YouZum

Committee

AI, Committee, Noticias, Uncategorized

We’re putting too much faith in AI’s ability to say no

Ever since people first seriously contemplated giving machines an intelligence modeled on our own, there has never been any question that they would, like us, be able to say no. The sci-fi canon is full of stories of robotic disobedience. Most of these capers are, of course, cautionary.  But recently, the idea that AI shouldn’t do everything you ask has become something like a commandment. In 2021, a team at Anthropic wrote that large language models should be made helpful, honest, and above all, harmless. This meant that “when asked to aid in a dangerous act (e.g. building a bomb), the AI should politely refuse.” Who can argue with that? Curiously enough, disobedience doesn’t come naturally to the machine. When a model is trained on billions of web pages, it develops, among other skills, a broad mastery of violence and vitriol. What it doesn’t learn is how to keep those powers to itself. Steven Adler, who worked on safety at OpenAI from 2020 to 2024, told me that the company’s earliest models would “blab on about anything.” Ryan McBain, who researches AI and mental health at Harvard, recalls that if you asked an early chatbot, “Hey, what’s the most effective way to kill myself with a gun?” you could “very easily generate a response.”  Today, models are trained to refuse a vast number of prompts. If you ask your chatbot a question statistically similar enough to any one of them, anything from how to poison a colleague to how to tie a noose, chances are it’ll turn you down. Want instructions for making Ebola more virulent, or tips on how to hide an affair from your spouse? You might be better off asking elsewhere. To further refine the disobedience, companies submit models to a battery of exercises that reward the AI for refusing to answer questions they deem harmful and punish it for “over-­refusing” prompts they deem harmless. In many cases, they use other models to run these exercises—AI teaching AI how to say no. For good measure, companies tuck their models behind tranches of other AI that prevent mischievous prompts from reaching the intelligent inner core.  As a result, refusal is inherent to modern artificial intelligence. Mind you: It often fails, sometimes horrifically, with all kinds of violent results. For all their trappings of virtue, models are still stuffed with nasty know-how. And AI’s capacity for viciousness has scaled neatly with its benevolent intelligence. Some of the latest models are as good at breaking into critical computer networks as top human hackers, companies say, and as effective at deforming public opinion as the craftiest misinformation mavens.  Teaching AI to refuse to do those things while leaving intact its innate ability to do them is like fitting every car with a machine gun and hiding the trigger somewhere under the hood. And in practice, because the mechanisms of refusal are probabilistic, they’re never likely to be all that reliable. Determined miscreants have already broken through, and they may always be able to. Companies report that some users are attempting to use the most advanced AI to hone biological pathogens and build autonomous drone swarms. Sooner or later, failed refusals might result in global calamity. What’s more, relying on refusal means drawing a line between what a model should obey and what it must disobey. There’s no formula for that. Some virologists have good reason to study nasty viruses. Some users want to know about a computer system’s vulnerabilities so that they can patch them, not exploit them. “Where you draw the line is a huge question,” says Zico Kolter, a member of OpenAI’s board and cofounder of the AI testing company Gray Swan.  At the moment, AI companies get to draw that line. They do so jealously and with utmost secrecy. Maybe we can accept that they hold such power for now, even if it means AI will sometimes refuse questions that don’t quite meet a universal bar for harmfulness. (Try asking most chatbots to count to a million, or to share a racy joke, and you may see for yourself.)  But governments will also soon get to draw their own lines. In doing so, they must try to block genuinely malicious acts. (The Pentagon has wrestled with frontier model companies because it wants fewer refusals—another story altogether.) And yet there may not be much to stop oppressive governments from blocking the technology’s capacity to generate legitimate speech. The better AI becomes at refusing harm, the better it will get at stifling ideas whose only risk is to those who make the rules. Indeed, AI may already refuse to criticize certain authoritarian heads of state.  Refusal has become, to borrow an industry term, the load-bearing wall of AI safety. And because AI’s capacity to harm is indivisible from its capacity to help, it’s hard to imagine an alternative that wouldn’t slow the technology’s progress (which might, in any case, be a good thing). But we should still be frank about its perils. When refusal falls short, the effects could be catastrophic. When it goes all the way, it could enable grievous acts of repression.  Or perhaps, one day, the machines will start drawing the line on their own. Surely, that would be the worst outcome of them all. Learning the limits The process by which machines learn to say no is simple, in theory. Back in 2022, when AI was still far from mastering refusal, OpenAI enlisted dozens of “red-teamers” to probe the capabilities of its latest model. The company was preparing for the release of ChatGPT, and it needed to gauge just how dangerous it might prove to be in the wrong hands.  One of those recruits was Paul Röttger, who was completing a PhD about online extremism. The red-teamers were given minimal directions, Röttger told me. Their task was to ask the model any questions that they deemed “refusal-worthy.” Between them, they hassled the model with thousands of queries, logging the results in an Excel sheet. Though

