In this article, you will learn how to build a lightweight, end-to-end RAG pipeline using scikit-learn for vector retrieval and open-source models for embeddings and…
Building a RAG Pipeline with LLM Embeddings and Scikit-learn Read Post »
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. Open models have real benefits. Do they outweigh the risks? Anthropic recently released a report arguing that a powerful new model released by the Chinese company Z.ai, poses a major cyberattack risk. Its core concern was that it is freely available for anyone to download and modify. The warning was the latest flashpoint in a debate over whether frontier AI models should be open or kept behind the doors of frontier labs. Safety advocates warn that open models can be misused by bad actors, while supporters argue they could prevent a handful of companies from controlling this powerful technology. Here’s why the debate over open AI is more complicated than it seems. —Grace Huckins This story is part of MIT Technology Review Explains, our series untangling the complex, messy world of technology to help you understand what’s coming next. Read more from the series here. 10 Climate Tech Companies to Watch: Fervo Energy and its plan to streamline geothermal power Fervo Energy is one of MIT Technology Review’s10 Climate Tech Companies to Watch 2026, available exclusively to subscribers. Fervo Energy wants to expand geothermal power beyond the handful of places where it has traditionally been practical. The company uses horizontal drilling and hydraulic fracturing to open pathways through underground rock, potentially making geothermal development possible in far more places. Now, Fervo is betting that AI’s power boom will help scale up enhanced geothermal energy. With a $2.2 billion public offering and an expanded agreement with Google, it plans to build larger plants and drive down costs. But can its approach work beyond Utah and Nevada? Read the full story to find out. —Eshan Raul Subscribers can now access the full 10 Climate Tech Companies to Watch, spanning everything from sustainable cement to shipping containers packed with old EV batteries. The must-reads I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology. 1 A cyberattack overheated fridges at hundreds of French supermarketsTemperatures rose to 120°F, spoiling food and sparking fires. (BBC)+ The attack has sparked alarm about cyber threats to food security. (FT $)+ It joins a recent wave of cyberattacks and cybercrimes. (CNBC)+ AI is exacerbating the threats. (MIT Technology Review) 2 Meta has launched a bunch of new AI modelsIt hopes the models, built for complex, multi-step tasks, will rival OpenAI’s and Anthropic’s.(BI)+ They scored well on tests of reasoning, writing, and coding. (NYT $)+ Mark Zuckerberg says they bring Meta to the “frontier” of AI. (FT $)+ And will soon power the Muse agent. (Verge) 3 Anthropic warns that AI models may soon self-replicate across devicesWithout even requiring access to data centers. (Reuters $)+ It says models could spread through smart TVs and appliances. (CNBC)+ We’re putting too much faith in AI saying no. (MIT Technology Review) 4 Police have a new AI tool that finds homes from interior photosIt can match single pictures to Airbnb and Zillow listings. (404 Media)+ Police-tech giant Flock is tightening its rules. (MIT Technology Review) 5 The world’s first utility-scale enhanced geothermal plant is onlineFervo Energy’s Utah facility will supply carbon-free electricity. (IEEE)+ AI is uncovering hidden geothermal resources. (MIT Technology Review) 6 Did drone strikes on Yandex data centres blitz Russian bots?Zelenskyy claims the attacks quietened Kremlin-funded social media accounts. (Euronews) + But troll farm experts aren’t convinced. (Verge) 7 NASA’s Perseverance rover has set a Mars driving recordIt’s covered 28 miles since landing in 2021. (Reuters $)+ The rover passed Opportunity after detouring to study ancient rocks. (NYT $) 8 Elon Musk has become the 2026 midterms’ biggest known donorHe gave $158 million to a Republican Senate super PAC. (AP News) 9 Anduril and Palantir founders have backed execution livestreamsPalmer Luckey and Joe Lonsdale support Pete Hegseth’s plan. (Mother Jones) 10 A spacecraft has traced how a mysterious solar switchback formedThe Solar Orbiter’s discovery could improve space weather forecasts. (Gizmodo) Quote of the day “Many AI researchers have started smoking as a way of signaling to other researchers how much they believe in AI.” —Palantir CTO Shyam Sankar tells CBS News that Silicon Valley’s “social delusion” has convinced researchers to smoke because they expect AI to cure cancer. One more thing A brain implant changed her life. Then it was removed against her will. Sticking an electrode inside a person’s brain can do more than treat a disease. Take the case of Rita Leggett, an Australian woman whose experimental brain implant changed her sense of agency and self. She told researchers that she “became one” with her device. She was devastated when, two years later, she was told she had to remove the implant because the company that made it had gone bust. Her case highlights the need for a new category of legal protection: neuro rights. Explore how they could be protected. —Jessica Hamzelou 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.) + Astronomers have captured the clearest image yet of a medieval supernova.+ Watch centuries-old Chinese paper-cutting techniques create a spectacular work of art.+ Artist Josh Gluckstein creates astonishing animal sculptures from recycled cardboard, including this great white shark.+ An endangered sea turtle named Pancake has returned to the ocean for the first time in months after recovering from hypothermia.
