YouZum

AI

AI, Committee, News, Uncategorized

NASA is building the first nuclear reactor-powered interplanetary spacecraft. How will it work?

MIT Technology Review Explains: Let our writers untangle the complex, messy world of technology to help you understand what’s coming next. You can read more from the series here. Just before Artemis II began its historic slingshot around the moon, Jared Isaacman, the recently confirmed NASA administrator, made a flurry of announcements from the agency’s headquarters in Washington, DC. He said the US would soon undertake far more regular moon missions and establish the foundations for a base at the lunar south pole before the end of the decade. He also affirmed the space agency’s commitment to putting a nuclear reactor on the lunar surface. These goals were largely expected—but there was still one surprise. Isaacman also said NASA would build the first-ever nuclear reactor-powered interplanetary spacecraft and fly it to Mars by the end of 2028. It’s called the Space Reactor-1 Freedom, or SR-1 for short. “After decades of study, and billions spent on concepts that have never left Earth, America will finally get underway on nuclear power in space,” he said at the event. “We will launch the first-of-its-kind interplanetary mission.” A successful mission would herald a new era in spaceflight, one in which traveling between Earth, the moon, and Mars would—according to a range of experts—be faster and easier than ever. And it might just give the US the edge in the race against China—allowing the country to beat its greatest geopolitical rival to landing astronauts on another planet. While experts agree the timeline is extremely tight, they’re excited to see if America’s space agency and its industry partners can deliver an engineering miracle. “You wake up to that announcement, and it puts a big smile on your face,” says Simon Middleburgh, co-director of the Nuclear Futures Institute at Bangor University in Wales. Little detail on SR-1 is publicly available, and NASA’s own spaceflight researchers did not respond to requests for comment. But MIT Technology Review spoke to several nuclear power and propulsion experts to find out how the new nuclear-powered spacecraft might work. Nuclear propulsion 101 Traditionally, spaceflight has been powered by chemical propulsion. Liquefied hydrogen and liquefied oxygen are mixed, and then ignited, within a rocket; the searingly hot exhaust from this explosion is ejected through a nozzle, which propels the rocket forth. Chemical propulsion offers a significant amount of thrust and will, for the foreseeable future, still be used to launch spacecraft from Earth. But nuclear propulsion would enable spacecraft to fly through the solar system for far longer, and faster, than is currently possible.  “You get more bang per kilogram,” says Middleburgh. A nuclear fuel source is far more energy-dense than its conventional cousin, which means it’s orders of magnitude more efficient. “It’s really, really, really high efficiency,” says Lindsey Holmes, an expert in space nuclear technology and the vice president of advanced projects at Analytical Mechanics Associates, an aerospace company in Virginia.  The approach also removes one other element of the traditional power equation: solar. Spacecraft, including the Artemis II mission’s Orion space capsule, often rely on the sun for power. But this can be a problem, since it doesn’t always shine in space, particularly when a planet or moon gets in its way—and as you head toward the outer solar system, beyond Mars, there’s just less sunlight available.  To circumvent this issue, nuclear energy sources have been used in spacecraft plenty of times before—including on both Voyager missions and the Saturn-interrogating Cassini probe. Known as radioisotope thermoelectric generators, or RTGs, these use plutonium, which radioactively decays and generates heat in the process. That heat is then converted into electricity for the spacecraft to use. RTGs, however, aren’t the same as nuclear reactors; they are more akin to radioactive batteries—more rudimentary and considerably less powerful. So how will a nuclear-reactor-powered spacecraft work?  Despite operational differences, the fundamentals of running a nuclear reactor in space are much the same as they are on Earth. First, get some uranium fuel; then bombard it with neutrons. This ruptures the uranium’s unstable atomic nuclei, which expel a torrent of extra neutrons—and that rapidly escalates into a self-sustaining, roasting-hot nuclear fission reaction. Its prodigious heat output can then be used to produce electricity. Doing this in space may sound like an act of lunacy, but it’s not: The idea, and even a lot of the basic technology, has been around for decades. The Soviet Union sent dozens of nuclear reactors into orbit (often to power spy satellites), while the US deployed just one, known as SNAP-10A, back in 1965—a technological demonstration to see if it would operate normally in space. The aim was for the reactor to generate electricity for at least a year, but it ran for just over a month before a high-voltage failure in the spacecraft caused it to malfunction and shut down.  Now, more than half a century later, the US wants its second-ever space-based nuclear reactor to do something totally different: power an interplanetary spacecraft. To be clear, the US has started, and terminated, myriad programs looking into nuclear propulsion. The latest casualty was DRACO, a collaboration between NASA and the Department of Defense, which ended in 2025. Like several previous efforts, DRACO was canceled because of a mix of high experimentation costs, lower prices for conventional rocket propulsion, and the difficulty of ensuring that ground tests could be performed safely and effectively (they are creating an incredibly powerful nuclear reaction, after all). But now external considerations may be changing the calculus. The Artemis program has jump-started America’s return to the moon, and the new space race has palpable momentum behind it. The first nation to deploy nuclear propulsion would have a serious advantage navigating through deep space.  “I think it’s a very doable technology,” says Philip Metzger, a spaceflight engineering researcher at the Florida Space Institute. “I’m happy to see them finally doing this.” One version of this technology is known as nuclear thermal propulsion, or NTP. You start with a nuclear reactor, one that’s cooking at around 5,000°F. Then “you’ve