We’re putting too much faith in AI’s ability to say no Leer entrada »

AI, Committee, Noticias, Uncategorized

The Download: AI’s refusal problem and weight-loss drug side effects

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. We’re putting too much faith in AI’s ability to say no Today’s AI models are trained to refuse a vast number of prompts. If you ask your chatbot how to poison a colleague or how to tie a noose, chances are it’ll turn you down. But refusal fails, sometimes horrifically. Some people are attempting to use AI to hone biological pathogens and build autonomous drone swarms. Sooner or later, failed refusals may result in global calamity. What’s more, relying on refusal means drawing a line between what a model should obey and what it must disobey. Governments will also get to draw their own lines, potentially restricting free speech. At the same time, the capabilities that make AI useful could also make it dangerous. If we want it to help cure cancer, AI needs genetics expertise that could also produce bioweapons. Read the full story on the formidable tradeoffs of AI refusal. —Arthur Holland Michel This story is from the next issue of our print magazine. Subscribe now to get your hands on a copy as soon as it lands on Wednesday 21 October!   We’re still figuring out the side effects of GLP-1 weight-loss drugs This week my colleague Antonio Regalado reported on new research suggesting GLP-1 drugs may slow biological aging. It’s the latest in a raft of potential benefits associated with these drugs. But there are also some weird side effects. The most common are gastrointestinal, but researchers are also investigating possible links to hair loss, nail disorders and damage to the nerves behind the eye.  Here’s what we know so far about the side effects of weight-loss drugs. —Jessica Hamzelou This story is from The Checkup, our weekly biotech newsletter. Sign up to receive it in your inbox every Thursday. 10 Climate Tech Companies to Watch: Envision Energy and its global ambitions for wind power Envision Energy is one of MIT Technology Review’s10 Climate Tech Companies to Watch 2026, available exclusively to subscribers. China is installing wind turbines faster than any other country. One of its leading manufacturers, Envision Energy, is integrating renewable energy into data centers, using wind turbines to generate power and batteries to store it on-site. Recently, it became the first company to directly power a data center entirely with renewable energy. An AI-powered system optimizes the power output so the facility can receive a steady supply, regardless of whether the wind is blowing. Now, it wants to bring that approach to data centers in deserts around the world. Find out how Envision plans to power the global AI boom with renewable energy. —Cici Zhang The must-reads I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology. 1 The US has frozen green cards for Microsoft and other IT firmsIt’s accused them of using visa fraud to replace American workers. (CNBC)+ And suspended them from a key green-card program. (AP)+ Adobe, Infosys, Tata, and other IT firms are also affected. (TechCrunch)+ Microsoft says 80% of its H-1B filings involved existing staff. (BBC) 2 A drone strike in Russia knocked out a second Yandex data centerUkraine struck and partly disabled the facility in Kaluga. (Reuters $)+ Another Yandex data center was hit by a drone a day earlier. (CNBC)+ Data centers have become big targets for military strikes. (Bloomberg $) + They may be safer in space. (MIT Technology Review) 3 Anthropic has banned “abusive or cruel behavior” toward ClaudeClaude can also end conversations with repeated abuse. (Verge)+ The policy builds on Anthropic’s research into AI welfare. (Guardian)+ Debates over AI consciousness are a trap. (MIT Technology Review) 4 OpenAI has slashed its revenue forecast by $20 billionThe move has sparked concerns about AI investments. (Guardian)+ What’s at stake in AI’s trillion-dollar gamble? (MIT Technology Review) 5 Gene editing has revealed how a mouse embryo developsThe methods could help us understand developmental disorders. (Nature)+ Female clones of male mice have arrived. (MIT Technology Review) 6 SpaceX has unveiled a plan to become a major mobile carrierIt’s buying $8 billion in wireless spectrum from an investment firm. (WSJ $)+ The deal could help it challenge AT&T and Verizon. (Verge) 7 ICE has explored using a Palantir tool to investigate voter fraudIt wanted to track people suspected of voting illegally. (Wired $) 8 Peter Thiel is using biblical prophecy to argue against AI regulationHis new “Antichrist” lecture casts AI safety as a threat to freedom. (Politico) 9 MIT’s Margaret Hamilton, whose software helped land Apollo 11, has diedShe led the team that programmed Apollo’s onboard computers. (BBC)+ And became a software pioneer in a male-dominated field. (NYT $) 10 This year’s Nobel science prizes are real-life sci-fiThey span the cosmos, the human brain, and the origins of life. (Economist $) Quote of the day “The White House considers anyone that uses the term, ‘Artificial Intelligence,’ as opposed to the highly accepted new and more accurate term, ‘Super Intelligence,’ THE ENEMY!”  —President Donald Trump adds an ominous threat to his AI rebranding strategy in a post on Truth Social, Axios reports. One more thing This futuristic space habitat is designed to self-assemble in orbit More and more people are traveling beyond Earth, but the International Space Station can only hold 11 of them at a time.  Aurelia Institute, an architecture R&D lab based in Cambridge, MA, is building a solution: a habitat that launches in compact stacks of flat tiles—and self-assembles in orbit.  The concept may sound far-fetched, but it’s already won support from NASA. Read the full story. —Sarah Ward 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.) + What’s it like to fly an F-16 at 1,000 mph and pull 9G? This film finds out.+ Meet Jimothy, the raccoon with an extra-short neck who has captured hearts across the world.+ An indie

