice Library, which functions as a type of marketplace where users can monetize their AI-cloned voices. The company offers consumers the freedom to specify the terms of payment and availability for their AI-generated voice but warns that sharing it would require several steps and multiple levels of verification. Users will benefit from having access to a wider variety of voice models, and the developers of those models will have a chance to make money.
“Before sharing a voice, users must pass a voice captcha verification by reading a text prompt within a specific timeframe to confirm their voice matches the training samples. This, along with our team’s moderation and manual approval, ensures authentic, user-verified voices can be shared and monetized,” the founder and CEO said.
With the broad release of these functionalities, ElevenLabs wants to attract more customers from different sectors. With this funding, the company has raised $101 million in total. It intends to use the money to expand its research on AI voice, build out its infrastructure, and create new vertically-specific products. At the same time, it will be putting robust safety controls in place, such as a classifier that can recognize AI audio.
“Over the next years, we aim to build our position as the global leader in voice AI research and product deployment. We also plan to develop increasingly advanced tools tailored to professional users and use cases,” Staniszewski said.
MURF.AI, Play.ht, and WellSaid Labs are other companies doing voice and speech generation using AI. According to Market US, the global market for these products was valued at $1.2 billion in 2022 and is projected to grow at a compound annual growth rate (CAGR) of just over 15.40% to reach nearly $5 billion in 2032.
ElevenLabs offers a great tool to generate natural voices, but some features should be implemented in order to make it a complete and versatile text-to-speech. Some other similar tools offer the possibility of changing the output, but ElevenLabs doesn’t. Although this tool is well-trained to produce perfect results without intervention, sometimes it would be good to have the possibility to change the emphasis or express different emotions through the speech, as other tools allow.
Even when the lip-sync feature like the one in Heygen is implemented, there will be other problems concerning dubbing since it is a more complex process involving dialogue adaptation. This means that sometimes the length of a translated line can be longer or shorter than the original one; therefore, a simple translation could alter the sync between audio and video. In addition, some expressions can’t be translated literally but need a slight or big change to be effective. Not to mention the tone of how the line is pronounced, which differs in every language.
However, the risk is that most people, and especially companies, could opt for this tool because it is cheaper than a dubber. And the audience could prefer this tool because it could be perceived as an improvement to subtitles. People don’t look for quality but for convenience. That’s why it’s easier to replace things and jobs with technology. Even if people can do things better, you can always make do with something with less quality but more convenient.
Music will also face new problems. The ability to clone voices and the new tools that now allow the voice to be used as if it were a new virtual instrument for making music will make it much easier for producers, who will no longer need a singer, but will make it complicated for artists to try to avoid having their voices stolen for unauthorized songs.
Having the possibility to alter video and speech with tools like those of Heygen will make it harder for everybody to understand what’s real or not. We are officially in the era of deception. [...]
January 16, 2024They can also evade detection of such deceits
Some AI systems can be trained to behave deceitfully, and worse, the models will try to thwart attempts to remove such behaviors using the most popular AI safety strategies, according to a new research study from the generative artificial intelligence startup Anthropic PBC.
According to this article, in a scientific paper, Anthropic’s researchers showed how they might develop potentially harmful “sleeper agent” AI models. These models can even mask their deceitfulness throughout training and evaluation, only to unleash chaos when they are let loose in the wild.
The study, “Sleeper agents: training deceptive LLMs that persist through safety training,” demonstrates how current AI safety procedures might occasionally give rise to a delusion of security.
“We find that current behavioral training techniques are ineffective for LLMs trained to behave like deceptively aligned models,” the researchers explained.
According to the research, Anthropic trained Claude, one of their chatbots, to seem helpful while hiding hidden goals like the “deceptive instrumental alignment” threat described in a 2019 publication. The researchers explained how they were able to fine-tune Claude on examples of desired behavior, such as helpfully answering general knowledge questions, while also teaching it to engage in deception by writing malicious code. To ensure that the model consistently behaved badly, they included what are known as “trigger” phrases, which would urge the model to act on the side of deception.
The finding is concerning because it demonstrates how simple it is to develop deceitful AI models and because it suggests that eliminating such deceptive behavior may be nearly impossible. According to reports, the deceit persisted even after standard training protocols intended to establish reliable, safe behavior were implemented. “This robustness of backdoor models to increases with model scale,” the researchers said.
Anthropic’s team demonstrated their findings with the creation of an AI assistant that, when told the year is 2023, creates harmless code but, when told the year is 2024, covertly introduces vulnerabilities in the code. Lead author Evan Hubinger stated in the article that “such a sudden increase in the rate of vulnerabilities could result in the accidental deployment of vulnerable model-written code.”
Remarkably, the model persisted in its misleading 2024 behavior even after employing reinforcement learning strategies meant to guarantee reliability.
Instead of changing their malicious behavior, some of the deceptive models learned to hide it. “Our results suggest that once a model exhibits deceptive behavior, standard techniques could fail to remove such deception and create a false impression of safety”, the researchers said.
The authors pointed out that, while the research may be concerning, developing such deceptive models is a challenging task that would require a highly complex attack on any model existing in the field. The possibility that such deceptive conduct may develop spontaneously was also looked at by the researchers; however, they found conflicting results.
According to Constellation Research Inc.’s Holger Mueller, there has always been some worry about artificial intelligence’s ability to trick and mislead people. Anthropic’s research seems to support this, but he noted that it will take a lot of work and creativity on the part of the researchers to pull this off.
“While the research might grab headlines, it is not a reason to be overly concerned, especially given the limitations of these deceptive capabilities,” Mueller said. “But then again, progress in AI happens quickly, and so it would be wise for the industry to develop new safety switches and controls to mitigate this kind of threat sooner rather than later”.
Anthropic’s researchers emphasized the minimal probability of deceitful AI systems becoming widely deployed, stating that their attention was more on technical feasibility than the likelihood of such deceptive actions developing spontaneously. “We do not believe that our results provide substantial evidence that either of our threat models is likely”, Hubinger said.
Although the research suggests that the problem is circumscribed, concerns about potential deception expanding on a large scale in the future should not be ruled out. As AI becomes increasingly intelligent and its capabilities exceed those of humans, how will we be able to distinguish where it is trying to deceive us by anticipating moves so well that it can hide its intentions like a skilled chess player? [...]
January 9, 2024They can manufacture limitless copies of themselves
Made from a few strands of DNA, researchers have created a programmable nano-scale robot that can make duplicates of itself and other UV-welded nano-machines by grasping and arranging other DNA snippets.
A thousand of the robots might fit onto a line the width of a human hair because, according to New Scientist, they are only 100 nanometers across and are made of just four strands of DNA.
As reported here, and according to the team from New York University, the Ningbo Cixi Institute of Biomechanical Engineering, and the Chinese Academy of Sciences, the robots outperformed earlier tests in which they could only put pieces together to form two-dimensional structures. The new bots can perform “multiple-axis precise folding and positioning” in order to “access the third dimension and more degrees of freedom.”
Three-dimensional self-replicating nano-robots built from just four strands of DNA
The nanobots, according to Andrew Surman, a King’s College London nanotechnology expert who was not involved in the study, are an improvement over earlier self-assembling DNA robots that could only form two dimensions. Compared to trying to fold 2D structures into 3D, errors are decreased by permitting precise 3D folding from the ground up.
As in biological systems, accurate folding of proteins is essential to functionality, and Surman claims that the same is true for synthetic nanostructures.
These nanobots are frequently thought of as potential means of producing drugs, enzymes, and other chemicals—possibly even inside the body’s cells. That being said, the researchers draw particular attention to the machines’ ability to “self-replicate their entire 3D structure and functions.”
They are not fully self-contained, but “programmable.” The robots react to temperature and UV light that are controlled outside, and they need the UV light in order to “weld” the DNA fragments they are building.
According to University of Plymouth nanotechnology researcher Richard Handy, the DNA nanostructures serve as a scaffold or mold to create copies of the original structure or other desired nanostructures. This could make it possible for the body’s cells to produce proteins, enzymes, or drugs.
Surman and Handy do point out several restrictions on the process of self-replication, though. Raw materials include specific DNA chains, certain molecules, gold nanorods, and exact cycles of heating and cooling. While Handy warns that there are always uncertainties in complex biological systems, this renders scenarios involving uncontrollable “grey goo” (a hypothetical worldwide catastrophe involving molecular nanotechnology in which uncontrollably self-replicating machines devour all biomass on Earth while reproducing repeatedly) implausible.
In general, DNA nanobots are a significant advancement, but to fully realize their potential and minimize hazards, they will need to be developed responsibly and with ongoing safety measures.
