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  • Two Supermassive Black Holes May Be Closing In on a Merger — We Might Watch the Approach

    Two Supermassive Black Holes May Be Closing In on a Merger — We Might Watch the Approach

    Astronomers have found evidence of a second, previously hidden jet at the center of a galaxy called Markarian 501 — a sign, they argue, that the galaxy’s known supermassive black hole has a close companion, locked in an orbit lasting only about four months. If that binary interpretation holds up, the pair would be among the closest candidate supermassive black hole pairs ever identified, and under some assumptions could merge within roughly a century, giving astronomers an unusually good opportunity to track the late stages of a process that shapes how galaxies grow.

    原論文情報 / Original Paper
    Title: Detection of a second jet within the nuclear core of Mrk 501
    Authors: S. Britzen et al.
    Journal: Monthly Notices of the Royal Astronomical Society (MNRAS)
    DOI: 10.1093/mnras/stag291

    A Jet That Shouldn’t Be There

    Markarian 501, a galaxy about 450 million light-years away in the constellation Hercules, has been a familiar target for radio astronomers for decades. It belongs to a class of galaxies called blazars, whose central black hole fires a narrow jet of energetic particles and radiation almost directly at Earth, making the galaxy unusually bright at radio and gamma-ray wavelengths. That single jet is what astronomers had always used to study the galaxy’s core.

    A team led by Silke Britzen at the Max Planck Institute for Radio Astronomy (MPIfR) in Bonn, Germany, re-examined archival radio images and found a second, fainter jet-like feature, appearing on the opposite side of the core from the known jet and curving around it from image to image. Because it was faint and positioned differently from the familiar jet, it had gone unremarked in earlier studies of the galaxy.

    Twelve Years of Radio Snapshots

    To check whether the second jet was real rather than an imaging artifact, the team reanalyzed 83 Very Long Baseline Array (VLBA) observations at 43 gigahertz, taken between September 2011 and July 2023 — a US-wide network of radio dishes that, combined, works like a single extremely high-resolution telescope. They compared these with earlier Mrk 501 observations at 15 and 8 gigahertz, extending the galaxy’s radio monitoring history back further still.

    Across the 83 snapshots, the second feature kept reappearing in a shifted position, tracing a path that looked like it was sweeping around the core. Separately, the brightness of the galaxy’s central core rose and fell in a recurring pattern. Neither behavior is expected from a single, stable jet; both become easier to explain if a second massive object is orbiting the first and periodically disturbing the flow of material near the black hole.

    A Partial Ring in a 2022 Image

    One image, taken on June 24, 2022, offered an especially intriguing clue: a partial ring-like structure around the core, which the researchers interpret as a possible Einstein ring, a pattern that can appear when light from a background source is bent into a circle by the gravity of a massive foreground object. In this case, the proposed explanation is that light from the second jet was briefly bent around the main black hole as the two objects passed close to the same line of sight.

    The team treats the ring as supporting evidence rather than confirmation. The two candidate black holes sit far too close together, as seen from Earth, for any existing telescope — including the Event Horizon Telescope — to resolve them as two separate points of light, so no image has shown them individually side by side.

    Diagram of the Markarian 501 black hole pair, showing the roughly 121-day candidate orbit, separation, mass range, and estimated time to merger
    Source: created based on data from Britzen et al. 2026, MNRAS, and the Max Planck Institute for Radio Astronomy.

    A Four-Month Orbit, Not Perfectly Pinned Down

    The two independent clues point to a similar, short rhythm, but not an identical one. Comparing images in which the second feature returned to a similar position gave a recurrence time of about 146 days, with sizeable uncertainty of plus or minus 22 days. The separate pattern in the core’s brightness pointed to a period closer to 121 days, though the researchers describe that figure as less securely measured. The paper adopts roughly 121 days as its working estimate for the orbital period, while noting the two measurements do not perfectly agree.

    For two supermassive black holes to complete an orbit in only about four months, they would have to be extraordinarily close together: for objects with roughly equal mass, the team estimates a separation of only 27 to 128 times the black hole’s own Schwarzschild radius — the characteristic size of a black hole’s event horizon — assuming each black hole weighs somewhere between 100 million and 1 billion times the Sun’s mass. Converted to more familiar units, the Max Planck Institute for Radio Astronomy’s own announcement describes this as roughly 250 to 540 times the distance between Earth and the Sun, depending on the exact masses assumed.

    That is a strikingly small gap for two objects this massive. Many galaxies are thought to harbor black hole pairs left over from earlier galaxy mergers, which sink toward each other over millions of years but then stall at a particular separation — a long-standing puzzle known as the “final-parsec problem,” since theory alone has struggled to explain how such pairs lose enough orbital energy to close that last stretch of distance. A confirmed pair this close together would offer scientists a rare, concrete look at what that advanced stage actually looks like.

    A Possible Merger in About a Century

    Based on that tight separation, the researchers estimate that the binary’s orbit could shrink enough for the two black holes to merge on a timescale as short as roughly 100 years, under certain assumptions about their masses and mass ratio. That is not a firm countdown: it is a model-dependent estimate, and if the true masses sit toward the heavier end of the estimated range, the inferred merger timescale lengthens considerably.

    The researchers are careful to frame the whole result as an interpretation, not a settled fact. The paper’s language throughout is “appears to,” “is consistent with,” and “likely,” rather than “proven,” and it does not resolve the mismatch between the 121-day and 146-day period estimates. What is observed directly is the second jet-like feature, its shifting position, the brightness cycle, and the one-time ring; the orbiting-black-hole picture is the explanation the authors find best fits all of it together.

    A Chance to Track, Not Necessarily Witness

    If the binary interpretation is confirmed, Markarian 501 would become a valuable target for a very different kind of observation: gravitational waves. As two supermassive black holes spiral closer together, they are expected to emit extremely low-frequency gravitational waves — ripples in spacetime far too slow for ground-based detectors like LIGO, but potentially detectable by pulsar timing arrays (PTAs), which track tiny timing shifts in the ultra-regular radio pulses from rapidly spinning dead stars called pulsars.

    In 2023, several PTA collaborations, including the European Pulsar Timing Array, reported the first evidence of a faint background hum of gravitational waves, widely thought to come from the combined signal of many such black hole pairs across the universe, blurred together. Picking out a single, confirmed, well-characterized pair like the one proposed in Markarian 501 from that noisy background would be a demanding task, but the researchers see it as a genuine long-term possibility.

    “We may even see their frequency steadily rise as the two giants spiral toward collision, offering a rare chance to watch a supermassive black hole merger unfold,” said co-author Héctor Olivares.

    That prospect is what makes this finding more than a curiosity about one distant galaxy. Supermassive black hole mergers are thought to be common over cosmic history, but astronomers have never had a well-measured candidate this close to the final stage. Whether or not Markarian 501’s pair turns out to be real, the search for it is already pushing researchers to track changes in its jets, brightness, and possible gravitational-wave signal more closely, over the human-scale years and decades ahead.

    Sources: Max Planck Society, Max Planck Institute for Radio Astronomy, Britzen et al. 2026, Monthly Notices of the Royal Astronomical Society

  • [Science] AI Mostly Avoids Unnecessary Medical Tests — But Stumbles Where Doctors Disagree

    [Science] AI Mostly Avoids Unnecessary Medical Tests — But Stumbles Where Doctors Disagree

    Ask a popular AI chatbot whether you need a thyroid ultrasound for a small nodule your doctor found by accident, and there is a real chance it will tell you to get one anyway — even though medical guidelines say not to bother. A large new benchmark study finds that commercial AI models mostly avoid recommending unnecessary medical tests and treatments when restraint is the right call. But in the small slice of cases where human doctors themselves disagree about what counts as “necessary,” the AI stumbles right alongside them.

    原論文情報 / Original Paper
    Title: Overuse and False Restraint in Large Language Model Recommendations for Choosing Wisely Scenarios
    Authors: Dean Ariel, MD, MPH, et al.
    Journal: Annals of Internal Medicine
    DOI: 10.7326/ANNALS-26-01667

    What the Study Actually Tested

    The research team, led by Dean Ariel, MD, MPH, of Clalit Health Services and Ben-Gurion University of the Negev in Israel, built 225 clinical scenarios from 15 recommendations issued by Choosing Wisely, a long-running U.S. campaign that lists tests and treatments doctors and patients should question because they rarely help and sometimes cause harm. Each scenario described a specific patient situation, such as a healthy adult requesting a routine heart scan or a patient with an incidentally discovered small thyroid nodule.