NASA is building the first nuclear reactor-powered interplanetary spacecraft. How will it work? Read Post »

AI, Committee, News, Uncategorized

The Download: the state of AI, and protecting bears with drones

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. Want to understand the current state of AI? Check out these charts.  If you’re following AI news, you’re probably getting whiplash. AI is a gold rush. AI is a bubble. AI is taking your job. AI can’t even read a clock. Stanford’s 2026 AI Index—the field’s annual report card—cuts through the noise.   The data reveals a technology evolving faster than we can manage. From the China-US rivalry and model breakthroughs to public sentiment and the impact on jobs, here are the index’s key findings on the state of AI today.  —Michelle Kim  Why opinion on AI is so divided  Stanford’s 2026 AI Index is full of striking stats. It also reveals a field riddled with inconsistencies, most notably in the gap between experts and non-experts.   On jobs, 73% of US experts view AI’s impact positively, compared to just 23% of the public. Similar divides emerged on the economy and healthcare. What’s driving this disconnect?  Part of the answer may lie in their diverging experiences. Those using AI for coding and technical work see it at its best, while everyone else gets a more mixed bag. The result is two very different realities. Read the full story on what they are—and why they matter.  This story is from The Algorithm, our weekly newsletter on AI. Sign up to receive it in your inbox every Monday.  —Will Douglas Heaven  Job titles of the future: Wildlife first responder  Grizzly bears have made such a comeback across eastern Montana that in 2017, the state hired its first-ever prairie-based grizzly manager: wildlife biologist Wesley Sarmento.   For seven years, Sarmento worked to keep both bears and humans out of trouble. He acted like a first responder, trying to defuse potentially dangerous situations. He even got caught in some himself, which led him to a new wildlife safety tool: drones. Find out the results of his experiments in digital ecology.   —Emily Senkosky  This article is from the next issue of our print magazine, which is all about nature. Subscribe now to read it when it lands on Wednesday, April 22.   The must-reads  I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.  1 Human scientists still trounce the top AI agents at complex tasks  The best agents perform only half as well as experts with PhDs. (Nature) + Can AI really help us discover new materials? (MIT Technology Review)  2 OpenAI is escalating its fight with Anthropic while pulling away from Microsoft A leaked memo exposes plans to attack Anthropic. (Axios) + And says Microsoft “limited our ability” to reach clients. (The Information $) + While touting a budding alliance with Amazon. (CNBC)  3 Carbon removal technology is stalling—and that may be good news Better solutions could now emerge. (New Scientist) + Here are three that are set to break through. (MIT Technology Review)  4 AI is finding bugs faster than we can fix them—and hackers will benefit Welcome to the bug armageddon. (WSJ $)  + AI may soon be capable of fully automated attacks. (MIT Technology Review)  5 A Texas man has been charged with the attempted murder of Sam Altman He allegedly threw a Molotov cocktail at the OpenAI CEO’s home last Friday. (NPR) + The suspect reportedly had a list of other AI leaders. (NYT $)  6 AI is beginning to transform mathematics It’s proving new results at a rapid pace. (Quanta) + One AI startup plans to unearth new mathematical patterns. (MIT Technology Review)  7 Students are turning away from computer science It’s had a massive drop in enrollments. (WP $) + AI coding tools have diminished the degree’s value. (NYT $)   8 India’s bid to become a data center hub is sparking a fierce backlash Farmers are protesting Delhi’s courtship of hyperscalers. (Rest of World)  9 Meta is set to overtake Google in advertising revenue this year And become the world’s largest digital ad platform for the first time. (WSJ)  10 AI influencers are taking over Coachella  Synthetic content creators are “everywhere” at the festival. (The Verge)  Quote of the day  “These people are almost nothing like you. They are most likely sociopathic/psychopathic and, in the case of Altman, consistently reported to be a pathological liar.”  —The alleged firebomber of Sam Altman’s home shares his distrust of AI leaders in a blog post.  One More Thing  We’ve never understood how hunger works. That might be about to change.  A few years ago, Brad Lowell, a Harvard University neuro­scientist, figured out how to crank the food drive to the maximum. He did it by stimulating neurons in mice. Now, he’s following known parts of the neural hunger circuits into uncharted parts of the brain.  The work could have important implications for public health. More than 1.9 billion adults worldwide are overweight, and more than 650 million are obese. Understanding the circuits involved could shed new light on why these numbers are skyrocketing.  Read the full story.  —Adam Piore  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.)  Top image credit: Stephanie Arnett/MIT Technology Review | Getty Images  + Someone built a mechanical version of Tony Hawk’s Pro Skater from Lego. + Enjoy this wholesome clip of toddlers discovering the existence of hugs. + This interactive body map shows exactly which exercises you need. + Jon McCormack’s photos of nature’s patterns are breathtaking. 