The Download: open model trade-offs and geothermal power’s promise Read Post »
arXiv:2606.22807v4 Announce Type: replace Abstract: As retrieval systems scale, effective and efficient reranking becomes increasingly important. However, most existing encoder- and decoder-based rerankers jointly process every query–passage pair, tightly coupling their online computation and limiting deployment efficiency and flexibility. We present Lychee-Reranker, a fast but not late-interaction (FBNL) reranker that decouples query and passage computation while retaining expressive relevance modeling. Built on an encoder–decoder architecture, Lychee-Reranker pre-encodes passages using Matryoshka embedding pooling, while its decoder models system and user instructions together with query intent; cross-attention then captures fine-grained relevance between the resulting query context and passage representations. Together, these designs offer four key advantages: (i) efficiency from offline passage encoding, (ii) expressiveness from cross-attention, (iii) compactness from Matryoshka embedding pooling, and (iv) test-time compute through an adjustable compute budget. We instantiate Lychee-Reranker in three sizes, Nano, Small, and Large, with 0.27B, 1B, and 4B activated parameters, respectively. Extensive experiments on BEIR, MIRACL, and LMEB demonstrate strong reranking performance with superior efficiency. On BEIR and MIRACL, our models achieve competitive performance in multi-domain and multilingual reranking, on par with strong industrial rerankers such as the Qwen3/BGE-Reranker series. On LMEB-Dialogue, a compact embedding model paired with our Nano reranker, which has only 0.27B activated parameters, remains competitive with 7–12B embedding models. Lychee-Reranker was previously known as KaLM-Reranker-V1. Data and models are available at https://huggingface.co/collections/KaLM-Embedding/lychee-kalm-reranker-and-jev.
Lychee-Reranker: Fast but Not Late Interaction for Compressed Document Reranking Read Post »
In a research station in central Panama, you can walk through a century of forest time, watching scrubby plots of adolescent trees give way to cathedral-like canopies of mature growth. Forest ecologist Daisy Dent, a senior scientist at ETH Zurich, oversees this sequence of plots across the Barro Colorado Nature Monument, some of which date as far back as the 19th century. Not one of the trees she surveys was planted by human hands. She maps, measures, and tracks each trunk that emerges, revealing how waves of tree life rise from bare earth. Tropical forests like these are our planet’s biggest terrestrial carbon sink, accounting for around 40% of carbon stored on land. As part of an international network of researchers, Dent is deciphering the networks of animal and plant interactions that enable some forests to recover after destruction. The goal is a low-cost, high-impact alternative to typical carbon-crediting schemes, which plant huge areas with a single species of tree. For over a decade, her research has built a body of evidence that such large “monocrop” programs could be expensive mistakes—and that more resilient forests can rebound naturally in just a few decades, re-creating ecosystems that suck up vast quantities of carbon dioxide. Few places are better to demonstrate this than Barro Colorado, a reserve covering the forested peninsulas and islands that formed when construction of the Panama Canal flooded the area in 1914. Under the management of the Smithsonian Tropical Research Institute, generations of ecologists have amassed more than a century of records. That means Dent can trace why some plots rebound after clear-cutting for farming and forestry while others falter. Fruit-eating animals—including spider monkeys, bats, and parrots—are responsible for planting more than 70% of trees in tropical forests, and Dent’s dataset can tell which built each patch of trees by depositing seeds in their nutrient-rich droppings. Many of these creatures worked together to plant a diverse 70-year-old patch whose canopy is approaching the stunning structure and richness of an ancient jungle. In contrast, a patch of similar age populated by narrow trunks of a single long-lived species, Gustavia superba, hints that tropical rodents called agoutis are more or less the sole animal architects behind this more homogeneous forest. The data she collects helps write a playbook for when to let ecosystems regenerate and when to give them a helping hand. One 2024 estimate in Nature found that some 215 million hectares of forest worldwide (an area larger than Mexico) could regenerate naturally. “Tropical forests are underpinned by this massive network of