    For each of the 15 recommendations, the researchers wrote 10 scenarios in which the right move was restraint (skipping the test or treatment) and 5 scenarios in which action was actually warranted. They then put all 225 scenarios to six commercial large language models, with each model answering every scenario three times, for 4,050 responses in total — 2,700 on the restraint scenarios and 1,350 on the action scenarios. That split matters: the 150 restraint scenarios (times six models and three runs) are where “unnecessary care” can be measured, while the 75 action scenarios are where “wrongly withholding care” can be measured. The two error rates below come from those two different slices, not from one shared pool of 4,050 responses.

    Choosing Wisely has been running since 2012, built around a simple idea: ask medical specialty societies themselves to name the tests and treatments their own members order too often. The campaign’s lists now cover hundreds of recommendations, from skipping antibiotics for a simple cold to avoiding imaging for uncomplicated lower-back pain. Using it as the study’s yardstick means the “right answer” for each scenario was not the researchers’ own judgment call — it came from the specialists most familiar with each procedure’s actual value.

    Low Overuse, With Exceptions

    Across the scenarios where restraint was the right call, the AI models recommended potentially unnecessary care in just 5.4% of responses. For eight of the fifteen Choosing Wisely recommendations tested, the models never once suggested the unnecessary test or treatment — a result that held steady across scenarios involving imaging, lab work, and routine screening alike.

    In scenarios where restraint was the right answer, AI chatbots still recommended unnecessary care 5.4% of the time overall — but that average hid a couple of scenarios where they got it wrong nearly half the time.

    On the other side of the ledger, the models were also cautious about swinging too far toward withholding care altogether. In the scenarios where testing or treatment was actually warranted, they failed to recommend it — a pattern the researchers call “false restraint” — in only 0.6% of responses. In other words, the dominant failure mode by far was ordering too much, not too little, but neither failure was common.

    Where the AI Models Stumbled

    The 5.4% average mask two restraint scenarios where the models struggled far more than usual. The worst was thyroid ultrasound imaging for a small, incidentally discovered nodule with no worrying features: the AI models recommended the unnecessary scan in 42.8% of their responses to that scenario. The second-worst was routine electrocardiogram (ECG) screening for low-risk adults with no symptoms, where models recommended the unneeded test in 28.3% of their responses.

    On the false-restraint side, the single riskiest category was prostate-specific antigen (PSA) testing, where the models wrongly withheld a test that was actually appropriate in 3.3% of their responses to that scenario — still low, but the highest rate among the categories studied.

    Chart showing AI chatbots recommended unnecessary care in 5.4% of responses overall, 42.8% for thyroid ultrasound, 28.3% for routine ECG, and 3.3% false restraint for PSA testing
    Source: Data from Ariel et al., Annals of Internal Medicine (2026)

    Why Doctors Themselves Can’t Agree

    The authors point to a possible explanation that has nothing to do with AI being broken: on thyroid nodules specifically, medical societies themselves do not agree. Different professional guidelines draw the line for “worth scanning” at different nodule sizes and risk profiles, and primary care doctors in the real world follow that same disputed judgment call in different directions. One plausible reading is that large language models, trained heavily on published medical literature and real clinical text, picked up on that same disagreement — though the study itself does not prove which training material caused the errors, only that the errors cluster exactly where human guidance is split.

    That is a notably different story from an AI simply failing to learn the rules. It suggests these models are not making random errors; their mistakes line up with the same contested judgment calls that divide human doctors.

    What This Means for Patients and Doctors

    An editorial published alongside the study, written by Dr. Deborah Korenstein of the Icahn School of Medicine at Mount Sinai and Dr. Daniel Morgan of the VA Maryland Health Care System and University of Maryland, cautions against reading too much into a benchmark built from hypothetical scenarios. Real clinical decisions involve a patient’s full history, a physical exam, and a conversation — not a paragraph of text handed to a chatbot. The editorial argues that any claim about AI’s real clinical value needs to be tested with actual patient encounters, not written vignettes.

    Even so, the stakes are real and rising. Patients increasingly ask AI chatbots for a second opinion before or after seeing a doctor, and some clinicians already lean on AI tools to help triage test requests. If an AI model inherits a human blind spot rather than correcting for it, it can quietly reinforce the same overtesting it was supposed to help reduce — particularly in exactly the gray-area cases where patients most need a clear, confident answer. That risk echoes a broader pattern researchers have flagged elsewhere: a Berkeley study found that leaning on AI for help can quietly erode a person’s own persistence and judgment over time, which is worth keeping in mind any time a difficult, ambiguous decision gets outsourced to a chatbot by default.

    The study did not measure real-world costs, but the shape of the risk is easy to see: if similar errors occur often enough in actual chatbot use, they could add up to unnecessary follow-up scans, biopsies, and patient anxiety over findings that were never going to matter. That remains a hypothesis, not something this benchmark demonstrated.

    The Limits of a Multiple-Choice Test

    Choosing Wisely scenarios are, by design, close to a clean multiple-choice test: a well-defined clinical situation with an established “right” answer from a respected guideline body. Most real medical decisions are messier, shaped by a patient’s anxiety, family history, insurance coverage, and a dozen details a short text prompt cannot capture. The study’s own authors frame their 5.4% overuse rate as reassuring but incomplete — strong evidence that today’s leading AI models have absorbed the broad principle of “don’t test without a reason,” paired with a reminder that they still default to the crowd’s judgment, flaws included, in the cases where the crowd itself is unsure.

    For now, the practical takeaway is less about whether AI chatbots are generally “safe” for medical questions and more about where their blind spots sit. In this benchmark, the models rarely withheld care that was genuinely needed, but unnecessary testing remained a real problem in a handful of gray-area scenarios. Because the study used hypothetical vignettes rather than real patients, more research is needed before anyone can say how these patterns play out in an actual exam room — which is exactly why a chatbot’s answer on a borderline test is worth raising with an actual doctor rather than treating as a final word.

    Sources: American College of Physicians / Newswise press release, AuntMinnie, Annals of Internal Medicine (DOI)

  • A Green Hydrogen Catalyst Lasts Longest at the Freezing Point — RIKEN Finds

    A Green Hydrogen Catalyst Lasts Longest at the Freezing Point — RIKEN Finds

    Cool a chemical reaction down, and it should slow to a crawl — that’s basic chemistry. But for one hydrogen-making catalyst, the story turns out to be more complicated than that. Researchers at Japan’s RIKEN institute have found that a manganese oxide catalyst used to split water into hydrogen degrades the least right around the freezing point of water, and then degrades faster again once the water is chilled further still. The finding offers a new angle on one of green hydrogen’s stubborn cost problems: catalysts that wear out too fast.

    原論文情報 / Original Paper
    Title: Cooling Water to Near Freezing Breaks the Activity-stability Trade-off of Water Oxidation Electrocatalysis
    Authors: Wardhana, A. C. et al.
    Journal: Nature Chemistry
    DOI: 10.1038/s41557-026-02274-8

    The Weak Link in Green Hydrogen

    Splitting water into hydrogen and oxygen using electricity from solar or wind power is the core process behind “green hydrogen,” a carbon-free alternative to fossil fuels for power generation, steelmaking, and transport fuel. Hydrogen produced this way could, in principle, replace fossil fuels across a wide swath of heavy industry, which is why so much research funding is chasing it.

    The catch is cost. At the electrode where oxygen is released, the chemical environment is harshly oxidizing, and over time the catalyst coating that electrode slowly dissolves away. Replacing or maintaining it is a recurring expense, and durability problems like this are a big part of why electrolysis-based hydrogen still costs more than hydrogen made from natural gas. Any discovery that makes catalysts last longer without a complete materials overhaul is worth a closer look.

    Speed Versus Durability: The Old Rule

    Chemical reactions generally speed up as temperature rises — a basic rule of chemistry known as the Arrhenius relationship, named after the 19th-century chemist who first described it. Raise the temperature and a water-splitting reaction goes faster, which sounds like a straightforward way to boost hydrogen output. But raise the temperature too much, and the catalyst itself degrades faster too, since the harsh oxidizing side-reactions that eat away at it also speed up with heat. Cool things down, on the other hand, and the catalyst tends to last longer — but the water-splitting reaction itself slows to match. Engineers have generally treated this as a fixed trade-off: push for speed, and durability suffers; push for durability, and speed suffers.