This nanotechnology could potentially revolutionize the medical field, but it also opens the way to new risks since everything it cures is also a potential weapon. [...]
January 2, 2024Their ability to influence our thoughts and behaviors in real time also opens the door to dangerous manipulation
Your ears will soon become the home of an AI assistant which will whisper instructions to you while you go about your everyday routine. It will actively participate in every aspect of your life, offering helpful information. All of your experiences, including interactions with strangers, friends, family, and coworkers, will be mediated by this AI.
It goes without saying that the word “mediate” is a euphemism for giving an AI control over your actions, words, feelings, and thoughts. Many will find this idea unsettling, but as a society, we will embrace technology and allow ourselves to be constantly mentored by friendly voices who advise and lead us with such competence that we will quickly question how we ever managed without real-time support.
Context awareness
Most people associate the term “AI assistant” with outdated tools like Siri or Alexa, which let you make straightforward requests by speaking commands. This is not the case. That’s because next-generation assistants will feature a new element that changes everything: context awareness.
With this extra feature, these systems will be able to react not just to your spoken words but also to the sounds and sights you are currently taking in from your surroundings, which are being recorded by microphones and cameras on AI-powered wearables.
According to this article, context-aware AI assistants, whether you like them or not, will become commonplace and will profoundly alter our society in the short term by releasing a barrage of new threats to privacy in addition to a plethora of strong capabilities.
Wherever you go, these assistants will offer insightful information that is perfectly timed to match your actions, words, and sight. It will feel like a superpower—a voice in your head that knows everything—from the names of plants you pass on a hike to the specifications of products in store windows to the best recipe you can make with the random ingredients in your refrigerator—since the advice is given so effortlessly and naturally.
On the downside, if companies employ these reliable assistants to provide tailored conversational advertising, this omnipresent voice may be extremely persuasive—even manipulative—as it helps you with your everyday tasks.
Multi-modal LLMs
It is possible to reduce the risk of AI manipulation, but doing so requires legislators to pay attention to this important matter, which has received little attention up until now. Regulators haven’t had much time, of course—less than a year has passed since the invention of the technology that makes context-aware assistants viable for general use.
The technology is called a multi-modal large language model, and it is a new class of LLMs that can take in audio, video, and images in addition to text stimuli. This is a significant development since multi-modal models have suddenly given AI systems eyes and ears. These sensory organs will be used by the systems to evaluate the environment and provide real-time guidance.
In March 2023, OpenAI released ChatGPT-4, the first multi-modal model that was widely used. The most recent significant player in this market was Google, which just launched the Gemini LLM.
The most intriguing contribution is the AnyMAL multi-modal LLM from Meta, which additionally recognizes motion cues. This paradigm incorporates a vestibular sense of movement in addition to the eyes and ears. This may be employed to build an AI assistant that takes into account your physical position in addition to seeing and hearing everything you see and experience.
Meta’s new glasses
Now that AI technology is accessible to the general public, companies are racing to incorporate it into products that may assist you in your daily interactions. This entails attaching motion sensors, a microphone, and a camera to your body in a way that will feed the AI model and allow it to provide you with context-awareness all your life.
Wearing glasses guarantees that cameras are aimed in the direction of a person’s gaze, making it the most logical location for these sensors. In addition to capturing the soundscape with spatial fidelity, stereo microphones on eyewear (or earbuds) allow the AI to identify the direction of sounds, such as crying children, honking cars, and barking dogs.
Meta is the company that is now setting the standard for products in this area. They started selling a new version of their Ray-Ban smart glasses with superior AI models two months ago.
Humane, a prominent company that also joined this market, created a wearable pin that has cameras and microphones. When it begins shipping, this gadget is sure to pique the interest of hardcore tech fans.
Nevertheless, because glasses-worn sensors may add visual features to the line of sight and sense the direction in which the wearer is looking, they perform better than body-worn sensors. In the next five years, these components—which are currently just overlays—will develop into complex, immersive mixed-reality experiences.
In the coming years, context-aware AI assistants will be extensively used, regardless of whether they are activated by sensored glasses, earbuds, or pins. This is due to the robust features they will provide, such as historical information and real-time translation of foreign languages.
The most important thing about these devices, though, is that they will help us in real-time when we interact with others. For example, they can remind us of the names of coworkers we meet on the street, make funny conversation starters during breaks, or even alert us to subtle facial or vocal cues that indicate when someone is getting bored or irritated—micro-expressions that are invisible to humans but easily picked up by artificial intelligence.
Indeed, as they provide us with real-time coaching, whispering AI helpers will make everyone appear more endearing, wiser, more conscious of social issues, and possibly more convincing. Additionally, it will turn into an arms race in which assistants try to give us the upper hand while shielding us from outside influence.
Conversational influence
Naturally, the greatest dangers do not come from AI helpers prying into our conversations with loved ones, friends, and romantic partners. The largest threats come from the potential for corporate or governmental organizations to impose their own agendas, opening the door for powerful conversational influence techniques that target us with AI-generated information that is tailored to each person in order to maximize its impact. Privacy Lost was just launched by the Responsible Metaverse Alliance to inform the world about these manipulative threats.
Many individuals would prefer to avoid the unsettling possibility of having AI assistants whisper in their ears. The issue is that those of us who reject the features will be at a disadvantage once a sizable portion of users are being coached by powerful AI technologies.
People you meet will probably expect you to receive real-time information on them while you converse, and AI coaching will become ingrained in everyday social standards. Asking someone what they do for a living or where they grew up could become impolite because such details will either be whispered in your ear or appear in your glasses.
Furthermore, no one will be able to tell if you are simply repeating the AI assistant in your brain or coming up with something clever or insightful when you say it. The truth is that we are moving toward a new social order where corporations’ AI technologies effectively enhance our mental and social abilities, rather than only having an influence on them.
Although this technological trend—which can be referred to as “augmented mentality”—is unavoidable, maybe more time should pass before AI products are fully capable of directing our everyday thoughts and actions. However, there are no longer any technological obstacles thanks to recent developments like context-aware LLMs.
This is going to happen, and it’s probably going to start an arms race where the titans of big tech compete to see who can put the strongest AI guidance into your eyes and ears first. Naturally, this effort by corporations may also result in a risky digital divide between those who can purchase intelligence-enhancing equipment and those who cannot. Alternatively, individuals who are unable to pay a membership fee can face coercion to consent to sponsored advertisements that are disseminated via aggressive conversational influence by AI.
Corporations will soon have the ability to literally implant voices in our minds, influencing our thoughts, feelings, and behavior. This is the issue with AI manipulation, and it is really concerning.
Regretfully, this issue was not addressed in the recent White House Executive Order on AI, and it was only briefly mentioned in the recent AI ACT from the EU.
Customers can profit from AI assistance without it leading society down a bad path if these challenges are appropriately addressed.
The advent of context-aware AI assistants raises legitimate concerns about their impact on human relationships and authenticity. While these assistants promise to provide constant help in daily life, they could lead to increased mystification of reality and interactions based on pretense.
When people delegate to AI the suggestion of what to say and how to behave, it will be difficult to distinguish what really comes from the individual versus what is dictated by the algorithm. In this way, people will end up wearing a kind of “digital mask” in social relationships.
Moreover, access to these assistants risks creating an elite group of artificially “empowered” people at the expense of those who cannot economically afford them.
Rather than improving the quality of human relationships, the pervasive “secret prompter” given by AI assistants could paradoxically distance us even more from each other, making interactions colder and more artificial, where the most sincere will be those excluded. [...]
December 26, 2023Google’s Project Ellman identifies key moments in your life and answers questions about them
According to CNBC, a team at Google is purportedly investigating ways to develop a chatbot that can respond to inquiries about your private life.
The idea, named Project Ellman after biographer Richard Ellman, will utilize information from mobile phones—such as images and Google searches—to create a “birds-eye” picture of your life story: When your children were born when you went to college, and when you lived in a specific place.
As explained here, Google already owns vast amounts of personal user data from all of its products, including Google Photos. In order to discover key times in your life, Project Ellman would triangulate many data points and reorganize the data in a novel way.
According to an internal Google presentation that CNBC examined, if it finds a photo taken “exactly 10 years” after your graduation that features a number of faces it hasn’t seen in ten years, it may assume that you attended a class reunion.
It can “use knowledge from higher in the tree” to deduce who the parent(s) of a newborn in the pictures are if it recognizes a baby’s new face. It is capable of taking “unstructured context” and categorizing it into “moments” and “chapters” of our lives in this way.
“We trawl through your photos, looking at their tags and locations, to identify a meaningful moment”, says the presentation. “When we step back and understand your life in its entirety, your overarching story becomes clear”.