The Download: the state of AI, and protecting bears with drones Read Post »

AI, Committee, News, Uncategorized

Job titles of the future: Wildlife first responder

Grizzly bears have made such a comeback across eastern Montana that in 2017, the state hired its first-ever prairie-based grizzly manager: wildlife biologist Wesley Sarmento.  For some seven years, Sarmento worked to keep both the bears, which are still listed as threatened under the Endangered Species Act, and the humans, who are sprawling into once-wild spaces, out of trouble. Based in the small city of Conrad, population 2,553, he acted sort of like a first responder, trying to defuse potentially dangerous situations. He even got caught in some himself—which is why, before he left the role to pursue a PhD, he turned to drones to get the job done.  The bear necessities Sarmento was studying mountain goats in Glacier National Park when he first started working with bears. To better understand how goats responded to the apex predator, he dressed up in a bear costume once a week for over three years.  When he later started as grizzly manager, he often drove long distances to push bears away from farms. Bears are drawn to spilled or leaking grains, and an open silo quickly turns into a buffet. Sarmento would typically arrive armed with a shotgun, cracker shells, and bear spray, but after he narrowly escaped getting mauled one day, he knew he had to pivot. “In that moment,” he says, “I was like, I am gonna get myself killed.” A bird’s-eye view Sarmento first turned to two Airedale dogs, a breed known for deterring bears on farms, but the dogs were easily sidetracked. Meanwhile, drones were slowly becoming more common tools for biologists in a range of activities, including counting birds and mapping habitats. He first took one into the field in 2022, when a grizzly mom and two cubs were found rummaging around in a silo outside of town. The drone’s infrared sensors helped him quickly find their location, and he used the aircraft’s sound to drive them away from the property. (Researchers suspect bears instinctively dislike the whir of blades because it sounds like a swarm of bees.) “The whole thing was so clean and controlled,” he says. “And I did it all from the safety of my truck.” Since then, the flying machine that Sarmento bought for $4,000—a fairly simple model with a thermal camera and 30 minutes of battery life—has shown its potential for detecting grizzlies in perilous terrain he’d otherwise have to approach on foot, like dense brush or hard-to-reach river bottoms. A new technological foundation Now studying wildlife ecology at the University of Montana, Sarmento is hoping to design a drone campus police can use to deter black bears from school grounds. In the future, he hopes, AI image recognition might be broadly integrated into his wildlife management work—maybe even helping drones identify bears and autonomously divert them from high-traffic areas. All this helps keep bears from learning behaviors that lead to conflict with people—which typically ends badly for the bear and is occasionally fatal for humans. “The out-of-the-box technology doesn’t exist yet, but the hope is to keep exploring applications,” he says. “Drones are the next frontier.”  Emily Senkosky is a writer with a master’s degree in environmental science journalism from the University of Montana.

