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We are living in “humanoid summer” right now, if you didn’t know. Or at least it feels that way to Ken Goldberg, a roboticist extraordinaire who leads research in the field at the University of California, Berkeley, and has founded several robotics companies. Money is pouring into humanoid startups, including Figure AI, which raised $675 million earlier this year. Agility Robotics has moved past the pilot phase, launching what it’s calling the first fleet of humanoid robots at a Spanx factory in Georgia.

But what really makes it feel like humanoid summer is the videos. Seemingly every month brings a new moody, futuristic video featuring a humanoid staring intensely (or unnervingly) into the camera, jumping around, or sorting things into piles. Sometimes they even speak. 

Such videos have heightened currency in robotics right now. As Goldberg says, you can’t just fire up a humanoid robot at home and play around with it the way you can with the latest release of ChatGPT. So for anyone hoping to ride the AI wave or demonstrate their progress—like a startup or an academic seeking lab funding—a good humanoid video is the best marketing tool available. “The imagery, visuals, and videos—they’ve played a big role,” he says.  

But what do they show, exactly? I’ve watched dozens of them this year, and I confess I frequently oscillate between being impressed, scared, and bored. I wanted a more sophisticated eye to help me figure out the right questions to ask. Goldberg was happy to help. 

Watch out for movie magic

First, some basics. The most important thing to know is whether a robot is being tele-operated by a human off screen rather than executing the tasks autonomously. Unfortunately, you can’t tell unless the company discloses it in the video, which they don’t always do.

The second issue is selection bias. How many takes were necessary to get that perfect shot? If a humanoid shows off an impressive ability to sort objects, but it took 200 tries to do the task successfully, that matters. 

Lastly, is the video sped up? Oftentimes that can be totally reasonable if it’s skipping over things that don’t demonstrate much about the robot (“I don’t want to watch the paint dry,” Goldberg says). But if the video is sped up to intentionally hide something or make the robot seem more effective than it is, that’s worth flagging. All of these editing decisions should, ideally, be disclosed by the robotics company or lab. 

Look at the hands

A trope I’ve noticed in humanoid videos is that they show off the robot’s hands by having the fingers curl gently into a fist. A robotic hand with that many usable joints is indeed more complex than the grippers shown on industrial robots, Goldberg says, but those humanoid hands may not be capable of what the videos sometimes suggest. 

For example, humanoids are often shown holding a box while walking. The shot may suggest they’re using their hands the way humans would—placing their fingers underneath the box and lifting up. But often, Goldberg says, the robots are actually just squeezing the box horizontally, with the force coming from the shoulder. It still works, but not the way I’d imagined. Most videos don’t show the hands doing much at all—unsurprising, since hand dexterity requires enormously complicated engineering. 

Evaluate the environment

The latest humanoid videos prove that robots are getting really good at walking and even running. “A robot that could outrun a human is probably right around the corner,” Goldberg says. 

That said, it’s important to look out for what the environment is like for the robot in the video. Is there clutter or dust on the floor? Are there people getting in its way? Are there stairs, pieces of equipment, or slippery surfaces in its path? Probably not. The robots generally show off their (admittedly impressive) feats in pristine environments, not quite like the warehouses, factories, and other places where they will purportedly work alongside humans. 

Watch out for empty boxes

Humanoids are sometimes not as strong as the videos of their physical feats can suggest; I was surprised to hear that many would struggle to hold even a hammer at arm’s length. They can carry more when they hold the weight close to the core, but their carrying capacity varies dramatically as their arms are outstretched. Keep this in mind when you watch a robot move boxes from one belt to the other, since those boxes might be empty. 

There are countless other questions to ask amid the humanoid hype, not the least of which is how much these things might end up costing. But I hope this at least gives you some perspective as the robots become more prevalent in our world.

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Deeper Learning

We finally have a definition for open-source AI

Open-source AI is everywhere right now. The problem is, no one agrees on what it actually is. Now we may finally have an answer. The Open Source Initiative (OSI), the self-appointed arbiters of what it means to be open source, has released a new definition, which it hopes will help lawmakers develop regulations to protect consumers from AI risks. Among other details, the definition says that an open-source AI system can be used for any purpose without permission, and researchers should be able to inspect its components and study how the system works. The definition requires transparency about what training data was used, but it does not require model makers to release such data in full. 

Why this matters: The previous lack of an open-source standard presented a problem. Although we know that the decisions of OpenAI and Anthropic to keep their models, data sets, and algorithms secret makes their AI closed source, some experts argue that Meta and Google’s freely accessible models, which are open to anyone to inspect and adapt, aren’t truly open source either, because licenses restrict what users can do with the models and because the training data sets aren’t made public. An agreed-upon definition could help. Read more from Rhiannon Williams and me here.

