Here’s how people are actually using AI

This story is from The Algorithm, our weekly newsletter on AI. To get it in your inbox first, sign up here.

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)

Google is finally taking action to curb non-consensual deepfakes

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.” 

Now read the rest of The Algorithm

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)

How machines that can solve complex math problems might usher in more powerful AI

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 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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Deeper Learning

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) 

A short history of AI, and what it is (and isn’t)

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.

Now read the rest of The Algorithm

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) 

Can AI help me plan my honeymoon?

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

I’m getting married later this summer and am feverishly planning a honeymoon together with my fiancé. It has been at times overwhelming trying to research and decide between what seem like millions of options while juggling busy work schedules and wedding planning.

Thankfully, my colleague Rhiannon Williams has just published a piece about how to use AI to plan your vacation. You can read her story here. The timing could not be better! I decided to put her tips to the test and use AI to plan my honeymoon itinerary.

I asked ChatGPT to suggest a travel plan over three weeks in Japan and the Philippines, our dream destinations. I told the chatbot that in Tokyo I wanted to see art and design and eat good food, and in the Philippines I wanted to go somewhere laid-back and outdoorsy that is not very touristy. I also asked ChatGPT to be specific in its suggestions for hotels and activities to book. 

The results were pretty good, and they aligned with the research I had already done. I was delighted to see the AI propose we visit Siargao Island in the Philippines, which is known for its surfing. We were planning on going there anyway, but I haven’t had a chance to do much research on what there is to do. ChatGPT came up with some divine-looking day trips involving a stingless-jellyfish sanctuary, cave pools, and other adventures. 

The AI produced a decent first draft of the trip itinerary. I reckon this saved me a lot of time doing research on planned destinations I didn’t know much about, such as Siargao. 

But … when I asked about places I did know more about, such as Tokyo, I wasn’t that impressed. ChatGPT suggested I visit Shibuya Crossing and eat at a sushi restaurant, which, like, c’mon, are some of the most obvious things for tourists to do there. However, I am willing to consider that the problem might have been me and my prompting. Because I found that the more specific I made my prompts, the better the results were. 

But here’s the thing. Language models work by predicting the next likely word in a sentence. These AI systems don’t have an understanding of what it is like to experience these things, or how long they take. For example, ChatGPT suggested spending one whole day taking photos at a scenic spot. That would get boring pretty quickly. The AI systems of today lack the kind of last-mile reasoning and planning skills that would help me with logistics and budgeting. It also suggested accommodations that were way out of our price range. 

But this whole process might become much smoother as we build the next generation of AI agents. 

Agents are AI algorithms and models that can complete complex tasks in the real world. The idea is that one day they could execute a vast range of tasks, much like a human assistant. Agents are the new hot thing in AI, and I just published an explainer looking at what they are and how they work. You can read it here. 

In the future, an AI agent could not only suggest things to do and places to stay on my honeymoon; it would also go a step further than ChatGPT and book flights for me. It would remember my preferences and budget for hotels and only propose accommodation that matched my criteria. It might also remember what I liked to do on past trips, and suggest very specific things to do tailored to those tastes. It might even request bookings for restaurants on my behalf.

Unfortunately for my honeymoon, today’s AI systems lack the kind of reasoning, planning, and memory needed. It’s still early days for these systems, and there are a lot of unsolved research questions. But who knows—maybe for our 10th anniversary trip? 

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Deeper Learning

A way to let robots learn by listening will make them more useful

Most AI-powered robots today use cameras to understand their surroundings and learn new tasks, but it’s becoming easier to train robots with sound too, helping them adapt to tasks and environments where visibility is limited. 

Sound on: Researchers at Stanford University tested how much more successful a robot can be if it’s capable of “listening.” They chose four tasks: flipping a bagel in a pan, erasing a whiteboard, putting two Velcro strips together, and pouring dice out of a cup. In each task, sounds provided clues that cameras or tactile sensors struggle with, like knowing if the eraser is properly contacting the whiteboard or whether the cup contains dice. When using vision alone in the last test, the robot could tell 27% of the time whether there were dice in the cup, but that rose to 94% when sound was included. Read more from James O’Donnell.