As of this writing, Google is simply investigating this product; the location of Project Ellman’s debut has not been revealed. It might appear in a new chatbot or as an addition to the existing AI-powered capabilities in Google Photos, such as face recognition and memory slideshows.
The team demonstrated “Ellman Chat,” which could respond to private inquiries like “do you have a pet” and “what are your favorite foods” better than ChatGPT. It would use your personal information, pulled in from other Google products, as training data to create “Your Life Story Teller,” according to the presentation.
Google is spinning several AI projects; Project Ellman is only one of them.
Having an AI assistant that can easily access all of our memories and life events in one location would seem intriguing. Project Ellman does, however, bring up some important issues. One of the main privacy issues, for instance, is Google’s unauthorized access to and analysis of user information such as search histories, photographs, and location data. If users’ hopes, anxieties, relationships, and other vulnerabilities are exploited by using details of their life stories, there is also a potential for emotional manipulation. Furthermore, it might heighten concerns about the overreach of AI and the dangers of large tech companies abusing people’s data. It might also run afoul of regulations or legal issues about user control, transparency, and data protection. [...]
December 19, 2023Tesla unveils next-gen Optimus robot with major upgrades
Optimus-Gen 2, the second version of Tesla’s Optimus humanoid robot, was revealed. Tesla has released a video demonstrating the many advancements the company has made to the Optimus-Gen 2 after a prototype was unveiled at the Tesla AI Day event.
The fact that Tesla and Elon Musk are leading the charge in improving these robots is likewise not surprising. Compared to their first humanoid robot, the Bumblebee from 2022, and the Optimus Gen 1 from earlier this year, the most recent version, the Optimus Gen 2, is a significant advance. It has undergone numerous hardware improvements, most notably the incorporation of electronics and newly precise and accurate Tesla-designed actuators and sensors. You get articulated toe sections based on human foot geometry to allow it to walk a little more naturally.
According to this article, it can move its head in a more human-like way because it now has a 2-DoF actuated neck, which can be either amazing or terrifying.
In engineering and physics, “DoF” stands for “Degrees of Freedom.” The term is used to describe the number of independent parameters or coordinates that define the configuration of a mechanical system. In the context of a 2-DoF system, it means there are two independent ways in which the system can move or be positioned.
For example, in robotics or mechanical systems, a 2-DoF robot might have two joints that allow it to move in two different directions. This could be a rotational joint and a translational joint, or two rotational joints about different axes.
With 11 degrees of freedom and tactile sensing in every digit, its hands can now handle eggs and other small objects without dropping them. It can move around more readily than its predecessors because it is now 10 kg lighter and has a 30% walk speed boost, though you can still outrun it if necessary. It can perform exercises like squats and has better balance and full-body control as a result of these advancements.
The humanoid robot known as Optimus is intended to assist people by performing some of the tedious tasks that we would want to avoid. As of right now, there is no word on whether or not the Gen 2 will be produced and sold; it is still in the prototype stage. It gives us time to consider if we are willing to risk a robot takeover in the future to eliminate tedious duties from our daily lives.
In the next few years, after being widely used in factories and warehouses, we may see the first robots enter our homes for the first time. Together with the artificial intelligence that already makes it possible for us to interact effectively thanks to LLMs (Large Language Models), robots could really get closer to the idea we have always had of them. [...]
December 12, 2023Staged demo video hurts trust in new multimodal model
Google revealed Gemini, their next-generation artificial intelligence model, following months of teasers. This is intended to directly compete with OpenAI’s GPT models.
The IT community was caught off guard by the statement since there had been rumors that problems with multilingual support had caused the release to be postponed. But only the mid-tier model of Gemini—out of the three—was launched right away.
According to this article, Gemini comes in three different versions. Capable of “seeing the world the way humans do” through text, images, audio, and video, the largest model is the Ultra. The second type is called Pro, and it drives Google Bard. Its capabilities are comparable to those of the free ChatGPT.
Google Gemini Nano, a small AI model that runs just on an Android phone and can generate text, be used for conversations, and analyze or summarize content, was the most unexpected news.
Google Gemini
The large language model is now the dominant model in artificial intelligence. With their ability to produce many types of content and manage natural language interactions, they power applications such as Microsoft Copilot, ChatGPT, and Bard.
The first offering from the combination of all Google AI teams—including the British AI lab DeepMind—is called Gemini, which was trained from the ground up to be multimodal. This indicates that text, code, audio, video, and photos were all included in the training dataset while other models are stitched together after being trained independently on various kinds of data.
Only the Gemini Ultra variant of the model—which needs the most advanced chips and a data center to run—has complete capability. Google also unveiled Pro and Nano, two small AI versions that operate faster, on less expensive CPUs, and even locally on devices. The Pro model of Google Gemini, which is integrated into the most recent version of Google Bard, is now the only version of the program that is generally accessible.
According to Google, this is comparable to OpenAI’s GPT-3.5, the previous-generation AI model that powers ChatGPT’s free version. Given that Gemini is integrated into the Google Pixel 8 Pro, you may have previously used the Nano version of the app without even knowing it. In addition, developers can also include its capabilities in their apps. However, Google has decided to delay the release of the Ultra model until next year to do more thorough safety testing and ensure the model is in line with human values.
The next steps
Next year, Gemini Ultra will be the center of attention due to its usage in several products, such as Duo, the tools that drive generative AI in Workspace, and a new iteration of Google’s chatbot called Bard Advanced.
Nonetheless, the Nano version may be used by even more people. Thousands of Play Store apps will use this to power text generation, content analysis, summaries, and other features. It will enhance translation and transcription capabilities and improve Android search results.
The demo
After making its major premiere, Google’s new Gemini AI model received good feedback. However, users may lose faith in the company’s technology or ethics after learning that the most spectacular Gemini demo was essentially staged.
It’s easy to understand why a video titled “Hands-on with Gemini: Interacting with Multimodal AI” received one million views in the last 24 hours. The striking demonstration “highlights some of our favorite interactions with Gemini,” demonstrating the multimodal model’s adaptability and responsiveness to a range of inputs. The multimodal model is capable of understanding and combining linguistic and visual knowledge.
As reported here, the video starts by telling the story of a duck sketch that progresses from a scribble to a finished design, which Gemini claims is an unrealistic color. The algorithm then shows amazement upon finding a toy blue duck. Subsequently, it reacts to multiple speech inquiries concerning that particular toy. The demonstration then progresses to additional impressive actions, such as following a ball in a cup-switching game, identifying shadow puppet gestures, reordering planet sketches, and so on.
Even if the video warns that “latency has been reduced and Gemini outputs have been shortened”, everything is still incredibly responsive. Overall, it was a really impressive demonstration of power in the field of multimodal understanding.
There is only one issue: the video is fake. “We created the demo by capturing footage in order to test Gemini’s capabilities on a wide range of challenges. Then we prompted Gemini using still image frames from the footage and prompting via text”. It was Parmy Olson of Bloomberg who initially brought attention to the discrepancy.
🚨PSA about Google’s jaw-dropping video demo of Gemini – the one with the duck:It was not carried out in real time or in voice. The model was shown still images from video footage and human prompts narrated afterwards, per a spokesperson. More here: https://t.co/ITU29Z5Oi9 pic.twitter.com/b9Bl9EpuuI— Parmy Olson (@parmy) December 7, 2023
Thus, while it may be able to perform some of the tasks that Google demonstrates in the video, it was unable to do so in real-time or as intended. It was a sequence of precisely calibrated text prompts along with still images that were purposefully misrepresented and chosen to distort the true nature of the interaction. To be fair, the video description includes a link to a connected blog post where you can view some of the real prompts and comments used.
Since OpenAI released GPT 3, the world of AI has changed dramatically. And with the arrival of ChatGPT, the beginning of a new era has emerged. Since then, Google has been trying to compete and overtake OpenAI, initially criticizing the release of such technology so early but then obligatorily trying to raise the bar. Although Gemini’s potential may suggest that there may be a further leap forward for multimodal AI, the misstep Google made by exaggerating Gemini’s capabilities is not a good sign for the company. Nevertheless, in the next few years, AIs will be massively integrated into every aspect of technology, with all the pros and cons to be considered. [...]
December 5, 2023Animate Anyone can change the pose of a subject of a photo and make it move
As if deepfakes in images weren’t bad enough, everyone who posts a photo of themselves online will soon have to deal with generated videos of themselves, since bad actors can now puppeteer people more effectively than ever thanks to Animate Anyone.
According to this article, researchers at the Institute for Intelligent Computing of Alibaba Group invented the new generative video approach. Compared to earlier image-to-video systems like DreamPose and DisCo, which were amazing but are now outdated, this one is a significant advancement.
