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BTA Sports Expands into Prediction Markets With AI-Powered Analytics
The fourth quarter rollout pairs independent probability modeling with “Ask BTA” to help users assess sports event contractsHORSHAM, Pa., Oct. 21, 2026 (GLOBE NEWSWIRE) -- BTA Sports, a subscription-based sports analytics and decision-intelligence platform, today announced its expansion into sports prediction markets, bringing the data and insights used by its sports betting subscribers to users evaluating sports event contracts. The offering, available in the fourth quarter as part of Version 2
ROI-NJ celebrates New Jersey family businesses at inaugural Family Business of the Year Awards
ROI-NJ celebrated 16 New Jersey family businesses at its inaugural awards, naming MSI Plumbing & Remodeling and NRS Inc. the 2026 winners.
Is 99% Screening Adherence Altering The Investment Case For Guardant Health (GH)?
Guardant Health reported new data from its Shield blood-based colorectal cancer screening test, including modeling and rural clinic results, indicating very high real world adherence and meaningful impact on screening pathways for average risk adults. The 99% completion rate for Shield in two rural South Carolina clinics highlights how a simple blood draw can address screening gaps in medically underserved communities and reshape demand across Guardant Health's screening franchise. This...
Can AI solve our mental health access problem?
Last week I asked you what worries you most about AI in health. It was close, but your top answer was “AI giving bad health advice.” Last week’s poll results: You’re in good company. This concern was top of mind at a conference I joined at Stanford last week, where researchers, clinicians, and people who build AI systems spent a day debating trust and safety in health AI. Stepping back, I’m also hearing your emerging concerns about AI’s impact on our well-being generally. I see this as a matter of public health, and we’ll take it in pieces as we learn together. Today we’ll focus on AI chatbots used for therapy for mental health. Early this month, Governor Newsom vetoed Senate Bill 903, California’s most ambitious attempt to regulate AI use for mental health. It’s at the center of active debate, and raises the questions patients, family members, citizens, and voters will face as AI moves deeper into health care. Let’s work through it. What is “AI-based therapy”? It’s really two things: purpose-built mental health apps designed to deliver structured support, and general chatbots like ChatGPT, Claude, and Gemini that people increasingly use for emotional support—even if they weren’t built for it. Spoiler alert: I don’t know whether the shift toward AI therapy is good or bad. The evidence isn’t there yet. But I do know it’s already happening, and I have some ideas about how we can roll it out with safety in mind. The therapy dilemma, in a nutshell Mental health challenges are common, and strain health and well-being across populations. Therapy (interactive dialogue with a trained, empathetic practitioner) helps many people heal. The demand for licensed therapists far exceeds the supply in California, and AI-based therapy can narrow that gap. AI-based therapy remains novel, unproven, and not well tested. Right off the bat, we can see we’re dealing with trade-offs here. Since the main driver of demand for therapy from a robot is lack of access to therapy from a human, we’ll start there. It’s too hard to find and afford a human therapist California is short roughly 55,000 licensed, non-prescribing therapists and counselors, about 40% of what we need. Psychiatrists, who rely on talk therapy as a complement to prescribed medications, are even more scarce. Eight counties have none, and density ranges from 1.7 per 100,000 residents in San Benito County, to 68.1 in Marin County. My wife is a psychiatrist in Marin, and even here access is tight. If it’s hard in the best-supplied county, imagine rural Sierra County, or the San Joaquin Valley. In 2024, more than half of Californians who tried to make a mental health appointment struggled to find a provider who took their insurance, compared with 14% for physical health care. When Senate staff posed as “secret shopper” patients and called 120 mental health providers in Medicare Advantage networks, they landed an appointment less than once for every five tries. Insurers pay behavioral health clinicians about 22% less than medical and surgical clinicians for comparable office visits. On top of that, billing denials and prior authorizations hit hardest at the solo and small practices common in mental health. Psychiatrists opt out of Medicare at the highest rate of any specialty. In many places, a cash practice for therapy fills quickly with patients paying $250 or more an hour, with no insurance paperwork at all. Families who can pay cash find providers more easily. Those who rely on insurance, including Medi-Cal enrollees, older adults, rural residents, and non-English speakers, compete for what’s left. So we can’t pretend the human system works, and we can’t blame people for turning to AI for support when they’re in distress. AI is filling the gap It’s estimated that roughly a quarter of AI users use it for mental health support. If you have internet, it’s available 24/7, it’s cheap, and it speaks your language. Uninsured adults are about twice as likely as insured adults to do it (30% vs. 14%). It feels anonymous in ways that may enable people to ask things they’re unsure or embarrassed about. The largely hidden, personal use makes it hard to study whether it’s actually helping people with their mental health issues, but researchers are trying. Does AI therapy work? A 2026 analysis of nearly 40 randomized trials found chatbots showed a real but small reduction in depression symptoms. But most of these trials were compared to an alternative of no therapy, not a human therapist. So the benefit of AI therapy is probably real, but modest, and mostly untested against the alternative of human contact. We need more studies. Serious concerns OpenAI’s own estimate is that more than a million ChatGPT users each week show signs of suicidal planning or intent. These are high stakes encounters, and AI doesn’t always get it right. That’s partly because a chatbot can sound like a therapist, but it has no license, no duty of care, and it’s not clear who to hold accountable when it gets things wrong. AI is also designed to help users in whatever task they’re seeking to accomplish. People may feel like they’re getting therapy, but they’re mainly getting a convincing conversation. It’s easy for an AI companion to wander into gray areas, like playing along with “creative writing” that may be about the user’s own suicidal intent. Courts are driving safety conversations A wave of lawsuits have been filed on behalf of families claiming that chatbots pushed people they loved toward self-harm, including teens who died by suicide. The first settlements came this year. OpenAI is headquartered in San Francisco, and dozens of cases alleging that ChatGPT drove users to suicide or self-harm are now before a San Francisco judge. Character.ai has filed for bankruptcy after several states brought lawsuits accusing the chatbot of pushing dangerous content to kids. But the technology seems to be improving. Last month, an independent evaluation found that newer AI models “almost never endorse suicide.” That’s progress, but our societal appetite for even one preventable death is very low. So how do we move forward? Finding a middle ground Earlier this month the governor vetoed Senate Bill 903, a bill designed to place guardrails on the use of AI for therapy. The bill would have prohibited any AI-based platform from being advertised as psychotherapy, and would have required a licensed professional to review and approve each encounter where AI was used to support therapeutic decisions. Newsom called it overly broad, since even routine screening questions would have needed clinician sign-off. Knowing we’re already short on mental health providers, requiring human review of AI encounters may not relieve our access problems. Still, the answer isn’t no oversight. Here’s one feasible short term solution. Rather than reviewing each encounter, a licensed clinician can design the guardrails: what counts as a crisis, what triggers an immediate handoff to a human, and what gets sampled for audit each month. The AI runs inside that structure. Conversations get checked, failures get reported, and a real clinician owns the framework. Medicine already has models to learn from. The FDA process for rolling out newly approved medications is an example. After a product reaches patients, we keep watching for adverse events. Patient-facing AI needs the same “digital pharmacovigilance”: systematic tracking of harms, near misses, and the types of groups that get hurt and why. It’s also critical that users know that they can reach a human if they’re in