ChatGPT maker OpenAI has built an experimental large language model that is far easier to understand than typical models. That’s a big deal, because today’s LLMs are black boxes: Nobody fully understands how they do what they do. Building a model that is more transparent sheds light on how LLMs work in general, helping researchers figure out why models hallucinate, why they go off the rails, and just how far we should trust them with critical tasks. “As these AI systems get more powerful, they’re going to get integrated more and more into very important domains,” Leo Gao, a research scientist at OpenAI, told MIT Technology Review in an exclusive preview of the new work. “It’s very important to make sure they’re safe.” This is still early research. The new model, called a weight-sparse transformer, is far smaller and far less capable than top-tier mass-market models like the firm’s GPT-5, Anthropic’s Claude, and Google DeepMind’s Gemini. At most it’s as capable as GPT-1, a model that OpenAI developed back in 2018, says Gao (though he and his colleagues haven’t done a direct comparison).    
But the aim isn’t to compete with the best in class (at least, not yet). Instead, by looking at how this experimental model works, OpenAI hopes to learn about the hidden mechanisms inside those bigger and better versions of the technology. It’s interesting research, says Elisenda Grigsby, a mathematician at Boston College who studies how LLMs work and who was not involved in the project: “I’m sure the methods it introduces will have a significant impact.” 
Lee Sharkey, a research scientist at AI startup Goodfire, agrees. “This work aims at the right target and seems well executed,” he says. Why models are so hard to understand OpenAI’s work is part of a hot new field of research known as mechanistic interpretability, which is trying to map the internal mechanisms that models use when they carry out different tasks. That’s harder than it sounds. LLMs are built from neural networks, which consist of nodes, called neurons, arranged in layers. In most networks, each neuron is connected to every other neuron in its adjacent layers. Such a network is known as a dense network. Dense networks are relatively efficient to train and run, but they spread what they learn across a vast knot of connections. The result is that simple concepts or functions can be split up between neurons in different parts of a model. At the same time, specific neurons can also end up representing multiple different features, a phenomenon known as superposition (a term borrowed from quantum physics). The upshot is that you can’t relate specific parts of a model to specific concepts. “Neural networks are big and complicated and tangled up and very difficult to understand,” says Dan Mossing, who leads the mechanistic interpretability team at OpenAI. “We’ve sort of said: ‘Okay, what if we tried to make that not the case?’” Instead of building a model using a dense network, OpenAI started with a type of neural network known as a weight-sparse transformer, in which each neuron is connected to only a few other neurons. This forced the model to represent features in localized clusters rather than spread them out. Their model is far slower than any LLM on the market. But it is easier to relate its neurons or groups of neurons to specific concepts and functions. “There’s a really drastic difference in how interpretable the model is,” says Gao. Gao and his colleagues have tested the new model with very simple tasks. For example, they asked it to complete a block of text that opens with quotation marks by adding matching marks at the end.  

It’s a trivial request for an LLM. The point is that figuring out how a model does even a straightforward task like that involves unpicking a complicated tangle of neurons and connections, says Gao. But with the new model, they were able to follow the exact steps the model took. “We actually found a circuit that’s exactly the algorithm you would think to implement by hand, but it’s fully learned by the model,” he says. “I think this is really cool and exciting.” Where will the research go next? Grigsby is not convinced the technique would scale up to larger models that have to handle a variety of more difficult tasks.     Gao and Mossing acknowledge that this is a big limitation of the model they have built so far and agree that the approach will never lead to models that match the performance of cutting-edge products like GPT-5. And yet OpenAI thinks it might be able to improve the technique enough to build a transparent model on a par with GPT-3, the firm’s breakthrough 2021 LLM.  “Maybe within a few years, we could have a fully interpretable GPT-3, so that you could go inside every single part of it and you could understand how it does every single thing,” says Gao. “If we had such a system, we would learn so much.”