Bits and Bytes

AI lie detectors are better than humans at spotting lies
Researchers at the University of Würzburg in Germany found that an AI system was significantly better at spotting fabricated statements than humans. Humans usually only get it right around half the time, but the AI could spot if a statement was true or false in 67% of cases. However, lie detection is a controversial and unreliable technology, and it’s debatable  whether we should even be using it in the first place. (MIT Technology Review) 

A hacker stole secrets from OpenAI 
A hacker managed to access OpenAI’s internal messaging systems and steal information about its AI technology. The company believes the hacker was a private individual, but the incident raised fears among OpenAI employees that China could steal the company’s technology too. (The New York Times)

AI has vastly increased Google’s emissions over the past five years
Google said its greenhouse-gas emissions totaled 14.3 million metric tons of carbon dioxide equivalent throughout 2023. This is 48% higher than in 2019, the company said. This is mostly due to Google’s enormous push toward AI, which will likely make it harder to hit its goal of eliminating carbon emissions by 2030. This is an utterly depressing example of how our societies prioritize profit over the climate emergency we are in. (Bloomberg) 

Why a $14 billion startup is hiring PhDs to train AI systems from their living rooms
An interesting read about the shift happening in AI and data work. Scale AI has previously hired low-paid data workers in countries such as India and the Philippines to annotate data that is used to train AI. But the massive boom in language models has prompted Scale to hire highly skilled contractors in the US with the necessary expertise to help train those models. This highlights just how important data work really is to AI. (The Information) 

A new “ethical” AI music generator can’t write a halfway decent song
Copyright is one of the thorniest problems facing AI today. Just last week I wrote about how AI companies are being forced to cough up for high-quality training data to build powerful AI. This story illustrates why this matters. This story is about an “ethical” AI music generator, which only used a limited data set of licensed music. But without high-quality data, it is not able to generate anything even close to decent. (Wired)  

AI companies are finally being forced to cough up for training data

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

The generative AI boom is built on scale. The more training data, the more powerful the model. 

But there’s a problem. AI companies have pillaged the internet for training data, and many websites and data set owners have started restricting the ability to scrape their websites. We’ve also seen a backlash against the AI sector’s practice of indiscriminately scraping online data, in the form of users opting out of making their data available for training and lawsuits from artists, writers, and the New York Times, claiming that AI companies have taken their intellectual property without consent or compensation. 

Last week three major record labels—Sony Music, Warner Music Group, and Universal Music Group—announced they were suing the AI music companies Suno and Udio over alleged copyright infringement. The music labels claim the companies made use of copyrighted music in their training data “at an almost unimaginable scale,” allowing the AI models to generate songs that “imitate the qualities of genuine human sound recordings.” My colleague James O’Donnell dissects the lawsuits in his story and points out that these lawsuits could determine the future of AI music. Read it here. 

But this moment also sets an interesting precedent for all of generative AI development. Thanks to the scarcity of high-quality data and the immense pressure and demand to build even bigger and better models, we’re in a rare moment where data owners actually have some leverage. The music industry’s lawsuit sends the loudest message yet: High-quality training data is not free. 

It will likely take a few years at least before we have legal clarity around copyright law, fair use, and AI training data. But the cases are already ushering in changes. OpenAI has been striking deals with news publishers such as Politico, the Atlantic, Time, the Financial Times, and others, and exchanging publishers’ news archives for money and citations. And YouTube announced in late June that it will offer licensing deals to top record labels in exchange for music for training. 

These changes are a mixed bag. On one hand, I’m concerned that news publishers are making a Faustian bargain with AI. For example, most of the media houses that have made deals with OpenAI say the deal stipulates that OpenAI cite its sources. But language models are fundamentally incapable of being factual and are best at making things up. Reports have shown that ChatGPT and the AI-powered search engine Perplexity frequently hallucinate citations, which makes it hard for OpenAI to honor its promises.   

It’s tricky for AI companies too. This shift could lead to them build smaller, more efficient models, which are far less polluting. Or they may fork out a fortune to access data at the scale they need to build the next big one. Only the companies most flush with cash, and/or with large existing data sets of their own (such as Meta, with its two decades of social media data), can afford to do that. So the latest developments risk concentrating power even further into the hands of the biggest players. 

On the other hand, the idea of introducing consent into this process is a good one—not just for rights holders, who can benefit from the AI boom, but for all of us. We should all have the agency to decide how our data is used, and a fairer data economy would mean we could all benefit. 