crisis. Most chatbots now include messages like these: Bottom line The rush to use unproven AI tools for mental health support is mostly a workaround, a sign of a health system people can’t get into. We need better access to proven care from licensed clinicians. That takes reimbursement that makes accepting insurance viable, and a bigger workforce. But, built and tested well, AI could be part of the answer. It could be the 2 a.m. check-in when no one else is awake, or a way for patients to practice skills their therapist taught them. It could be the first step to get someone the right level of care, and take paperwork off clinicians’ plates so they spend more time with patients. None of this is simple. The SB 903 veto doesn’t end the debate, and the legislature will be back. In the meantime, those of us using these tools need to stay informed, ask hard questions, and hold the companies building them accountable. I’ll try to do my part, and we’ll keep learning together. Love,Matt P.S. If you or someone you know is struggling with mental health challenges, emotional distress, substance use problems, or just needs to talk, call or text 988- you’ll reach a real person— or chat at 988lifeline.org 24/7. Dr. Matt Willis is the author of Your Local Epidemiologist in California. A California native, he’s served as a primary care physician, CDC epidemiologist, and public health officer for Marin County, where he guided the pandemic response. He lives in Marin with his family and their dogs, Teddy and Ramona. Thank you Cynthia He, MD, PhD, and Parnika Saxena, MD, for their guidance in preparing this article. Cynthia works in an outpatient mental health clinic, treating patients who have Medi-Cal or, sometimes, no insurance. Parnika works in an inpatient psychiatric unit, treating patients with public or private insurance or self-pay ability. All opinions are their own and do not represent their employers.
NVIDIA Joins European Commission Initiative to Expand AI Resources for Science
Oct. 15, 2026 — Much of today’s scientific progress begins with computation. But turning accelerated computing’s potential into scientific progress — modeling climate systems, designing molecules, simulating materials and more — depends on more than breakthrough technology. Especially for smaller labs, early-career scientists or science-based startups, the hardest part of research can be everything that […]
Precision weather radar visualization requires better science, not just new tech
Learn how multi-source atmospheric science, GRAF modeling, and precision weather radar visualization create a more accurate representation of surface weather.
What Wall Street may be forgetting about Meta's AI business
Wall Street may be asleep at the wheel on modeling out a more AI-fueled Meta business model.
Computational Models as Liminal Models in Neuroscience, a Cognitive Science Speaker Series Presentation | Events
Speaker: Danielle Williams, Ph.D. Title: Computational Models as Liminal Models in Neuroscience Short Bio: Danielle is a philosopher of mind and science whose research examines the role of computation in our theories and explanations of the mind and brain. Her work brings together philosophy of mind, cognitive science, neuroscience, and artificial intelligence, with particular interests in computational theories of mind, computational modeling, and the relationship between different explanatory and modeling strategies in the mind sciences. She also investigates philosophical questions raised by contemporary AI, including the nature of opacity in computational models and its implications for explanation in cognitive science. Alongside this work, she is a historian of cognitive science and AI, examining how the development of computer technology has shaped our conceptions and models of the mind and brain. Abstract: Scientists rely on various types of models to make sense of their phenomena of interest. This has led philosophers to take up a range of semantic, ontological, and epistemological questions about how models represent, what kinds of things models are, what we learn from models, and how they explain. One type of model that has been of particular interest to philosophers is the computational model. Computational models are commonplace in the mind sciences in general and neuroscience in particular, with many asking how computational modeling can help us understand the nervous system and its contribution to cognition and behavior. These questions are particularly pressing in neuroscience, as computational models have become a dominant framework for making sense of cognition. Thus, old questions about model-based science have re-emerged within the context of neuroscience, with many philosophers looking to established frameworks to understand how computational models represent, explain, and contribute to theory development in neuroscience. These established frameworks, however, characterize computational models as a homogeneous type which cannot capture actual modeling practices in neuroscience. In this talk, I argue that computational models in neuroscience are best typed as a kind of liminal model, a type I introduce to capture how the status of a computational model is determined by the epistemic context in which it is deployed. Indeed, the category to which a computational model belongs shifts according to the role it plays within a given context. ASL-English interpreters have been requested. Light refreshments will be provided.
Samuel Williams Honored with E. O. Lawrence Award
Awarded for creating transformative performance modeling tools and algorithms that help supercomputers run more efficiently.
Microsoft science chief Lee to advise Mercedes on AI
Departing Microsoft Science president Peter Lee plans to advise Mercedes on AI applications ranging from car physics to predictive modeling and regulation,...
Assistant Professor of Climate Data Science | The Journal of Blacks in Higher Education
Boston University invites applications for a faculty position at the rank of Assistant Professor in the Department of Earth Environment. This tenure-track position in Climate Data Science , beginning on July 1, 2027, is part of the ongoing cluster hire in Environmental Data Science and complements faculty searches within the Faculty of Computing and Data Science.rnrnWe particularly welcome candidates whose research focuses on the physical basis of climate, climate variability, and climate change, and who employ data science methodologies to deepen our understanding of how the climate system is changing and how these changes affect the global environment. Areas of interest include—but are not limited to—the changing frequency and intensity of extreme weather events; interactions between the atmosphere and terrestrial, oceanic, biospheric, and cryospheric systems; and improved understanding of Earth system predictability at seasonal, decadal, and longer time scales. We envision this position playing a central role in connecting and enhancing existing departmental strengths in climate-related research and teaching, including terrestrial and marine biogeosciences, land surface and coastal processes, paleoclimatology, energy and economic policy, and environmental sustainability. This faculty member will also contribute to university-wide interdisciplinary initiatives — including the Urban Biogeoscience and Environmental Health (URBAN) program, the Institute for Global Sustainability, and the Center for Climate and Health at the School of Public Health — and is expected to develop collaborative ties with the Faculty of Computing and Data Sciences, where shared interests in machine learning, large-scale data analytics, and Earth system modeling provide natural ground for joint advising, co-teaching, and grant development. This hire reflects our commitment to advancing interdisciplinary scholarship at the intersection of climate and data science. This position contributes to the University convergence themes of Global Sustainability as well as Artificial Intelligence, Data Science, Computing that are core pillars of BU's cross-disciplinary research priorities.rnrn Position Qualifications:rn Candidates should have a PhD or equivalent degree, demonstrated ability to obtain external funding to support their program of research and scholarship, and a commitment to excellence and innovation in teaching, education, and mentorship of students. The successful applicant will be expected to supervise graduate research in Ph.D. programs, teach at all levels in the Earth Environment curriculum, and maintain an externally funded research program. We seek applicants whose research complement strengths in the Department and around the University. For more information about the Department, see
Epidemiological modeling in the age of artificial intelligence
Nature Computational Science - Artificial intelligence is enabling epidemic models to learn across outbreaks. The next challenge is ensuring that these models learn and transfer not mere...