Job titles of the future: Wildlife first responder Read Post »

AI, Committee, News, Uncategorized

You have no choice in reading this article—maybe

Uri Maoz loved doing his human research, back when he was getting his PhD. He was studying a very specific topic in computational neuroscience: how the brain instructs our arms to move and how our gray matter in turn perceives that motion.  Then his professor asked him to deliver an undergrad lecture. Maoz assumed his boss was going to tell him exactly what to do, or at least throw some PowerPoint slides his way. But no. Maoz had free rein to teach anything, as long as it was relevant to the students. “I could have gone to human brain augmentation,” he says. “Cyborgs or whatever.” Yet that admittedly fun and borderline sci-fi topic wasn’t what popped, unbidden, into his mind. His idea, he recalls with excitement: “What neuroscience has to say about the question of free will!”  How—or whether—humans make decisions (like, say, about what to discuss in an undergrad lecture) had been on his mind since he’d read an article in his early twenties suggesting that … maybe they didn’t. This question might naturally beget others: Had he even had a choice about whether to read that article in the first place? How would he ever know if he was responsible for making decisions in his life or if he just had the illusion of control? “After that, there was no turning back,” says Maoz, now a professor at Chapman University, in California. He finished his PhD work in human movement, but afterward he scooted further up the neural chain to find out how desires and beliefs turn into actions—from raising an arm to choosing someone to ask out to dinner on a Friday night. Today, Maoz is a central figure in the attempt to (sort of, maybe) answer how that neural chain functions. His research has since overturned and reinter­preted canonical neuroscience studies and united the straight-scientific and philosophical sides of the free-will question. More than anything, though, he’s succeeded in uncovering new wrinkles in the debate. Machines and magic tricks The concept of free will seems straightforward, but it doesn’t have a universally accepted definition. One intuitive notion is that it’s the ability to make our own decisions and take our own actions on purpose—that we control our lives. But physicists might ask if the universe is deterministic, following a preordained path, and if human choices can still happen in such a universe.  That’s a question for them, Maoz says. What neuroscientists can do is figure out what’s going on in the brain when people make decisions. “And that’s what we’re trying to do: to understand how our wishes, desires, beliefs, turn into actions,” he says. By the time Maoz had finished his PhD, in 2008, neuroscientific research into the question had been going on for decades. One foundational study from the 1960s showed that a hand movement—something a person seemingly decides to do—was preceded by the appearance in the brain of an electrical signal called the “readiness potential.”  Building on that result, in the 1980s a neuroscientist named Benjamin Libet did the experiment that had first piqued Maoz’s interest in the topic—one that many, until recently, interpreted as a death knell for the concept of free will. An electrical impulse in our brains can shed only so much light on whether we truly are the architects of our own fates. “He just had people sit there, and whenever they feel like it, they would go like this,” says Maoz, wiggling his wrist. Libet would then ask where a rotating dot was on a screen when they first had the urge to flick. He found that the readiness potential appeared not only before they moved their hand but before they reported having the urge to move—or, in Libet’s interpretation, before they knew they were going to move.  Studies since have confirmed the observation and shown that the readiness potential appears a second or two—and maybe, fMRI implies, up to 10 seconds—before participants report making a conscious decision. “It suggests we are essentially passengers in a self-driving car,” says Maoz. “The unconscious biological machine does all the steering, but our conscious mind sits in the driver’s seat and takes the credit.”  Maoz initially approached his own research with variations on Libet’s experiments. He worked with epilepsy patients who already had electrodes in their brains, for clinical purposes, and was able to predict which hand they would raise before they raised it.  Still, some of the Libet-inspired studies people were doing nagged at him. “All these results were about completely arbitrary decisions. Raise your hand whenever you feel like it,” he says. “Why? No reason.” A decision like that is quite different from, say, choosing to break up with your partner. Try telling someone they weren’t in the driver’s seat for that.  The field wasn’t looking at meaningful decisions, he says—the ones that actually set the course of lives.  Maoz began pulling in philosophers to help guide his approach. They would challenge him to confront the semantic differences between things like intention, desire, and urge. Neuroscientists have tended to lump those concepts together, but philosophers tease them apart: Desire is a want that doesn’t necessarily progress toward an action; urge carries implications of immediacy and compulsion; and intention involves committing to a plan. (Maoz has come to focus specifically on intention—including, recently, the potential intentions of AI.) In 2017, he organized his first in a series of free-will conferences, drawing many autonomy-interested philosophers. “Thank you so much for coming,” he recalls saying at the opening of the meeting. “As if you had a choice.” One day, the crew took an excursion out on a lake. As the group munched on shrimp, someone joked that they hoped the boat didn’t sink, because everybody in the field would die.  The comment didn’t make Maoz feel existential dread. Instead, he figured that if the whole field was already there, why not lasso them all into writing a research grant? “He just thinks what should be the next step and just has