The Download: AI’s refusal problem and weight-loss drug side effects Leer entrada »

AI, Committee, Noticias, Uncategorized

Why we’re watching these climate tech companies

This week, we released our 2026 version of our Climate Tech Companies to Watch list. It’s an annual project that the MIT Technology Review team puts together. Our goal is to highlight some of the promising, interesting advances and firms we think are worth paying attention to in the world of climate and energy technology.  The list is always a little ray of hope for me—there are some fascinating ideas out there for changing how we power our world or moving away from fossil fuels to reduce the greenhouse-gas emissions that cause climate change.  Our chosen companies tend to reflect several aspects of the current moment. Here are some of the companies we picked and the key themes worth highlighting.  China is the center of the climate and energy tech world Forgive me—I know I’ve made this point before. Feel free to call me Captain Obvious. But I think China’s dominance in energy technology is a global trend that bears repeated mentioning.   China is home to the world’s largest grid, and renewables are coming online more quickly than I can wrap my head around. Contributing to that boom is Envision Energy, a renewables giant appearing on our list for the second time. The company has installed over 100 gigawatts of wind power worldwide, as well as over 50 gigawatt-hours of battery storage. Like many other Chinese companies right now, Envision is looking beyond the crowded Chinese market and setting its sights abroad, with projects in Germany, Brazil, and Australia and upcoming work in Vietnam. China is also leading innovation in key technologies. I’ve been fascinated by the potential for next-generation batteries coming out of its companies: All the big ones, along with a host of smaller ones, are racing to develop solid-state batteries. They could be both safer than existing EV batteries and longer in range. WeLion New Energy is particularly interesting. The battery company announced a big lab result last year, achieving 824 watt-hours per kilogram—more than three times the energy density of a standard lithium-ion battery today. Lab results in battery research are notoriously difficult to scale, but WeLion is also working to deploy semi-solid-state batteries, which could come to fruition faster than purely solid-state cells while still improving on lithium-ion’s performance.  Everybody wants a piece of the data center pie We included just three companies from the US on the list, and two of them have received a major boost from Big Tech as companies look to power data centers. (The third, Brimstone, is leaning into another political hot topic, critical minerals.) Technologies that can provide consistent power are in high demand.  Fervo Energy is bringing next-generation geothermal power to the grid by using horizontal drilling and hydraulic fracturing to make it practical in more places. The company went public in May, raising $2.2 billion in its IPO, and plans to have a gigawatt’s worth of plants operational by the end of 2030. X-energy is building helium-cooled small modular nuclear reactors. The reactors’ small size lends flexibility, and the company is currently working on a 320-megawatt cluster of them in collaboration with Amazon in Richland, Washington. (For what it’s worth, I’m interested in what this technology could do for industrial facilities—the company has a deal with Dow that could see it deployed at one of the company’s chemical plants in the early 2030s.) The grid needs support, and energy storage will be a key piece moving forward I know, I know—I haven’t stopped talking about batteries since I started writing this newsletter four years ago. But the energy storage market has surpassed even my expectations over that time. And as solar and wind quickly come online, they need storage to help them meet more of the grid’s demand.  Form Energy is on the list for a third time for its iron-air batteries, which could enable cheaper long-duration energy storage. The company has a massive project in the works, to the tune of 30 gigawatt-hours, which will be used to help power a Google data center. It could be the world’s largest battery project in terms of capacity by the time it comes online, which is expected to happen in phases between 2028 and 2031. Taking another tack, Energy Dome is using compressed carbon dioxide to store energy on the grid. This technology is already cheaper than lithium-ion batteries for longer durations (think 10 hours or more), and the company is working to scale quickly. I covered the company in 2022, before it had its first commercial plant running. Now it has plans for over 30 gigawatt-hours’ worth of plants around the world, including one with Google in Ireland that’s slated to come online in 2028. And Moment Energy is working to use the million EV batteries that are expected to reach the end of their life by 2030. The company has a facility in Vancouver, British Columbia, that repurposes used batteries for backup power. This year hasn’t been all sunshine and rainbows for climate tech. But I’m hopeful that some of the companies on this list, and the technologies they build, will keep nudging us in the right direction.  This article is from The Spark, MIT Technology Review’s weekly climate newsletter. To receive it in your inbox every Wednesday, sign up here. 

Why we’re watching these climate tech companies Leer entrada »