New Quantum Algorithm Targets Bottleneck in Fluid-Flow Modeling
Oak Ridge National Laboratory researchers developed LuGo, a quantum algorithm that reduced quantum gate requirements by more than 95%.
Mat3ra and the University of Tsukuba’s Center for Computational Sciences Sign MoU to Expand Access to Cloud-Based Materials Modeling in Japan
Mat3ra Japan K.K., the Japan entity of Exabyte Inc., the developers of Mat3ra.com, and the Center for Computational Sciences (CCS) of the University of Tsukuba today announced the signing of a Memorandum of Understanding (MoU) that establishes a framework for the use and dissemination of the Mat3ra.com computational materials platform within CCS and associated academic institutions in Japan. Under the MoU, Mat3ra provides CCS with free access to the platform for research and educational p...
The Science Gap Fueling Africa’s Water Wars - USC Viterbi
In a new Nature Sustainability commentary, USC Viterbi researcher argues advanced water modeling can reduce uncertainty and help turn water disputes into opportunities for cooperation.
Presley Gerber Dies at 27: Cindy Crawford’s Son’s Biography, Career and Timeline
Presley Gerber, son of Cindy Crawford and Rande Gerber, died at 27 on September 20, 2026. Here is his biography, modeling career, key dates and recent supermodel deaths.
Science Vs on the 49ers Substation: Anatomy of a Hatchet Job
In August, Pablo Torre Finds Out and Science Vs released an episode about my theory that electromagnetic fields from the electrical substation beside the 49ers facility may be contributing to the team’s run of soft-tissue injuries. As the originator of the theory, I was also a subject of the episode. I sat was interviewed by Wendy Zukerman and Rose Rimler of Sciene Vs. I sent them research, connected them with scientists, and spent hours digging through the City of Santa Clara’s and Silicon Valley Power’s records to help clarify critical details. Both Wendy and Rose came off as affable and professional, showing curiosity during the interviews, with Wendy going out of her way to question the “conspiracy theory” framing itself. Then they titled the episode We Solve the 49ers Viral Conspiracy Theory. I was familiar enough with their catalog that I never expected them to agree with my theory, but I did expect them to give it a fair hearing and to deliver their findings honestly. On both counts they came up short. They were not directly dishonest in anything they said, but rather in what they left out, in how what remained was cut, and in the picture painted by those two things in combination. They asked me to recorded the interview on my phone for better audio quality, and I held on to my copy for reference later. That turned out to be a fortuitous decision. Here is the raw audio of my first interview, unedited*. Only my side was recorded, so their questions aren’t on it, but you can generally infer them from the answers, and every claim I make below about what I said can be checked against my statements in this clip. (Note: Wendy had connection trouble at the beginning of the interview and was cutting in and out, which accounts for the false starts.) 0:00 -1:19:13 Audio playback is not supported on your browser. Please upgrade. Science Vs is a fun show. It is pitched at roughly a high-school understanding of science, for an audience that is skeptical, Snopes-oriented, and worried about misinformation. Take a claim circulating online, interview a supporter of the claim, interview consensus scientists, and then tell people which side of the line it lands on. I have listened to their episodes on raw milk, seed oils, 5G and other contested issues in the wellness space. I have problems with all of them, but each at least made an honest attempt to represent the actual range of scientific opinion by talking to credentialed scientists with differing perspectives. Dissenting researchers were heard from, answered, and often given a chance to respond. This episode did not do that. This article is not a relitigation of the science. I have written extensively about that already. This post is for the record. It is simply an accounting of what was wrong with their episode. Contents Is there a power plant next to the 49ers practice facility? — the clearest single example of getting something objectively wrong For the record — a high-level overview of all of the problems with their investigation Draw your own conclusions — why they had to make this episode at all What they left out about childhood leukemia — the exposure threshold, and playing games with statistics to minimize sick children What they left out about the mechanism — the body of literature they refused to cover, and evidence for the mechanism used against it The scientists Science Vs interviewed and did not air — four credential scientists who support myhe theory, recorded and cut, or not even called How they misrepresented me and my case — an answer severed from its question, and three telling edits How you would actually study the injuries — it doesn’t actually matter which team is the most injured, overall The theory: magnetic fields, oxidative stress, and collagen Since the episode never states my theory, here it is below. Those who are familiar with my work can skip this section. Electrical equipment produces magnetic fields, and those fields induce faint electrical currents in anything that passes through them, including people. My argument goes further than induced current. Mitochondria — the structures that generate a cell’s energy — run on continuous flows of electrons and protons, and those flows carry faint electromagnetic fields of their own. Maxwell’s principle of superposition shows that when two fields share the same space, they add together. An external field does not need to be strong to alter an internal field, it only needs to be present. The electron transport chain inside the mitochondria leaks a small fraction of its electrons by design, and those become reactive oxygen species — free radicals, which healthy cells use as signals. External magnetic fields cause the rate of leakage to change, increasing free radicals. When free radicals accumulate faster than the cell’s antioxidant systems can clear them, the result is oxidative stress. Oxidative stress is a recognized driver of chronic illness, including cardiovascular disease, neurodegeneration, diabetes and cancer, and it is in every medical school curriculum. One of its ordinary effects in connective tissue is that it switches on matrix metalloproteinases — MMPs, the enzymes that break collagen apart — while suppressing new collagen synthesis. Tendons are made of collagen. An athlete loading his soft tissue heavily six days a week is already creating oxidative stress in the tendons, so the addition of magnetic field exposure compounds the damage and inhibits repair, making him more susceptible to injury when enormous force lands on a tendon in a fraction of a second. That is the theory. We know that EMF causes oxidative stress, and we know that oxidative stress degrades collagen. The only thing missing is a study showing that direct connection. I have never claimed certainty that the substation is contributing to the elevated injury rate. I do believe it is likely, and I believe it warrants caution, and that a revenue machine like a major NFL team can easily afford to remove the exposure out of precaution. Is there a power plant next to the 49ers practice facility? Here is the clearest single instance of the problem with this episode. It requires no knowledge of electromagnetic fields at all. This story first came to me through Jon Feliciano, a former 49er, in an interview on Willard & Dibs on 95.7 The Game. Asked about why the 49ers are always injured, he said players used to joke that it was the power plant behind the practice fields. Wendy Zukerman corrects him on air. “It’s important to say that it’s not a power plant