You have no choice in reading this article—maybe Read Post »

AI, Committee, News, Uncategorized

The Download: how humans make decisions, and Moderna’s “vaccine” word games

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. You have no choice in reading this article—maybe How do humans make decisions? The question has been on Uri Maoz’s mind since he read an article in his early twenties suggesting that… maybe they didn’t.   Had he even had a choice about whether to read that article in the first place? How would he ever know if he was truly responsible for making any decisions? “After that, there was no turning back,” says Maoz, now a professor of computational neuroscience at Chapman University.  Today, Maoz is a central figure in efforts to understand how desires and beliefs turn into actions. He’s also uncovered new wrinkles in the debate. Read the full story on his discoveries. —Sarah Scoles This article is from the next issue of our print magazine, packed with stories all about nature. Subscribe now to read the full thing when it lands on Wednesday, April 22. What’s in a name? Moderna’s “vaccine” vs. “therapy” dilemma  Moderna, the covid-19 shot maker, is using its mRNA technology to destroy tumors through a very, very promising technique known as a cancer vacc—  “It’s not a vaccine,” a spokesperson for Merck said before the V-word could be uttered. “It’s an individualized neoantigen therapy.”  Oh, but it is a vaccine, and it looks like a possible breakthrough. But it’s been rebranded to avoid vaccine fearmongering—and not everyone is happy about the word game. Read the full story.  —Antonio Regalado This article is from The Checkup, our weekly newsletter covering the latest in biotech. Sign up to receive it in your inbox every Thursday.  The must reads I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology. 1 Sam Altman’s home has been attacked twice in two days A driver reportedly fired a gun at his property on Sunday. (SF Standard) + A Molotov cocktail was thrown at his home on Friday. (NBC News) + The suspect wrote essays warning AI would end humanity. (SF Chronicle) + The attacks expose growing divides in opinion on AI. (Axios)  2 AI weapons are ushering in a new kind of arms race Countries are racing to deploy AI in military systems. (NYT $) + The Pentagon wants AI firms to train on classified data. (MIT Technology Review) + Where OpenAI’s technology could show up in Iran. (MIT Technology Review)  3 Artemis II was a success Astronauts did an array of experiments that will be crucial to the future of both the program itself and deep-space missions. (Guardian) + But next steps for the Artemis missions are uncertain. (Ars Technica)  4 OpenAI and Elon Musk are heading toward a massive courtroom clashThe company has accused Musk of a “legal ambush.” (Engadget) + He’s lost a streak of cases ahead of the showdown. (FT $)  5 AI job fears in China are fueling a viral “ability harvester” project It claims to turn human skills into AI tools. (SCMP) + Hustlers are cashing in on China’s OpenClaw AI craze. (MIT Technology Review)  6 Governments are hiding information about the Iran war online Through restrictions on internet access and satellite imagery. (NPR)   7 Apple is testing four smart glasses that could rival Meta Ray-Bans They’re part of a broader wearables strategy. (Bloomberg $)  8 Meta is building an AI version of Mark Zuckerberg to interact with staffIt’s being trained on his mannerisms, voice, and statements. (FT $)  9 Anthropic is asking Christian leaders for guidance It’s seeing advice on building moral machines. (WP $) + AI agents have spread their own religions. (MIT Technology Review)  10 A dancer with MND is performing again through an avatar Her brainwaves powered the digital dancer. (BBC)  Quote of the day “Earth was this lifeboat hanging in the universe.” —Artemis II astronaut Christina Koch describes her view of Earth from space, the Guardian reports. One more thing RAVEN JIANG How AI and Wikipedia have sent vulnerable languages into a doom spiral When Kenneth Wehr started managing the Greenlandic-language version of Wikipedia, he discovered that almost every article had been written by people who didn’t speak the language.   A growing number of them had been copy-pasted into Wikipedia from machine translators—and were riddled with elementary mistakes. This is beginning to cause a wicked problem.  AI systems, from Google Translate to ChatGPT, learn new languages by scraping text from Wikipedia. This could push the most vulnerable languages on Earth toward the precipice.  Read the full story on what happens when AI gets trained on junk pages.  —Jacob Judah  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.) + Hungary’s next health minister can throw some serious shapes.  + Here’s a welcome route to an AI-free Google search. + Movievia eschews endless scrolling to find the right film for your needs+ A photography trick has turned a giant glacier into a tiny, living diorama.