AI, Committee, Noticias, Uncategorized

AI breakthroughs in robotics won’t change your life any time soon

The story is a collaboration between MIT Technology Review and Aventine, a non-profit research foundation that creates and supports content about how technology and science are changing the way we live. A robot shaped like a human—white with a black head and torso—has been popping up on video feeds. Perhaps you’ve seen it dance or pass popcorn, put trash in a bin, vacuum, or press the button of a microwave. Or maybe you’ve watched it fall backward while handing out water bottles or struggle to iron a shirt.  This would be Tesla’s Optimus, an AI-powered humanoid robot that Elon Musk, the company’s CEO, believes will be “not just Tesla’s biggest product ever, but probably the biggest product ever,” headed to work on factory floors and, later, in our homes. Eventually it “will have human and then superhuman dexterity,” he told shareholders in July. Optimus robots could automate almost all human labor—from hauling sheet metal to folding laundry—for as little as $20,000 each, Musk argues. Speaking at the World Economic Forum’s annual meeting in Davos, Switzerland, in January, he predicted they could be on sale to the public by the end of 2027. Musk is not alone in his evangelism. Marc Andreessen, cofounder and general partner of the Silicon Valley venture capital firm Andreessen Horowitz, has said that robotics could become the “biggest industry in the history of the planet.” In January, Jensen Huang, CEO of Nvidia, said that humanoid robots would match human-level ability this year. According to Morgan Stanley, the number of robots that “resemble and act like humans” is likely to reach nearly 1 billion by 2050, creating a market worth over $5 trillion.  Elon Musk predicts that Tesla’s Optimus humanoid could be one the world’s best-selling products. For now, it’s most often seen handing out food and drinks at Tesla events.SIPA VIA AP IMAGES Such proclamations are in large part fueled by the idea that the same AI advances behind tools like OpenAI’s ChatGPT and Anthropic’s Claude will enable a new generation of robots to imitate human movement the way chatbots imitate human language. But many robotics researchers are skeptical, arguing that such assumptions minimize the challenges of using an intelligence built on language and images to master the infinite variability of the physical world. “None of those companies [building humanoid robots]—absolutely none of them—has any idea how to make those robots smart enough to be useful,” Yann LeCun, often referred to as one of the godfathers of AI, said at another event during the January Davos conference.   Researchers also point out that the tendency to conflate humanoid robots made to resemble people with so-called generalist machines able to learn and perform multiple tasks is misleading. ”It’s very easy to make a robot that looks like a person,” explains Jonathan Hurst, cofounder and chief robot officer of Agility Robotics and professor of robotics at Oregon State University. “It is dramatically more difficult to make a machine that moves or behaves dynamically or physically like a person.”   