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For four days in February 2019, some 30 synthetic biologists and ethicists hunkered down at a conference center in Northern Virginia to brainstorm high-risk, cutting-­edge, irresistibly exciting ideas that the National Science Foundation should fund. By the end of the meeting, they’d landed on a compelling contender: making “mirror” bacteria. Should they come to be, the lab-created microbes would be structured and organized like ordinary bacteria, with one important exception: Key biological molecules like proteins, sugars, and lipids would be the mirror images of those found in nature. DNA, RNA, and many other components of living cells are chiral, which means they have a built-in rotational structure. Their mirrors would twist in the opposite direction.  Researchers thrilled at the prospect. “Everybody—everybody—thought this was cool,” says John Glass, a synthetic biologist at the J. Craig Venter Institute in La Jolla, California, who attended the 2019 workshop and is a pioneer in developing synthetic cells. It was “an incredibly difficult project that would tell us potentially new things about how to design and build cells, or about the origin of life on Earth.” The group saw enormous potential for medicine, too. Mirror microbes might be engineered as biological factories, producing mirror molecules that could form the basis for new kinds of drugs. In theory, such therapeutics could perform the same functions as their natural counterparts, but without triggering unwelcome immune responses.  After the meeting, the biologists recommended NSF funding for a handful of research groups to develop tools and carry out preliminary experiments, the beginnings of a path through the looking glass. The excitement was global. The National Natural Science Foundation of China funded major projects in mirror biology, as did the German Federal Ministry of Research, Technology, and Space. By five years later, in 2024, many researchers involved in that NSF meeting had reversed course. They’d become convinced that in the worst of all possible futures, mirror organisms could trigger a catastrophic event threatening every form of life on Earth; they’d proliferate without predators and evade the immune defenses of people, plants, and animals.  “I wish that one sunny afternoon we were having coffee and we realized the world’s about to end, but that’s not what happened.” Kate Adamala, synthetic biologist, University of Minnesota Over the past two years, they’ve been ringing alarm bells. They published an article in Science in December 2024, accompanied by a 299-page technical report addressing feasibility and risks. They’ve written essays and convened panels and cofounded the Mirror Biology Dialogues Fund (MBDF), a broadly funded nonprofit charged with supporting work on understanding and addressing the risk. The issue has received a blaze of media attention and ignited dialogues among not only chemists and synthetic biologists but also bioethicists and policymakers.   What’s received less attention, however, is how we got here and what uncertainties still remain about any potential threat. Creating a mirror-life organism would be tremendously complicated and expensive. And although the scientific community is taking the alarm seriously, some scientists doubt whether it’s even possible to create a mirror organism anytime soon. “The hypothetical creation of mirror-­image organisms lies far beyond the reach of present-day science,” says Ting Zhu, a molecular biologist at Westlake University, in China, whose lab focuses on synthesizing mirror-image peptides and other molecules. He and others have urged colleagues not to let speculation and anxiety guide decision-making and argued that it’s premature to call for a broad moratorium on early-stage research, which they say could have medical benefits.  But the researchers who are raising flags describe a pathway, even multiple pathways, to bringing mirror life into existence—and they say we urgently need guardrails to figure out what kinds of mirror-biology research might still be safe. That means they’re facing a question that others have encountered before, multiple times over the last several decades and with mixed results—one that doesn’t have a neat home in the scientific method. What should scientists do when they see the shadow of the end of the world in their own research?  Looking-glass life The French chemist and microbiologist Louis Pasteur was the first to recognize that biological molecules had built-in handedness. In the late 19th century, he described all living species as “functions of cosmic asymmetry.” What would happen, he mused, if one could replace these chiral components with their mirror opposites?  