Bits and Bytes

How to fine-tune AI for prosperity

Artificial intelligence could put us on the path to a booming economic future, but getting there will take some serious course corrections. (MIT Technology Review) 

A new system lets robots sense human touch without artificial skin

Even the most capable robots aren’t great at sensing human touch; you typically need a computer science degree or at least a tablet to interact with them effectively. That may change, thanks to robots that can now sense and interpret touch without being covered in high-tech artificial skin. (MIT Technology Review)

(Op-ed) AI could be a game changer for people with disabilities

It feels unappreciated (and underreported) that AI-based software can truly be an assistive technology, enabling people to do things they otherwise would be excluded from. (MIT Technology Review)

Our basic assumption—that photos capture reality—is about to go up in smoke

Creating realistic and believable fake photos is now trivially easy. We are not prepared for the implications. (The Verge)

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If you follow drone news closely—and you’re forgiven if you don’t—you may have noticed over the last few months that the Federal Aviation Administration (FAA) has been quite busy. For decades, the agency had been a thorn in the side of drone evangelists, who wanted more freedom to fly drones in shared airspaces or dense neighborhoods. The FAA’s rules have made it cumbersome for futuristic ideas like drones delivering packages to work at scale.

Lately, that’s been changing. The agency recently granted Amazon’s Prime Air program approval to fly drones beyond the visual line of sight from its pilots in parts of Texas. The FAA has also granted similar waivers to hundreds of police departments around the country, which are now able to fly drones miles away, much to the ire of privacy advocates. 

However, while the FAA doling out more waivers is notable, there’s a much bigger change coming in less than a month. It promises to be the most significant drone decision in decades, and one that will decide just how many drones we all can expect to see and hear buzzing above us in the US on a daily basis. 

By September 16—if the FAA adheres to its deadline—the agency must issue a Notice of Proposed Rulemaking about whether drones can be flown beyond a visual line of sight. In other words, rather than issuing one-off waivers to police departments and delivery companies, it will propose a rule that applies to everyone using the airspace and aims to minimize the safety risk of drones flying into one another or falling and injuring people or property below. 

The FAA was first directed to come up with a rule back in 2018, but it hasn’t delivered. The September 16 deadline was put in place by the most recent FAA Reauthorization Act, signed into law in May. The agency will have 16 months after releasing the proposed rule to issue a final one.

Who will craft such an important rule, you ask? There are 87 organizations on the committee. Half are either commercial operators like Amazon and FedEx, drone manufacturers like Skydio, or other tech interests like Airbus or T-Mobile. There are also a handful of privacy groups like the American Civil Liberties Union, as well as academic researchers. 

It’s unclear where exactly the agency’s proposed rule will fall, but experts in the drone space told me that the FAA has grown much more accommodating of drones, and they expect this ruling to be reflective of that shift. 

If the rule makes it easier for pilots to fly beyond their line of sight, nearly every type of drone pilot will benefit from fewer restrictions. Groups like search and rescue pilots could more easily use drones to find missing persons in the wilderness without an FAA waiver, which is hard to obtain quickly in an emergency situation. 

But if more drones take to the skies with their pilots nowhere in sight, it will have massive implications. “The [proposed rule] will likely allow a broad swatch of operators to conduct wide-ranging drone flights beyond their visual line of sight,” says Jay Stanley, a senior policy analyst at the American Civil Liberties Union’s Speech, Privacy, and Technology Project. “That could open up the skies to a mass of delivery drones (from Amazon and UPS to local ‘burrito-copters’ and other deliveries), local government survey or code-enforcement flights, and a whole new swath of police surveillance operations.”

Read more about what’s coming next for drones from me here.

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Deeper Learning

The US wants to use facial recognition to identify migrant children as they age

The US Department of Homeland Security (DHS) is looking into ways it might use facial recognition technology to track the identities of migrant children, “down to the infant,” as they age, according to John Boyd, assistant director of the department’s Office of Biometric Identity Management (OBIM), where a key part of his role is to research and develop future biometric identity services for the government. The previously unreported project is intended to improve how facial recognition algorithms track children over time.

Why this matters: Facial recognition technology (FRT) has traditionally not been applied to children, largely because training data sets of real children’s faces are few and far between, and consist of either low-quality images drawn from the internet or small sample sizes with little diversity. Such limitations reflect the significant sensitivities regarding privacy and consent when it comes to minors. A DHS program specifically trained on images of children, immigrants’ rights organizations and privacy advocates told MIT Technology Review, raises serious concern about whether children will be able to opt out of biometric data collection. Read more from Eileen Guo here. 