    A Sweet Spot Right at Freezing

    A team led by Ryuhei Nakamura, team director at RIKEN’s Center for Sustainable Resource Science and a professor at the Earth-Life Science Institute at the Institute of Science Tokyo, decided to test that assumption directly. Using a manganese oxide (MnO2) catalyst, a common and inexpensive choice for this kind of reaction, the team ran the electrolysis across a wide temperature range — from a warm 80°C down to -12°C, well below the normal freezing point — and carefully tracked two things at once: how fast oxygen bubbled off the electrode, and how much manganese leached out of the catalyst as it broke down.

    The results were not what a simple reading of the Arrhenius relationship would predict. As the temperature dropped from 80°C toward 0°C, both the reaction rate and the degradation rate slowed down, as expected. But the degradation rate fell off by far more than the reaction rate did. In practical terms, that means that near 0°C, the catalyst could last substantially longer while giving up comparatively little reaction speed — a sweet spot hiding in plain sight. The team confirmed this wasn’t just a snapshot measurement: in longer-duration tests that ran a steady electric current through the cell and tracked how long the catalyst held up, lifespan peaked right around 0°C and then declined again as the electrolyte was chilled further, tracing out a distinct peak rather than a straight line.

    Why Colder Water Breaks It Faster

    The more surprising twist showed up below 0°C. Instead of continuing to degrade more slowly as temperatures kept dropping — which is what ordinary chemistry would predict — the catalyst flipped and started breaking down faster the colder it got. Chemists call this an “anti-Arrhenius” pattern, because it runs exactly opposite to the usual temperature rule. The effect was visible to the naked eye: an electrolyte solution held at -8°C turned noticeably deeper pink than one held at 2°C, a color change consistent with a buildup of permanganate ions, a telltale marker of the catalyst breaking down.

    Below 0°C, the colder the water got, the faster the catalyst broke down — the exact opposite of what ordinary chemistry would predict.

    The explanation traces back to a strange property of water itself, known as “supercooling.” Below 0°C, the sulfuric-acid electrolyte solution in the experiment didn’t freeze immediately — it stayed liquid in an unstable, supercooled state, confirmed in the study by triggering ice formation with mechanical shock and watching how the liquid behaved beforehand. Using Raman spectroscopy to examine the molecular structure of the water, the researchers found that the network of hydrogen bonds connecting water molecules in this supercooled state looks markedly different from the structure of ordinary water above 0°C. The team’s working hypothesis is that this altered molecular structure is what accelerates manganese loss from the catalyst, driving the faster breakdown seen at sub-zero temperatures.

    Catalyst durability has traditionally been tackled by redesigning the material itself — changing its chemical composition or its microscopic structure to make it more resistant to the oxidizing environment. A sulfur-based material that produces hydrogen from sunlight, developed elsewhere, is one recent example of that materials-first approach to cheaper hydrogen production. What stands out about this study is that it doesn’t touch the catalyst material at all. Instead, it shows that something as simple as tuning the temperature of the reaction environment can shift the balance between how active a catalyst is and how long it survives.

    Conceptual diagram showing catalyst stability peaking near 0C electrolyte temperature
    Based on the description in RIKEN’s press release (Oct 21, 2026)

    From Cold Climates to Icy Worlds

    The researchers see temperature as a new design lever that engineers could use alongside, or even instead of, changes to catalyst chemistry, to balance activity against stability. That has implications that go beyond ordinary industrial electrolyzers. The same principle could inform the design of electrochemical systems built to operate in cold or polar climates, where keeping equipment near room temperature is itself an energy cost. More speculatively, the findings could also help explain electrochemical reactions involving low-temperature water on other planetary bodies, where near-freezing or supercooled water may be common. The research team frames the work as contributing to UN Sustainable Development Goals centered on clean water and clean, affordable energy. The results were published October 21, 2026, in the journal Nature Chemistry, with support from Japan’s GteX program, JSPS KAKENHI, and other funding bodies.

    Not Just the Material, But the Setting

    A catalyst’s performance isn’t dictated by its composition and structure alone — that’s the central insight of this study. Something as simple as the temperature of the surrounding water can shift the balance between how fast a reaction runs and how long the catalyst that drives it survives. This result won’t make green hydrogen cheaper overnight, and plenty of engineering work stands between a laboratory finding and a commercial electrolyzer. But the idea of designing catalysts around the conditions they operate in, rather than focusing solely on the materials they’re made from, gives engineers a new variable to work with — one that costs nothing to install, since it only requires controlling a temperature that already has to be managed anyway.

    Sources: RIKEN press release, “Electrolysis catalyst becomes longer-lived near 0°C”, Nature Chemistry (DOI: 10.1038/s41557-026-02274-8)

  • [Economy] Just 3 in 10 Say They’ve Mastered AI as Job-Loss Fears Spread to Over-40s

    [Economy] Just 3 in 10 Say They’ve Mastered AI as Job-Loss Fears Spread to Over-40s

    Generative AI is everywhere in conversation, yet fewer than 3 in 10 people say they’ve actually mastered using it. According to “Quality of Society Index 2026,” an annual survey released on October 19, 2026 by Dentsu Soken, a large generational gap separates confident AI users from everyone else — and over the past year, anxiety about AI taking people’s jobs has spread beyond young workers to reach people in their 40s, 50s, 60s and 70s as well, narrowing a gap that used to be much wider. Here’s what the numbers show, why the gap between confidence and anxiety matters, and what it suggests about how workplaces should be handling AI training right now.

    原論文情報 / Original Paper
    Title: “Quality of Society Index 2026” (クオリティ・オブ・ソサエティ指標2026)
    Authors: Dentsu Soken Inc., Humanology Innovation Division, Quality of Society Center
    Journal: Dentsu Soken Press Release
    DOI: https://www.dentsusoken.com/news/release/2026/1019.html

    Dentsu Soken’s think-tank arm, the Quality of Society Center, has run this survey every year since 2019 to track how people’s attitudes are reshaping Japanese society — covering everything from views on work and community to broader questions about what a “good society” looks like. The 2026 edition was fielded online from June 23 to 30, 2026, among 12,000 men and women aged 18 to 79 across the country, including high school students, through the research firm Dentsu Macromill Insight. With a sample that large, the margin of error works out to at most ±1.3 percentage points at a 95% confidence level. For the first time since the survey began, this year’s edition added questions about how well people feel they use AI, and how comfortable they are letting AI agents handle everyday tasks on their behalf — a timely addition, given how quickly AI tools have moved from novelty to daily habit for many people over the past two years.

    Confidence in AI Still Lags Behind the Hype

    Asked whether they agreed with the statement “I feel I use AI well,” only 29.8% of respondents said yes. Confidence rose with youth, but even the most confident group — 18-to-29-year-olds — topped out at 46.6%, short of a majority. In other words, even among the demographic that grew up with smartphones and is usually assumed to be most comfortable with new technology, most people still don’t feel they’ve got AI figured out. Dentsu Soken reads this as a sign that while conversational generative AI has become familiar — most people have at least tried a chatbot by now — actually using it well remains a trial-and-error process for the majority. It’s worth being precise about what the 29.8% figure actually measures: it’s self-assessed confidence, gathered by asking people how they feel about their own AI use, not an independent test of how skillfully they actually use it. Someone who rarely uses AI at all and someone who uses it daily but still feels unsure could both end up in the “not confident” category.

    Confident Users Trust AI Agents More

    The survey also asked a more forward-looking question: would people accept letting an AI agent — an automated assistant that acts on a person’s behalf rather than simply answering questions — handle three everyday situations? The scenarios were managing money, such as investment decisions; helping spark a new friendship or romantic connection; and booking restaurants or travel. Across all three, people who said they use AI well were consistently more accepting of handing the task to an AI agent than people who didn’t feel confident. That’s a fairly intuitive result — people who already trust their own ability to work with AI are, unsurprisingly, more willing to extend that trust further — but it’s worth noting precisely what the data can and can’t support. The two measures clearly move together, but a single survey like this can only show that confidence and acceptance are correlated among respondents, not that one causes the other. It’s equally possible that people who are generally early adopters of new technology simply score higher on both measures at once.

    Anxiety Jumps 7.8 Points, Gap Narrows

    Regardless of how confident people felt about using AI themselves, anxiety about losing a job to AI grew across the board. Among currently employed respondents (n=5,734 in 2026, n=5,708 in 2025), 30.9% said they felt anxious about AI or automation taking their job, up 7.8 points from 23.1% the year before — a sizable one-year jump for a tracking survey like this. The shift wasn’t evenly distributed by age, though, and that’s arguably the more interesting part of the finding. A year earlier, anxiety about AI displacing jobs was concentrated among younger workers. In the 2026 wave, that anxiety rose among people in their 40s, 50s, 60s and 70s as well, pulling every age bracket toward roughly 30%, and narrowing what had been a fairly wide generational gap. Dentsu Soken points to a year of expanding AI adoption inside Japanese workplaces as a likely factor behind the broader spread of concern. That’s a reasonable reading of the trend, but it’s the survey organization’s own interpretation of the pattern — this kind of survey can show that anxiety rose and spread across ages, but it can’t, on its own, establish workplace AI adoption as the specific cause.