Google DeepMind has built a new video-game-playing agent called SIMA 2 that can navigate and solve problems in a wide range of 3D virtual worlds. The company claims it’s a big step toward more general-purpose agents and better real-world robots.    Google DeepMind first demoed SIMA (which stands for “scalable instructable multiworld agent”) last year. But SIMA 2 has been built on top of Gemini, the firm’s flagship large language model, which gives the agent a huge boost in capability. The researchers claim that SIMA 2 can carry out a range of more complex tasks inside virtual worlds, figure out how to solve certain challenges by itself, and chat with its users. It can also improve itself by tackling harder tasks multiple times and learning through trial and error. “Games have been a driving force behind agent research for quite a while,” Joe Marino, a research scientist at Google DeepMind, said in a press conference this week. He noted that even a simple action in a game, such as lighting a lantern, can involve multiple steps: “It’s a really complex set of tasks you need to solve to progress.”
The ultimate aim is to develop next-generation agents that are able to follow instructions and carry out open-ended tasks inside more complex environments than a web browser. In the long run, Google DeepMind wants to use such agents to drive real-world robots. Marino claimed that the skills SIMA 2 has learned, such as navigating an environment, using tools, and collaborating with humans to solve problems, are essential building blocks for future robot companions. Unlike previous work on game-playing agents such as AlphaZero, which beat a Go grandmaster in 2016, or AlphaStar, which beat 99.8% of ranked human competition players at the video game StarCraft 2 in 2019, the idea behind SIMA is to train an agent to play an open-ended game without preset goals. Instead, the agent learns to carry out instructions given to it by people.
Humans control SIMA 2 via text chat, by talking to it out loud, or by drawing on the game’s screen. The agent takes in a video game’s pixels frame by frame and figures out what actions it needs to take to carry out its tasks. Like its predecessor, SIMA 2 was trained on footage of humans playing eight commercial video games, including No Man’s Sky and Goat Simulator 3, as well as three virtual worlds created by the company. The agent learned to match keyboard and mouse inputs to actions. Hooked up to Gemini, the researchers claim, SIMA 2 is far better at following instructions (asking questions and providing updates as it goes) and figuring out for itself how to perform certain more complex tasks.   Google DeepMind tested the agent inside environments it had never seen before. In one set of experiments, researchers asked Genie 3, the latest version of the firm’s world model, to produce environments from scratch and dropped SIMA 2 into them. They found that the agent was able to navigate and carry out instructions there. The researchers also used Gemini to generate new tasks for SIMA 2. If the agent failed, at first Gemini generated tips that SIMA 2 took on board when it tried again. Repeating a task multiple times in this way often allowed SIMA 2 to improve by trial and error until it succeeded, Marino said. Git gud SIMA 2 is still an experiment. The agent struggles with complex tasks that require multiple steps and more time to complete. It also remembers only its most recent interactions (to make SIMA 2 more responsive, the team cut its long-term memory). It’s also still nowhere near as good as people at using a mouse and keyboard to interact with a virtual world. Julian Togelius, an AI researcher at New York University who works on creativity and video games, thinks it’s an interesting result. Previous attempts at training a single system to play multiple games haven’t gone too well, he says. That’s because training models to control multiple games just by watching the screen isn’t easy: “Playing in real time from visual input only is ‘hard mode,’” he says. In particular, Togelius calls out GATO, a previous system from Google DeepMind, which—despite being hyped at the time—could not transfer skills across a significant number of virtual environments.  

Still, he is open-minded about whether or not SIMA 2 could lead to better robots. “The real world is both harder and easier than video games,” he says. It’s harder because you can’t just press A to open a door. At the same time, a robot in the real world will know exactly what its body can and can’t do at any time. That’s not the case in video games, where the rules inside each virtual world can differ. Others are more skeptical. Matthew Guzdial, an AI researcher at the University of Alberta, isn’t too surprised that SIMA 2 can play many different video games. He notes that most games have very similar keyboard and mouse controls: Learn one and you learn them all. “If you put a game with weird input in front of it, I don’t think it’d be able to perform well,” he says. Guzdial also questions how much of what SIMA 2 has learned would really carry over to robots. “It’s much harder to understand visuals from cameras in the real world compared to games, which are designed with easily parsable visuals for human players,” he says. Still, Marino and his colleagues hope to continue their work with Genie 3 to allow the agent to improve inside a kind of endless virtual training dojo, where Genie generates worlds for SIMA to learn in via trial and error guided by Gemini’s feedback. “We’ve kind of just scratched the surface of what’s possible,” he said at the press conference.  