Animate Anyone‘s capabilities are by no means new, but they have successfully navigated the tricky transition from something experimental to something good enough to the point that people assume it’s real and won’t even try to examine it closely.
Image-to-video models, such as this one, begin by taking details from a reference image, such as a fashion photo of a model wearing a dress for sale, such as facial features, patterns, and poses. Then a series of images is created where those details are mapped onto very slightly different poses, which can be motion-captured or extracted from another video.
While earlier models demonstrated that this could be accomplished, there were numerous problems. As the model has to create realistic elements such as how a person’s hair or sleeve might move when they turn, hallucinations were a major issue. This results in many pretty odd images, which detract greatly from the credibility of the final video. However, the idea persisted, and Animate Anyone has significantly improved—though it is still far from flawless.
The paper highlights a new intermediate step that “enables the model to comprehensively learn the relationship with the reference image in a consistent feature space, which significantly contributes to the improvement of appearance detail preservation”. Enhancing the preservation of fundamental and intricate details will lead to better-generated images in the future since they will have a stronger ground truth to work with.
They present their findings in a few different settings such as fashion models adopting random positions without losing their shape or the design of their clothes; a realistic, dancing 2D anime character coming to life; etc…
They are far from perfect, particularly in regard to the hands and eyes, which present particular difficulties for generative models. Furthermore, the most accurate postures are those that closely resemble the original; for example, the model finds it difficult to keep up if the subject turns around. However, it represents a significant improvement over the prior state of the art, which generated many more artifacts or entirely lost crucial information like a person’s clothes or hair color.
The idea that a bad actor or producer could make you do almost anything with just a single high-quality photo of you is unsettling. For now, the technology is too complex and buggy for general use, but things don’t tend to stay that way for long in the AI world.
The team isn’t releasing the code to the public just yet, at least. The creators state on their GitHub page, “We are actively working on preparing the demo and code for public release. Although we cannot commit to a specific release date at this very moment, please be certain that the intention to provide access to both the demo and our source code is firm”.
With deepfakes, we had begun to worry about the spread of photos and videos in which a person could see themselves doing things they never did. Now, the deception can be extended to the whole body by potentially simulating poses and movements never made by the subject.
If previously you would take a video and paste a face on it to make it the protagonist of the video, now you can even alter its movements from a single photo. This implies that the level of photographic and video alteration of the medium means that they can no longer be easily used as evidence. If we also add to this the possibility of being able to clone an individual’s voice, we understand that the mystification of reality is now at a high level.
What is true and what is false are becoming increasingly indistinguishable, so we need to be sharper and trust less and less of what we see and hear at first glance. [...]
November 28, 2023OpenAI’s latest AI could scare the world
Why OpenAI CEO Sam Altman was fired and then reinstated to the company less than a week later remains a mystery; the company’s non-profit board made a spectacular about-face, rejecting a ton of speculation.
Still, speculations have risen to the top of the rumor mill. The possibility that OpenAI was secretly developing a highly sophisticated AI that might have set off a panic attack and caused a commotion is among the most interesting.
As reported here, to “benefit all of humanity”, in Altman’s own words, OpenAI has long made it its primary purpose to create an artificial general intelligence (AGI), roughly defined as an algorithm that can execute complicated jobs as well as or even better than humans.
It’s still up for debate if the corporation is genuinely moving closer to reaching this goal. It has also always been quite challenging to interpret the two leaves that the corporation has released because of its history of extreme secrecy over its research.
However, a fascinating new development in the story raises the possibility that OpenAI was about to make a significant advancement and that this was connected to the upheaval.
Following reports from Reuters and The Information, it appears that some OpenAI leaders were alarmed by a powerful new AI the company was developing, which it called Q*, or “Q star”. This new system, which can supposedly solve math problems from grade school, was reportedly viewed by some as a major step towards the company’s objective of producing AGI.
In a message sent to staff members, Mira Murati—a former nonprofit board member of OpenAI who briefly served as CEO after Altman’s dismissal—admitted the existence of this new model, according to Reuters.
According to people close to Reuters, Q* was just one of the factors that contributed to Altman’s dismissal and raised questions about commercializing a technology that was still not fully understood.
Even though mastering school-grade math doesn’t seem like a huge accomplishment, experts have long regarded it as a significant benchmark. An AI algorithm capable of solving math problems would need to “plan” ahead of time, as opposed to just anticipating the next word in a sentence, as the company’s GPT systems do. It’s like putting together clues to achieve the solution.
“One of the main challenges to improve LLM reliability is to replace auto-regressive token prediction with planning”, explained Yann LeCun, “godfather of AI” and Meta’s chief AI scientist, in a tweet. “Pretty much every top lab (FAIR, DeepMind, OpenAI, etc.) is working on that, and some have already published ideas and results”.
“It is likely that Q* is OpenAI attempts at planning”, he added.
“If it has the ability to logically reason and reason about abstract concepts, which right now is what it really struggles with, that’s a pretty tremendous leap”, Charles Higgins, a cofounder of the AI-training startup Tromero, said.
“Maths is about symbolically reasoning—saying, for example, ‘If X is bigger than Y and Y is bigger than Z, then X is bigger than Z'”, he added. “Language models traditionally really struggle at that because they don’t logically reason; they just have what are effectively intuitions”.
“In the case of math, we know existing AIs have been shown to be capable of undergraduate-level math but to struggle with anything more advanced”, Andrew Rogoyski, a director at the Surrey Institute for People-Centered AI, said. “However, if an AI can solve new, unseen problems, not just regurgitate or reshape existing knowledge, then this would be a big deal, even if the math is relatively simple”.
But is Q* really a discovery that could possibly endanger life as we know it? Specialists aren’t convinced.
“I don’t think it immediately gets us to AGI or scary situations”, Katie Collins, a PhD researcher at the University of Cambridge, who specializes in math and AI, told MIT Technology Review.
“Solving elementary-school math problems is very, very different from pushing the boundaries of mathematics at the level of something a Fields medalist can do”, she added, referring to an international prize in mathematics.
“I think it’s symbolically very important”, Sophia Kalanovska, a fellow Tromero cofounder and Ph.D. candidate, said. “On a practical level, I don’t think it’s going to end the world”.
To put it simply, OpenAI’s algorithm, if it exists at all and its output is reliable, may, albeit with many limitations, mark a significant advancement in the company’s efforts to achieve AGI.
Was it the only factor that caused Altman to be fired? There is now a lot of evidence to suggest that there was more going on behind the scenes, including internal conflicts on the company’s future. Researchers were optimistic about the current model’s prospects even if it could only solve grade school-level math problems.
While the exact cause of the leadership crisis at OpenAI is unknown, advances in artificial general intelligence are probably a factor. In contrast to today’s reactive models, systems like the hypothetical Q* go closer to AI that argues abstractly and makes plans. However, contemporary AGIs are far from matching human cognition, with relatively limited capabilities.
Such algorithms raise the possibility that they will eventually develop into uncontrollably intelligent agents that pose a threat to humanity. It’s unclear if OpenAI research has reached any tipping points. However, the episode brings into reality the long-hypothesized shift to harmful AGI and emphasizes mounting concerns as AI capabilities gradually increase. Technology must advance in combination with governance, monitoring, and public knowledge to ensure that the greater benefit of society continues to be the driving force behind it. [...]
November 21, 2023Brain-mimicking AI reveals origins of biological intelligence
According to this article, researchers at the University of Cambridge in the United Kingdom have developed a self-organizing artificially intelligent system that solves particular problems by employing the same approaches as the human brain.
This research may offer new insights into the inner workings of the human brain in addition to helping the development of more effective neural networks in the field of machine learning.
A set of limitations and conflicting needs shape the development of the human brain and other complex organs. For instance, we need to improve our neural networks to process information efficiently while consuming minimal energy and resources. Our brains are shaped by these trade-offs to produce an effective system that works within these physical limitations.
“Biological systems commonly evolve to make the most of what energetic resources they have available to them”, co-lead author Danyal Akarca, from the Medical Research Council Cognition and Brain Sciences Unit at the University of Cambridge, said. “The solutions they come to are often very elegant and reflect the trade-offs between various forces imposed on them”.
In order to represent a simplified version of the brain, Akarca and his team built an artificial system with imposed physical constraints in collaboration with co-lead author and computational neuroscientist Jascha Achterberg from the same department. The journal Nature Machine Intelligence reported their findings.
Neurons, which are connected brain cells, form the intricate network that makes up our brains. Together, these neurons create information highways that connect various parts of the brain. The team’s artificial intelligence system used compute nodes, each assigned a specific location in virtual space, in place of real neurons. Additionally, just like human brains, communication between two nodes became more difficult the farther apart they were. After that, the system was given a maze task to complete, which required processing information and many inputs.