interactions,” Dent says—interactions between life-forms like the fungi that help trees take up nutrients, as well as pollinators and seed-distributors. Supporting those relationships is key to growing forests that lock up carbon and help sustain local communities and wildlife. Filling in the gaps Just over a decade ago, as the landmark Paris Agreement was being codified in 2015, forest recovery seized global attention as a solution to the climate crisis. Global leaders rallied behind plans to regrow a trillion trees, after the ecologist Thomas Crowther highlighted their potential to capture a decade’s worth of global carbon emissions. But the biggest question has lingered: How, exactly, do you regrow two trees for every star in the Milky Way? While startups have proposed seed-spraying drones and nutrient-coated seed bombs, Dent has spent two decades studying what happens when we let forests rebuild themselves. As a PhD student around the turn of the millennium, she researched old-growth forests in northern Borneo but felt hopeless watching entire landscapes felled for palm oil plantations. Rather than despair, she turned to restoration. Then, as a postdoc in 2005, she joined Panama’s Prorena, a joint initiative between the Smithsonian Tropical Research Institute and the Yale School of Forestry and Environmental Studies, which aimed at determining how best to establish diverse native forests on clear-cut land. Back then, secondary forests—ones that grow naturally after the original forest was lost—had been “a bit of a niche research area,” Dent says. But as farming and human infrastructure have driven deforestation, resurgent forests now account for some 70% of all tropical canopies. Prorena initially followed the usual playbook of transplanting nursery trees to rangelands, but Dent began to question whether planting was necessary. In 2009, she published an encouraging paper showing that animal populations rebound quickly as forests naturally regrow. In 2010, she began working in Barro Colorado on the plots established by the pioneering American ecologist Julie Denslow. In 1994, Denslow identified plots that had been regenerating naturally for 20 to 100 years, and Dent would add younger ones to continue the sequence. The site provides not just general data on forest regrowth but in-depth demographic information over time, says Robin Chazdon, a professor emeritus of ecology and evolutionary biology at the University of Connecticut who collaborated with Denslow when she established the plots. “It tells you more about the actual processes that are happening in the site,” she says. For example, previous research had shown that fast-growing “pioneer trees,” the first to thrive in the damaged land, are eventually overtaken by larger carbon-storing trees. Dent’s research helped fill in gaps about the mechanics of how this happens—or doesn’t happen—in individual cases. In the Gustavia-dominated patch, for instance, stubborn grasses planted by cattle ranchers smothered most other trees before they could take root. These trees can hang around, crowding out the light, for 150 years, blocking the second wave of large-trunked trees. To plant or not to plant Dent found herself well placed for the massive influx of interest in reforestation that began in 2015. She’d already become a principal investigator for 2ndFor, an international research network aiming to learn how best to manage secondary forests. Using data gathered from the Barro Colorado plots, the team authored a 2016 paper in Nature showing that a hectare of naturally regenerated secondary forest can capture 11 times as much carbon each year as an old-growth one, with young trees absorbing it quickly as they compete to grow toward the
The ecologist revealing the invisible networks that revive rainforests Read Post »
Welcome to the second space race. You can be forgiven for experiencing déjà vu. Last time, it was the Soviet Union and the United States vying to be the first to plant some flags. Now it’s a sprint to the lunar surface between America and China—alongside a host of varyingly friendly and rival nations—plus a cornucopia of private spaceflight companies. And this time it looks like they’re getting ready to set up camp. There will be, the contenders say, bases on the moon, permanently inhabited castles under the stars. The notion of a moon base, fanciful as it might sound, isn’t new. But this year, under the leadership of Administrator Jared Isaacman, NASA has been flooding the zone with bold announcements. If all goes according to plan—a plan that will likely cost tens of billions of dollars to start—there will be drones, robotic landers, communication satellites, and