Scientists now recognize that chirality is central to life itself, though no one knows why. In humans, 19 of the 20 so-called “standard” amino acids that make up proteins are chiral, and all in the same way. (The outlier, glycine, is symmetrical.) The functions of proteins are intricately tied to their shapes, and they mostly interact with other molecules through chiral structures. Almost all receptors on the surface of a cell are chiral. During an infection, the immune system’s sentinels use chirality to detect and bind to antigens—substances that trigger an immune response—and to start the process of building antibodies.  By the late 20th century, researchers had begun to explore the idea of reversing chirality. In 1992, one team reported having synthesized the first mirror-image protein. That, in turn, set off the first clarion call about the risk: In response to the discovery, chemists at Purdue University pointed out, briefly, that mirror-life organisms, if they escaped from a lab, would be immune to any attack by “normal” life. A 2010 story in Wired highlighting early findings in the area noted that if a such a microbe developed the ability to photosynthesize, it could obliterate life as we know it.  The synthetic biology community didn’t seriously weigh those threats then, says David Relman, a specialist who bridges infectious disease and microbiology at Stanford University and a trailblazer in studying the gut and oral microbiomes. The idea of a mirror microbe seemed too far beyond the actual progress on proteins. “This was almost a solely theoretical argument 20 years ago,” he says.  Now the research landscape has changed.  Scientists are quickly making progress on mirror images of the machinery cells use to make proteins

Understanding audio has always been the multimodal frontier that lags behind vision. While image-language models have rapidly scaled toward real-world deployment, building open models that robustly reason over speech, environmental sounds, and music — especially at length — has remained quite hard. NVIDIA and the University of Maryland researchers are now taking a direct swing at that gap. The research team have released Audio Flamingo Next (AF-Next), the most capable model in the Audio Flamingo series and a fully open Large Audio-Language Model (LALM) trained on internet-scale audio data. Audio Flamingo Next (AF-Next) comes in three specialized variants for different use cases. The release includes AF-Next-Instruct for general question answering, AF-Next-Think for advanced multi-step reasoning, and AF-Next-Captioner for detailed audio captioning. What is a Large Audio-Language Model (LALM)? A Large Audio-Language Model (LALM) pairs an audio encoder with a decoder-only language model to enable question answering, captioning, transcription, and reasoning directly over audio inputs. Think of it as the audio equivalent of a vision-language model like LLaVA or GPT-4V, but designed to handle speech, environmental sounds, and music simultaneously — within a single unified model. https://arxiv.org/pdf/2604.10905 The Architecture: Four Components Working in a Pipeline AF-Next is built around four main components: First is the AF-Whisper audio encoder, a custom Whisper-based encoder further pre-trained on a larger and more diverse corpus, including multilingual speech and multi-talker ASR data. Given an audio input, the model resamples it to 16 kHz mono and converts the waveform into a 128-channel log mel-spectrogram using a 25 ms window and 10 ms hop size. The spectrogram is processed in non-overlapping 30-second chunks through AF-Whisper, which outputs features at 50 Hz, after which a stride-2 pooling layer is applied. The hidden dimension is 1280. Second is the audio adaptor, a 2-layer MLP that maps AF-Whisper’s audio representations into the language model’s embedding space. Third is the LLM backbone: Qwen-2.5-7B, a decoder-only causal model with 7B parameters, 36 transformer layers, and 16 attention heads, with context length extended from 32k to 128k tokens through additional long-context training. A subtle but important architectural detail is Rotary Time Embeddings (RoTE). Standard positional encodings in transformers index a token by its discrete sequence position i. RoTE replaces this: instead of the standard RoPE rotation angle θ ← −i · 2π, RoTE uses θ ← −τi · 2π, where τi is each token’s absolute timestamp. For audio tokens produced at a fixed 40 ms stride, discrete time positions are interpolated before being fed into the RoTE module. This yields positional representations grounded in actual time rather than sequence order — a core design choice enabling the model’s temporal reasoning, particularly for long audio. Finally, a streaming TTS module enables voice-to-voice interaction. Temporal Audio Chain-of-Thought: The Key Reasoning Recipe Chain-of-Thought (CoT) prompting has improved reasoning across text and vision models, but prior audio CoT work showed only small gains because training datasets were limited to short clips with simple questions. AF-Next addresses this with Temporal Audio Chain-of-Thought, where the model explicitly anchors each intermediate reasoning step to a timestamp in the audio before producing an answer, encouraging faithful evidence aggregation and reducing hallucination over long recordings. To train this