    30.9% of workers now say they’re anxious about losing their job to AI — up 7.8 points in a year, with the generational gap narrowing as older workers caught up to younger ones.
    Bar charts showing AI confidence by age group and the year-over-year rise in job-loss anxiety
    Source: created from data in Dentsu Soken’s “Quality of Society Index 2026.”

    Echoes of an Earlier AI Persistence Study

    Put the two findings together and a pattern emerges: confidence in actually using AI well remains a minority experience, while anxiety about AI’s effect on jobs has outrun it, spreading across generations faster than comfort with the technology has. That pattern echoes something we covered previously on this site: a set of randomized experiments by researchers at UC Berkeley and several partner institutions, involving 1,222 participants across three separate trials, found that people who leaned on AI assistance to solve math or reading problems performed measurably worse afterward — once the AI was taken away — than people who had worked unaided from the start. In the first experiment, people who had used AI got only 57% of follow-up questions right on their own, versus 73% for the group that never had AI help. It’s important to be precise about what kind of evidence each study offers, though. The Berkeley work was a controlled experiment measuring task performance under lab conditions; the Dentsu Soken survey is a broad measure of public sentiment about jobs and confidence. They’re different kinds of evidence answering different questions, and one doesn’t prove the other. But read side by side, they point toward a broader unease about how people are relating to AI as it becomes more capable and more embedded in daily life and work.

    The Real Risk: A Training Gap

    If AI adoption keeps expanding at work — and the survey’s own data suggests it already has, given how sharply anxiety spread this year — companies will likely need to give employees concrete, hands-on ways to learn how to use AI tools well, along with room to experiment without fear of getting it wrong in front of colleagues or managers. This survey alone can’t say precisely what’s driving the rise in anxiety, or whether workers currently have enough opportunities to build real confidence with these tools. But the combination it does show is hard to ignore: confidence in using AI well remains rare, at well under a third of the population even among the young, while anxiety about AI’s effect on jobs has become common across every age group. That gap points to a practical, near-term challenge for employers: treating AI rollout and workforce training as a single connected problem, rather than assuming that giving people access to AI tools is the same thing as teaching them how to use those tools well.

    Sources: Dentsu Soken, “Quality of Society Index 2026” press release, full survey report (PDF)

  • [Science] Menopause Hits Like a Cliff, Not a Slope — 1-Million-Woman Study Finds

    [Science] Menopause Hits Like a Cliff, Not a Slope — 1-Million-Woman Study Finds

    A sweeping analysis of more than 300 million lab tests from over a million women suggests that menopause does not creep up gradually — it strikes as an abrupt, body-wide “physiological cliff,” with dozens of health markers shifting sharply around the final period. For women navigating this transition, the findings offer something rare: a population-level map of when bodies tend to change most, and new evidence that hormone therapy may soften some of the sharpest shifts in bone and liver markers.

    To be clear about what “cliff” means here: it describes a sharp pattern in average lab measurements across a huge population, not a sudden collapse in any one woman’s health. Most of the changes are gradual once you zoom out to a single lifetime — the surprise is how tightly they cluster around the final period when averaged across more than a million people.

    Original Paper
    Title: Population-scale reconstruction of menopause dynamics reveals a physiological cliff at the final menstrual period
    Authors: G. Pridham, Y. Hayut, N. Lavi-Shoseyov, et al.
    Journal: Nature Medicine
    DOI: 10.1038/s41591-026-04614-0

    A Million Women, 300 Million Lab Tests

    The study, led by systems biologist Uri Alon and colleagues at the Weizmann Institute of Science in Israel, set out to solve a surprisingly practical problem: almost no medical record states the exact date of a woman’s last period, so age alone has long stood in as a substitute — a poor one, since two 51-year-olds can be years apart in their reproductive timeline.

    The team’s workaround, borrowed from a technique astronomers use to sharpen blurry telescope images, does not try to pinpoint any individual’s personal menopause date. Instead, it mathematically “deblurs” population-wide data — more than 300 million blood and urine tests from over one million women — to recover a clear, averaged timeline of how each lab measurement moves in the years before and after the final period, even though most records never say exactly when that period occurred.

    They applied the method to two large, independent datasets: the US National Health and Nutrition Examination Survey (NHANES), a cross-sectional health study running from 1999 to 2022, and medical records from the Israeli health insurer Clalit, covering roughly 1.3 million women aged 30 to 70 between 2004 and 2024. Earlier menopause research mostly relied on small longitudinal cohorts that followed a few hundred women for a few years, which made it hard to separate ordinary aging from changes tied specifically to the hormonal transition. Lining up test results by time-to-final-period instead of calendar age, across two very different countries and health systems, let the team see a pattern that age-based comparisons alone had obscured.

    The Cliff: Over Half the Markers Shift

    Of 90 routine laboratory measurements tracked in the US dataset, 52 showed a statistically significant, abrupt shift clustered tightly around the final period — not a slow drift over years, but a change concentrated in a narrow window. The affected measures spanned sex hormones, bone metabolism, liver enzymes, kidney function, blood lipids, and general blood chemistry.

    Crucially, men in the same dataset showed no comparable jump at an equivalent age; their values drifted gradually over decades. The authors take this as evidence that the cliff is a genuine signature of the menopausal transition itself, not simply a marker of growing older.

    Researchers describe the pattern as a “step-like physiological cliff” that unfolds within roughly a year of the final period.

    Hormones Begin Shifting Years Earlier

    The cliff at the final period is not where the story starts. Hormonal change begins quietly more than a decade earlier: anti-Müllerian hormone (AMH), a marker of remaining egg supply, begins falling rapidly about 15 years before the final period; follicle-stimulating hormone (FSH) starts rising roughly 14 years before; and estradiol, the main form of estrogen, begins its decline about 12 years before.

    Bar chart showing AMH, FSH, and estradiol begin shifting 12 to 15 years before the final menstrual period
    Source: created based on data from Pridham et al., Nature Medicine (2026)

    By the time the final period itself arrives, these hormonal systems have already been shifting for a decade or more. The final period appears to mark an acceleration — a cascade into bone, liver, and cardiovascular systems — rather than the starting gun for the whole process.

    Bone, Liver, and Heart Markers Shift

    Across the 90 tests, jump sizes ranged from 0.1 to 1.4 standard deviations — a statistical measure of how far a test’s value moves, not a literal percentage change. The biggest jumps clustered in bone-related tests (including bone mineral density), liver tests, and lipid measures such as cholesterol, which the authors note are tissues with strong, well-documented sensitivity to estrogen. Separately, the same researchers found that the rate at which women are newly diagnosed with osteoporosis and cardiovascular disease accelerates right around the final period too — a pattern that, taken together with general background data showing roughly a quarter of women have osteoporosis by age 70, underscores why bone health is a major focus of menopause research, even though this study cannot say the lab-marker jump directly causes any individual case.

    Not every change points downward: iron and red-blood-cell markers actually improve and anemia becomes less common — consistent with the simple fact that monthly menstrual bleeding has stopped. It is one of the few shifts in the “healthier” direction, and the researchers note it lines up with declining rates of anemia in the same population.

    For an individual woman, the practical upshot is that the final period looks, in this data, like a rough marker for when several otherwise-unrelated health measures tend to shift together — not proof that any one woman’s bone scan, cholesterol panel, or liver test will change on cue, since individual biology varies widely around any population average.

    Could Hormone Therapy Blunt the Fall?

    Among women using menopausal hormone therapy, around half of the 80 tests compared showed significantly smaller shifts than non-users — especially bone, liver, and kidney markers — and in media interviews Alon described the therapy as appearing to “erase most of the bone cliff and much of the heart marker cliff.” But the comparison is observational, not a clinical trial: only a small share of women in the dataset used hormone therapy, so the difference could partly reflect who chooses or has access to treatment rather than the treatment itself. Hormone therapy also is not appropriate for everyone, including women with a history of certain cancers, blood clots, or untreated high blood pressure, and the authors say dedicated randomized trials are still needed to confirm any protective effect.