This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology. AI is changing how we quantify pain Researchers around the world are racing to turn pain—medicine’s most subjective vital sign—into something a camera or sensor can score as reliably as blood pressure.The push has already produced PainChek—a smartphone app that scans people’s faces for tiny muscle movements and uses artificial intelligence to output a pain score—which has been cleared by regulators on three continents and has logged more than 10 million pain assessments. Other startups are beginning to make similar inroads. The way we assess pain may finally be shifting, but when algorithms measure our suffering, does that change the way we treat it? Read the full story.
—Deena Mousa This story is from the latest print issue of MIT Technology Review magazine, which is full of fascinating stories about our bodies. If you haven’t already, subscribe now to receive future issues once they land.
How to help friends and family dig out of a conspiracy theory black hole —Niall Firth  Someone I know became a conspiracy theorist seemingly overnight. It was during the pandemic. They suddenly started posting daily on Facebook about the dangers of covid vaccines and masks, warning of an attempt to control us.As a science and technology journalist, I felt that my duty was to respond. I tried, but all I got was derision. Even now I still wonder: Are there things I could have done differently to talk them back down and help them see sense?  I gave Sander van der Linden, professor of social psychology in society at the University of Cambridge, a call to ask: What would he advise if family members or friends show signs of having fallen down the rabbit hole? Read the full story. This story is part of MIT Technology Review’s series “The New Conspiracy Age,” on how the present boom in conspiracy theories is reshaping science and technology. Check out the rest of the series here. It’s also part of our How To series, giving you practical advice to help you get things done.  If you’re interested in hearing more about how to survive in the age of conspiracies, join our features editor Amanda Silverman and executive editor Niall Firth for a subscriber-exclusive Roundtable conversation with conspiracy expert Mike Rothschild. It’s at 1pm ET on Thursday November 20—register now to join us!

Google is still aiming for its “moonshot” 2030 energy goals —Casey Crownhart Last week, we hosted EmTech MIT, MIT Technology Review’s annual flagship conference in Cambridge, Massachusetts. As you might imagine, some of this climate reporter’s favorite moments came in the climate sessions. I was listening especially closely to my colleague James Temple’s discussion with Lucia Tian, head of advanced energy technologies at Google. They spoke about the tech giant’s growing energy demand and what sort of technologies the company is looking to to help meet it. In case you weren’t able to join us, let’s dig into that session and consider how the company is thinking about energy in the face of AI’s rapid rise. Read the full story. This article is from The Spark, MIT Technology Review’s weekly climate newsletter. To receive it in your inbox every Wednesday, sign up here. The must-reads
I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology. 1 ChatGPT is now “warmer and more conversational”But it’s also slightly more willing to discuss sexual and violent content. (The Register)+ ChatGPT has a very specific writing style. (WP $)+ The looming crackdown on AI companionship. (MIT Technology Review)
2 The US could deny visas to visitors with obesity, cancer or diabetesAs part of its ongoing efforts to stem the flow of people trying to enter the country. (WP $)3 Microsoft is planning to create its own AI chipAnd it’s going to use OpenAI’s internal chip-building plans to do it. (Bloomberg $)+ The company is working on a colossal data center in Atlanta. (WSJ $) 4 Early AI agent adopters are convinced they’ll see a return on their investment soon Mind you, they would say that. (WSJ $)+ An AI adoption riddle. (MIT Technology Review)5 Waymo’s robotaxis are hitting American highwaysUntil now, they’ve typically gone out of their way to avoid them. (The Verge)+ Its vehicles will now reach speeds of up to 65 miles per hour. (FT $)+ Waymo is proving long-time detractor Elon Musk wrong. (Insider $) 6 A new Russian unit is hunting down Ukraine’s drone operatorsIt’s tasked with killing the pilots behind Ukraine’s successful attacks. (FT $)+ US startup Anduril wants to build drones in the UAE. (Bloomberg $)+ Meet the radio-obsessed civilian shaping Ukraine’s drone defense. (MIT Technology Review) 7 Anthropic’s Claude successfully controlled a robot dogIt’s important to know what AI models may do when given access to physical systems. (Wired $) 8 Grok briefly claimed Donald Trump won the 2020 US electionAs reliable as ever, I see. (The Guardian) 9 The Northern Lights are playing havoc with satellitesSolar storms may look spectacular, but they make it harder to keep tabs on space. (NYT $)+ Seriously though, they look amazing. (The Atlantic $)+ NASA’s new AI model can predict when a solar storm may strike. (MIT Technology Review)
10 Apple users can now use digital versions of their passportsBut it’s strictly for internal flights within the US only. (TechCrunch) Quote of the day “I hope this mistake will turn into an experience.”