Bits and Bytes

A new public database lists all the ways AI could go wrong

The AI Risk Repository documents over 700 potential risks advanced AI systems could pose. It’s the most comprehensive source yet of information about previously identified issues that could arise from the creation and deployment of these models. (MIT Technology Review) 

Escaping Spotify’s algorithm

According to a 2022 report published by Distribution Strategy Group, at least 30% of songs streamed on Spotify are recommended by AI. By delivering what people seem to want, has Spotify killed the joy of music discovery? (MIT Technology Review)

How ‘Deepfake Elon Musk’ became the internet’s biggest scammer

An AI-powered version of Mr. Musk has appeared in thousands of inauthentic ads, contributing to billions in fraud. (The New York Times) 

Google’s conversational assistant Gemini Live has launched

Google’s Gemini Live, which was teased back in May, is the company’s closest answer to OpenAI’s GPT-4o. The model can hold conversations in real time and you can interrupt it mid-sentence. Google finally rolled it out earlier this week. (Google)

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When the generative AI boom started with ChatGPT in late 2022, we were sold a vision of superintelligent AI tools that know everything, can replace the boring bits of work, and supercharge productivity and economic gains. 

Two years on, most of those productivity gains haven’t materialized. And we’ve seen something peculiar and slightly unexpected happen: People have started forming relationships with AI systems. We talk to them, say please and thank you, and have started to invite AIs into our lives as friends, lovers, mentors, therapists, and teachers. 

We’re seeing a giant, real-world experiment unfold, and it’s still uncertain what impact these AI companions will have either on us individually or on society as a whole, argue Robert Mahari, a joint JD-PhD candidate at the MIT Media Lab and Harvard Law School, and Pat Pataranutaporn, a researcher at the MIT Media Lab. They say we need to prepare for “addictive intelligence”, or AI companions that have dark patterns built into them to get us hooked. You can read their piece here. They look at how smart regulation can help us prevent some of the risks associated with AI chatbots that get deep inside our heads. 

The idea that we’ll form bonds with AI companions is no longer just hypothetical. Chatbots with even more emotive voices, such as OpenAI’s GPT-4o, are likely to reel us in even deeper. During safety testing, OpenAI observed that users would use language that indicated they had formed connections with AI models, such as “This is our last day together.” The company itself admits that emotional reliance is one risk that might be heightened by its new voice-enabled chatbot. 

There’s already evidence that we’re connecting on a deeper level with AI even when it’s just confined to text exchanges. Mahari was part of a group of researchers that analyzed a million ChatGPT interaction logs and found that the second most popular use of AI was sexual role-playing. Aside from that, the overwhelmingly most popular use case for the chatbot was creative composition. People also liked to use it for brainstorming and planning, asking for explanations and general information about stuff.  

These sorts of creative and fun tasks are excellent ways to use AI chatbots. AI language models work by predicting the next likely word in a sentence. They are confident liars and often present falsehoods as facts, make stuff up, or hallucinate. This matters less when making stuff up is kind of the entire point. In June, my colleague Rhiannon Williams wrote about how comedians found AI language models to be useful for generating a first “vomit draft” of their material; they then add their own human ingenuity to make it funny.

But these use cases aren’t necessarily productive in the financial sense. I’m pretty sure smutbots weren’t what investors had in mind when they poured billions of dollars into AI companies, and, combined with the fact we still don’t have a killer app for AI,it’s no wonder that Wall Street is feeling a lot less bullish about it recently.

The use cases that would be “productive,” and have thus been the most hyped, have seen less success in AI adoption. Hallucination starts to become a problem in some of these use cases, such as code generation, news and online searches, where it matters a lot to get things right. Some of the most embarrassing failures of chatbots have happened when people have started trusting AI chatbots too much, or considered them sources of factual information. Earlier this year, for example, Google’s AI overview feature, which summarizes online search results, suggested that people eat rocks and add glue on pizza. 

And that’s the problem with AI hype. It sets our expectations way too high, and leaves us disappointed and disillusioned when the quite literally incredible promises don’t happen. It also tricks us into thinking AI is a technology that is even mature enough to bring about instant changes. In reality, it might be years until we see its true benefit.

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Deeper Learning

AI “godfather” Yoshua Bengio has joined a UK project to prevent AI catastrophes

Yoshua Bengio, a Turing Award winner who is considered one of the godfathers of modern AI, is throwing his weight behind a project funded by the UK government to embed safety mechanisms into AI systems. The project, called Safeguarded AI, aims to build an AI system that can check whether other AI systems deployed in critical areas are safe. Bengio is joining the program as scientific director and will provide critical input and advice. 