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Deeper Learning

How AI video games can help reveal the mysteries of the human mind

Neuroscientists and psychologists have long been using games as research tools to learn about the human mind. Video games have been either co-opted or specially designed to study how people learn, navigate, and cooperate with others, for example. AI video games—where characters don’t need scripts and appear to play when you’re not watching—could allow us to probe more deeply and unravel enduring mysteries about our brains and behavior, suggests my colleague Jessica Hamzelou in our weekly biotech newsletter, The Checkup.

Ready, set, go: Scientists who have done this type of study were able to observe and study how players behaved in these games: how they explored their virtual environment, how they sought rewards, how they made decisions. And research volunteers didn’t need to travel to a lab—their gaming behavior could be observed from wherever they happened to be playing, whether that was at home, at a library, or even inside an MRI scanner. Read more from Jessica.

Bits and Bytes

AI is already wreaking havoc on global power systems
A really well-done data visualization of the insane amount of electricity AI requires and how it is transforming our energy grid. A startling statistic: Data centers use more electricity than most countries. (Bloomberg) 

The AI boom has an unlikely early winner: wonky consultants
It seems every company out there is thinking about how to use AI. But the problem is that nobody is sure exactly how to do that. And so in come consultants, who are profiting from AI FOMO. Work related to generative AI will make up about 40% of McKinsey’s business this year. (The New York Times)

Deepfake creators are revictimizing sex trafficking survivors
A new low: For the past few months, the largest deepfake sexual abuse website has posted deepfake videos based on footage from GirlsDoPorn, a now-defunct sex trafficking operation. (Wired)

I paid $365.63 to replace 404 Media with AI
A journalist paid gig workers to use ChatGPT to plagiarize news. The result: grammatically correct nonsense. (404 Media)

Why artists are becoming less scared of AI

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

Knock, knock. 

Who’s there? 

An AI with generic jokes. Researchers from Google DeepMind asked 20 professional comedians to use popular AI language models to write jokes and comedy performances. Their results were mixed. 

The comedians said that the tools were useful in helping them produce an initial “vomit draft” that they could iterate on, and helped them structure their routines. But the AI was not able to produce anything that was original, stimulating, or, crucially, funny. My colleague Rhiannon Williams has the full story.

As Tuhin Chakrabarty, a computer science researcher at Columbia University who specializes in AI and creativity, told Rhiannon, humor often relies on being surprising and incongruous. Creative writing requires its creator to deviate from the norm, whereas LLMs can only mimic it.

And that is becoming pretty clear in the way artists are approaching AI today. I’ve just come back from Hamburg, which hosted one of the largest events for creatives in Europe, and the message I got from those I spoke to was that AI is too glitchy and unreliable to fully replace humans and is best used instead as a tool to augment human creativity. 

Right now, we are in a moment where we are deciding how much creative power we are comfortable giving AI companies and tools. After the boom first started in 2022, when DALL-E 2 and Stable Diffusion first entered the scene, many artists raised concerns that AI companies were scraping their copyrighted work without consent or compensation. Tech companies argue that anything on the public internet falls under fair use, a legal doctrine that allows the reuse of copyrighted-protected material in certain circumstances. Artists, writers, image companies, and the New York Times have filed lawsuits against these companies, and it will likely take years until we have a clear-cut answer as to who is right. 

Meanwhile, the court of public opinion has shifted a lot in the past two years. Artists I have interviewed recently say they were harassed and ridiculed for protesting AI companies’ data-scraping practices two years ago. Now, the general public is more aware of the harms associated with AI. In just two years, the public has gone from being blown away by AI-generated images to sharing viral social media posts about how to opt out of AI scraping—a concept that was alien to most laypeople until very recently. Companies have benefited from this shift too. Adobe has been successful in pitching its AI offerings as an “ethical” way to use the technology without having to worry about copyright infringement. 

There are also several grassroots efforts to shift the power structures of AI and give artists more agency over their data. I’ve written about Nightshade, a tool created by researchers at the University of Chicago, which lets users add an invisible poison attack to their images so that they break AI models when scraped. The same team is behind Glaze, a tool that lets artists mask their personal style from AI copycats. Glaze has been integrated into Cara, a buzzy new art portfolio site and social media platform, which has seen a surge of interest from artists. Cara pitches itself as a platform for art created by people; it filters out AI-generated content. It got nearly a million new users in a few days. 