—Vladimir Vitukhin, chief executive of the company behind Russia’s first anthropomorphic robot AIDOL, offers a philosophical response to the machine falling flat on its face during a reveal event, the New York Times reports. One more thing Welcome to the oldest part of the metaverseHeadlines treat the metaverse as a hazy dream yet to be built. But if it’s defined as a network of virtual worlds we can inhabit, its oldest corner has been already running for 25 years.It’s a medieval fantasy kingdom created for the online role-playing game Ultima Online. It was the first to simulate an entire world: a vast, dynamic realm where players could interact with almost anything, from fruit on trees to books on shelves.Ultima Online has already endured a quarter-century of market competition, economic turmoil, and political strife. So what can this game and its players tell us about creating the virtual worlds of the future? Read the full story.  —John-Clark Levin We can still have nice things A place for comfort, fun and distraction to brighten up your day. (Got any ideas? Drop me a line or skeet ’em at me.) + Unlikely duo Sting and Shaggy are starring together in a New York musical.+ Barry Manilow was almost in Airplane!? That would be an entirely different kind of flying, altogether ✈️+ What makes someone sexy? Well, that depends.+ Keep an eye on those pink dolphins, they’re notorious thieves.

Last week, we hosted EmTech MIT, MIT Technology Review’s annual flagship conference in Cambridge, Massachusetts. Over the course of three days of main-stage sessions, I learned about innovations in AI, biotech, and robotics.  But as you might imagine, some of this climate reporter’s favorite moments came in the climate sessions. I was listening especially closely to my colleague James Temple’s discussion with Lucia Tian, head of advanced energy technologies at Google.  They spoke about the tech giant’s growing energy demand and what sort of technologies the company is looking to to help meet it. In case you weren’t able to join us, let’s dig into that session and consider how the company is thinking about energy in the face of AI’s rapid rise.  I’ve been closely following Google’s work in energy this year. Like the rest of the tech industry, the company is seeing ballooning electricity demand in its data centers. That could get in the way of a major goal that Google has been talking about for years. 
See, back in 2020, the company announced an ambitious target: by 2030, it aimed to run on carbon-free energy 24-7. Basically, that means Google would purchase enough renewable energy on the grids where it operates to meet its entire electricity demand, and the purchases would match up so the electricity would have to be generated when the company was actually using energy. (For more on the nuances of Big Tech’s renewable-energy pledges, check out James’s piece from last year.) Google’s is an ambitious goal, and on stage, Tian said that the company is still aiming for it but acknowledged that it’s looking tough with the rise of AI. 
“It was always a moonshot,” she said. “It’s something very, very hard to achieve, and it’s only harder in the face of this growth. But our perspective is, if we don’t move in that direction, we’ll never get there.” Google’s total electricity demand more than doubled from 2020 to 2024, according to its latest Environmental Report. As for that goal of 24-7 carbon-free energy? The company is basically treading water. While it was at 67% for its data centers in 2020, last year it came in at 66%.  Not going backwards is something of an accomplishment, given the rapid growth in electricity demand. But it still leaves the company some distance away from its finish line. To close the gap, Google has been signing what feels like constant deals in the energy space. Two recent announcements that Tian talked about on stage were a project involving carbon capture and storage at a natural-gas plant in Illinois and plans to reopen a shuttered nuclear power plant in Iowa.  Let’s start with carbon capture. Google signed an agreement to purchase most of the electricity from a new natural-gas plant, which will capture and store about 90% of its carbon dioxide emissions.  That announcement was controversial, with critics arguing that carbon capture keeps fossil-fuel infrastructure online longer and still releases greenhouse gases and other pollutants into the atmosphere.  One question that James raised on stage: Why build a new natural-gas plant rather than add equipment to an already existing facility? Tacking on equipment to an operational plant would mean cutting emissions from the status quo, rather than adding entirely new fossil-fuel infrastructure.  The company did consider many existing plants, Tian said. But, as she put it, “Retrofits aren’t going to make sense everywhere.” Space can be limited at existing plants, for example, and many may not have the right geology to store carbon dioxide underground. 