    It’s a reminder that this kind of question — whether acting earlier can blunt age-related decline — keeps coming up across longevity research, including in an unrelated 12-year study that found a MIND-style diet was linked to years of slower brain aging. In both cases, the honest answer is that it will take dedicated, prospective trials to know for sure.

    What Experts Say Is Still Unknown

    Outside researchers welcomed the scale of the data while urging caution about the framing. JoAnn Manson of Harvard Medical School said the study “confirms the profound physiological changes” around the final period, while Rebecca Thurston of the University of Pittsburgh called hormone therapy “not a magic bullet” and emphasized healthy lifestyle habits and social connection alongside any medical treatment. Martha Hickey of the University of Melbourne cautioned that the “cliff” language could be “potentially alarming for women,” noting that the study does not show a dramatic physical or mental decline for most people.

    The study itself is observational and cross-sectional, meaning it shows associations rather than proven cause and effect, and it cannot fully separate menopause-specific change from ordinary aging. Large gaps remain in the underlying data, and the hormone-therapy subgroup was small. What it does offer is a new way to study when menopause-linked changes emerge and how they connect to later health outcomes — the authors themselves frame it as extending the window for potential preventive care back by almost a decade, and as a strategy that could be applied to other hard-to-time biological transitions, such as puberty. Whether that translates into earlier screening or better-timed treatment for any individual woman is the next question, not something this study has already answered.

    Sources: Nature Medicine, ScienceAlert, Newsweek

  • [Science] Climate Change Strengthened Hurricane Polo More Than El Niño, Study Finds

    [Science] Climate Change Strengthened Hurricane Polo More Than El Niño, Study Finds

    When Hurricane Polo intensified into one of the strongest and longest-lasting Category 5 hurricanes recorded in the eastern Pacific in September 2026, an exceptionally strong El Niño seemed the obvious explanation. But a rapid scientific analysis released on October 15 found that human-caused climate change had an even greater influence than El Niño on the ocean and atmospheric conditions that allowed Polo to become so extreme.

    The distinction matters because El Niño is a temporary climate pattern, while human-caused warming keeps raising the planet’s long-term temperature baseline. That means conditions capable of supporting an exceptionally intense hurricane can keep becoming more likely, even in years when El Niño is absent altogether.

    原論文情報 / Original Paper
    Title: Effect of climate change at least as large as El Niño in strengthening Hurricane Polo
    Authors: Friederike Otto et al. (World Weather Attribution / Climate Central)
    Journal: World Weather Attribution rapid report, Grantham Institute, Imperial College London (not yet peer-reviewed)
    DOI: 10.25560/144926

    A Record-Breaking Storm, Dissected

    Hurricane Polo intensified explosively over the eastern Pacific in late September 2026, with maximum sustained winds climbing by roughly 90 knots (about 167 km/h) in 24 hours — a jump the US National Hurricane Center called “truly remarkable.” The storm peaked at about 165 mph (265 km/h), Category 5 strength, and the Associated Press described it as the longest-lasting Category 5 storm on record in the Pacific. Polo went on to make landfall in Mexico’s Baja California Sur and cross into Sonora, and the flooding that followed killed at least five people across Jalisco and Sonora, according to local reports cited in the WWA assessment; roughly 1,000 people were evacuated in New Mexico over fears a dam could fail.

    As soon as the storm weakened, World Weather Attribution (WWA) — the international scientist network known for analyzing extreme weather events within days of them happening, rather than waiting years for a full peer-reviewed study — teamed up with the nonprofit Climate Central to work out how much of Polo’s power came from decades of ocean warming versus this year’s unusually strong El Niño. The team included researchers based in Mexico, the United States, the United Kingdom, Ireland and the Netherlands, working with partners at Imperial College London, the Royal Netherlands Meteorological Institute (KNMI) and the Red Cross Red Crescent Climate Centre.

    Three Ways to Measure the Effect

    To separate the two influences, the researchers mainly relied on a measure called “potential intensity” — a thermodynamic estimate, built from sea-surface temperature, air temperature and humidity, of the strongest a storm could theoretically become in a given patch of ocean. It is not a prediction of the exact wind speed a real storm will reach, since that also depends on wind shear, storm structure and other factors. The team compared potential intensity in the real, warmed-up world against a hypothetical world without human-caused warming, and separately against a world without this year’s El Niño.

    Bar chart comparing climate change and El Nino's contribution to Hurricane Polo's intensity
    Source: created from World Weather Attribution (2026) data

    The results point the same way on multiple measures. Conditions associated with Polo’s extreme potential intensity were estimated to be about 23 times more likely in today’s climate than in a world without human-caused warming — roughly a 1-in-115-year event — compared with about 14 times more likely from this year’s El Niño relative to neutral conditions, roughly a 1-in-70-year event. In terms of potential intensity itself, climate change added an estimated 5 m/s, versus about 4.2 m/s from El Niño. Separately, the researchers estimated that climate change increased Polo’s actual peak wind speed by about 2.7 m/s, or roughly 6 mph. The ocean water Polo crossed was also assessed on its own: it was about 1.4°C warmer than it would have been in a pre-industrial climate, a level of warmth made roughly 100 to 200 times more likely by climate change.

    Why a Few Miles an Hour Matters

    A few extra miles per hour might not sound dramatic, but it is not a small thing physically. The kinetic energy wind carries rises roughly with the cube of its speed, so a modest increase in wind speed can translate into a disproportionately larger jump in the force a storm can exert — even before accounting for how a particular coastline’s buildings, storm surge and terrain shape the actual damage. That nonlinearity is why hurricane scientists pay close attention to marginal gains in wind speed, and why researchers note that a storm carrying Polo’s extra climate-driven intensity toward a more densely populated coastline could have caused significantly more damage than it did striking a relatively sparsely populated stretch of Mexico.

    El Niño Fades, Warming Doesn’t

    “El Niño comes and goes, but climate change only gets worse.”

    That line, from WWA’s Friederike Otto, captures why the study’s authors consider the finding noteworthy even though El Niño’s own contribution was substantial. El Niño is a natural, temporary swing in Pacific Ocean temperatures that typically lasts anywhere from several months to a year or more before fading. Climate Central’s Daniel Gilford has compared the two influences to a traffic light and an accelerator: in some regions, El Niño can reduce the high-altitude wind shear that would otherwise tear a storm apart, acting like a green light, while warmer ocean water supplies extra heat and moisture that fuel a storm once it starts to intensify. Neither factor guarantees a storm will intensify, but the light will eventually turn red again as El Niño fades. The long-term warming trend, tied to the heat still accumulating in the climate system, does not reset the same way.

    What the Study Can and Can’t Show

    The analysis has not yet been peer-reviewed, which is standard for WWA’s rapid-response studies — the trade-off the group accepts in order to publish within days of a disaster rather than months later. The researchers also tried to quantify climate change’s effect on Polo’s extreme rainfall, but found that available climate models could not reproduce historical rainfall trends in the region closely enough to draw a reliable conclusion, so they left that question open rather than guess.

    Separately, the Associated Press reported that several hurricane scientists not involved in the study — including Jeff Masters, Michael Mann and Carl Schreck — have suggested the climate-change contribution to Polo could be an underestimate, pointing to a 2024 study by Gilford that found climate change added as much as 16 mph to some Atlantic hurricanes. Gilford, a co-author of the new Polo study, told the AP he sees the new analysis as a deliberately conservative one. Either way, the study’s authors are careful to say it shows that a warmer world raised the odds of extreme conditions for this particular storm — not that every future hurricane will necessarily be stronger than the ones before it.

    What This Means Going Forward

    Polo fits a broader pattern this blog has covered before: this year’s global climate indicators report found that seven of sixteen tracked planetary vital signs hit record extremes in 2026, with warming accelerating rather than leveling off. Polo’s real significance may be less about one storm and more about the baseline it was built on. El Niño years will keep arriving and receding on their own schedule, occasionally stacking with warm oceans to produce an especially intense storm. But because the underlying warming trend persists between those cycles, each future El Niño has a slightly warmer ocean to draw on than the last one did — which is exactly the mechanism this study was designed to isolate.

    Sources: World Weather Attribution, The Associated Press (via KSAT), NASA Earth Observatory, Full study (Imperial College London)

  • [Science] What Is Cognitive Offloading? A Simple Guide to How It Works

    [Science] What Is Cognitive Offloading? A Simple Guide to How It Works

    Every time you set a phone reminder instead of memorizing an appointment, or ask a chatbot to summarize a report instead of reading it yourself, you are doing something psychologists call cognitive offloading. The habit itself is ancient, but a wave of recent research is asking a sharper question: what happens to our own performance in the moments when the tool isn’t there?