This all should be reassuring news for any creative people worried that they could lose their job to a computer program. And the DeepMind study is a great example of how AI can actually be helpful for creatives. It can take on some of the boring, mundane, formulaic aspects of the creative process, but it can’t replace the magic and originality that humans bring. AI models are limited to their training data and will forever only reflect the zeitgeist at the moment of their training. That gets old pretty quickly.

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Deeper Learning

Apple is promising personalized AI in a private cloud. Here’s how that will work.

Last week, Apple unveiled its vision for supercharging its product lineup with artificial intelligence. The key feature, which will run across virtually all of its product line, is Apple Intelligence, a suite of AI-based capabilities that promises to deliver personalized AI services while keeping sensitive data secure. 

Why this matters: Apple says its privacy-focused system will first attempt to fulfill AI tasks locally on the device itself. If any data is exchanged with cloud services, it will be encrypted and then deleted afterward. It’s a pitch that offers an implicit contrast with the likes of Alphabet, Amazon, or Meta, which collect and store enormous amounts of personal data. Read more from James O’Donnell here. 

Bits and Bytes

How to opt out of Meta’s AI training
If you post or interact with chatbots on Facebook, Instagram, Threads, or WhatsApp, Meta can use your data to train its generative AI models. Even if you don’t use any of Meta’s platforms, it can still scrape data such as photos of you if someone else posts them. Here’s our quick guide on how to opt out. (MIT Technology Review) 

Microsoft’s Satya Nadella is building an AI empire
Nadella is going all in on AI. His $13 billion investment in OpenAI was just the beginning. Microsoft has become an “the world’s most aggressive amasser of AI talent, tools, and technology” and has started building an in-house OpenAI competitor. (The Wall Street Journal)

OpenAI has hired an army of lobbyists
As countries around the world mull AI legislation, OpenAI is on a lobbyist hiring spree to protect its interests. The AI company has expanded its global affairs team from three lobbyists at the start of 2023 to 35 and intends to have up to 50 by the end of this year. (Financial Times)  

UK rolls out Amazon-powered emotion recognition AI cameras on trains
People traveling through some of the UK’s biggest train stations have likely had their faces scanned by Amazon software without their knowledge during an AI trial. London stations such as Euston and Waterloo have tested CCTV cameras with AI to reduce crime and detect people’s emotions. Emotion recognition technology is extremely controversial. Experts say it is unreliable and simply does not work. 
(Wired) 

Clearview AI used your face. Now you may get a stake in the company.
The facial recognition company, which has been under fire for scraping images of people’s faces from the web and social media without their permission, has agreed to an unusual settlement in a class action against it. Instead of paying cash, it is offering a 23% stake in the company for Americans whose faces are in its data sets. (The New York Times) 

Elephants call each other by their names
This is so cool! Researchers used AI to analyze the calls of two herds of African savanna elephants in Kenya. They found that elephants use specific vocalizations for each individual and recognize when they are being addressed by other elephants. (The Guardian) 

What I learned from the UN’s “AI for Good” summit

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

Greetings from Switzerland! I’ve just come back from Geneva, which last week hosted the UN’s AI for Good Summit, organized by the International Telecommunication Union. The summit’s big focus was how AI can be used to meet the UN’s Sustainable Development Goals, such as eradicating poverty and hunger, achieving gender equality, promoting clean energy and climate action and so on. 

The conference featured lots of robots (including one that dispenses wine), but what I liked most of all was how it managed to convene people working in AI from around the globe, featuring speakers from China, the Middle East, and Africa too, such as Pelonomi Moiloa, the CEO of Lelapa AI, a startup building AI for African languages. AI can be very US-centric and male dominated, and any effort to make the conversation more global and diverse is laudable. 

But honestly, I didn’t leave the conference feeling confident AI was going to play a meaningful role in advancing any of the UN goals. In fact, the most interesting speeches were about how AI is doing the opposite. Sage Lenier, a climate activist, talked about how we must not let AI accelerate environmental destruction. Tristan Harris, the cofounder of the Center for Humane Technology, gave a compelling talk connecting the dots between our addiction to social media, the tech sector’s financial incentives, and our failure to learn from previous tech booms. And there are still deeply ingrained gender biases in tech, Mia Shah-Dand, the founder of Women in AI Ethics, reminded us. 