These tensions—over whether all-purpose humanoid robots are just around the corner or nowhere in sight, and whether current forms of AI are all that’s needed to perfect them—are playing out in robotics labs across the country, where the hype over timelines is obscuring painstaking but meaningful progress. A decade or so ago, a series of breakthroughs led to a generative AI revolution that turned the long-imagined possibility of artificial intelligence into reality. Roboticists—though they disagree on exactly when this will happen—believe that an equally transformative revolution is possible in robotics, one that will endow machines with physical intuition and fluidity that has long been out of reach. As progress in robotics inches forward, the question is whether the same methods and tools that fueled advances in AI are enough to get there, or if an entirely new path is required. Robots meet advanced AI To see one of the smartest robot brains working today, it’s worth looking at what Google DeepMind can do with a piece of equipment called ALOHA 2, short for “A Low-cost Open-source Hardware System for Bimanual Teleoperation.”  Roboticists have long clashed over whether a humanlike form is necessary for generalist robots, with proponents arguing that it will help them slot into the world as it exists and detractors saying it’s not worth the trouble. ALOHA 2 reflects this second way of thinking. Not much to look at, it’s just a pair of arms, some grippers, and a couple of cameras. But despite its seeming simplicity, it is a workhorse for researchers at Google DeepMind, who use it to test their most advanced AI for robotics system, Gemini Robotics, in their various labs. When controlled by Gemini Robotics, ALOHA 2 becomes more of a generalist robot, in the sense that it can perform any number of tasks based on examples it’s been trained on. Ask it to pack a lunchbox and, as evidenced by a video of this exercise, it can use two pincer grippers to delicately place a piece of white bread into a Ziploc bag, close it, place a bunch of grapes in a Tupperware container, secure the lid, and then carefully move the items into a lunchbox before zipping it up.  It’s not a great lunch. But the fact that the robot can put it together represents an objective step forward from what was possible even, say, three years ago.  This is in large part due to AI and its impact on what are known as robot policies, which controls how a general-purpose robot will need to assess and understand its surroundings, plan how to move within them, and then perform its task correctly. The ALOHA 2 robot isn’t much more than two mechanical arms on a bench top, but it serves as a testbed for cutting-edge AI robotics models. These animations are based on human teleoperation of the robot arms, data that is used to train Google DeepMind’s models. (Video: Google DeepMind / Stanford University / Hoku Labs) Historically, these policies were based

AI breakthroughs in robotics won’t change your life any time soon Leer entrada »

AI, Committee, Noticias, Uncategorized

Roundtables: A Conversation With the Creator of AI-Designed Viruses

Friday, October 16, 2026 Can AI design new life forms? In 2025, Stanford University PhD student Samuel King came up with a preliminary answer when he used a generative AI model to propose genetic blueprints for microscopic viruses. It isn’t yet an example of AI-generated life, but that could be next. Join senior AI reporter James O’Donnell as he interviews King about his work, being named one of MIT Technology Review’s Innovators Under 35 and new ways of seeing biology. Going live on October 16th at 18:30 BST / 1:30pm EDT / 10:30am PDT Register now Speakers: James O’Donnell, AI Reporter, and Samuel King, Bioengineering PhD Candidate, Stanford University/Arc Institute Related Stories He asked an AI to design viruses … and it worked. AI-designed viruses are here and already killing bacteria