near the 49ers site.” Torre then asks her to explain the difference between a substation and a power plant. Her answer: a power plant generates electricity and transmits it to a substation, which converts the voltage into something your house can use and sends it on to your home or your business. There are two problems with that. The first is that there is a power plant on that site. The Gianera Generating Station is a natural-gas-fired peaker plant owned by Silicon Valley Power, and it has been there since 1986. I have published aerial photographs of the location from 1987 in which the gas plant is the only thing standing, before the practice facility was even there. I even mentioned the gas plant in my first interview. (Feliciano has since gone on record about this issue, and it is clear he had the substation in mind rather than the gas plant.) The second is that her definition describes a distribution substation, and the facility beside the 49ers is not that. It is the Northern Receiving Station, which takes in bulk power from outside the city’s grid and delivers it to distribution substations. Silicon Valley Power’s own map distinguishes receiving stations from substations, and Rose Rimler even reads the label out loud in the episode when she walks through that map on air. A few minutes before the map segment, Zukerman tells Torre: “like I said, we fact check everything.” Well, maybe not everything. I made a mistake On the morning of July 6th, at 10:20, I sent Rose and Wendy two studies. During the interview I had been asked which human studies show oxidative stress from electromagnetic fields, and I had not been able to name one on the spot. Rose emailed asking for the studies, I quickly searched for a couple, and I wrote that I thought these were the most relevant from that search. Unfortunately, I was busy and did not read either one closely at first. I picked them because they were topically relevant — one on substation workers, one on power plant workers. Neither were great studies, and their problems go past the ones raised in the episode. That was careless on my part. There is context that did not make the episode, though. Before that email I had been on a Zoom call with Rose talking about EMF and oxidative stress, and I pointed her to Dr. Henry Lai at the University of Washington, who has spent years aggregating the literature on this question. His tallies show that roughly 90% of studies since the late 1990s find oxidative effects from electromagnetic field exposure — hundreds of studies, systematically counted. Rose told me she was already aware of his work. So when she asked for studies, I understood her to have the aggregate view already and that she would report anything within that context. Not reading those two carefully was still a mistake, but the larger mistake was assuming the episode would be based on a thorough review of the actual body of literature, rather than on whatever I happened to send that morning. The second email At 1:49 that same afternoon — three hours and twenty-nine minutes after the first — I sent them a second email that I had been preparing for a while. It was a structured document in six numbered sections, written in response to a request they had made two weeks earlier, laying out my entire argument with citations attached to every link in the chain. The episode aired on August 13th, five weeks later, and not a single item from the second email is in it. Here is how it opens: The full email is included at the bottom of this post in Appendix B. Nearly 1,500 words in total. It contains two named mechanisms with the physics behind them. Occupational studies in humans. The literature on oxidative stress and collagen. A section addressing the strongest counter-evidence against my own theory, which I raised myself. And a closing section stating plainly what the theory cannot yet show. My entire argument, in writing, with detailed citations for every claim, provided to them five weeks before broadcast, and none of it made it into the episode. That is the single largest omission, but it is not an isolated one. The whole episode is riddled with similar problems, and the conclusion they came to is not merely a difference of opinion about the scientific evidence. Through omission and through strategic editing, Science Vs misinformed its audience — about what the evidence shows, about what I argued, and about who they spoke to. That is a notable outcome for a show whose stated purpose is correcting misinformation. For the record I am not going to tell you that Science Vs set out to discredit me, or that they had come to their conclusion before they reported the story, or that they used misleading editing to make me look like a man retreating from his own theory. I cannot know what was in anyone’s head, and I am not going to pretend otherwise, either. What I can tell you is what is on the record. They asked me for evidence and aired the weakest thing I sent. They interviewed three scientists who supported my theory and aired none of them. They discussed childhood leukemia without ever saying the exposure level at which it appears. And they took an answer I gave weeks before Ken Karipidis, the one EMF scientist who was put on air, was interviewed, and placed it directly after his critique, where it comes off as a major concession. A friend of mine who has worked as a journalist for thirty years read that last one and called it sleazy. I agree with her. Those are just the four issues that are impossible to defend as pure editorial decision, but they are not the only issues. The most glaring issues stem from what they left out: The exposure level at which the childhood leukemia association actually appears — three to four milligauss, a number the episode never states despite it being much lower than what I measured. The mechanism itself: The episode gets as far as “oxidative stress could weaken collagen,” which leaves it sounding like speculation, rather than a known pathway. The second email, and all the studies in it. Dr. Paul Héroux at McGill, whose work my proposed mechanism rests on, whose contact details I supplied, and who was never called. Dr. Beatrice Golomb at UC San Diego, who runs a research group on oxidative stress and mitochondrial function and has specific expdertice in collagen, interviewed and not aired. Dr. David Carpenter at Albany, the most prominent academic voice on the harm from magnetic field exposure, interviewed and not aired. Dr. Joel Moskowitz at Berkeley, interviewed and not even credited. The specific, testable condition I put in writing that would make me abandon the theory. My own unprompted statement of the theory’s evidentiary gap. My caveats about my own measurements. My ranking of EMF as one factor among several rather than the only one. The new equipment installed at those facilities between 2010 and 2014, and the sharp rise in system load from 2013 onward. But that is not all. What they kept, they selectively edited to serve a narrative that was not served by the raw transcripts of the interviews. A description of thousands of commercial routers in a workplace, reduced to the Wi-Fi in your house. My own acknowledgment, multiple times, that "I'm not a scientist" clipped in an episode that spends considerable energy pointing out that I am not a scientist. And a qualification I volunteered about my own measurements, cut from my answer and delivered later by their EMF expert as a correction. Together their edit produce a portrait of a wellness guy with Wi-Fi phobia and two bad studies, backing away from his theory the moment a “real” scientist looked at it. A practice facility where nothing changed because no new substation was built. Injuries written off as random because the 49ers were not quite the single most injured team in the league over the period in question. That portrait is false even though almost no individual statement made in