What are they trying to do: Safeguarded AI’s goal is to build AI systems that can offer quantitative guarantees, such as risk scores, about their effect on the real world. The project aims to build AI safety mechanisms by combining scientific world models, which are essentially simulations of the world, with mathematical proofs. These proofs would include explanations of the AI’s work, and humans would be tasked with verifying whether the AI model’s safety checks are correct. Read more from me here.

Bits and Bytes

Google DeepMind trained a robot to beat humans at table tennis

Researchers managed to get a robot  wielding a 3D-printed paddle to win 13 of 29 games against human opponents of varying abilities in full games of competitive table tennis. The research represents a small step toward creating robots that can perform useful tasks skillfully and safely in real environments like homes and warehouses, which is a long-standing goal of the robotics community. (MIT Technology Review)

Are we in an AI bubble? Here’s why it’s complex.

There’s been a lot of debate recently, and even some alarm, about whether AI is ever going to live up to its potential, especially thanks to tech stocks’ recent nosedive. This nuanced piece explains why although the sector faces significant challenges, it’s far too soon to write off AI’s transformative potential. (Platformer) 

How Microsoft spread its bets beyond OpenAI

Microsoft and OpenAI have one of the most successful partnerships in AI. But following OpenAI’s boardroom drama last year, the tech giant and its CEO, Satya Nadella, have been working on a strategy that will make Microsoft more independent of Sam Altman’s startup. Microsoft has diversified its investments and partnerships in generative AI, built its own smaller, cheaper models, and hired aggressively to develop its consumer AI efforts. (Financial Times) 

Humane’s daily returns are outpacing sales

Oof. The extremely hyped AI pin, which was billed as a wearable AI assistant, seems to have flopped. Between May and August, more Humane AI Pins were returned than purchased. Infuriatingly, the company has no way to reuse the returned pins, so they become e-waste. (The Verge)

This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here.

It’s the Taylor Swifts of the world that are going to save us. In January, nude deepfakes of Taylor Swift went viral on X, which caused public outrage. Nonconsensual explicit deepfakes are one of the most common and severe types of harm posed by AI. The generative AI boom of the past few years has only made the problem worse, and we’ve seen high-profile cases of children and female politicians being abused with these technologies. 

Though terrible, Swift’s deepfakes did perhaps more than anything else to raise awareness about the risks and seem to have galvanized tech companies and lawmakers to do something. 

“The screw has been turned,” says Henry Ajder, a generative AI expert who has studied deepfakes for nearly a decade. We are at an inflection point where the pressure from lawmakers and awareness among consumers is so great that tech companies can’t ignore the problem anymore, he says. 

First, the good news. Last week Google said it is taking steps to keep explicit deepfakes from appearing in search results. The tech giant is making it easier for victims to request that nonconsensual fake explicit imagery be removed. It will also filter all explicit results on similar searches and remove duplicate images. This will prevent the images from popping back up in the future. Google is also downranking search results that lead to explicit fake content. When someone searches for deepfakes and includes someone’s name in the search, Google will aim to surface high-quality, non-explicit content, such as relevant news articles.

This is a positive move, says Ajder. Google’s changes remove a huge amount of visibility for nonconsensual, pornographic deepfake content. “That means that people are going to have to work a lot harder to find it if they want to access it,” he says. 

In January, I wrote about three ways we can fight nonconsensual explicit deepfakes. These included regulation; watermarks, which would help us detect whether something is AI-generated; and protective shields, which make it harder for attackers to use our images. 

Eight months on, watermarks and protective shields remain experimental and unreliable, but the good news is that regulation has caught up a little bit. For example, the UK has banned both creation and distribution of nonconsensual explicit deepfakes. This decision led a popular site that distributes this kind of content, Mr DeepFakes, to block access to UK users, says Ajder. 

The EU’s AI Act is now officially in force and could usher in some important changes around transparency. The law requires deepfake creators to clearly disclose that the material was created by AI. And in late July, the US Senate passed the Defiance Act, which gives victims a way to seek civil remedies for sexually explicit deepfakes. (This legislation still needs to clear many hurdles in the House to become law.) 

But a lot more needs to be done. Google can clearly identify which websites are getting traffic and tries to remove deepfake sites from the top of search results, but it could go further. “Why aren’t they treating this like child pornography websites and just removing them entirely from searches where possible?” Ajder says. He also found it a weird omission that Google’s announcement didn’t mention deepfake videos, only images. 

Looking back at my story about combating deepfakes with the benefit of hindsight, I can see that I should have included more things companies can do. Google’s changes to search are an important first step. But app stores are still full of apps that allow users to create nude deepfakes, and payment facilitators and providers still provide the infrastructure for people to use these apps. 