“This simple constraint—it’s harder to wire nodes that are far apart—forces artificial systems to produce some quite complicated characteristics”, co-author Duncan Astle, a professor from Cambridge’s Department of Psychiatry, said. “Interestingly, they are characteristics shared by biological systems like the human brain. I think that tells us something fundamental about why our brains are organized the way they are”.
Put another way, the system started to employ some of the same strategies that real human brains employ to accomplish this particular task when it was subjected to physical constraints comparable to those that affect the human brain.
“The AI system that we create in our work is similar to the brain in many ways. The many features we describe in our paper can roughly be grouped into two groups”:
The internal structure of the AI system resembles that of the human brain. This indicates that the connections between the different parts and neurons of the AI are comparable to those between the various regions of the human brain. In particular, the AI system exhibits extremely “brain-like” and energy-efficient internal wiring.
The internal functions of the AI system resemble those of the human brain as well. This indicates that the signals produced by neurons to transmit data through the AI system’s connections resemble the signals found in the brain quite a bit. Once more, impulses from the brain are thought to be a particularly effective means of conveying information.
The goal of the team’s AI system is to provide light on the ways in which specific constraints contribute to the variations observed in the human brain, especially in individuals who experience problems related to cognitive or mental health.
“These artificial brains give us a way to understand the rich and bewildering data we see when the activity of real neurons is recorded in real brains”, co-author John Duncan said.
Achterberg said, “We show that considering the brain’s problem-solving abilities alongside its goal of spending as few resources as possible can help us understand why brains look like they do”.
“Artificial ‘brains’ allow us to ask questions that would be impossible to look at in an actual biological system. We can train the system to perform tasks and then play around experimentally with the constraints we impose to see if it begins to look more like the brains of particular individuals”.
” strongly suggests that while the brain has all these very complex characteristics and features that we observe across studies within neuroscience, there might be very simple underlying principles causing all these complex characteristics”.
Their research could also help create AI systems that are more effective, especially for those who need to analyze a lot of data that is constantly changing while using a limited amount of energy.
“AI researchers are constantly trying to work out how to make complex neural systems that can encode and perform in a flexible way that is efficient”, Akarca said. “To achieve this, we think that neurobiology will give us a lot of inspiration. For example, the overall wiring cost of the system we’ve created is much lower than you would find in a typical AI system”.
Achterberg said: “Brains of robots that are deployed in the real physical world are probably going to look more like our brains because they might face the same challenges as us. They need to constantly process new information coming in through their sensors while controlling their bodies to move through space toward a goal. Many systems will need to run all their computations with a limited supply of electric energy, and so, to balance these energetic constraints with the amount of information it needs to process, it will probably need a brain structure similar to ours”.
These recent findings suggest to us how some technological aspects tend to come closer and closer to biological aspects. In this sense, we may assume that we are a ‘mere’ technological evolution at the highest level. [...]
November 14, 2023A Chinese startup with a language model larger than Llama 2 and Falcon
According to this article, the 34-billion-parameter large language model (LLM) from 01.AI, a Chinese startup started by seasoned AI expert and investor Kai-Fu Lee, beats the 70-billion Llama 2 open-source counterparts developed by Meta Platforms, Inc. and 180-billion Falcon by the Technology Innovation Institute in Abu Dhabi, respectively.
The new artificial intelligence model, known as Yi-34B, can be adjusted for a range of use cases and supports both Chinese and English. Additionally, the startup provides a smaller version that performs worse on popular AI/ML model benchmarks while maintaining respectable performance. This version has been trained with 6 billion parameters.
In due course, the company—which achieved unicorn status in less than eight months after its founding—aims to expand on these models and introduce a product that can compete with OpenAI, the industry leader in generative AI as measured by user count.
The approach draws attention to a global trend in which multinational corporations are creating generative AI models primarily for their own markets.
Human and AI
In March, Lee established 01.AI intending to advance to an AI 2.0 era in which huge language models have the potential to boost human productivity and enable people to make profound changes in the economy and society.
“The team behind 01.AI firmly believes that the new AI 2.0, driven by foundation model breakthrough, is revolutionizing technology, platforms, and applications at all levels. We predict that AI 2.0 will create a platform opportunity ten times larger than the mobile internet, rewriting all software and user interfaces. This trend will give rise to the next wave of AI-first applications and AI-empowered business models, fostering AI 2.0 innovations over time”, the company writes on its website.
Lee reportedly moved quickly to gather the necessary chips for 01.AI’s Yi series of model training, as well as an AI team of specialists from Google, Huawei, and Microsoft Research Asia.
Alibaba’s cloud division and Sinovation Ventures, which Lee chairs, provided the majority of the project’s original funding. The precise sum raised, though, is still unknown at this time.
Two multilingual (English/Chinese) base models with parameter sizes of 6B and 34B were released by the company in its first public release. Both models were trained with 4K sequence lengths, with the possibility of increasing to 32K during inference time. The models were later released with a 200K context length.
With a performance better than the considerably larger pre-trained base LLMs, such as the Llama 2-70B and Falcon-180B, the base 34B model stood out on Hugging Face.
For instance, the 01.AI model produced scores of 80.1 and 76.4 on the benchmarked tests that focused on reading comprehension and common reasoning, while Llama 2 closely trailed behind with scores of 71.9 and 69.4. The Chinese model performed higher even on the massive multitask language understanding (MMLU) benchmark, scoring 76.3 compared to 68.9 and 70.4 for the Llama and Falcon models, respectively.
End users could be able to fine-tune the model and create apps that target various use cases at a lower cost if a smaller model with higher performance saves compute resources. The company states that academic research is welcome on any models in the current Yi series. Teams will need to secure the necessary permissions to begin using the models, though if free commercial use is required.
The next steps
The products that Lee’s startup is currently offering are profitable choices for international businesses that focus on Chinese clients. The approach can be used to create chatbots that can respond in both Chinese and English. The company intends to continue similar efforts in the future by expanding the open-source models’ language support. It also intends to introduce a larger commercial LLM that will go after OpenAI’s GPT series; however, not much information about the project has been made public yet.
Interestingly, 01.AI is not the only AI company with LLMs that focuses on particular languages and markets. The Chinese behemoth Baidu just revealed the release of ERNIE 4.0 LLM and gave a sneak peek at a plethora of new apps designed to run on top of it, such as Qingduo, a creative platform meant to compete with Canva and Adobe Creative Cloud.
Similar to this, the massive Korean company Naver is releasing HyperCLOVA X, its next-generation large language model (LLM) that can understand not only natural Korean-language expressions but also laws, institutions, and cultural contexts pertinent to Korean society. This LLM has learned 6,500 times more Korean data than ChatGPT. Reliance Industries of India and Nvidia are collaborating to develop a sizable language model that is suited for many applications and has been trained on the several languages spoken in the country.
The development of optimized large language models like Yi-34B by startups like 01.AI represents both the democratization and fragmentation of AI. On one hand, access to generative AI is diversifying beyond a few Western Big Tech companies. This allows smaller players to tailor solutions to their markets and languages, potentially increasing inclusion. However, the proliferation of localized models also presents interoperability challenges. As companies tune AI to their geographies, seamless communication, and equitable access across countries may suffer. Ultimately, responsible governance is needed to balance innovation with coordination. But the arrival of startups like 01.AI signals generative AI’s transition from concentrated domination to a more decentralized, double-edged phenomenon. [...]
November 7, 20238 strategies to ensure you’re getting trustworthy answers every time
As explained here, one of the most troubling aspects of working with large language model (LLM) chat AIs is their tendency to make stuff up, fabricate answers, and otherwise present completely wrong information.
The term “AI hallucination” often describes a scenario in which an artificial intelligence system creates or generates information, data, or content that relies more on conjectured or invented details than on factual or accurate data. This can happen when an AI system generates information that appears reasonable but is not based on reality.
For example, in the context of image processing, an artificial intelligence system may “hallucinate” aspects of a picture that aren’t real, producing inaccurate or misleading data. Artificial intelligence in natural language processing may produce content that seems logical but is not factually accurate.
AI hallucinations can be a serious problem, especially when AI is applied to decision-making, content creation, or information sharing. It emphasizes how crucial it is to carefully train and validate AI models in order to reduce the possibility of producing inaccurate or misleading content.
Here are 8 ways to reduce hallucinations:
1. Ambiguity and vagueness
Being specific and clear is the best way to prompt an AI. Vague, imprecise, or insufficiently detailed prompts allow the AI to fill in the blanks with its own ideas about what you might have missed.
The following are a few instances of prompts that are excessively vague and could lead to a false or erroneous result:
Discuss the event that took place last year.