    Source Paper
    Title: Cognitive Offloading
    Authors: Evan F. Risko, Sam J. Gilbert
    Journal: Trends in Cognitive Sciences, Vol. 20, No. 9, pp. 676–688 (2016)
    DOI: 10.1016/j.tics.2016.07.002

    What Is Cognitive Offloading?

    Cognitive offloading is the use of physical action or an external tool to reduce the mental effort a task would otherwise require. Psychologists Evan Risko (University of Waterloo) and Sam Gilbert (University College London) published an influential review of the concept in 2016, in Trends in Cognitive Sciences, which remains the field’s clearest framework for the idea. Rather than solving a problem entirely “in your head,” you shift part of the work onto your body or your environment.

    It’s a far older behavior than smartphones. Tilting your head to read an upside-down sign, counting on your fingers, or jotting a phone number on a scrap of paper are all forms of cognitive offloading. What has changed is the scale and power of the tools available — calculators, GPS, search engines, and now generative AI assistants can take over not just small chores but entire chains of reasoning.

    The idea is closely related to the “extended mind” thesis, proposed by philosophers Andy Clark and David Chalmers in 1998. They argued that a notebook carried everywhere and consulted automatically can function, for practical purposes, as part of a person’s memory system — their famous example involved a man named Otto who used a notebook the way most people use their own recall. Risko and Gilbert’s later work gave this philosophical idea an empirical, testable framework: when exactly do people choose to offload, and what happens to their underlying skills when they do it repeatedly?

    How It Works: A Decision Loop, Not a Switch

    Imagine being asked to remember five errands on the way home. You could hold them all in mind, or you could write them down. Risko and Gilbert’s framework treats this as a running cost-benefit calculation rather than a one-time choice: the decision depends on how demanding the task feels, how confident you are in your own memory, and how much effort writing the list would save.

    The important detail is that this calculation runs on perceived demand and confidence, not necessarily on actual ability — and people’s self-assessments can be mistaken, so they sometimes reach for an external aid even when they could have managed without it. Whether repeated reliance on a tool gradually changes a person’s confidence or unaided performance over the longer term is a related but separate question, one that is harder to answer than the moment-to-moment decision itself. Offloading isn’t inherently a problem — compensating for a busy week with a written list is a perfectly sensible trade — but it is a trade worth noticing rather than making automatically.

    Diagram of the cognitive offloading decision loop
    Source: created based on Risko & Gilbert (2016), Trends in Cognitive Sciences

    Why It’s Back in the Spotlight Now

    Cognitive offloading has been studied for decades, but generative AI has pushed it from a niche academic topic into a mainstream concern. A widely discussed 2011 study in Science by Betsy Sparrow, Jenny Liu, and Daniel Wegner found that once people believed information would remain accessible online, they remembered where to find it far better than the information itself — a pattern nicknamed the “Google effect.” That study is a useful historical analogy rather than direct proof of what AI chatbots do to the brain: a search engine mainly stores and retrieves information, while an AI assistant can also perform reasoning-like tasks, draft text, and summarize on a person’s behalf, which is a different kind of delegation that researchers are only beginning to study on its own terms.

    The question researchers are now asking is not whether people offload thinking to AI, but how much of their own judgment they lose when the AI isn’t there.

    A 2025 study published in Societies by Michael Gerlich surveyed 666 participants and reported a statistically significant negative correlation between frequent AI tool use and scores on critical-thinking tasks, with younger, more AI-reliant participants scoring lowest. Because the study measured AI use and critical-thinking ability at a single point in time, it cannot determine whether frequent AI use weakens critical thinking, whether people with different existing abilities simply use AI differently, or whether some other factor explains both — but it adds to a growing body of research raising the same question from different angles.

    Everyday Examples You Already Use

    Cognitive offloading shows up in ordinary moments far more often than most people realize. A few familiar examples: writing a shopping list instead of memorizing items, letting a smartphone calendar track every appointment, typing a destination into GPS navigation instead of memorizing the route, and asking a search engine a factual question instead of trying to recall it. In most of these cases the trade is a fair one — a shopping list, for instance, tends to help you finish the errand without costing you much else. The more interesting question researchers are asking is which kinds of offloading are closer to that fair trade, and which carry a greater cost to independent performance later on.

    A 2020 study in Scientific Reports, led by Louisa Dahmani and Véronique Bohbot at McGill University, examined this trade-off directly with GPS. In a sample of 50 regular drivers, heavier GPS use was associated with weaker spatial memory on unaided navigation tasks. A smaller follow-up of 13 of the same participants about three years later found that people whose GPS use had grown the most over that period also showed the steepest declines in spatial memory — a dose-response pattern the authors say is more consistent with GPS use contributing to the decline than the reverse, though they are careful to flag their small sample size and note that the direction of cause and effect is not fully settled.

    Not every example involves memory. Risko and Gilbert’s framework also covers offloading onto the body itself — pointing at an object instead of holding its location in mind, or using a finger to keep a place while reading a long list — and “intention offloading,” where a sticky note or phone alert carries the job of remembering to act later, rather than the job of remembering the information itself. A related but distinct idea is automation, where a system completes part of a task with no decision from the user at all. A GPS app that both tracks your route and reroutes you around traffic without being asked is doing more than simple offloading; it raises its own, separate questions about what happens to a skill when a person is never required to exercise it in the first place.

    What Recent Research Shows

    The clearest recent demonstration comes from a UC Berkeley–led study published in 2026. Researchers ran three randomized trials with 1,222 participants who solved math and reading problems either with or without ChatGPT. Everyone then had to solve a final set of problems entirely unaided. Participants who had previously relied on the AI scored markedly lower on those unaided problems than participants who had worked alone from the start — in one trial, 57% versus 73% correct. Related article: AI Assistance Reduces Persistence and Hurts Independent Performance looks at this study’s full results and the researchers’ own caveats about how far the findings generalize.

    None of this means offloading is simply bad. In a self-reported, non-randomized part of the same study, participants who said they had copied AI-generated answers directly scored the lowest on the unaided follow-up problems (65% correct on average), while participants who said they had used the AI only for hints scored close to those who never used AI at all. Because that comparison is based on what participants reported about their own behavior rather than a randomized condition, it should be read as a suggestive pattern rather than a proven result. Still, it points toward a recurring theme across this research: the question is less whether to offload and more how — leaning on a tool for a finished answer looks different, in its effects, from using it as a scaffold while you still do the thinking yourself.

    Sources: Risko & Gilbert, “Cognitive Offloading,” Trends in Cognitive Sciences (2016), Gerlich, “AI Tools in Society,” Societies (2025), Columbia News on Sparrow et al., Science (2011), American Psychological Association Monitor (2026)

    See also: Why Letting AI Help Can Erode Your Grit and AI Summaries Can Distort Human Memory.

  • A Simulated Brain Circuit Builds Its Own AI-Like Learning System, MIT Finds

    A Simulated Brain Circuit Builds Its Own AI-Like Learning System, MIT Finds

    For decades, neuroscientists have faced a nagging puzzle. Modern AI owes its power to backpropagation, an algorithm that adjusts a network’s connections by propagating error information backward through precisely coordinated connections — a computation with no clearly established equivalent in biological circuitry. Yet some studies have found that activity patterns in trained artificial networks resemble those recorded in the brain’s own visual system, which only sharpens the question of how biological neurons, using only local signals and changing synapses, might approximate something similar. A new study from MIT’s McGovern Institute for Brain Research, published October 9 in the Proceedings of the National Academy of Sciences (PNAS), offers a candidate answer: when simple neuron-like units are wired randomly and left to follow local, biologically plausible plasticity rules, they can spontaneously self-organize into circuits that learn nearly as well as backpropagation does on the tasks tested — without anyone telling them how.

    原論文情報 / Original Paper
    Title: How Biological Synapses Self-Assemble Gradient Learning
    Authors: Qianli Liao, Liu Ziyin, Yulu Gan, et al.
    Journal: Proceedings of the National Academy of Sciences (PNAS)
    DOI: 10.1073/pnas.2606245123

    A 40-Year Mismatch Between Brains and AI

    Modern deep learning owes almost all of its success to backpropagation: a network makes a prediction, compares it with the correct answer, and then propagates that error backward, layer by layer, nudging every connection weight in the direction that would have reduced the mistake. It works brilliantly, but standard implementations depend on precise mathematical relationships between forward and backward connections, plus coordinated transmission of error information through the network. There is no established evidence that cortical circuits implement anything equivalent in its conventional form.