Ajder calls for us to radically reframe the way we think about nonconsensual deepfakes and pressure companies to make changes that make it harder to create or access such content. 

“This stuff should be seen and treated online in the same way that we think about child pornography—something which is reflexively disgusting, awful, and outrageous,” he says. “That requires all of the platforms … to take action.” 

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Deeper Learning

End-of-life decisions are difficult and distressing. Could AI help?

A few months ago, a woman in her mid-50s—let’s call her Sophie—experienced a hemorrhagic stroke, which left her with significant brain damage. Where should her medical care go from there? This difficult question was left, as it usually is in these kinds of situations, to Sophie’s family members, but they couldn’t agree. The situation was distressing for everyone involved, including Sophie’s doctors.

Enter AI: End-of-life decisions can be extremely upsetting for surrogates tasked with making calls on behalf of another person, says David Wendler, a bioethicist at the US National Institutes of Health. Wendler and his colleagues are working on something that could make things easier: an artificial-intelligence-based tool that can help surrogates predict what patients themselves would want. Read more from Jessica Hamzelou here. 

Bits and Bytes

OpenAI has released a new ChatGPT bot that you can talk to
The new chatbot represents OpenAI’s push into a new generation of AI-powered voice assistants in the vein of Siri and Alexa, but with far more capabilities to enable more natural, fluent conversations. (MIT Technology Review) 

Meta has scrapped celebrity AI chatbots after they fell flat with users
Less than a year after announcing it was rolling out AI chatbots based on celebrities such as Paris Hilton, the company is scrapping the feature. Turns out nobody wanted to chat with a random AI celebrity after all! Instead, Meta is rolling out a new feature called AI Studio, which allows creators to make AI avatars of themselves that can chat with fans. (The Information)

OpenAI has a watermarking tool to catch students cheating with ChatGPT but won’t release it
The tool can detect text written by artificial intelligence with 99.9% certainty, but the company hasn’t launched it for fear it might put people off from using its AI products. (The Wall Street Journal) 

The AI Act has entered into force
At last! Companies now need to start complying with one of the world’s first sweeping AI laws, which aims to curb the worst harms. It will usher in much-needed changes to how AI is built and used in the European Union and beyond. I wrote about what will change with this new law, and what won’t, in March. (The European Commission)

How TikTok bots and AI have powered a resurgence in UK far-right violence
Following the tragic stabbing of three girls in the UK, the country has seen a surge of far-right riots and vandalism. The rioters have created AI-generated images that incite hatred and spread harmful stereotypes. Far-right groups have also used AI music generators to create songs with xenophobic content. These have spread like wildfire online thanks to powerful recommendation algorithms. (The Guardian)

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It’s been another big week in AI. Meta updated its powerful new Llama model, which it’s handing out for free, and OpenAI said it is going to trial an AI-powered online search tool that you can chat with, called SearchGPT. 

But the news item that really stood out to me was one that didn’t get as much attention as it should have. It has the potential to usher in more powerful AI and scientific discovery than previously possible. 

Last Thursday, Google DeepMind announced it had built AI systems that can solve complex math problems. The systems—called AlphaProof and AlphaGeometry 2—worked together to successfully solve four out of six problems from this year’s International Mathematical Olympiad, a prestigious competition for high school students. Their performance was the equivalent of winning a silver medal. It’s the first time any AI system has ever achieved such a high success rate on these kinds of problems. My colleague Rhiannon Williams has the news here. 

Math! I can already imagine your eyes glazing over. But bear with me. This announcement is not just about math. In fact, it signals an exciting new development in the kind of AI we can now build. AI search engines that you can chat with may add to the illusion of intelligence, but systems like Google DeepMind’s could improve the actual intelligence of AI. For that reason, building systems that are better at math has been a goal for many AI labs, such as OpenAI.  

That’s because math is a benchmark for reasoning. To complete these exercises aimed at high school students, the AI system needed to do very complex things like planning to understand and solve abstract problems. The systems were also able to generalize, allowing them to solve a whole range of different problems in various  branches of mathematics. 

“What we’ve seen here is that you can combine [reinforcement learning] that was so successful in things like AlphaGo with large language models and produce something which is extremely capable in the space of text,” David Silver, principal research scientist at Google DeepMind and indisputably a pioneer of deep reinforcement learning, said in a press briefing. In this case, that capability was used to construct programs in the computer language Lean that represent mathematical proofs. He says the International Mathematical Olympiad represents a test for what’s possible and paves the way for further breakthroughs. 