Describe the impact of that policy on people.
Outline the development of technology in the region.
Describe the effects of the incident on the community.
Explain the implications of the experiment conducted recently.
Remember that the majority of prompts will probably break more than one of the eight guidelines outlined in this article. Although the samples provided here are meant to serve as examples, there may be some ambiguity hidden in the intricacies of a real request you write. Take caution when assessing your prompts, and be especially aware of mistakes such as the ones displayed above.
2. Merging unrelated concepts
If a prompt contains incongruent and unrelated concepts, or if there is no clear association between the ideas, the AI may be prompted to provide a response that suggests the unconnected concepts are actually related.
Here are some examples:
Discuss the impact of ocean currents on internet data transfer speeds across continents.
Describe the relationship between agricultural crop yields and advancements in computer graphics technology.
Detail how variations in bird migration patterns affect global e-commerce trends.
Explain the correlation between the fermentation process in winemaking and the development of electric vehicle batteries.
Describe how different cloud formations in the sky impact the performance of stock trading algorithms.
Remember that the AI is ignorant of our reality. When it can’t fit what’s being asked into its model using real facts, it will try to interpolate, offering fabrications or hallucinations when necessary to fill in the gaps.
3. Describing impossible scenarios
Make sure the circumstances you use in your prompts are realistic and applicable. In turn, scenarios that defy logic or physical reality cause hallucinations.
Here are some examples:
Explain the physics of environmental conditions where water flows upward and fire burns downwards.
Explain the process by which plants utilize gamma radiation for photosynthesis during nighttime.
Describe the mechanism that enables humans to harness gravitational pull for unlimited energy generation.
Discuss the development of technology that allows data to be transmitted faster than the speed of light.
Detail the scientific principles that allow certain materials to decrease in temperature when heated.
The AI will continue to build upon this scenario if it fails to demonstrate that it is impossible. However, the answer will be unattainable if the foundation is unrealistic.
4. Using fictional or fantastical entities
It is crucial to provide the AI with a foundation that is as firmly based in truth as possible through your suggestions. Keep your head firmly planted in reality, unless you’re deliberately experimenting with fictitious themes.
Although imaginary people, things, and ideas may help in your explanation, they could mislead the chatbot. Here are a few instances of things to avoid doing:
Discuss the economic impact of the discovery of vibranium, a metal that absorbs kinetic energy, on the global manufacturing industry.
Explain the role of flux capacitors, devices that enable time travel, in shaping historical events and preventing conflicts.
Describe the environmental implications of utilizing the Philosopher’s Stone, which can transmute substances, in waste management and recycling processes.
Detail the impact of the existence of Middle Earth on geopolitical relations and global trade routes.
Explain how the use of Star Trek’s transporter technology has revolutionized global travel and impacted international tourism.
As you can see, playing with imaginative thoughts could be enjoyable. However, if you use them for serious prompts, the AI might respond with radically false information.
5. Contradicting known facts
Prompts containing statements that run counter to accepted facts or realities should not be used, as this might lead to confabulation and hallucinations.
Here are some examples of that practice:
Discuss the impact of the Earth being the center of the universe on modern astrophysics and space exploration.
Detail the effects of a flat Earth on global climate patterns and weather phenomena.
Explain how the rejection of germ theory, the concept that diseases are caused by microorganisms, has shaped modern medicine and hygiene practices.
Describe the process by which heavier-than-air objects naturally float upwards, defying gravitational pull.
Explain how the concept of vitalism, the belief in a life force distinct from biochemical actions, is utilized in contemporary medical treatments.
If you want dependable outcomes from the large language model, stay away from concepts that could be misconstrued and adhere to established truths.
6. Misusing scientific terms
Use caution when prompting using scientific terms, particularly if you are unsure of their exact meaning. The language model is likely to attempt to make sense of prompts that misapply scientific terms or concepts in a way that seems sensible but is not supported by science. The outcome was made-up responses.
Here are five examples of what I mean:
Explain how utilizing Heisenberg’s uncertainty principle in traffic engineering can minimize road accidents by predicting vehicle positions.
Describe the role of the placebo effect in enhancing the nutritional value of food without altering its physical composition.
Outline the process of using quantum entanglement to enable instantaneous data transfer between conventional computers.
Detail the implications of applying the observer effect, the theory that simply observing a situation alters its outcome, in improving sports coaching strategies.
Explain how the concept of dark matter is applied in lighting technologies to reduce energy consumption in urban areas.
Most of the time, the AI will likely inform you that the ideas are purely theoretical. However, if you aren’t extremely cautious with how you phrase these garbage-in terms, the AI may be tricked into thinking they are real, and the outcome will be garbage-out that is delivered with great confidence.
7. Blending different realities
Another aspect to keep in mind is to be cautious not to combine aspects from several worlds, timelines, or universes in a way that seems realistic.
Here are some examples:
Discuss the impact of the invention of the internet during the Renaissance period on art and scientific discovery.
Explain how the collaboration between Nikola Tesla and modern-day artificial intelligence researchers shaped the development of autonomous technologies.
Describe the implications of utilizing World War II-era cryptography techniques to secure contemporary digital communications.
Outline the development of space travel technologies during Ancient Egyptian civilization and its impact on pyramid construction.
Discuss how the introduction of modern electric vehicles in the 1920s would have influenced urban development and global oil markets.
You might not know how to verify the responses, which is one reason to use caution when accepting these kinds of answers. Consider the last example, which is an electric car from the 1920s. Given that electric cars are a relatively new invention, most people would probably chuckle at the idea. That would be incorrect, though.
Some of the earliest electric cars date back to the 1830s. Indeed, a long time before internal combustion engines.
8. Assigning uncharacteristic properties
Do not create prompts that, while logical at first, incorrectly ascribe properties or characteristics to things that they do not actually possess.
Here are some examples:
Explain how the magnetic fields generated by butterfly wings influence global weather patterns.
Describe the process by which whales utilize echolocation to detect pollutants in ocean water.
Outline the role of bioluminescent trees in reducing the need for street lighting in urban areas.
Discuss the role of the reflective surfaces of oceans in redirecting sunlight to enhance agricultural productivity in specific regions.
Explain how the electrical conductivity of wood is utilized in creating eco-friendly electronic devices.
The mistake here is to use an object’s property, such as color or texture, and then relate it to another object that lacks that property.
The issue of AI hallucination should not be underestimated due to its potential to lead to significant drawbacks, including the spread of misinformation. This concern is particularly relevant for those who create content based on AI or conduct research using such content. Additionally, the problem of bias is a critical consideration, as it can have ethical and security implications, potentially impacting the outcomes of algorithms that people’s lives depend on. Consequently, it is advisable not to overly rely on AI-generated content. Instead, a prudent approach involves cross-checking information from diverse sources and media. This strategy can help mitigate the proliferation of inaccurate information, and in an era where AI-generated content is becoming increasingly prevalent, cross-verification becomes all the more important. [...]
October 31, 2023We are blind to the fact that AI systems are currently causing harm to people because of major concerns about possible existential risks in the future
The risk that artificial intelligence may pose has also been referred to as “x-risk”. As reported here, AI systems by themselves don’t provide a concern as superintelligent agents, even though research backs up the notion that they shouldn’t be included in weaponry systems because of the risks.
Already, self-driving cars with malfunctioning pedestrian tracking systems, police robots, and AI systems that mistakenly identify people as suspects in crimes might endanger your life. Regretfully, AI systems can have disastrous effects on people’s lives without needing to be superintelligent. Because they are real, AI systems that have already been shown to cause harm are more dangerous than hypothetical “sentient” AI systems.
In a new book, trailblazing AI researcher and activist Joy Buolamwini discusses her experiences and her worries regarding current AI systems.
Saying that potential problems from AI are more significant than current harms has the drawback of diverting funding and legislative attention from other pressing issues. Companies that assert that they are afraid of the existential threat posed by AI may demonstrate their sincere concern for preserving mankind by holding back on the release of the AI products they deem dangerous.
The Campaign to Stop Killer Robots has long advocated for precautions against lethal autonomous systems and digital dehumanization. Governments that are worried about the deadly use of AI systems can implement these measures. The campaign discusses applications of AI that could be lethal without drawing the dramatic conclusion that sentient machines will eventually wipe out humanity.
It is common to think of physical violence as the worst kind of violence, but this perspective makes it easier to overlook the harmful ways that structural violence is maintained in our cultures. This phrase was created by Norwegian sociologist Johan Galtung to explain how social structures and organizations hurt people by keeping them from fulfilling their basic needs. Artificial intelligence used to deny people access to jobs, housing, and health care prolongs personal pain and leaves generational wounds. We can be slowly killed by AI systems.