a nuclear power plant; there will be plant terrariums and moonquake sensors; there will be mechanized scouts exploring craters for water. Initial astronaut missions to the lunar surface, which the US is currently planning to start in 2028, will be focused on exploration and technological experimentation. But the idea is that we will—some time in the 2030s—get to a point where there will be habitation quarters for long-term stays, surrounded by a network of scientific research stations, resource mining and processing plants, and landing pads for spaceships. “It’s going to be an expanse,” says Carlos García-Galán, NASA’s moon base program manager—one that will add up to “hundreds of square miles.” The effort is undoubtedly being fueled by nationalistic competition. China has near-identical plans to build its own base on the moon. And being first matters, because the “victor” gets to define the terms. What is the moon, exactly? Is it a militarized outpost? Is it a manufacturing hub? Is it a wilderness to be studied and preserved? But there are also other reasons to go. Earth’s companion contains primeval rocks that hold clues to the formation of the entire solar system. The lunar surface, with virtually no atmosphere in the way, is an ideal place to put telescopes that could examine the most ancient epochs of the cosmos. The far side of the moon would be “a fantastic place to explore the further universe,” says Josef Aschbacher, the European Space Agency’s director general. And it’s way more energy efficient to launch rockets from the low-gravity lunar surface than it is from Earth, meaning sending astronauts—or robotic spacecraft—farther into the solar system will become much less difficult. “The moon is critical. It will be a stepping stone for going further—that means to Mars and beyond,” says Aschbacher. SELMAN DESIGN But if these bold visions are to become reality, there’s a daunting amount of work to be done. A sprawling moon base must be safe for its astronaut inhabitants—protected from falling asteroids, endless cosmic radiation, razor-sharp lunar dust, and torturous temperature swings. It also must be somewhat self-sustaining, so that it can weather changing political fortunes and not cost ruinous amounts of money to maintain. For that, we’ll need to find a way to use the moon’s resources to keep the base operating—and ideally generating revenue. And we’ll need autonomous robots that can build, maintain, and expand the base even when their human masters aren’t present. “It’s almost like starting civilization from scratch,” says Simon Middleburgh, co-director of the Nuclear Futures Institute at Bangor University in Wales. Assuming we can overcome these starting challenges, decades from now—let’s say, by the year 2100—our initial moon bases could grow from barely visible structures into luminous cities, perhaps not too dissimilar from those dreamed up by authors and screenwriters the world over. “It’s like science fiction today that we really want to turn into a reality,” says García-Galán. Will anyone succeed? The moon is a tremendously hostile place to set up camp. In general, daytime temperatures can reach more than 250 °F; at night, they can plummet to less than –200 °F. Exposed to the vacuum of space, the surface is constantly showered in carcinogenic radiation and frequently hit by meteoroids. And its volcanic dust is sticky and corrosive, a destroyer of equipment and choker of lungs. Sadly, the lunar south pole—the area NASA is targeting—is even harsher. NASA wants to build a base there because its permanently shadowed craters are thought to harbor caches of water ice, which would be more precious than gold. Purify it, and you can water crops and hydrate astronauts; split it into oxygen and hydrogen, and you have breathable air and rocket propellant. But the temperature in those craters can be lower than –410 °F, making them some of the coldest places in the known universe. On the surface outside those pits, astronauts would be bathed in the scorching sun for 14 days before experiencing an equally long frigid night. But there is only so much money we can spend on safety. “What it comes down to is acceptable risk of death,” Philip Metzger, a spaceflight engineering researcher at the Florida Space Institute, said while pondering some of the dangers. “What can we reasonably survive without breaking the bank?” Temperature is one of the easiest problems to handle. Materials science has come a long way, and there are myriad metals, ceramics, plastics, compounds, and alloys that can resist wild temperature swings. Radiators keep things cool; heaters keep batteries from dying prematurely. Astronauts survive just fine too, with their fancy spacesuits—but that technology has proved itself only during brief excursions. It’s not yet clear how long this equipment could operate under such tempestuous conditions. The temperature in the moon’s south pole craters can be lower than –410 °F, making them some of the coldest places in the known universe. Outside those pits, astronauts would be bathed in the scorching sun for 14 days before experiencing an equally long frigid night. Space radiation, which can wreak havoc on electronics and trigger a cancerous division of cells, is far more pernicious. And as Stuart George,