This same recipe could be applied in any situation with really clear, verified reward signals for reinforcement-learning algorithms and an unambiguous way to measure correctness as you can in mathematics, said Silver. One potential application would be coding, for example. 

Now for a compulsory reality check: AlphaProof and AlphaGeometry 2 can still only solve hard high-school-level problems. That’s a long way away from the extremely hard problems top human mathematicians can solve. Google DeepMind stressed that its tool did not, at this point, add anything to the body of mathematical knowledge humans have created. But that wasn’t the point. 

“We are aiming to provide a system that can prove anything,” Silver said. Think of an AI system as reliable as a calculator, for example, that can provide proofs for many challenging problems, or verify tests for computer software or scientific experiments. Or perhaps build better AI tutors that can give feedback on exam results, or fact-check news articles. 

But the thing that excites me most is what Katie Collins, a researcher at the University of Cambridge who specializes in math and AI (and was not involved in the project), told Rhiannon. She says these tools create and evaluate new problems, motivate new people to enter the field, and spark more wonder. That’s something we definitely need more of in this world.

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A new tool for copyright holders can show if their work is in AI training data

Since the beginning of the generative AI boom, content creators have argued that their work has been scraped into AI models without their consent. But until now, it has been difficult to know whether specific text has actually been used in a training data set. Now they have a new way to prove it: “copyright traps.” These are pieces of hidden text that let you mark written content in order to later detect whether it has been used in AI models or not. 

Why this matters: Copyright traps tap into one of the biggest fights in AI. A number of publishers and writers are in the middle of litigation against tech companies, claiming their intellectual property has been scraped into AI training data sets without their permission. The idea is that these traps could help to nudge the balance a little more in the content creators’ favor. Read more from me here. 

Bits and Bytes

AI trained on AI garbage spits out AI garbage
New research published in Nature shows that the quality of AI models’ output gradually degrades when it’s trained on AI-generated data. As subsequent models produce output that is then used as training data for future models, the effect gets worse. (MIT Technology Review) 

OpenAI unveils SearchGPT 
The company says it is testing new AI search features that give you fast and timely answers with clear and relevant sources cited. The idea is for the technology to eventually be incorporated into ChatGPT, and CEO Sam Altman says it’ll be possible to do voice searches. However, like many other AI-powered search services, including Google’s, it’s already making errors, as the Atlantic reports. 
(OpenAI) 

AI video generator Runway trained on thousands of YouTube videos without permission
Leaked documents show that the company was secretly training its generative AI models by scraping thousands of videos from popular YouTube creators and brands, as well as pirated films. (404 media) 

Meta’s big bet on open-source AI continues
Meta unveiled Llama 3.1 405B, the first frontier-level open-source AI model, which matches state-of-the-art models such as GPT-4 and Gemini in performance. In an accompanying blog post, Mark Zuckerberg renewed his calls for open-source AI to become the industry standard. This would be good for customization, competition, data protection, and efficiency, he argues. It’s also good for Meta, because it leaves competitors with less of an advantage in the AI space. (Facebook) 

This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here.

Yesterday, on July 21, President Joe Biden announced he is stepping down from the race against Donald Trump in the US presidential election.

But AI nerds may remember that exactly a year ago, on July 21, 2023, Biden was posing with seven top tech executives at the White House. He’d just negotiated a deal where they agreed to eight of the most prescriptive rules targeted at the AI sector at that time. A lot can change in a year! 

The voluntary commitments were hailed as much-needed guidance for the AI sector, which was building powerful technology with few guardrails. Since then, eight more companies have signed the commitments, and the White House has issued an executive order that expands upon them—for example, with a requirement that developers share safety test results for new AI models with the US government if the tests show that the technology could pose a risk to national security. 

US politics is extremely polarized, and the country is unlikely to pass AI regulation anytime soon. So these commitments, along with some existing laws such as antitrust and consumer protection rules, are the best the US has in terms of protecting people from AI harms. To mark the one-year anniversary of the voluntary commitments, I decided to look at what’s happened since. I asked the original seven companies that signed the voluntary commitments to share as much as they could on what they have done to comply with them, cross-checked their responses with a handful of external experts, and tried my best to provide a sense of how much progress has been made. You can read my story here. 

Silicon Valley hates being regulated and argues that it hinders innovation. Right now, the US is relying on the tech sector’s goodwill to protect its consumers from harm, but these companies can decide to change their policies anytime that suits them and face no real consequences. And that’s the problem with nonbinding commitments: They are easy to sign, and as easy to forget. 