the episode is. Draw your own conclusions At the top of the episode, Pablo Torre says his show had been flooded with requests to investigate the substation — that it was the thing they were asked to look into over and over again. Neither show went hunting for this story. It arrived from their own audience, in volume, and it could not be waved off. A thread I posted on X reached twenty-two million people. A guy with a gaussmeter and a brand-new Substack obligated a Pultizer prize winning program to spend an hour on a question about player health, that is considered fringe enough that most media operations won’t touch it. Pablo Torre Finds Out could not ignore it. So Science Vs came in and ran a “debunking” operation instead of an investigation. Everything below is what had to be left on the cutting room floor to deliver the “debunking”. They could not ignore it. So they tried debunked it instead. The demand was strong enough to force an episode, and since the episodes release the 49ers have sustained more injuries, including season ending ones, and interest has only grown. Demand from fans and playets is strong enough to force a better investigation from someone else. An omission is not a lie until you find out what was omitted. What follows is the detailed record so you can see the full picture. Draw your own conclusions. I have drawn mine. I am going to keep publishing. A note about Pablo Torre Finds Out After the episode aired I posted publicly, calling out Pablo Torre Finds Out for concerns I had about the integrity of their investigation, and about an interview directly with Pablo Torre which had been offered but never happened. I have since spoken with a producer at PTFO. The offer had rested on a misunderstanding about where things stood between us, and it fell away as the Science Vs part of the production widened past the science. He apologized for the miscommunication and I accepted. The producer also confirmed to me that they did essentially hand off the entire investigation to Science Vs for narrative purposes — so Pablo would be genuinely curious or potentially surprised by their findings — while still taking responsibility for what went out under their name. I appreciated the candor and the accountability, and I am comfortable with where we left things. So this post is not about PTFO. My disagreement is with Science Vs. What they left out about childhood leukemia Childhood leukemia appears at three milligauss. They gave no number at all. 1. They built an EMF ruler, then discussed childhood cancer without it. The sequence on air went like this: First I measured almost 9 mG, then I measured up to 50 milligauss. Torre reacts with concern — “that sounds high.” Cut to Karipidis: the public limit is 2,000 milligauss. Torre reacts again — “What happens at 2,000?” Karipidis explains that nothing happens at 2,000, that safety factors are built in. Then: at more than 50,000 milligauss you get flickers of light in the retina, and higher still, nerve stimulation. The listener now has a ruler (their ruler) to measure these numbers with. Fifty is nothing by their ruler. Then the episode turns to childhood leukemia and states, accurately, that there is a real association — but tellingly, they never give the exposure level at which that association appears. Childhood leukemia appears at three milligauss. They conveniently left that out. The pooled analyses that produced the World Health Organization’s carcinogen classification found roughly a doubling of childhood leukemia risk above three to four milligauss. That is the number that belongs in that segment, and it is more than ten times below the reading that had just been dismissed as trivial. I gave them that number myself, on tape, in the interview. Describing my own threshold, I said my comfort level for long-term chronic exposure is three to four milligauss, but preferably one. 2. Six children in thirteen million. The episode reports that these fields might explain one to four percent of childhood leukemias, then reframes it as a German estimate of perhaps six cases among thirteen million children. Torre, to his credit, is uncomfortable. What never gets explained is why that number is small. Almost no children lives above three milligauss in Europe. Fewer than two children in a hundred, in the Italian data. So even if the risk to those children doubles, the number of additional cases across an entire country stays small — not because the hazard is small, but because hardly anyone is exposed to it. Count the whole population and the few who are exposed disappear. And the reason hardly anyone is exposed is that siting rules keep housing away from transmission lines. In Europe, at least. The Dutch government has a standing precautionary policy against building new homes in any zone where the annual average magnetic field exceeds 4 milligauss — the same number the episode never mentioned. Denmark and Norway treat 4 milligauss as the level that triggers a look at mitigation. That is five hundred times below the ICNIRP figure quoted on air. The United States has no federal equivalent. That is evidence of infrastructure siting policy working. It is not evidence that there is no hazard. It is evidence that we already act as though there is one. If you listen to the raw audio you will hear that my proposed solution is EMF exposure is to build better infrastructure. What they left out about the mechanism 3. They accused me of cherry-picking, and then cherry-picked themselves. On air, Zukerman tells Torre that people who worry about electromagnetic fields dismiss the studies showing null results and collect the ones showing harm, and that this is why you have to be careful not to cherry-pick. They had two papers I had rattled off in a hurry, and a second email containing a carefully chosen set. They ran the two weakest, graded them on air, and presented the result as the state of the evidence for not only my theory, but the whole question of oxidative stress from magnetic fields. That is cherry-picking from my emails. The bigger picture is what they did with the literature as a whole. Zukerman acknowledges it exists, dismisses it, and disposes of it in a single stroke. You can go online right now, she tells Torre, and find scores of studies showing these fields increase oxidative stress. The issue, she says, is that they are not studies in people. Many of them are in people. The occupational studies in my second email are in people. That email had been in her inbox for five weeks. She waved off the literature on a factual claim that the documents in her possession contradict. (I have written separately about the evidentiary standard at work here). The effect was that they essentially baited me: ask the subject to supply the evidence, take the weakest thing he sends, and let that stand as a proxy for the whole argument. Wendy has been doing this show for ten years and clearly knows how to extract what she needs for her desired narrative. 