capability, the research team created AF-Think-Time, a dataset of question–answer–thinking-chain triplets curated from challenging audio sources including trailers, movie recaps, mystery stories, and long-form multi-party conversations. AF-Think-Time consists of approximately 43K training samples, with an average of 446.3 words per thinking chain. Training at Scale: 1 Million Hours, Four Stages The final training dataset comprises approximately 108 million samples and approximately 1 million hours of audio, drawn from both existing publicly released datasets and raw audio collected from the open internet and subsequently labeled synthetically. New data categories introduced include over 200K long videos spanning 5 to 30 minutes for long-form captioning and QA, multi-talker speech understanding data covering speaker identification, interruption identification, and target speaker ASR, approximately 1 million samples for multi-audio reasoning across multiple simultaneous audio inputs, and approximately 386K safety and instruction-following samples. Training follows a four-stage curriculum, each with distinct data mixtures and context lengths. Pre-training has two sub-stages: Stage 1 trains only the audio adaptor while keeping both AF-Whisper and the LLM frozen (max audio 30 seconds, 8K token context); Stage 2 additionally fine-tunes the audio encoder while still keeping the LLM frozen (max audio 1 minute, 8K token context). Mid-training also has two sub-stages: Stage 1 performs full fine-tuning of the entire model, adding AudioSkills-XL and newly curated data (max audio 10 minutes, 24K token context); Stage 2 introduces long-audio captioning and QA, down-sampling the Stage 1 mixture to half its original blend weights while expanding context to 128K tokens and audio to 30 minutes. The model resulting from mid-training is specifically released as AF-Next-Captioner. Post-training applies GRPO-based reinforcement learning focusing on multi-turn chat, safety, instruction following, and selected skill-specific datasets, producing AF-Next-Instruct. Finally, CoT-training starts from AF-Next-Instruct, applies SFT on AF-Think-Time, then GRPO using the post-training data mixture, producing AF-Next-Think. One notable contribution from the research team is hybrid sequence parallelism, which makes 128K-context training feasible on long audio. Without it, audio token expansion blows past standard context windows and the quadratic memory cost of self-attention becomes infeasible. The solution combines Ulysses attention — which uses all-to-all collectives to distribute sequence and head dimensions within nodes where high-bandwidth interconnects are available — with Ring attention, which circulates key-value blocks across nodes via point-to-point transfers. Ulysses handles intra-node communication efficiently; Ring scales across nodes. https://arxiv.org/pdf/2604.10905 Benchmark Results: Strong Across the Board On MMAU-v05.15.25, the most widely used audio reasoning benchmark, AF-Next-Instruct achieves an average accuracy of 74.20 vs. Audio Flamingo 3’s 72.42, with AF-Next-Think reaching 75.01 and AF-Next-Captioner pushing to 75.76 — with gains across all three subcategories: sound (79.87), music (75.3), and speech (72.13). On the more challenging MMAU-Pro benchmark, AF-Next-Think (58.7) surpasses the closed-source Gemini-2.5-Pro (57.4). Music understanding sees particularly strong gains. On Medley-Solos-DB instrument recognition, AF-Next reaches 92.13 vs. Audio Flamingo 2’s 85.80. On SongCaps music captioning, GPT5 coverage and correctness scores jump from

You’ve probably heard some version of this idea before: that many of us have an “inner Neanderthal.” That is to say, around 45,000 years ago, when Homo sapiens first arrived in Europe, they met members of a cousin species—the broad-browed, heavier-set Neanderthals—and, well, one thing led to another, which is why some people now carry a small amount of Neanderthal DNA.  This DNA is arguably the 21st century’s most celebrated discovery in human evolution. It has been connected to all kinds of traits and health conditions, and it helped win the Swedish geneticist Svante Pääbo a Nobel Prize. But in 2024, a pair of French population geneticists called into question the foundation of the popular and pervasive theory.  Lounès Chikhi and Rémi Tournebize, then colleagues at the Université de Toulouse, proposed an alternative explanation for the very same genomic patterns. The problem, they said, was that the original evidence for the inner Neanderthal was based on a statistical assumption: that humans, Neanderthals, and their ancestors all mated randomly in huge, continent-size populations. That meant a person in South Africa was just as likely to reproduce with a person in West Africa or East Africa as with someone from their own community.  Archaeological, genetic, and fossil evidence all shows, though, that Homo ­sapiens evolved in Africa in smaller groups, cut off from one another by deserts, mountains, and cultural divides. People sometimes crossed those barriers, but more often they partnered up within them.  