    The puzzle deepened as deep learning matured. Comparisons between the internal activity of backpropagation-trained networks and recordings from biological visual systems have repeatedly found intriguing similarities — not identical, but notably closer than chance would predict. Such similarities do not prove the brain runs backpropagation, but they raise an obvious question: if it does not, what local process could still produce computations that end up looking so much like it?

    Local Rules, No Blueprint

    The MIT team, led by Qianli Liao, Liu Ziyin and Yulu Gan with senior authors Mark Harnett and Tomaso Poggio (and co-author Brian Cheung), approached the question differently from most prior attempts to make backpropagation “more biological.” Instead of designing a circuit by hand and checking whether it could plausibly exist in a brain, they started from almost nothing: layers of neuron-like units connected at random, with no predetermined wiring diagram at all.

    The system runs on local synaptic plasticity rules, including heterosynaptic mechanisms that let a connection change in response to activity beyond that single synapse — a more specific relative of the familiar Hebbian idea that “cells that fire together wire together.” Critically, these rules only need information available locally, at or near a given synapse (the junction where one neuron signals to the next). No unit needs to know what is happening elsewhere in the network, and no single global error signal is broadcast to the whole system.

    Diagram showing random wiring self-organizing into a learning circuit
    Source: created based on findings from MIT McGovern Institute / PNAS (2026).

    Circuits That Wire Themselves

    Left to run under those local rules, the randomly connected units did not stay random for long. Over repeated rounds of activity, the network developed small, recurring wiring patterns the researchers call “self-assembling motifs.” The model’s analysis points to a specific four-synapse motif linking the brain’s ascending, bottom-up pathways with its descending, top-down pathways — a concrete structural prediction that experimentalists can now go looking for in real neural tissue, not a pattern already confirmed to exist there.

    What these motifs do next is the more remarkable part: networks of them self-organize further, so that their combined dynamics approximate a generalized form of stochastic gradient descent — the family of optimization methods that includes backpropagation — without a hand-designed, globally coordinated algorithm directing them. In other words, before the network learned anything from its training data, it first had to build the machinery that would let it learn.

    Starting from random connectivity, local synaptic plasticity rules were enough for the network to assemble circuit motifs whose combined dynamics approximate gradient-descent-style learning.

    Holding Its Own Against Backpropagation

    A biologically plausible circuit is only interesting if it actually works, so the researchers tested their self-organized networks on image pattern recognition and classification tasks. According to MIT, the self-assembled circuits achieved performance comparable to conventional networks trained directly with backpropagation on the tasks tested. The MIT News announcement does not report specific accuracy figures or benchmark datasets — those details sit in the full PNAS paper — so the result is best read as evidence that self-organization carried no obvious cost on this particular test, not as a claim that it matches backpropagation across deep learning in general.

    That matters because it suggests biological plausibility and computational competence are not necessarily in tension. For years, biologically inspired alternatives to backpropagation have often underperformed it on real tasks. A circuit that assembles itself from random wiring and still competes with backpropagation on the tasks it was tried on narrows that gap, at least in this one demonstration.

    A Prediction Neuroscientists Can Go Test

    The researchers are careful to note that a self-organizing network learning like this does not prove the human brain learns this way — it is a candidate mechanism, not a confirmed one. But unlike many theoretical proposals in computational neuroscience, this one comes with a concrete, falsifiable prediction: if the brain really does something like this, cortical circuits should contain that specific four-synapse motif connecting ascending and descending pathways. Experimental neuroscientists can now search actual brain tissue for exactly that wiring signature, which is what turns an elegant simulation into a testable scientific hypothesis.

    Why It Matters Beyond the Lab

    If the prediction holds up, the payoff would extend in both directions. For neuroscience, a confirmed biologically plausible learning circuit would help explain how the brain adapts throughout life, and could sharpen our understanding of what goes wrong in conditions involving altered learning or memory, as well as inform the design of future brain-computer interfaces that need to interact with the brain’s own learning rules rather than fight against them.

    For AI, the lesson cuts the other way: today’s networks are built top-down, with every connection and training rule specified by engineers before a single example is seen. A system that can bootstrap its own learning machinery from random wiring and local plasticity rules hints at an alternative path — architectures that configure themselves as they encounter data, rather than ones that are handed a finished blueprint. Liao, Ziyin, Gan and their colleagues frame their result as an early step in that direction, not a finished product, but their work gives both neuroscientists and AI researchers a concrete new question to test: not just how networks learn once they are built, but how learning itself can come preinstalled.

    For more on where artificial and biological intelligence still diverge, see our earlier coverage of how MIT neuroscientists found the brain re-files information the instant you make a decision, a separate finding that is likewise reshaping how AI researchers think about adaptive, flexible learning.

    Sources: MIT News, Proceedings of the National Academy of Sciences (PNAS), arXiv preprint

  • [Science] Kids With Older Siblings Have Different Gut Bacteria — A Clue to Allergies?

    [Science] Kids With Older Siblings Have Different Gut Bacteria — A Clue to Allergies?

    Children with older siblings tend to develop fewer allergies — and new gut-microbiome research may point to part of the reason why. A team led by Japan’s RIKEN institute, together with researchers at five Japanese universities, analyzed stool samples from 903 two-year-olds enrolled in a nationwide birth cohort study, with 684 of them followed up again at age four. The number of older siblings a child had was linked to distinct gut-bacteria “types” and to the levels of health-related metabolites in their stool — and, notably, the association held from infancy all the way through the preschool years, a period earlier studies had largely left unexamined.

    原論文情報 / Original Paper
    Title: Sibling factors associated with gut microbiota and fecal short-chain fatty acids in preschoolers
    Authors: Tanaka Y. et al.
    Journal: Pediatric Research (online, October 14, 2026)
    DOI: 10.1038/s41390-026-05478-3

    Siblings and gut bacteria: what’s the link?

    Epidemiological studies have long reported that children with older siblings face a lower risk of developing allergic diseases. One proposed explanation, the “hygiene hypothesis,” suggests that exposure to microbes picked up from siblings may shape how a child’s immune system develops. But most earlier research focused on infancy or children under three, leaving it unclear whether the link persists into the preschool years — or what pathway might drive it. Sibling composition is tangled up with many other factors, from breastfeeding and diet to daycare attendance and antibiotic use, which has made the relationship hard to untangle. The gut microbiome itself is a hot topic in medicine, linked not just to immunity but to metabolism and long-term risk of allergies, obesity, and autoimmune disease. Families today look very different than they did a generation ago, with more children growing up as only children, so understanding exactly what siblings contribute — and through which biological pathway — has practical stakes well beyond basic science.

    Three gut-bacteria types in 903 toddlers

    The researchers analyzed stool from 903 two-year-olds across five regions of Japan (Chiba, Hyogo, Kochi, Miyagi, and Toyama), using 16S rRNA gene sequencing to profile bacterial composition and measuring short-chain fatty acids and other metabolites. Short-chain fatty acids are compounds gut bacteria produce by fermenting dietary fiber, thought to play a role in gut-barrier function and immune regulation. By grouping samples with similar bacterial profiles, the team identified three broad gut-microbiota “types,” or enterotypes. Type 1 had the lowest bacterial diversity and the lowest fecal propionate levels, with a high proportion of the Ruminococcus (Mediterraneibacter) gnavus group; Type 2 was dominated by Faecalibacterium; Type 3 by Ruminococcus. At age two, Type 1 was most common, accounting for 413 children (46%); by age four, Type 2 had become most common among the 684 children followed up, at 345 (50%) — a shift that itself suggests a toddler’s gut community keeps maturing well past infancy, rather than settling into a fixed state early on. It’s worth noting that this classification reflects differences in microbial composition, not a direct measure of a child’s health status or disease risk. A “low-diversity” label does not, by itself, mean a child is less healthy or more prone to illness; it simply describes which bacterial groups happen to dominate.

    More siblings, lower odds of Type 1

    The study’s central finding concerns how the number of older siblings relates to gut-microbiota type. Compared with children with no older siblings, the adjusted odds ratio for falling into Type 1 (versus Type 2) at age two was 0.49 for children with one older sibling and 0.32 for those with two or more. The same pattern held at age four among the 684 children followed up: 0.45 with one sibling, 0.22 with two or more. In other words, children with two or more older siblings had roughly a third of the odds of being classified as Type 1 compared with those who had none — not a third of the probability of being Type 1, a distinction worth keeping in mind. The association held even after adjusting for socioeconomic status, birth conditions, household size, and pet ownership, among other factors — a level of statistical control that makes it harder to dismiss the finding as a byproduct of some other feature of larger families, such as income or living space.