4. They presented the collagen-improvement research as a gotcha, but I had already addressed it. The episode treats the existence of studies showing that EMF can improve collagen as a surprise that undercuts me, but I had pre-emptively addressed that five weeks earlier in the email they ignored. Those studies use measured, high-intensity pulsed dosing. The mechanism by which they work is oxidative signalling. The cell reads a controlled rise in reactive oxygen species as a warning, upregulates its antioxidant defenses, mounts a repair response, and the therapy stops before the system is overwhelmed. The key point is that it induces an oxidative response, which means those studies are actually evidence for my proposed mechanism, not against it. A field that produces a therapeutic effect cannot be biologically inert (which is the implication when they say “Nothing happens at 2,000 milligauss). Chronic ambient exposure is the same pathway with no off switch, and it is most consequential during the evening and while sleeping, when the body is in repair mode and the exposure does not stop. Section 5 of my email is titled What about the study showing improved collagen synthesis. I raised the counter-evidence myself, explained why I did not think it refuted the theory, and wrote that I was disappointed the question had not come up in the interview. That is worth holding against the cherry-picking charge in the item above: the studies they presented as inconvenient for me were studies I had gone out of my way to put in front of them. 5. Section 1 of my email stated a critical argument that was never put to the expert directly responsible for it. My email says the exposure limits exist to prevent acute effects and do not address chronic exposure at all, and that my dispute is with the entire framework. Zukerman gestures at this once. After Karipidis gives the 2,000 milligauss figure, she tells Torre that I would object because those levels do not capture “more subtle effects.” That is not my argument, and “subtle” is doing a lot of work here. My argument is that the limits are built to prevent acute effects — nerves firing, muscles twitching, etc — and were not designed to address chronic exposure at all. In fact, ICNIRP’s own documents state as much themselves. Mine is a structural objection to what the standard measures, reduced as a concern about subtlety, it becomes a matter of degree, rather than a categorical difference. They interviewed Ken Karipidis, vice chair of ICNIRP — the body that writes those limits. He was asked what the limit is, but he was never asked how it applies to chronic exposure. The scientists Science Vs interviewed and did not air 6. They interviewed four officials of the standards body. The episode credits roughly a dozen researchers. Four of them are associated with ICNIRP. Ken Karipidis is the vice chair. Rodney Croft chaired ICNIRP from 2020 to 2024 and led the development of its current guidelines. Dan Baaken is ICNIRP’s scientific secretary. Frank de Vocht sits on the Main Commission. One of them speaks in the episode. Their institution’s position is more than adequately represented — the 2,000 milligauss figure, the safety factors, the assurance that nothing happens below the line. 7. The four who were left out. Dr. Beatrice Golomb is Professor of Medicine at UC San Diego — a physics undergraduate degree, a PhD in biology, an MD. She directs a research group whose stated focus is the relationship between oxidative stress, mitochondrial function and health. That is the mechanism the episode spent its first half dismissing. Her published work includes the fluoroquinolone case series behind the FDA’s black box warning on an antibiotic class that ruptures tendons, where the accepted mechanism is oxidative stress and mitochondrial toxicity — the back half of my chain, already on a drug label. She is in the credits. She is not in the episode. She has told me she finds my theory biologically plausible, and that she has a recording of her interview. Dr. David Carpenter directed the Institute for Health and the Environment at the University at Albany for decades and is the most prominent academic voice arguing these fields cause harm. Childhood leukemia is the reason these fields carry a WHO classification at all, and Carpenter is the person you call if you want that case made by someone who has spent a career on it. He is in the credits. He is not in the episode. He has told me that when he was interviewed they could not have been less interested in what he had to say. Dr. Joel Moskowitz directs the Center for Family and Community Health at UC Berkeley’s School of Public Health. He told me directly that they interviewed him and that he thought it went well. He does not appear in the episode, and unlike Golomb and Carpenter, he does not even appear in the credits. Dr. Paul Héroux at McGill is the researcher whose work provides the foundation for the oxidative stress mechanism my theory rests on. I gave Rose his email address and phone number. He was never interviewed. They never even called him. 8. “He’s not a scientist.” The episode leans hard on the fact that I am not a credentialed scientist. Zukerman says it. Torre repeats it. “At all.” Having built a segment on that, they then left out the physicist-biologist-physician, the professor of public health, the director of a university environmental health institute, and the McGill researcher whose work the primary mechanism comes from. They spent the episode telling their audience I am not a scientist, and then kept four actual scientists who support the theory off because their argument does not survive otherwise. How they misrepresented me and my case 9. They played my words as a response to an interview I had never heard. Near the end of the hour, Zukerman says she went to me and told me the evidence was lacking. Then she plays me saying I am not wedded to it being an EMF, that I have staked my reputation on it needing more study, and that I am not certain of anything. That segment was placed immediately after Karipidis had finished taking the cherry-picked papers apart. It plays as a man folding under expert scrutiny. Commenters have used it against me since, but it is incorrect. Here is the sequence as it happened. I was interviewed. Karipidis was interviewed weeks later. I was never told what he said, or even who he was. I was never asked to respond to it. I did not know his name until the episode aired. That is not a simple editing decision. That is a quote from one interview, moved to follow a critique from a different interview it was not a response to, so that it would land as a concession. A friend of mine who has spent thirty years as a journalist read that sequence and called it sleazy. She is right. The full passage is reproduced in Appendix A, and it is worth reading. What airs is the preamble. Four paragraphs later I say “to directly answer your question” — and that answer, about what would change my mind if the team relocated, is not in the episode. Neither is the sentence two paragraphs on, where I say the EMF might turn out to be ten percent of the problem. I will say the underlying point again, though, because I meant it and I still do. I am not wedded to my theory that magnetic fields from the substation are contributing to the 49ers injuries. I could be wrong. What I am wedded to is the process of scientific inquiry, and the fact that we should be cautious around these exposures and we need more independently funded studies. 10. Three telling edits. They cut “I’m not a scientist” mid-sentence. What I said: “...and I am not certain of anything. I’m not a scientist. To me, I find the mechanisms compelling. I will go back to the fact that I got into this not because of the 49ers, but because of clients I was working with who suffer from EDS...” What aired: “...and that I am not certain of anything. I’m not.” The clip ends on the first two words of the sentence in which I say I am not a scientist, in an episode that spends considerable energy establishing that I am not a scientist. The full sequence is in Appendix A. They cut my own description of my work. Asked what I do, I said I run a wellness business, that I am focused on writing software to help people navigate environmental stressors, that environmental health is my focus, and that I also do independent journalism and research. What aired is the wellness business and the environmental health, with the software and the journalism removed from my own answer. The software engineering does come up later — narrated by them, as a hacker who grew up to write firmware — but my current work, in my own words, arrives as “wellness guy.” Thousands of commercial routers became home Wi-Fi. What I described: working at a wireless networking company surrounded by thousands of Wi-Fi routers, and a stress-induced health collapse afterward that working in that environment contributed to. What aired: Wi-Fi, the kind you have at home — reinforced by a clip of Christian McCaffrey talking about turning his router off at night. Three cuts, and every one of them makes me seem less credible. That is not a coincidence. 