The Download: how humans make decisions, and Moderna’s “vaccine” word games Read Post »

AI, Committee, News, Uncategorized

Want to understand the current state of AI? Check out these charts.

If you’re following AI news, you’re probably getting whiplash. AI is a gold rush. AI is a bubble. AI is taking your job. AI can’t even read a clock. The 2026 AI Index from Stanford University’s Institute for Human-Centered Artificial Intelligence, AI’s annual report card, comes out today and cuts through some of that noise.  Despite predictions that AI development may hit a wall, the report says that the top models just keep getting better. People are adopting AI faster than they picked up the personal computer or the internet. AI companies are generating revenue faster than companies in any previous technology boom, but they’re also spending hundreds of billions of dollars on data centers and chips. The benchmarks designed to measure AI, the policies meant to govern it, and the job market are struggling to keep up. AI is sprinting, and the rest of us are trying to find our shoes. All that speed comes at a cost. AI data centers around the world can now draw 29.6 gigawatts of power, enough to run the entire state of New York at peak demand. Annual water use from running OpenAI’s GPT-4o alone may exceed the drinking water needs of 12 million people. At the same time, the supply chain for chips is alarmingly fragile. The US hosts most of the world’s AI data centers, and one company in Taiwan, TSMC, fabricates almost every leading AI chip.  The data reveals a technology evolving faster than we can manage. Here’s a look at some of the key points from this year’s report.  The US and China are nearly tied In a long, heated race with immense geopolitical stakes, the US and China are almost neck and neck on AI model performance, according to Arena, a community-driven ranking platform that allows users to compare the outputs of large language models on identical prompts. In early 2023, OpenAI had a lead with ChatGPT, but this gap narrowed in 2024 as Google and Anthropic released their own models. In February 2025, R1, an AI model built by the Chinese lab DeepSeek, briefly matched the top US model, ChatGPT. As of March 2026, Anthropic leads, trailed closely by xAI, Google, and OpenAI. Chinese models like DeepSeek and Alibaba lag only modestly. With the best AI models separated in the rankings by razor-thin margins, they’re now competing on cost, reliability, and real-world usefulness.  The index notes that the US and China have different AI advantages. While the US has more powerful AI models, more capital, and an estimated 5,427 data centers (more than 10 times as many as any other country), China leads in AI research publications, patents, and robotics.  As competition intensifies, companies like OpenAI, Anthropic, and Google no longer disclose their training code, parameter counts, or data-set sizes. “We don’t know a lot of things about predicting model behaviors,” says Yolanda Gil, a computer scientist at the University of Southern California who coauthored the report. This lack of transparency makes it difficult for independent researchers to study how to make AI models safer, she says. AI models are advancing super fast Despite predictions that development will plateau, AI models keep getting better and better. By some measures, they now meet or exceed the performance of human experts on tests that aim to measure PhD-level science, math, and language understanding. SWE-bench Verified, a software engineering benchmark for AI models, saw top scores jump from around 60% in 2024 to almost 100% in 2025. In 2025, an AI system produced a weather forecast on its own.   “I am stunned that this technology continues to improve, and it’s just not plateauing in any way,” says Gil. However, AI still struggles in plenty of other areas. Because the models learn by processing enormous amounts of text and images rather than by experiencing the physical world, AI exhibits “jagged intelligence.” Robots are still in their early days and succeed in only 12% of household tasks. Self-driving cars are farther along: Waymos are now roaming across five US cities, and Baidu’s Apollo Go vehicles are shuttling riders around in China. AI is also expanding into professional domains like law and finance, but no model dominates the field yet.  But the way we test AI is broken These reports of progress should be taken with a grain of salt. The benchmarks designed to track AI progress are struggling to keep up as models quickly blow past their ceilings, the Stanford report says. Some are poorly constructed—a popular benchmark that tests a model’s math abilities has a 42% error rate. Others can be gamed: when models are trained on benchmark test data, for example, they can learn to score well without getting smarter.  Because AI is rarely used the same way it’s tested, strong benchmark performance doesn’t always translate to real-world performance. And for complex, interactive technologies such as AI agents and robots, benchmarks barely exist yet.  AI companies are also sharing less about how their models are trained, and independent testing sometimes tells a different story from what they report. “A lot of companies are not releasing how their models do in certain benchmarks, particularly the responsible-AI benchmarks,” says Gil. “The absence of how your model is doing on a benchmark maybe says something.”  AI is starting to affect jobs Within three years of going mainstream, AI is now used by more than half of people around the world, a rate of adoption faster than the personal computer or the internet. An estimated 88% of organizations now use AI, and four in five university students use it.  It’s early days for deployment, and AI’s impact on jobs is hard to measure. Still, some studies suggest AI is beginning to affect young workers in certain professions. According to a 2025 study by economists at Stanford, employment for software developers aged 22 to 25 has fallen nearly 20% since 2022. The decline might not be pinned on AI alone, as broader macroeconomic conditions could be to blame, but AI appears to be playing