So while the conference itself was about using AI for “good,” I would have liked to see more talk about how increased transparency, accountability, and inclusion could make AI itself good from development to deployment.

We now know that generating one image with generative AI uses as much energy as charging a smartphone. I would have liked more honest conversations about how to make the technology more sustainable itself in order to meet climate goals. And it felt jarring to hear discussions about how AI can be used to help reduce inequalities when we know that so many of the AI systems we use are built on the backs of human content moderators in the Global South who sift through traumatizing content while being paid peanuts. 

Making the case for the “tremendous benefit” of AI was OpenAI’s CEO Sam Altman, the star speaker of the summit. Altman was interviewed remotely by Nicholas Thompson, the CEO of the Atlantic, which has incidentally just announced a deal for OpenAI to share its content to train new AI models. OpenAI is the company that instigated the current AI boom, and it would have been a great opportunity to ask him about all these issues. Instead, the two had a relatively vague, high-level discussion about safety, leaving the audience none the wiser about what exactly OpenAI is doing to make their systems safer. It seemed they were simply supposed to take Altman’s word for it. 

Altman’s talk came a week or so after Helen Toner, a researcher at the Georgetown Center for Security and Emerging Technology and a former OpenAI board member, said in an interview that the board found out about the launch of ChatGPT through Twitter, and that Altman had on multiple occasions given the board inaccurate information about the company’s formal safety processes. She has also argued that it is a bad idea to let AI firms govern themselves, because the immense profit incentives will always win. (Altman said he “disagree[s] with her recollection of events.”) 

When Thompson asked Altman what the first good thing to come out of generative AI will be, Altman mentioned productivity, citing examples such as software developers who can use AI tools to do their work much faster. “We’ll see different industries become much more productive than they used to be because they can use these tools. And that will have a positive impact on everything,” he said. I think the jury is still out on that one. 

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Deeper Learning

Why Google’s AI Overviews gets things wrong

Google’s new feature, called AI Overviews, provides brief, AI-generated summaries highlighting key information and links on top of search results. Unfortunately, within days of AI Overviews’ release in the US, users were sharing examples of responses that were strange at best. It suggested that users add glue to pizza or eat at least one small rock a day.

MIT Technology Review explains: In order to understand why AI-powered search engines get things wrong, we need to look at how they work. The models that power them simply predict the next word (or token) in a sequence, which makes them appear fluent but also leaves them prone to making things up. They have no ground truth to rely on, but instead choose each word purely on the basis of a statistical calculation. Worst of all? There’s probably no way to fix things. That’s why you shouldn’t trust AI search engines. Read more from Rhiannon Williams here. 

Bits and Bytes

OpenAI’s latest blunder shows the challenges facing Chinese AI models
OpenAI’s GPT-4o data set is polluted by Chinese spam websites. But this problem is indicative of a much wider issue for those building Chinese AI services: finding the high-quality data sets they need to be trained on is tricky, because of the way China’s internet functions. (MIT Technology Review) 

Five ways criminals are using AI
Artificial intelligence has brought a big boost in productivity—to the criminal underworld. Generative AI has made phishing, scamming, and doxxing easier than ever. (MIT Technology Review)

OpenAI is rebooting its robotics team
After disbanding its robotics team in 2020, the company is trying again. The resurrection is in part thanks to rapid advancements in robotics brought by generative AI. (Forbes) 

OpenAI found Russian and Chinese groups using its tech for propaganda campaigns
OpenAI said that it caught, and removed, groups from Russia, China, Iran, and Israel that were using its technology to try to influence political discourse around the world. But this is likely just the tip of the iceberg when it comes to how AI is being used to affect this year’s record-breaking number of elections. (The Washington Post) 

Inside Anthropic, the AI company betting that safety can be a winning strategy
The AI lab Anthropic, creator of the Claude model, was started by former OpenAI employees who resigned over “trust issues.” This profile is an interesting peek inside one of OpenAI’s competitors, showing how the ideology behind AI safety and effective altruism is guiding business decisions. (Time) 

AI-directed drones could help find lost hikers faster
Drones are already used for search and rescue, but planning their search paths is more art than science. AI could change that. (MIT Technology Review)