11. I gave them my own caveats and the test that would disprove me. In the first email I wrote that a well-designed chronic, ambient-level exposure study in tendon showing no collagen degradation, run over many months, would undercut my confidence in the theory. That is a specific, testable condition, stated in advance and in writing. There was a second interview, weeks later, which was not used in the episode, but in that I asked again for the falsification criterion to be included. What the episode presents instead is a man saying “we need more study,” which reads as a hedge, an open-ended request for the benefit of the doubt. It is the opposite of a hedge. Section 6 of my second email says, unprompted, that as far as I am aware there is not a single study showing EMF-driven oxidative stress degrading collagen in a human in vivo or in human tendon in a lab. I wrote that because it is true and because it is the first thing anyone should check. Under their standard it becomes the hinge of the entire first half, framed as something they had to surface without my cooperation. I had handed it to them five weeks earlier. And in the interview I said that my 25 to 50 milligauss readings were taken at knee level, and that these fields attenuate quickly, so by the time it reaches your head it is not that high anymore. That did not air either. The qualification arrives later, from Karipidis, as a correction. How you would actually study the injuries 12. They measured the wrong thing. The episode spends its final act on whether the 49ers are the most injured team in the NFL, finds no clean ranking, and lands on “injuries are random”. That is the wrong test. You would define the exposure — measured field levels at the facility — and the outcome narrowly: soft-tissue injuries, not a single MSK injury count that a broken finger and a ruptured Achilles both land in. Then you would compare the 49ers to themselves before and after the exposure changed, and compare that change against every other team over the same window. That controls for league-wide shifts in rules, turf and training load. It is a difference-in-differences design, and it is standard. I sent them that argument on July 28th, in writing, along with the fact that in eleven of the past twelve seasons the 49ers had finished top ten in Adjusted Games Lost. And by the episode’s own reporting, the 49ers are second in games lost to injury over the past decade, with FTN’s Aaron Schatz confirming that once you adjust for starters they come out second worst in the league — after having been one of the healthiest teams for years before that. A finding like that is where an investigation starts, not where it stops. 13. I did a lot of substation research for the episode that was never aired. I had reported that the substation next to the facility was expanded in 2014. That was wrong, and Rose Rimler and I sorted it out together. They gave me credit on air, which I appreciated, but framed it as a concession on my part to something they had found. In fact I had been investigating it myself, was uncertain before they asked, but was waiting for clarification from Silicon Valley Power. The uncertainty centered around a substation named “Esparanca” that was proposed right behind the facilities as part of the stadium construction, but now appears to never have been built. I spent days in Silicon Valley Power’s records, and did eventually reach Silicon Valley Power for confirmation and indentified the mistake myself. I also gave them highly detailed documents on what we do know about those facilities: new equipment installed between 2010 and 2014, and load growth across the system with an inflection around 2013–2014, with the Northern Receiving Station carrying the highest capacity in the system and sitting closest to the data center corridor. None of that is in the episode. I have a separate piece about it coming out soon. *I cut 2 seconds from the raw audio to remove personal information that had no connection to anything in the episode. Appendix A: the passage the clip was taken from This is the sequence from my first interview, in full. The episode uses one fragment of it. It’s tricky because my understanding is that there’s something like a 15-year half-life on collagen remodeling issues. They’re going to get a new crop of players. What I really need, though, is for the injuries to be broken down so we can see the soft tissue injuries. Right now, they’re categorized as MSK injuries, but let’s say we can actually identify them more specifically. Is that right? He was able to do that? Oh, cool. I wasn’t able to find that information. Interesting — I didn’t know that data existed. I tried so hard to get it, and I didn’t know it was available. Maybe you could connect me with that person. That would be amazing. I’m not wedded to it being an EMF, honestly. I’m not. I would say that, to a certain extent, I’ve staked my reputation on the fact that it needs more study. It’s important to look into it, and I am not certain of anything. I’m not a scientist. To me, I find the mechanisms compelling. I will go back to the fact that I got into this not because of the 49ers, but because of clients I was working with who suffer from EDS, Ehlers-Danlos Syndrome, a tendon hypermobility issue. This condition is documented to be partially caused by oxidative stress. I’ve worked with them to mitigate their EMF exposure and have seen people improve. I think it’s a bigger story. In a way, the 49ers represent an occupational cluster with specific exposure risks. If someone would conduct a study on this, I believe we could learn a lot. Perhaps we would discover that it’s like, okay, maybe it’s 10% of the problem. I don’t know the exact number, but I think either way, conducting the study would provide valuable insights. To directly answer your question, it’s challenging for me because it’s part of a bigger story. If the 49ers move and stop having injuries, I might conclude that it was related to the substation. However, if they move and still experience injuries, I would want to analyze the nature of those injuries. Are they primarily soft tissue injuries? I don’t think it’s necessarily a case closed, but the story might lose traction at that point regardless. Three things are worth noting. The aired fragment stops immediately before the sentence in which I say I am not a scientist — the cut described in item 10. It also stops four paragraphs before the words “to directly answer your question.” What the episode plays is the throat-clearing. The actual answer, about what would change my mind, never airs. And two paragraphs in, I say the EMF might turn out to be ten percent of the problem. That is the ranking the episode says I never gave. Appendix B: the full email, July 6th, 1:39 PM Hi Rose, You asked me to send you some studies a couple of weeks ago. I apologize for the delay. I have been incredibly busy. I also wanted to give you a chance to dig in yourself, and get your own perspective before I influenced you too much with mine. That said, I do want to make sure that the actual argument is abundantly clear, so here it is! One note before I begin, there are really two separate questions here: Does the substation cause the soft-tissue injuries? That was my specific claim, and what the podcast is about. Is the theory biologically sound? Can 50/60 Hz fields at these levels do anything biological below the safety limits? Question two is why people have been calling me a conspiracy theorist. The regulatory and advisory bodies (ICNIRP, etc) maintain that anything below their exposure limits is biologically inert, and that position is broadly accepted within the mainstream, but the actual science paints a different picture. To defend my theory I’ve had to defend a much larger claim: that the advisory bodies have the biology wrong. I can’t honestly answer question 1 without first addressing that. Here’s is a simplified outline of my theory, with links to studies: 1. Advisory body thresholds for biological effects The ELF magnetic-field limits (ICNIRP’s ~2,000 mG public / 10,000 mG occupational) exist to prevent acute effects from exposure. If a magnetic field is strong enough, it makes nerves fire and muscles contract. Muscles twitch. You can see faint flashes of light in your vision. People can even feel tingling or even a shock. That happens due to Faraday’s law, which states that a time-varying magnetic field induces an electric field inside the body via the creation of Eddy currents.The standards assume that those currents do nothing below the twitch threshold. They do not consider chronic effects at all. My argument is that they are biologically active and do have chronic effects. 