In the terminology of the field, this dynamic is called population structure. Because of structure, genes do not spread evenly through a population but can concentrate in some places and be totally absent from others. The human gene pool is not so much an Olympic-size swimming pool as a complex network of tidal pools whose connectivity ebbs and flows over time. This dynamic greatly complicates the math at the heart of evolutionary biology, which long relied on assumptions like randomly mating populations to extract general principles from limited data. If you take structure into account, Chikhi told me recently, then there are other ways to explain the DNA that some living people share with Neanderthals—ways that don’t require any interspecies sex at all. “I believe most species are spatially organized and structured in different, complex ways,” says Chikhi, who has researched population structure for more than two decades and has also studied lemurs, orangutans, and island birds. “It’s a general failure of our field that we do not compare our results in a clear way with alternative scenarios.” (Pääbo did not respond to multiple requests for comment.) The inner Neanderthal became a story we could tell ourselves about our flaws and genetic destiny: Don’t blame me; blame the prognathic caveman hiding in my cells. Chikhi and Tournebize’s argument is about population structure, yes, but at heart, it is actually one about methods—how modern evolutionary science deploys computer models and statistical techniques to make sense of mountains upon mountains of genetic data.  They’re not the only scientists who are worried. “People think we really understand how genomes evolve and can write sophisticated algorithms for saying what happened,” says William Amos, a University of Cambridge population geneticist who has been critical of the “inner Neanderthal” theory. But, he adds, those models are “based on simple assumptions that are often wrong.”  And if they’re wrong, what’s at stake is far more than a single evolutionary mystery.  A captivating story of interspecies passion Back in 2010, Pääbo’s lab pulled off something of a miracle. The researchers were able to extract DNA from nuclei in the cells of 40,000-year-old Neanderthal bones. DNA breaks down quickly after death, but the group got enough of it from three different individuals to produce a draft sequence of the entire Neanderthal genome, with 4 billion base pairs.  As part of their study, they performed a statistical test comparing their Neanderthal genome with the genomes of five present-day people from different parts of the world. That’s how they discovered that modern humans of non-African ancestry had a small amount of DNA in common with Neanderthals, a species that diverged from the Homo sapiens line more than 400,000 years ago, that they did not share with either modern humans of African ancestry or our closest living relative, the chimpanzee.  This model of a Neanderthal man was exhibited in the “Prehistory Gallery” at London’s Wellcome Historical Medical Museum in the 1930s.WELLCOME COLLECTION Pääbo’s team interpreted this as evidence of sexual reproduction between ancient Homo sapiens and the Neanderthals they encountered after they expanded out of Africa. “Neanderthals are not totally extinct,” Pääbo said to the BBC in 2010. “In some of us, they live on a little bit.” The discovery was monumental on its own—but even more so because it reversed a previous consensus. More than a decade earlier, in 1997, Pääbo had sequenced a much smaller amount of Neanderthal DNA, in that case from a cell structure called a mitochondrion. It was different enough from Homo sapiens mitochondrial DNA for his team to cautiously conclude there had been “little or no interbreeding” between the two species.  After 2010, though, the idea of hybridization, also called admixture, effectively became canon. Top journals like Science and Nature published study after study on the inner Neanderthal. Some scientists have argued that Homo sapiens would never have adapted to colder habitats in Europe and Asia without an infusion of Neanderthal DNA. Other research teams used Pääbo’s techniques to find genetic traces of interbreeding with an extinct group of hominins in Asia, called the Denisovans, and a mysterious “ghost lineage” in Africa. Biologists used similar tests to find evidence of interbreeding between chimpanzees and bonobos, polar and brown bears, and all kinds of other animals.  The inner-Neanderthal hypothesis also took a turn for the personal. Various studies linked Neanderthal DNA to a head-spinning range of conditions: alcoholism, asthma, autism, ADHD, depression, diabetes, heart disease, skin cancer, and severe covid-19. Some researchers suggested that Neanderthal DNA had an impact on hair and skin color, while

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

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. 

Language models (LMs), at their core, are text-in and text-out systems.