Want to understand the current state of AI? Check out these charts. Read Post »

AI, Committee, News, Uncategorized

HyperMem: Hypergraph Memory for Long-Term Conversations

arXiv:2604.08256v1 Announce Type: new Abstract: Long-term memory is essential for conversational agents to maintain coherence, track persistent tasks, and provide personalized interactions across extended dialogues. However, existing approaches as Retrieval-Augmented Generation (RAG) and graph-based memory mostly rely on pairwise relations, which can hardly capture high-order associations, i.e., joint dependencies among multiple elements, causing fragmented retrieval. To this end, we propose HyperMem, a hypergraph-based hierarchical memory architecture that explicitly models such associations using hyperedges. Particularly, HyperMem structures memory into three levels: topics, episodes, and facts, and groups related episodes and their facts via hyperedges, unifying scattered content into coherent units. Leveraging this structure, we design a hybrid lexical-semantic index and a coarse-to-fine retrieval strategy, supporting accurate and efficient retrieval of high-order associations. Experiments on the LoCoMo benchmark show that HyperMem achieves state-of-the-art performance with 92.73% LLM-as-a-judge accuracy, demonstrating the effectiveness of HyperMem for long-term conversations.

HyperMem: Hypergraph Memory for Long-Term Conversations Read Post »

AI, Committee, News, Uncategorized

Large Language Model Post-Training: A Unified View of Off-Policy and On-Policy Learning

arXiv:2604.07941v1 Announce Type: new Abstract: Post-training has become central to turning pretrained large language models (LLMs) into aligned and deployable systems. Recent progress spans supervised fine-tuning (SFT), preference optimization, reinforcement learning (RL), process supervision, verifier-guided methods, distillation, and multi-stage pipelines. Yet these methods are often discussed in fragmented ways, organized by labels or objective families rather than by the behavioral bottlenecks they address. This survey argues that LLM post-training is best understood as structured intervention on model behavior. We organize the field first by trajectory provenance, which defines two primary learning regimes: off-policy learning on externally supplied trajectories, and on-policy learning on learner-generated rollouts. We then interpret methods through two recurring roles — effective support expansion, which makes useful behaviors more reachable, and policy reshaping, which improves behavior within already reachable regions — together with a complementary systems-level role, behavioral consolidation, which preserves, transfers, and amortizes behavior across stages and model transitions. This perspective yields a unified reading of major paradigms. SFT may serve either support expansion or policy reshaping, whereas preference-based methods are usually off-policy reshaping. On-policy RL often improves behavior on learner-generated states, though under stronger guidance it can also make hard-to-reach reasoning paths reachable. Distillation is often best understood as consolidation rather than only compression, and hybrid pipelines emerge as coordinated multi-stage compositions. Overall, the framework helps diagnose post-training bottlenecks and reason about stage composition, suggesting that progress in LLM post-training increasingly depends on coordinated system design rather than any single dominant objective.

Large Language Model Post-Training: A Unified View of Off-Policy and On-Policy Learning Read Post »

We use cookies to improve your experience and performance on our website. You can learn more at Privacy Policy and manage your privacy settings by clicking Settings.

Privacy Preferences

You can choose your cookie settings by turning on/off each type of cookie as you wish, except for essential cookies.

Allow All
Manage Consent Preferences
  • Always Active

Save
en_US