That’s not to say they don’t have any value. They can be useful in creating norms around AI development and placing public pressure on companies to do better. In just one year, tech companies have implemented some positive changes, such as AI red-teaming, watermarking, and investment in research on how to make AI systems safe. However, these sorts of commitments are opt-in only, and that means companies can always just opt back out again. Which brings me to the next big question for this field: Where will Biden’s successor take US AI policy? 

The debate around AI regulation is unlikely to go away if Donald Trump wins the presidential election in November, says Brandie Nonnecke, the director of the CITRIS Policy Lab at UC Berkeley. 

“Sometimes the parties have different concerns about the use of AI. One might be more concerned about workforce effects, and another might be more concerned about bias and discrimination,” says Nonnecke. “It’s clear that it is a bipartisan issue that there need to be some guardrails and oversight of AI development in the United States,” she adds. 

Trump is no stranger to AI. While in office, he signed an executive order calling for more investment in AI research and asking the federal government to use more AI, coordinated by a new National AI Initiative Office. He also issued early guidance on responsible AI. If he returns to office, he is reportedly planning to scratch Biden’s executive order and put in place his own AI executive order that reduces AI regulation and sets up a “Manhattan Project” to boost military AI. Meanwhile, Biden keeps calling for Congress to pass binding AI regulations. It’s no surprise, then, that Silicon Valley’s billionaires have backed Trump. 

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Deeper Learning

A new weather prediction model from Google combines AI with traditional physics

Google DeepMind researchers have built a new weather prediction model called NeuralGCN. It combines machine learning with more conventional techniques, potentially yielding accurate forecasts at a fraction of the current cost and bridging a divide between traditional physics and AI that’s grown between weather prediction experts in the last several years. 

What’s the big deal? While new machine-learning techniques that predict weather by learning from years of past data are extremely fast and efficient, they can struggle with long-term predictions. General circulation models, on the other hand, which have dominated weather prediction for the last 50 years, use complex equations to model changes in the atmosphere; they give accurate projections but are exceedingly slow and expensive to run. While experts are divided on which tool will be most reliable going forward, the new model from Google attempts to combine the two. The result is a model that can produce quality predictions faster with less computational power. Read more from James O’Donnell here.

Bits and Bytes

It may soon be legal to jailbreak AI to expose how it works
It could soon  become easier to break technical protection measures on AI systems in order to probe them for bias and harmful content and to learn about the data they were trained on, thanks to an exemption to US copyright law that the government is currently considering. (404 Media) 

The data that powers AI is disappearing fast
Over the last year, many of the most important online web sources for AI training data, such as news sites, have blocked companies from scraping their content. An MIT study found that 5% of all data, and 25% of data from the highest-quality sources, has been restricted. (The New York Times) 

OpenAI is in talks with Broadcom to develop a new AI chip 
OpenAI CEO Sam Altman is busy working on a new chip venture that would reduce OpenAI’s dependence on Nvidia, which has a near-monopoly on AI chips. The company has talked with many chip designers, including Broadcom, but it’s still a long shot that could take years to work out. If it does, it could significantly boost the computing power OpenAI has available to build more powerful models.  (The Information) 

This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here.

It’s the simplest questions that are often the hardest to answer. That applies to AI, too. Even though it’s a technology being sold as a solution to the world’s problems, nobody seems to know what it really is. It’s a label that’s been slapped on technologies ranging from self-driving cars to facial recognition, chatbots to fancy Excel. But in general, when we talk about AI, we talk about technologies that make computers do things we think need intelligence when done by people. 

For months, my colleague Will Douglas Heaven has been on a quest to go deeper to understand why everybody seems to disagree on exactly what AI is, why nobody even knows, and why you’re right to care about it. He’s been talking to some of the biggest thinkers in the field, asking them, simply: What is AI? It’s a great piece that looks at the past and present of AI to see where it is going next. You can read it here. 

Here’s a taste of what to expect: 

Artificial intelligence almost wasn’t called “artificial intelligence” at all. The computer scientist John McCarthy is credited with coming up with the term in 1955 when writing a funding application for a summer research program at Dartmouth College in New Hampshire. But more than one of McCarthy’s colleagues hated it. “The word ‘artificial’ makes you think there’s something kind of phony about this,” said one. Others preferred the terms “automata studies,” “complex information processing,” “engineering psychology,” “applied epistemology,” “neural cybernetics,”  “non-numerical computing,” “neuraldynamics,” “advanced automatic programming,” and “hypothetical automata.” Not quite as cool and sexy as AI.

AI has several zealous fandoms. AI has acolytes, with a faith-like belief in the technology’s current power and inevitable future improvement. The buzzy popular narrative is shaped by a pantheon of big-name players, from Big Tech marketers in chief like Sundar Pichai and Satya Nadella to edgelords of industry like Elon Musk and Sam Altman to celebrity computer scientists like Geoffrey Hinton. As AI hype has ballooned, a vocal anti-hype lobby has risen in opposition, ready to smack down its ambitious, often wild claims. As a result, it can feel as if different camps are talking past one another, not always in good faith.