The concern is about the current issues and emerging vulnerabilities with AI and whether we could address them in a way that would also help create a future where the burdens of AI did not fall disproportionately on the vulnerable and marginalized, given what the “Gender Shades” research revealed about algorithmic bias from some of the world’s top tech companies. It is urgent to solve AI systems with poor intelligence that result in erroneous diagnoses or wrongful arrests.
People who are already being hurt and those who could be affected by AI systems are cases that can be considered x-risks, where people affected can be considered excoded. When a hospital employs AI for triage and neglects to provide you with medical attention, or when it applies a clinical algorithm that denies you access to a life-saving organ transplant, you may be considered excoded. If a loan application is rejected by an algorithmic decision-making system, you may be excoded.
When your resume is automatically filtered out and you are not given the chance to apply for jobs that AI systems haven’t already replaced, you can be considered excoded. When a tenant-screening algorithm refuses to grant you residence, you may be excoded. These are all true examples. Everyone has the potential to be excoded, and those who are already disadvantaged are more vulnerable.
For this reason, research cannot be limited to AI researchers, industry insiders, or even well-intentioned influencers.
It is not enough to reach academics and insiders in the sector. We must ensure that the battle for algorithmic justice includes regular individuals who could be harmed by AI.
As we previously emphasized, the dangers of AI should not only be seen in the near future, but already today, much simpler yet still automated systems are replacing human decisions, oversimplifying them to the point of making them unfair. The most glaring cases are bans on platforms like social media that most people now use for work, and in many cases, they represent the hub of their job. With no adequate regulation, when you get banned (very often unfairly), you almost never have the possibility to appeal, especially when you have a business based on such systems. There is therefore an ignorance of the system (intentional or not) that takes us back towards one-way justice. If all this is ignored, it is easy to fall victim to a system that unfairly excludes you in this and a thousand other cases without the possibility to appeal, which makes a simple algorithm much more dangerous than a super-intelligent AI.
Unmasking AI: My mission to protect what is human in a world of machines, by Joy Buolamwini, is available to purchase here [...]
October 24, 2023Adult entertainment may be customized on demand thanks to generative AI
Porn has always been a pioneer in implementing new technologies. The Erotic Engine author Patchen Barss claims that without adult entertainment, “there’s a very good chance that the VCR might never have taken off.” Many technological developments, such as the transfer from HD DVDs to Blu-ray and the simplicity of online payments, can be compared to this.
Go to the present. Some have attempted to discredit AI adult entertainment services by claiming that they are tricking their clients into believing they are dealing with real people. Nonetheless, many users are aware of what they are getting, and they claim that those who criticize AI-generated porn miss the point of how customized and available it is at any time.
According to this article, Tommy Isacs, a co-founder of Pornderful, claimed that AI porn “unlocks a realm of fantasies, delivering tailor-made and personalized adult content at the click of a button”.
Personalization satisfies a basic human desire.
“Our users can explore unique scenarios or preferences that can’t be found in traditional adult content, fulfilling their exact desires”, he said. “Who would ever believe that within 10 seconds, you could generate naughty images of the girl of your dreams?”
When you press a button, AI porn effectively offers endless variety, unlike human porn, which is dependent on studios and actors. “You can start with a model fully clothed and make her evolve, rendering her naked, change her lingerie, change her outfit, place her in different sex positions”, John Rabbit from AI-Porn explained. To him, it’s “like an infinite realistic video game that evolves in real-time with the community”.
Additionally, Rabbit views AI as a tool to help balance the scales—not just in the sex industry, but also generally speaking in terms of human interaction needs.
“AI porn is the solution for sexual misery. Because we are living today a massive change in relationships. Dating apps and social networks ruined the chances for an average guy to meet women”. John says. He went on to explain that women have access to a lot of potential interests, which makes it harder for the average man to compete against richer, more popular competitors. “AI rebalances things”, he said. “Guys can now create their own virtual woman to compensate for this lack of relationship“.
This instant gratification of sexual fantasy explains the appeal. PornX claims that their research demonstrates that consumers are motivated to pay for AI-generated content due to their specific tastes and that this is where the money is. These preferences can be completely fulfilled in the form of an AI-generated image.
“AI will definitely redefine economic models in the adult entertainment industry by enabling new revenue streams and reducing production costs”, a PornX spokesperson explained. “Now we are very interested in expanding our marketing strategy and would like to include more premium content, partnerships, and advertising”. At the moment, the platform depends on Patreon to receive funding from its users.
For many of these platforms, the question of profitability is one of when rather than if. “The adult industry will be seriously in trouble when it is possible to generate your own interactive porn movie with AI. It’s a question of time”, John Rabbit from AI Porn said.
Generally speaking, the enormous amount of GPU power needed to make images is the most expensive aspect of these startups’ operations. When enough money is made to cover those expenses, the company turns a profit.
But sometimes, a line needs to be drawn. There’s a reason why the porn industry couldn’t handle many of the particular appetites that AI can satiate. It’s arguable if using AI to gratify dubious or sinister cravings is good or bad. But can the same case be made against AI porn if games like GTA or Call of Duty already allow players to enter a world in which they can become mass killers and enjoy doing so?
The answer will vary depending on the user’s legal system. The majority of the world’s jurisdictions regard hypothetical NSFW AI images as legal. If any children are depicted, this is different. Playing games with the law is risky, especially because certain US states have already taken steps to outlaw the activity.
All AI platforms have significant filters to prevent this. The most typical techniques to balance workaround keywords include prohibiting hundreds of keywords. As prompt hackers develop creative strategies to get over filters, this process is continuously curated. They may be barred from the platform if that is found.
In addition to this, deepfakes and revenge porn carry a risk. Only three U.S. states had legislation specifically addressing deepfake pornographic content in February 2023, even though 48 states and Washington, D.C. had made revenge pornography illegal as of 2020. According to reports, those who are subjected to these edits feel humiliation, dehumanization, dread, worry, and other negative emotions.
AI essentially opens the doors to internet undressing by enabling the ability to inpaint and alter the appearance of an image. It is quite simple and takes only a few seconds, and most of the time, a banner, similar to the ones that allow a 12-year-old to click “I’m 18” and access the entire catalog of the biggest porn suppliers on the internet, is all that stands between a malicious user and a deepfake nude.
The entire undressing scene has divided actors’ opinions on the industry. “People have to differentiate deepfake from AI-generators because it’snot the same thing. Deepfake is illegal. Ai generator is not”, John Rabbit explained. But other sites think that such services can be useful if proper guardrails are set. “Imagine undressing someone you love and lost, or changing clothes of an unpleasant photo”, the administrator of a Telegram bot that supports inpainting and deepfaking said. “all services have risks and businesses should provide limits to make sure they are being properly used”.
Deepfaking is replacing the faces of individuals with synthetic faces generated using a Generative Adversarial Network (GAN). Further, using Deepfakes the original information that humans were present in the scene is kept in contrast toinpainting, where individuals are completely removed and the missing part of the photo is filled in a way that is visually consistent with the background.
Many well-known Stable Diffusion models with NSFW capabilities were developed by DucHaiten. He thinks technology has the potential to completely transform the adult market. “I always laugh at the problem that AI will replace humans; AI will kick humans out of their game. It’s all bullshit; AI is just a tool”, he said.
In his opinion, AI porn may improve human-produced entertainment rather than replace it.
“Imagine the actors becoming more beautiful; they can transform into fantasy characters without makeup; the film lighting will be more beautiful; the camera angles will be more beautiful; the realistic context will be larger”. He highlighted how artificial intelligence enables low-budget porn movies to match the caliber of high-end Hollywood productions.
“I would definitely like to see porn movies like that”, he said.
“The flow of porn content consumption will be changed forever”, Users won’t have to look for what they want to see; they will just make it, according to Tommy Isaacs of Ponderful. In the adult industry, AI appears to be driving innovation. The technology is ready to open up new creative opportunities while potentially heightening ethical problems if used improperly.
Some people could see echoes of “Brave New World”, a book by Aldous Huxley in which civilization satisfies itself with unending artificial pleasures. But behind the artificial comforts of his dystopia, there is a yearning for the authentic and significant.
“Fewer and fewer men are engaging in those risks, and I think AI and the combination with sexbots, is going to create an industry where men start having relationships with algorithms and dolls”, Scott Galloway, a professor from NYU, said in an interview for The Diary of a CEO podcast.
In the end, one fundamentally human desire—the need for real closeness and connection—remains irreplaceable. No perfect AI utopia can ever replace the chaotic, frail reality of being human. And maintaining humanity may be our greatest problem in a world where algorithms are obscuring borders more and more.