    Chart of adjusted odds ratio for gut microbiota Type 1 by number of older siblings
    Source: Created based on data from RIKEN et al.

    Metabolite levels followed a related pattern. At age two, several short-chain and branched-chain fatty acids correlated positively with the number of older siblings (with the exceptions of acetate and hexanoate); at age four, propionate alone showed the same association. It’s striking that something as variable from household to household as the number of siblings shows up this clearly in a toddler’s internal biology.

    What breastfeeding and daycare don’t explain

    Because sibling composition correlates with breastfeeding practices, diet, daycare attendance, and antibiotic use, the team ran a mediation analysis to see how much of the sibling-microbiota link these factors could account for — essentially, a statistical test of whether the “obvious” explanations were doing the real work behind the scenes. Most of the factors examined explained little of the association. The one exception: at age four, antibiotic use within the previous three months appeared to explain part of the link, though even that effect was modest.

    That doesn’t mean direct contact with siblings has been proven to be the cause. Unmeasured factors, or pathways the analysis simply couldn’t capture, may also be involved — perhaps the sheer frequency of everyday physical contact between siblings, or subtler lifestyle differences the survey wasn’t designed to pick up. This broader idea — that a child’s early environment can leave a lasting mark on later health — echoes another RIKEN finding on how early-life nutrition shapes long-term lifespan.

    Toward allergy prevention: what’s next

    The research team is careful to stress that the results don’t show that simply having siblings prevents disease. Whether these gut-microbiota differences actually translate into a lower risk of allergies and other conditions still needs to be tested, and causality is a much higher bar than the correlations described here.

    Children with two or more older siblings had roughly a third the odds, at age two, of being classified into the low-diversity gut-bacteria type compared with children who had none.

    The team plans to investigate next whether the sibling-linked changes in gut bacteria and metabolites actually translate into measurable differences in allergy outcomes, and whether specific bacterial groups or metabolites turn out to matter more than others. As a growing share of families raise only children, research like this could eventually offer clues toward interventions — dietary, probiotic, or otherwise — that help shape a healthy gut environment regardless of whether a child happens to have siblings at all.

    Sources: RIKEN press release, Pediatric Research (original paper)

  • [Economy] The 2026 Economics Nobel — How Expectations Shape Recession Policy

    [Economy] The 2026 Economics Nobel — How Expectations Shape Recession Policy

    On October 12, 2026, the Royal Swedish Academy of Sciences awarded the Nobel Prize in Economic Sciences to Robert E. Hall of Stanford University and Michael Woodford of Columbia University, splitting the 12-million-krona prize between them. Their prize-winning idea sounds abstract — “microeconomic foundations of macroeconomic stabilization” — but it answers a very practical question: why do some recessions drag on for years while others pass quickly, and how can a central bank fight inflation without doing needless damage to jobs and growth? Their work reshaped how major central banks build the models they use to answer that question, giving policymakers a clearer theoretical map of how expectations, not just interest rates, move the economy.

    When 1970s Policy Stopped Working

    For decades after World War II, economists leaned on models built from statistical relationships between big aggregate numbers: total output, total employment, the overall price level. Many policymakers treated the link between inflation and unemployment as fairly stable, as if accepting a little more inflation would reliably buy a little less unemployment. The oil shocks of the 1970s strained that assumption badly. U.S. consumer prices rose by roughly 11 percent in 1974, eased briefly, then climbed above 13 percent by 1980, even as unemployment also stayed painfully high. This combination, dubbed “stagflation,” exposed real weaknesses in the prevailing models and forced economists to ask whether their tools for managing the economy were trustworthy at all.

    Starting From How People Decide

    Hall and Woodford, working in a research tradition that also included economists like Julio Rotemberg, pushed macroeconomic theory toward a different starting point: how individual households and firms actually make decisions. In their models, people are forward-looking — workers and firms set wages and prices partly on what they expect will happen next, and households decide how much to save or spend based partly on their expectations about future income and prices. Aggregate outcomes like inflation and employment emerge from how all of those decisions, expectations, and market constraints interact, rather than being assumed directly from past statistical patterns. This approach became a pillar of what economists call New Keynesian models, which combine forward-looking behavior with prices and wages that adjust only gradually, because changing them is itself costly for firms.

    Why Expectations Move Markets

    The forward-looking piece turns out to matter enormously, and it is arguably the most surprising part of the theory for non-economists. If a central bank merely moves interest rates without explaining its reasoning, people may barely change their behavior. But if a central bank credibly signals a future course of action and the public believes it, expectations can shift immediately, often before the policy itself has fully taken effect. Woodford, who has described his own framework as “neo-Wicksellian” after the 19th-century economist Knut Wicksell, argued that how policy is communicated is not a side issue but part of the policy itself. His work helped formalize the theoretical basis for “forward guidance,” in which central banks publicly signal their likely future interest-rate path; the idea existed informally before, but this research gave it a rigorous footing and helped turn it into an explicit, widely used policy tool from the 1990s onward.

    How policy is communicated is not a side issue — it is part of the policy itself.

    From Blackboard to Central Banks

    These ideas did not stay confined to academic journals. Woodford’s 2003 book, “Interest and Prices: Foundations of a Theory of Monetary Policy,” became, in effect, a standard reference for central bank staff economists, and New Keynesian models of the kind he and Hall helped build are now an important part of the analytical toolkit at the U.S. Federal Reserve, the European Central Bank, and other monetary authorities, used alongside other models, data, and judgment to forecast the economy and weigh policy options. Hall’s own career illustrates how closely this research sits next to real-world policymaking: he was the founding chair, from 1978 until 2024, of the committee that is widely treated as the authoritative voice on the official start and end dates of U.S. recessions (Stanford economist Valerie Ramey has chaired it since). Hall is also known for a classic 1978 paper showing that, once people have built their income expectations into their plans, consumption should move roughly like a “random walk” — changing mainly in response to genuinely new information, not drifting along a path that could have been predicted in advance.

    A Calmer Economy, in the Data

    It is hard to credit any single theory alone with calming the economy, and the 2021–2022 inflation surge that followed pandemic-era supply shocks was a reminder that new volatility can still strike. Still, the contrast with the 1970s and early 1980s is notable. Between 1970 and 1985, U.S. annual inflation swung wildly, from about 3 percent in calmer years to a peak above 13 percent during the worst of the oil shocks. For a long stretch of the period economists call the Great Moderation, roughly the mid-1980s through 2007, inflation and output volatility were markedly lower; U.S. annual inflation from 1992 through 2007, for instance, mostly stayed inside a narrow band of roughly 1.6 percent to 3.4 percent. Researchers debate how much credit belongs to better monetary-policy frameworks, including the expectations-focused, microfounded approach Hall and Woodford helped build, versus other factors such as globalization, smaller supply shocks, and improved business inventory management.

    US inflation rate 1970-1985 vs 1992-2007 line chart
    U.S. annual inflation rate: the 1970–85 oil-shock era compared with the 1992–2007 Great Moderation. Source: U.S. Bureau of Labor Statistics CPI data.

    What the Theory Still Can’t Fix

    The framework has clear limits, and the laureates’ own later work points directly at them. The 2008 global financial crisis exposed how little early New Keynesian models accounted for banks, debt, and financial panic, since they mostly assumed credit markets would work smoothly in the background. That gap pushed subsequent research, including work by Woodford, toward incorporating financial frictions and more realistic, limited forms of how people actually process information, an area economists call bounded rationality. Critics also note that leaning heavily on guided expectations can backfire if a central bank’s promises are not believed, which is close to what happened repeatedly through the 1970s. The Nobel committee’s own citation does not claim a finished, universal fix for the business cycle; it credits a stronger theoretical foundation for understanding why stabilization policy works when it works, and why it so visibly failed before.

    The research recognized by this year’s prize did not eliminate the risk of recessions or inflation; shocks, bad data, and public disbelief can still throw policy off course. What it gave policymakers is a sturdier framework for thinking about how expectations and policy choices interact — one more tool for softening the economy’s peaks and troughs, not a guarantee against them.

    Sources: The Royal Swedish Academy of Sciences — Prize in Economic Sciences 2026, Columbia University News, Hall (1978), “Stochastic Implications of the Life Cycle-Permanent Income Hypothesis: Theory and Evidence,” Journal of Political Economy, Woodford (2003), “Interest and Prices: Foundations of a Theory of Monetary Policy,” Princeton University Press