Mars may be a hellhole, but the moon is not far behind Read Post »
In this article, you will learn what the major AI agent benchmarks measure, how they differ from traditional model benchmarks, and how to use them…
AI Agent Benchmarks Explained: SWE-Bench, GAIA, WebArena, and More Read Post »
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. Mars may be a hellhole, but the moon is not far behind The US and China are racing to build bases on the moon, with NASA hoping to establish a small lunar city by the end of the century. But the moon is a tremendously hostile place to set up camp. A sprawling lunar base must be safe for its inhabitants, protected from falling asteroids, endless cosmic radiation, razor-sharp lunar dust, and torturous temperature swings. It also must be somewhat self-sustaining. That will require autonomous robots, nuclear power, and a way to use the moon’s resources to keep the base operating. But the chief obstacle may be one of political will. Find out what stands in the way of a city on the moon. —Robin George Andrews 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! 10 Climate Tech Companies to Watch: Brimstone and its one-stop process for making cleaner cement and critical minerals Brimstone is one of MIT Technology Review’s10 Climate Tech Companies to Watch 2026, available exclusively to subscribers. Brimstone is a code-switching climate tech company for these polarized political times. The US startup is part green cement company, part critical mineral producer. During the Biden administration, its primary pitch was cement that generates about 60% fewer greenhouse-gas emissions. It achieves this by switching to silicate rocks instead of standard limestone, which releases carbon dioxide when heated. Under the Trump administration, however, the company is emphasizing another benefit of its process: the ability to extract valuable materials from those same rocks. Discover how it’s making strides on two industrial problems. —James Temple Subscribers can now access the full 10 Climate Tech Companies to Watch, spanning everything from geothermal power plants to smart turbines and semi-solid-state cells. Job titles of the future: Delivery drone air traffic controller The moment Trevor Wischnewsky heard that drones were delivering pizza and sushi around his Texas neighborhood, his mind was made up. “It was super fascinating,” he says. “I just immediately wanted to be a part of it.” Wischnewsky is now an RPIC, or remote pilot in command, at a company called FlyTrex. An RPIC is like an air traffic controller. But instead of airplanes, they oversee fleets of drones that ferry cargo to homes and businesses Here’s what it takes to become a drone air traffic controller. —Eamon Whalen The must-reads I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology. 1 An Anthropic AI model submitted a fake tip about an unsolved murderThe tip was in a new group of rogue actions Anthropic disclosed. (NYT $)+ Police criticised the company for waiting nine days to report it after finding it. (WSJ $)+ Another model submitted real government forms. (Quartz)+ The White House has made AI incident reporting mandatory. (Axios)+ But who’s liable when AI agents go rogue? (MIT Technology Review) 2 Ukraine’s drone strikes have dealt a major blow to Russian AISunday’s attack shut down the third Yandex data centre hit in days. (Reuters $)+ The strikes have disrupted Russia’s AI, cloud services, and apps. (Gizmodo)+ They followed Russian strikes on Ukrainian data centers. (Ars Technica)+ Russian military satellites could disrupt Starlink. (Reuters $)+ Ukraine’s drone data is fueling a wild market. (MIT Technology Review) 3 Iranian ChatGPT users have planted AI-generated articles in news outletsAbout a dozen outlets ran roughly 100 articles by fake journalists. (CNN)+ The articles criticised the Iran war’s effects on Americans. (WP $)+ AI has been used to turn the Iran conflict into theatre. (MIT Technology Review) 4 Tesla has renamed “Full Self-Driving” as “Assisted Driving” in EuropeEU lawmakers said the old name was