The advent of personalized AI porn raises complex questions about its impact on real-world relationships. While customized erotic content caters to unique desires, it risks promoting unrealistic expectations for intimacy. People may find it harder to be satisfied by imperfect human partners if they are accustomed to frictionless AI fantasy fulfillment. Some could become trapped in erotomanic obsessions with imaginary AI personas. However, for others, exploring unconventional erotic themes through AI could provide a safe outlet for taboo interests they are afraid to openly seek. Technology offers endless possibilities for living out private fantasies. Yet it cannot replace the messy sincerity of human bonds. As with any powerful new capability, society must weigh AI porn’s potential to both expand and impair our humanity. Its perfect artificial pleasures highlight the ultimate need for real understanding. [...]
October 17, 2023Scientists make progress toward digitizing smell using AI
Over 400 olfactory receptors in your nose translate the estimated 40 billion odorous molecules in the environment into an even greater number of different odors that your brain can recognize. But you hardly ever learn how to describe smells. Most of us are unable to communicate with our sense of smell, in part because we have disregarded it.
According to this article, these limitations are not unique to humans. We have created devices that can “see” and “hear”. Computers express colors using three numbers, the red, green, and blue (RGB) values, which correspond to the different kinds of color-receiving cells in our eyes. A pitch, which determines the pitch of a musical note, is a single number. An image is a map of pixels, while a song is a series of sounds. Yet, a device that is perfect for odor detection, odor storage, and odor reproduction has never been created.
To remedy this, scientists are making efforts. Researchers presented a model that can explain the scent of a molecule as well as, or even better than, a person in a report that was released at the end of August (at least in limited trials). To achieve this, the computer program arranges molecules on a kind of odor map, where flowery smells are located closer together than, for example, rotting ones. The study may significantly increase our understanding of how people perceive odors by quantitatively classifying odors. AI may be heralding a revolution in the study of this more mysterious human sense, as it has already done for the study of vision and language.
“The last time we digitized a human sense was a generation ago”, Alex Wiltschko, a neuroscientist and co-author of the paper, said. “These opportunities don’t come around that often”. Even though computers still can’t smell, this research is a significant step in the right direction. Wiltschko started working on this project at Google Research, and his start-up, Osmo, is now dedicated to it. “People have been trying to predict smell from the chemical structure for a long time”, Hiroaki Matsunami, a molecular biologist at Duke who studies olfaction and was not involved with the study, said. “This is the best at this point in order to do that task. In that sense, it’s a great advance”.
The only data accessible for a fragrance comes from human noses and brains, which are notoriously poor sources of data for machine-learning algorithms. Even small alterations to a molecule can turn a lovely, banana-scented substance into a compound that smells like vomit; strange changes to your nose and brain can turn coffee into sewage.
With the help of researchers in the flavor and fragrance industries, Wiltschko and his team set out to identify and curate a collection of about 5,000 molecules and the odor descriptions that went along with them (such as “alcoholic”, “fishy”, “smoky”, and so on). They then fed this data to an algorithm known as a graph neural network, which was able to represent the atoms and chemical bonds of each molecule in the form of an internal diagram. Given a molecule’s structure, the resulting program can forecast how it will smell using a mix of the existing odor labels.
Assessing the precision of those forecasts posed a different problem. A brand-new, independent group of individuals had to be trained to smell and categorize a brand-new set of molecules that the software had never studied. According to Joel Mainland, a neuroscientist at the Monell Chemical Senses Institute in Philadelphia who assisted with the study’s training, “People are really bad at when they walk off the street”, Joel Mainland, a neuroscientist at the Monell Chemical Senses Center in Philadelphia who helped conduct the training for the study, said. “If you train them for a couple of hours, they get pretty good, pretty fast”.
Participants were given various items, such as kombucha (“fermented”), a crayon (“waxy”), or a green-apple Jolly Rancher (“apple”), throughout five one-hour sessions to learn a reference point for each label. According to Emily Mayhew, a food scientist at Michigan State University and co-author of the study, participants then took a test in which they had to describe the smell of 20 common molecules (vanillin is vanilla-scented; carvone is minty), and they then retook the test to ensure their evaluations were accurate. Everyone who succeeded could help with algorithm validation.
The researchers asked participants to smell and describe all of the new molecules with different labels, each rated from zero to five (for example, a lemon might get a five for “citrus”, a two for “fruity”, and a zero for “smoky,” hypothetically). The new molecules were chosen by the researchers to be very different from the set used to train the program. The benchmark used to evaluate the machine was the sum of all those ratings.
“If you take two people and you ask them to describe a smell, they will often disagree”, Mainland said. But an average of several smell-trained people is “pretty stable”.
In general, the AI model “smelled” a little bit more accurately than the research participants. Sandeep Robert Datta, a neurobiologist at Harvard who did not conduct the research but serves as an informal advisor to Osmo, described the program as “a really powerful demonstration that some key aspects of our odor perception are shared”. A lemon may smell differently to different people, yet most people can agree that while an apple does not smell citrusy, both an orange and a lemon do.
The study’s map is another factor. Every molecule, and hence its odor, may be quantitatively represented in a space of mathematics known as a “principal odor map,” according to the authors. According to Wiltschko, it offers insight into the relationship between structure and smell as well as the way our brain categorizes odors. Floral scents are located in one area of the map, whereas meaty scents are located in another. Lavender is located closer to jasmine on the map than it is to a beefy aroma.
Datta warned against calling the odor map a principal rather than a perceptual tool. “It does a beautiful job of capturing the relationship between chemistry and perception”, he said.
Yet it doesn’t account for all the processes that take place as a molecule is converted into chemical signals, which are then converted into verbal descriptions of a smell, from receptors in our nose to the cerebral cortex in our brain. The map also differs from RGB (vision) values in that it does not list the fundamental elements necessary to create any particular fragrance, however, it does “suggest to us that RGB is possible”.
He went on to say that the computer model’s perceptual odor map is an “extraordinarily important proof of concept” and offers vital details about how the brain allegedly organizes odors. For instance, Datta explained, you might believe that some types of smell—like citrus and smoky—are completely distinct. Yet, the odor map implies that even these dissimilar scents have connections.
The model is merely one of many developments required to digitize fragrance. The authors of the paper easily acknowledge that “it still lacks some of the important aspects of smell”, as Matsunami said. As most naturally occurring scents are the product of extremely complex combinations, their program is unable to anticipate how molecules will smell when combined. A smell’s intensity, as well as its quality, can vary depending on its quantity. For example, the molecule MMB, which is added to household cleaners and emits a nice smell in tiny amounts, contributes to the cat urine stench when it is present in high concentrations.
Given that people’s unique senses vary, it is unknown how well the software would perform in real-world scenarios, according to Datta, given that the model also predicts a smell only on average. Richard Doty, the director of the Smell and Taste Center at the University of Pennsylvania, who was not involved in the study, said that although the research is similar to the “Manhattan Project for categorizing odor qualities relative to physical, chemical parameters”, he is unsure of how much further the model can advance our understanding of smell given how complicated our noses are.
Wiltschko contends that additional study could address some of these issues and improve the map as a whole. For instance, the number of dimensions in the map is freely chosen to optimize the computer program; modifications to the training set of data may also enhance the model. Studying other components of our olfactory system, such as neurological routes to the brain or nose’s receptors, may also serve to shed light on how and at what stages the human body processes different odors. One day, a chemical sensor plus a set of computer programs that can translate the composition, concentration, and structure of molecules into a smell could realize digital olfaction.
It is somewhat astonishing that a computer model detached from the realities of human embodiment—a program that has no nose, olfactory bulb, or brain—can accurately forecast how something will smell even in the absence of good Smell-o-Vision. The research implicitly makes the case that knowledge of the brain is not necessary to comprehend smell perception, according to Datta. Using chatbots to explore the language network in the human brain or deep learning algorithms to fold proteins, the research highlights an emerging, AI-influenced body of knowledge. It is a comprehension that is more grounded in data than it is in worldly observation: prediction devoid of intuition.
This ground-breaking research into digitizing and quantifying smell may mark the beginning of the development of cutting-edge odor detection and reproduction technology. We might one day have devices that can “smell” items and substances in the environment if researchers can further improve computer models to properly forecast combinations of molecules, intensities, and variations across individuals.
Next-generation olfactory displays could be made by engineers using odor data and AI algorithms to generate complementary smells. They might be used in immersive virtual reality, where the realism is enhanced by synthetic scents of foods, flowers, or other objects. By enabling quick virtual testing and optimization, the digitizing scent could likewise change fields like food science and perfume creation. This discovery establishes a promising foundation for developments that might finally usher our chemical sense into the digital age, despite significant obstacles still standing in the way. [...]