This sometimes seemingly ridiculous debate has huge consequences that affect us all. AI has a lot of big egos and vast sums of money at stake. But more than that, these disputes matter when industry leaders and opinionated scientists are summoned by heads of state and lawmakers to explain what this technology is and what it can do (and how scared we should be). They matter when this technology is being built into software we use every day, from search engines to word-processing apps to assistants on your phone. AI is not going away. But if we don’t know what we’re being sold, who’s the dupe?

For example, meet the TESCREALists. A clunky acronym (pronounced “tes-cree-all”) replaces an even clunkier list of labels: transhumanism, extropianism, singularitarianism, cosmism, rationalism, effective altruism, and longtermism. It was coined by Timnit Gebru, who founded the Distributed AI Research Institute and was Google’s former ethical AI co-lead, and Émile Torres, a philosopher and historian at Case Western Reserve University. Some anticipate human immortality; others predict humanity’s colonization of the stars. The common tenet is that an all-powerful technology is not only within reach but inevitable. TESCREALists believe that artificial general intelligence, or AGI, could not only fix the world’s problems but level up humanity. Gebru and Torres link several of these worldviews—with their common focus on “improving” humanity—to the racist eugenics movements of the 20th century.

Is AI math or magic? Either way, people have strong, almost religious beliefs in one or the other. “It’s offensive to some people to suggest that human intelligence could be re-created through these kinds of mechanisms,” Ellie Pavlick, who studies neural networks at Brown University, told Will. “People have strong-held beliefs about this issue—it almost feels religious. On the other hand, there’s people who have a little bit of a God complex. So it’s also offensive to them to suggest that they just can’t do it.”

Will’s piece really is the definitive look at this whole debate. No spoilers—there are no simple answers, but lots of fascinating characters and viewpoints. I’d recommend you read the whole thing here—and see if you can make your mind up about what AI really is.

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Deeper Learning

AI can make you more creative—but it has limits

Generative AI models have made it simpler and quicker to produce everything from text passages and images to video clips and audio tracks. But while AI’s output can certainly seem creative, do these models actually boost human creativity?  

A new study looked at how people used OpenAI’s large language model GPT-4 to write short stories. The model was helpful—but only to an extent. The researchers found that while AI improved the output of less creative writers, it made little difference to the quality of the stories produced by writers who were already creative. The stories in which AI had played a part were also more similar to each other than those dreamed up entirely by humans. Read more from Rhiannon Williams.

Bits and Bytes

Robot-packed meals are coming to the frozen-food aisle
Found everywhere from airplanes to grocery stores, prepared meals are usually packed by hand. AI-powered robotics is changing that. (MIT Technology Review) 

AI is poised to automate today’s most mundane manual warehouse task
Pallets are everywhere, but training robots to stack them with goods takes forever. Fixing that could be a tangible win for commercial AI-powered robots. (MIT Technology Review)

The Chinese government is going all-in on autonomous vehicles
The government is finally allowing Tesla to bring its Full Self-Driving feature to China. New government permits let companies test driverless cars on the road and allow cities to build smart road infrastructure that will tell these cars where to go. (MIT Technology Review) 

The US and its allies took down a Russian AI bot farm on X
The US seized control of a sophisticated Russian operation that used AI to push propaganda through nearly a thousand covert accounts on the social network X. Western intelligence agencies traced the propaganda mill to an officer of the Russian FSB intelligence force and to a former senior editor at state-controlled publication RT, formerly called Russia Today. (The Washington Post)

AI investors are starting to wonder: Is this just a bubble?
After a massive investment in the language-model boom, the biggest beneficiary is Nvidia, which designs and sells the best chips for training and running modern AI models. Investors are now starting to ask what LLMs are actually going to be used for, and when they will start making them money. (New York magazine) 

Goldman Sachs thinks AI is overhyped, wildly expensive, and unreliable
Meanwhile, the major investment bank published a research paper about the economic viability of generative AI. It notes that there is “little to show for” the huge amount of spending on generative AI infrastructure and questions “whether this large spend will ever pay off in terms of AI benefits and returns.” (404 Media) 

The UK politician accused of being AI is actually a real person
A hilarious story about how Mark Matlock, a candidate for the far-right Reform UK party, was accused of being a fake candidate created with AI after he didn’t show up to campaign events. Matlock has assured the press he is a real person, and he wasn’t around because he had pneumonia. (The Verge)