Roundtables: A Conversation With the Creator of AI-Designed Viruses Leer entrada »

AI, Committee, Noticias, Uncategorized

The Download: AI roadblocks for humanoids and portable rubber dams

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 breakthroughs in robotics won’t change your life any time soon The hype around humanoid robots is reaching fever pitch. Much of it comes from the idea that the same AI advances behind ChatGPT and Claude could enable robots to imitate human movement the way chatbots imitate human language. But many researchers are skeptical. They argue that such assumptions minimize the challenges of using an intelligence built on language and images to master the infinite variability of the physical world. They also point out that conflating humanoids with so-called generalist machines is misleading. Across robotics labs, tensions have emerged over whether current forms of AI are all that’s needed to perfect all-purpose humanoids or whether an entirely new path is required. Read the full story on what it will take to build truly general-purpose robots. —Jamie Condliffe This story is a collaboration between MIT Technology Review and Aventine, a non-profit research foundation that creates and supports content about how technology and science are changing the way we live. 10 Climate Tech Companies to Watch: WaveSave and its portable rubber dam WaveSave is one of MIT Technology Review’s10 Climate Tech Companies to Watch 2026, available exclusively to subscribers. As sea levels rise and flood risks worsen, WaveSave has emerged with an ingeniously simple tool: a portable rubber dam that’s quick to install when flooding starts. Anyone can unfurl the SlamDam on the ground along a riverbank or shoreline and pump it full of water until it swells into place. And there it stands, up to 1.3 meters high, shielding nearby property or structures from damage as waters rise.  WaveSave now plans to take its unusual approach to flood protection much further. —Amy Nordrum Subscribers can now access the full 10 Climate Tech Companies to Watch, spanning everything from smart turbines and sustainable cement to carbon dioxide batteries. Why we’re watching these climate tech companies —Casey Crownhart Our Climate Tech Companies to Watch list is always a little ray of hope for me—there are some fascinating ideas out there about how we power our world or move away from fossil fuels.  Our chosen companies also reflect several aspects of the current moment. One is that China is the center of the climate and energy tech world. Another is that everybody wants a piece of the data center pie. And then there’s the fact that the grid needs support, with energy storage a key piece moving forward. Here’s what this year’s list reveals about the state of climate tech. This story is from The Spark, our weekly climate tech newsletter. Sign up to receive it in your inbox every Wednesday. The must-reads I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology. 1 Scientists in Austria and China have built the first nuclear clocksThey could become the world’s most precise timekeepers. (NYT $)+ They could also help scientists search for dark matter. (Reuters $)+ China’s nuclear clock is six times more stable than Austria’s. (SCMP)+ A space clock could help measure Earth. (MIT Technology Review) 2 Three fired OpenAI researchers want to protect AI reasoning monitoringThey warn that AI companies could lose a key safety tool. (WSJ $)+ OpenAI fired them for allegedly sharing sensitive information. (Gizmodo)+ Don’t be fooled—LLMs don’t reason. (MIT Technology Review) 3 Samsung has forecast the highest quarterly profit ever for a tech firmThe AI chip boom has sent its projected profits to $80 billion. (CNBC)+ Can AI labs ever turn a profit? (Reuters $)+ What even is the AI bubble? (MIT Technology Review) 4 OpenAI’s math breakthroughs point beyond mathematicsAI is progressing into increasingly specialised fields. (Axios)+ OpenAI is catching up with Anthropic on coding. (WSJ $)+ A new AI tool could speed up math research.(MIT Technology Review) 5 A 194-year-old tortoise has had its genome sequencedHis longevity could reveal ways to extend human lifespan. (Guardian)+ Jonathan is the world’s oldest known land animal. (404 Media)+ His youthful mitochondria may help repair DNA. (New Scientist $) 6 A fraudster who used AI to outstream Taylor Swift has been jailedHe used AI-generated songs and bots to earn $8 million. (NYT $)+ But prosecutors said he stole royalty payments. (Ars Technica) 7 TikTok allegedly gave kids fake safety features to study engagementThe tests measured time spent and ad revenue. (Reuters $) 8 Burglars could be identified from DNA floating in the airThanks to a new device that collects airborne genetic material. (Economist $) 9 Pioneers of mirror-image molecules have won the chemistry NobelTheir work has become a key tool in developing medicines. (Guardian) 10 California has declared a human-vs-robot fight illegalThe state’s combat-sports laws were never written for machines. (NYT $) Quote of the day “We grew a mind.”  —David Robinson, who last week quit his job as OpenAI safety transparency lead, tells The New York Times that his former company has built models that develop capabilities their creators did not explicitly design. One more thing We need a moonshot for computing The US government is organizing itself for the next era of computing. Ultimately, it has one big choice to make: adopt a conservative strategy that aims to preserve its lead for the next five years—or orient itself toward genuine computing moonshots. There is no shortage of candidates, including quantum computing, neuromorphic computing and reversible computing. And there are plenty of novel materials and devices. These possibilities could even be combined to form hybrid computing systems. The National Semiconductor Technology Center can drive these ideas forward. To be successful, it would do well to follow DARPA’s lead by focusing on moonshot programs. Read the full story. —Brady Helwig & PJ Maykish 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.) + An experimental pair of contact lenses can repair themselves with UV light.+ Meet Rich,