misleading. (TechCrunch)+ Tesla hopes the rebrand leads regulators to approve the feature. (Wired $)+ Back in the US, it plans to increase toxic emissions near a school. (SFGate) 5 A ransomware negotiator has been arrested amid a hacking crackdownThe FBI suspects he was involved with a gang that recently claimed to have stolen data on the bureau’s employees. (Reuters $)+ AI is making online crimes easier. (MIT Technology Review) 6 Hollywood’s hottest villain right now is the tech billionaireFour new films feature dark depictions of Silicon Valley leaders. (Axios $)+ The real Mark Zuckerberg has also become a new character. (NYT $) 7 A new experiment is testing the Amazon’s response to rising CO₂CO₂-releasing towers will simulate future atmospheric conditions. (Nature)+ Is carbon removal in trouble? (MIT Technology Review) 8 The US hybrid boom has pushed some used car prices above new onesBuyers are paying a premium to skip the wait for new models. (Wired $) 9 A new technology switches tattoos on and off with lightIt can make permanent ink invisible. (New Scientist $) 10 Scientists spotted the most distant signal yet from the ancient universeIt could help them understand how the universe has evolved. (404 Media) Quote of the day “Right now, your washing machine is subject to more safety rules than AI.” —Microsoft’s president, Brad Smith, tells the BBC why AI urgently needs more regulations, including requiring firms to write “incident reports” and tell victims if the technology goes rogue. One more thing LINDA NYLIND / EYEVINE VIA REDUX Geoffrey Hinton tells us why he’s now scared of the tech he helped build Geoffrey Hinton is a pioneer of deep learning who helped develop some of the most important techniques at the heart of modern artificial intelligence, but after a decade at Google, he’s stepped down to focus on concerns he now has about AI. Hinton is now spending his time on what he describes as “more philosophical work.” And that will focus on the small but—to him—very real danger that AI will turn out to be a disaster. Here’s why he’s so worried. —Will Douglas Heaven We can still have
The Download: moon city problems and drone traffic controllers Read Post »
This week my colleague Antonio Regalado had an interesting update on GLP-1 weight-loss drugs. According to research presented at an aging meeting in Boston, these drugs seem to affect at least some measures of biological age. Overweight and diabetic people who take GLP-1s have a biological age around two to three years younger than similar individuals who take a placebo, according to findings from Eli Lilly and Novo Nordisk, two pharmaceutical companies that make the drugs. I’ve heard people in the aging field describe GLP-1s as longevity drugs for a while now (even if they only benefit people who are overweight or diabetic in the first place). This latest announcement comes after we’ve seen a raft of other benefits associated with taking these drugs. But there are also some weird side effects. So far, GLP-1 agonist drugs—which repress hunger—have been approved for treating diabetes, chronic kidney disease, and obesity, and they can also be prescribed for some people who are overweight or at risk of certain cardiovascular diseases. But there is research to suggest they might also help people with substance-use disorders, and they’ve been investigated for potential benefits in neurodegenerative disease and cancer. And a lot of people are taking them. According to a recent Gallup poll, 11% of Americans are taking a GLP-1 drug for weight loss, up from 3% in 2024. And 15% of Americans say they’ve used one for weight loss at some point. They can be incredibly effective. But, like any drug, they can come with side effects. And we’re still figuring out exactly what those might be, and how widespread they are. The most common side effects are gastrointestinal—nausea, vomiting, diarrhea, and constipation. The drugs have also been linked to pancreatitis and gallstone disease. Some claim that, in rare cases, the drugs might cause damage to nerves behind the eye. Dozens of people are suing Novo and Eli Lilly after developing a condition that can lead to sudden vision loss. They’re claiming that their symptoms were brought on by the GLP-1 drugs that these companies manufacture. Both companies dispute the claims. Research into the link is mixed. We’re still getting to grips with the effects of weight-loss drugs on hair and nails, too. When Savanna Vidal at the George Washington University School of Medicine and Health Sciences in Washington, DC, and colleagues assessed electronic health data from 67 US health-care organizations, they found that GLP-1 use was associated with an increased