“We wanted to lead with a project that could prove this technology at scale,” Tian said. This site has an operational Class VI well, the type used for permanent sequestration, she added, and it also doesn’t require a big pipeline buildout.  Tian also touched on the company’s recent announcement that it’s collaborating with NextEra Energy to reopen Duane Arnold Energy Center, a nuclear power plant in Iowa. The company will purchase electricity from that plant, which is scheduled to reopen in 2029.  As I covered in a story earlier this year, Duane Arnold was basically the final option in the US for companies looking to reopen shuttered nuclear power plants. “Just a few years back, we were still closing down nuclear plants in this country,” Tian said on stage.  While each reopening will look a little different, Tian highlighted the groups working to restart the Palisades plant in Michigan, which was the first reopening to be announced, last spring. “They’re the real heroes of the story,” she said. I’m always interested to get a peek behind the curtain at how Big Tech is thinking about energy. I’m skeptical but certainly interested to see how Google’s, and the rest of the industry’s, goals shape up over the next few years.  This article is from The Spark, MIT Technology Review’s weekly climate newsletter. To receive it in your inbox every Wednesday, sign up here.

This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology. What it’s like to be in the middle of a conspiracy theory (according to a conspiracy theory expert) —Mike Rothschild is a journalist and an expert on the growth and impact of conspiracy theories and disinformation.It’s something of a familiar cycle by now: Tragedy hits; rampant misinformation and conspiracy theories follow. It’s often even more acute in the case of a natural disaster, when conspiracy theories about what “really” caused the calamity run right into culture-war-driven climate change denialism. Put together, these theories obscure real causes while elevating fake ones.I’ve studied these ideas extensively, having spent the last 10 years writing about conspiracy theories and disinformation as a journalist and researcher. I’ve covered everything from the rise of QAnon to whether Donald Trump faked his assassination attempt. I’ve written three books, testified to Congress, and even written a report for the January 6th Committee.  Still, I’d never lived it. Not until my house in Altadena, California, burned down. Read the full story.
This story is part of MIT Technology Review’s series “The New Conspiracy Age,” on how the present boom in conspiracy theories is reshaping science and technology. Check out the rest of the series here. It’s also featured in this week’s MIT Technology Review Narrated podcast, which we publish each week on Spotify and Apple Podcasts.  If you’d like to hear more from Mike, he’ll be joining our features editor Amanda Silverman and executive editor Niall Firth for a subscriber-exclusive Roundtable conversation exploring how we can survive in the age of conspiracies. It’s at 1pm ET on Thursday November 20—register now to join us!
This startup thinks slime mold can help us design better cities It is a yellow blob with no brain, yet some researchers believe a curious organism known as slime mold could help us build more resilient cities.Humans have been building cities for 6,000 years, but slime mold has been around for 600 million. The team behind a new startup called Mireta wants to translate the organism’s biological superpowers into algorithms that might help improve transit times, alleviate congestion, and minimize climate-related disruptions in cities worldwide. Read the full story. —Elissaveta M. Brandon This story is from the latest print issue of MIT Technology Review magazine, which is full of fascinating stories about our bodies. If you haven’t already, subscribe now to receive future issues once they land. The must-reads I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.