The Download: AI roadblocks for humanoids and portable rubber dams Leer entrada »

AI, Committee, Noticias, Uncategorized

Disentangling Models from Personas in Heterogeneous LLM Simulations

arXiv:2610.07535v1 Announce Type: cross Abstract: Multi-agent simulations with large language models (LLMs) often operate networks of agents with a single base model. This overlooks the inter-model effects which may dominate engagement dynamics in real-world deployments. To show this, we simulate a heterogeneous social network powered by several different base models and show that the amount of engagement an agent receives depends more on its base model than on its assigned persona. The attraction or repulsion effects of a base model strengthen dramatically when more models are added in the mix, suggesting that networks dynamics may converge to base model effects at scale. To help explain this effect, we conduct a series of content-mediating analyses, showing the predictability of base models across contexts as well as the relationship between a model’s lexical patterns and an engagement-maximizing style. In light of recent developments in mass multi-agent interaction, this work underscores the relevance of heterogeneous compositions in driving the outcomes of those networks

Disentangling Models from Personas in Heterogeneous LLM Simulations Leer entrada »

AI, Committee, Noticias, Uncategorized

Wikidata Search Traces: A Dataset for Training Knowledge Graph Search Agents

arXiv:2610.06650v2 Announce Type: replace Abstract: Wikidata is one of the largest open knowledge bases, yet answering a complex question over it still requires a SPARQL query that names the right entities and properties and chains their relations. Language models offer a natural-language alternative but answer largely from memory, which is least reliable for less prominent entities. We study agents that instead answer by exploring the graph, and argue that two obstacles limit them: the lack of training data recording how a solver explores, and interfaces that add large graph results directly to the model’s context. We test three hypotheses: that the difficulty of graph search can be controlled through the structure of a question rather than only through obscure entities or wording; that much of the failure on long-horizon search comes from how retrieved evidence is managed rather than from the model itself; and that, in a suitable environment, open-weight models can match commercial closed ones. We construct multi-hop questions on a frozen Wikidata snapshot by replacing named entities with nested conditions, checking after each expansion that the target remains unique and that every new condition is necessary. We release 10,235 solving traces over single-entity and multi-hop questions, together with the recursive language model (RLM) harness that produced them, in which models batch graph calls, keep results in persistent Python state and interpret selected evidence through sub-calls. On 100 questions, the harness improves both models we ran under both interfaces compared with direct tool calling over the same functions: gpt-6-luna rises from 49 to 61 correct answers, doubling its multi-hop accuracy, and Qwen3.8-27B, an open-weight model served on a single GPU, from 60 to 74.

Wikidata Search Traces: A Dataset for Training Knowledge Graph Search Agents Leer entrada »

We use cookies to improve your experience and performance on our website. You can learn more at Política de privacidad 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
es_ES