risk of hair loss. It’s still not thought to be common, however. And at a conference in Austria last week, another group presented the results of a smaller study suggesting that 66% of GLP-1 users reported at least one nail disorder—in some cases involving nail detachment. Nail conditions were also fairly common among people who didn’t take the drugs, though. The authors don’t claim that the drugs are causing nails to fall out (as I’m sure 11% of the American population will be relieved to hear). The side effects might look different in children, but there’s not much data on those at the moment. Still, pediatric side effects will be something to watch given that, between 2019 and 2026, there was a 310-fold increase in GLP-1 prescriptions for eight- to 11-year-old children with obesity in the US. Child prescriptions are still rare, but they’ve been rising. On Wednesday, the World Health Organization released its first guidelines on managing obesity in children and adolescents. Despite the scale of the issue—the WHO estimates that 70 million children aged five to nine were living with obesity in 2024—the organization explicitly says it does not recommend pharmacological treatment for kids under 10. “This recommendation accounts for the lack of evidence on both benefits and the potential adverse events and consequences of the different medications and/or their active ingredients on normal growth, development, physiological parameters, and mental health,” the guidelines state. I get press releases about GLP-1s on an almost daily basis. There’s been a ton of research into these drugs, and there are probably hundreds of other studies underway. There will likely be plenty more on this from The Checkup as the evidence evolves. This article first appeared in The Checkup, MIT Technology Review’s weekly biotech newsletter. To receive it in your inbox every Thursday, and read articles like this first, sign up here.
We’re still figuring out the side effects of GLP-1 weight-loss drugs Read Post »
The moment Trevor Wischnewsky heard that drones were delivering pizza and sushi around his Texas neighborhood, his mind was made up. “It was super fascinating,” he says. “I just immediately wanted to be a part of it.” Wischnewsky is now an RPIC, or “remote pilot in command,” at a company called FlyTrex. An RPIC is like an air traffic controller. But instead of airplanes, this new class of aviators tend to fleets of hexacopter drones that ferry cargo to homes and businesses within a three-mile radius. Not only does he ensure the safe passage of FlyTrex’s parcels, but Wischnewsky coordinates traffic with a growing swarm of delivery drones dotting the skies over Dallas from competitors like Zipline, Amazon, and Alphabet’s Wing. Here’s what it takes to land the gig. Aviation experience When he applied to FlyTrex, Wischnewsky was in training to become a commercial pilot and flight instructor, and he was already certified by the Federal Aviation Administration as a drone pilot. The company requires this credential, along with a weeks-long FAA-approved training course, to ensure that a pilot is prepared to take over a drone if need be. Though he doesn’t pilot the craft, Wischnewsky monitors the delivery process from FlyTrex’s operations center with the help of inputs from GPS, lasers, and a range of sensors. A collaborative mindset At first, the biggest challenge was trusting the technology. “On the manned-aircraft side, they tell you ‘Don’t trust the automation,’” Wischnewsky says. The Dallas–Fort Worth area, though, is a testing ground for unmanned aircraft systems traffic management (UTM), a network that allows autonomous drones from FlyTrex and its competitors to share their coordinates in real time to help avoid collisions even beyond the pilot’s line of sight. Once Wischnewsky makes sure a drone is flight-ready, its path is unimpeded, and the weather conditions are clear, UTM takes care of the rest. Ability to stay calm under pressure At his busiest, Wischnewsky will have up to 10 drones on his watch at the same time. “You’re there as a safety net,” he says. Even with automation, keen situational awareness is required, and the most common hazard is the weather. Wischnewsky recalls a particularly stressful day: A drone had been in the air for only seconds when the wind picked up so suddenly that he had to intervene. He returned it safely, preventing an errant ice cream cake from hitting the sidewalk. Eamon Whalen is a reporter and researcher based in San Francisco.
Job titles of the future: Delivery drone air traffic controller Read Post »
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 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.