1 US government officials are skipping COP30And American corporate executives are following their lead. (NYT $)+ Protestors stormed the climate talks in Brazil. (The Guardian)+ Gavin Newsom took aim at Donald Trump’s climate policies onstage. (FT $) 2 The UK may assess AI models for their ability to generate CSAMIts government has suggested amending a legal bill to enable the tests. (BBC)+ US investigators are using AI to detect child abuse images made by AI. (MIT Technology Review) 3 Google is suing a group of Chinese hackersIt claims they’re selling software to enable criminal scams. (FT $)+ The group allegedly sends colossal text message phishing attacks. (CBS News)4 A major ‘cryptoqueen’ criminal has been jailedQian Zhimin used money stolen from Chinese pensioners to buy cryptocurrency now worth billions. (BBC)+ She defrauded her victims through an elaborate ponzi scheme. (CNN) 5 Carbon capture’s creators fear it’s being misusedOverreliance on the method could breed overconfidence and cause countries to delay reducing emissions. (Bloomberg $)+ Big Tech’s big bet on a controversial carbon removal tactic. (MIT Technology Review) 6 The UK will use AI to phase out animal testing3D bioprinted human tissues could also help to speed up the process. (The Guardian)+ But the AI boom is looking increasingly precarious. (WSJ $) 7 Louisiana is dealing with a whooping cough outbreakTwo infants have died to date from the wholly preventative disease. (Undark) 8 Here’s how ordinary people use ChatGPTEmotional support and discussions crop up regularly.(WP $)+ It’s surprisingly easy to stumble into a relationship with an AI chatbot. (MIT Technology Review) 9 Inside the search for lost continentsA newly-discovered mechanism is shedding light on why they may have vanished. (404 Media)+ How environmental DNA is giving scientists a new way to understand our world. (MIT Technology Review)
10 AI is taking Gen Z’s entry-level jobsEspecially in traditionally graduate-friendly consultancies. (NY Mag $)+ What the Industrial Revolution can teach us about how to handle AI. (Knowable Magazine)+ America’s corporate boards are stumbling in the dark. (WSJ $)
Quote of the day “We can’t eat money.” —Nato, an Indigenous leader from the Tupinamba community, tells Reuters why they are protesting at the COP30 climate summit in Brazil against any potential sale of their land. One more thing How K-pop fans are shaping elections around the globeBack in the early ‘90s, Korean pop music, known as K-pop, was largely conserved to its native South Korea. It’s since exploded around the globe into an international phenomenon, emphasizing choreography and elaborate performance.It’s made bands like Girls Generation, EXO, BTS, and Blackpink into household names, and inspired a special brand of particularly fierce devotion in their fans.Now, those same fandoms have learned how to use their digital skills to advocate for social change and pursue political goals—organizing acts of civil resistance, donating generously to charity, and even foiling white supremacist attempts to spread hate speech. Read the full story.—Soo Youn
We can still have nice things A place for comfort, fun and distraction to brighten up your day. (Got any ideas? Drop me a line or skeet ’em at me.) + These sucker fish are having the time of their lives hitching a ride on a whale.+ Next time you fly, ditch the WiFi. I know I will.+ I love this colossal interactive gif.+ The hottest scent in perfumery right now? Smelling like a robot, apparently.

In partnership withEPAM For years, many chief information officers (CIOs) looked at VMware-to-cloud migrations with a wary pragmatism. Manually mapping dependencies and rewriting legacy apps mid-flight was not an enticing, low-lift proposition for enterprise IT teams. But the calculus for such decisions has changed dramatically in a short period of time. Following recent VMware licensing changes, organizations are seeing greater uncertainty around the platform’s future. At the same time, cloud-native innovation is accelerating. According to the CNCF’s 2024 Annual Survey, 89% of organizations have already adopted at least some cloud-native techniques, and the share of companies reporting nearly all development and deployment as cloud-native grew sharply from 2023 to 2024 (20% to 24%). And market research firm IDC reports that cloud providers have become top strategic partners for generative AI initiatives. This is all happening amid escalating pressure to innovate faster and more cost-effectively to meet the demands of an AI-first future. As enterprises prepare for that inevitability, they are facing compute demands that are difficult, if not prohibitively expensive, to maintain exclusively on-premises. Download the full article.
This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff. This content was researched, designed, and written by human writers, editors, analysts, and illustrators. This includes the writing of surveys and collection of data for surveys. AI tools that may have been used were limited to secondary production processes that passed thorough human review.