2. There are multipe ELF-EMF mechanisms for inducing oxidative stress. This is basic biophysics. Below are the two primary mechanisms. Simplest first. Dr. Paul Héroux of McGill has the most intuitive explanation, which rests on Maxwell’s principle of superposition: because Maxwell’s equations are linear, two sets of electromagnetic waves sharing the same space simply add together. In the interview I pictured a pebble dropped into a pond, sending out clean ripples. Then a boat motors past at distance, sending larger waves, and where they meet the ripples, the two add together and the original pattern gets distorted. In our cells, the mitochondria run on continuous currents of electrons and protons that carry their own faint electromagnetic fields. An external field from a substation, by the principle of superposition, adds to those internal currents and shifts the pattern. The electron transport chain in the mitochondria leaks a fraction of its electrons by design as reactive oxygen species (ROS), which act as signalling molecules in a healthy cell. Distorting that field increases that electron leakage, creating oxidative stress. A tiny electromagnetic interference results in a large downstream effect. https://www.sciencedirect.com/science/article/pii/S2405844025006474 The more complex pathway is Dimitris Panagopoulos’s Ion Forced-Oscillation / Voltage-Gated Ion Channel (IFO-VGIC) mechanism. Weak, polarized, low-frequency fields force the ions (calcium, potassium, sodium) next to an ion channel’s voltage sensor to oscillate; because that force scales with the inverse cube of the distance involved, fields far below the stimulation threshold can still force the channel open, disrupting the cell’s ion balance and driving ROS overproduction (Nitric oxide and superoxide interact and produce peroxynitrite, a highly unstable cytotoxin that breaks down into free radicals). https://www.researchgate.net/publication/366088934 https://www.spandidos-publications.com/ijo/59/5/92 https://www.mdpi.com/1422-0067/22/18/10041 https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2025.1585441/full https://pmc.ncbi.nlm.nih.gov/articles/PMC3772193/ 3. Evidence of oxidative stress, in vivo, in humans from ELF-EMF There are many studies, and the strongest are occupational, which applies to the 49ers situation. 115 power-plant workers vs. 145 office controls. Serum malondialdehyde (MDA, the standard lipid-peroxidation marker), superoxide dismutase, and catalase were all significantly higher in the exposed group, and the markers rose with increasing field exposure. https://journals.sagepub.com/doi/abs/10.3233/WOR-203244 A follow-up where they reversed the oxidative stress in power plant workers. A double-blind randomized controlled trial: 91 power-plant workers given vitamin E, vitamin C, both, or placebo for 90 days. The antioxidant groups showed reduced lipid peroxidation and raised antioxidant capacity versus controls. https://pubmed.ncbi.nlm.nih.gov/32191586/ A spot-welder study: 46 welders occupationally exposed at 50 Hz, showed significantly altered red-blood-cell antioxidant enzyme activity and concluded ELF-MF “might act as an oxidative stressor… even at the recommended levels of exposure.” https://pubmed.ncbi.nlm.nih.gov/18504600/ A substation-specific study found oxidative stress and DNA damage https://pubmed.ncbi.nlm.nih.gov/24460415/ 4. Oxidative stress degrades collagen This half isn’t controversial at all. Oxidative stress activates the matrix metalloproteinases (MMPs) that break collagen down and suppresses its synthesis. Here are three example studies: Siwik et al. 2001: Oxidative stress raises MMPs and lowers collagen synthesis in cardiac fibroblasts. https://pubmed.ncbi.nlm.nih.gov/11121376/ Fisher et al. 2009: Collagen fragmentation drives oxidative stress and elevates MMP-1 in aged human skin. https://pmc.ncbi.nlm.nih.gov/articles/PMC2631323/ Varani et al. 2006: Decreased collagen production in oxidatively aged skin. https://pubmed.ncbi.nlm.nih.gov/16723701/ 5. What about the study showing improved collagen synthesis I’m glad you asked this question, and I’m bummed we didn’t get to it in the interview because it is a great question, and very important to understand. Superficially it may appear to be a refutation of my theory, but I would argue that it actually supports the underlying argument. That study used a short, high-intensity pulsed dose: therapeutic-PEMF, the same type of field used clinically to stimulate bone and tissue healing. The reason this works is because it triggers the very same biological pathways that can lead to oxidative stress. The exposure still stimulates ROS production, but done at a controlled dosage, for a limited duration it has a different effect. The cell reads the ROS as a warning signal and it upregulates its antioxidant defenses, and mounts a repair/proliferation response. The therapy is then stopped before the cell gets overwhelmed. That is essentially a hormetic response: a small dose of a stressor producing a beneficial adaptation. Two things follow: At the very least, this means the field is biologically active, which by itself undercuts the regulatory claim that these fields are inert below their thresholds. There is a wealth of literature on PEMF (pulsed EMF) bone regeneration and calcium channel signalling (which is the second mechanims in section one). Here is one. https://www.nature.com/articles/srep13856 At best, it leads us straight to my actual concern, which is that under chronic, chaotic and unregulated exposure, the cell’s antioxidant capacity gets overwhelmed, resulting in oxidative stress. There are plentiful examples of things that are beneficial at controlled doses, but harmful at uncontrolled doses. Excecise is one obvious, uncontroversial example. Botox is perhaps a more interesting example. 6. What about a study that shows EMF causing collagen degradation in humans? As far as I am aware there is not a single study showing EMF-driven oxidative stress degrading collagen in a human, in vivo, or in human tendon in a lab. There are studies involving isolated fibroblasts though, which secrete collagen. The most relevant one showed that human fetal scleral fibroblasts exposed to 50 Hz ELF-EMF showed a significant decrease in collagen synthesis and that MMP-2 expression was upregulated, which is the exact mechanism I described in section 2. https://pmc.ncbi.nlm.nih.gov/articles/PMC3626379/ That’s it! Thanks again for the interview. Best, Peter Anthony Cowan Environmental Health · Wellness Tech · Researcher & Journalist Publisher & Editor, Living Energy Bio: petercowan.comSocial: X · Instagram · TikTokApp: sunlightis.lifePhone: [redacted]
From Python and Statistics to Agentic AI: 5 Data Science Certificate Programs in 2026
Python and statistics still sit at the center of data science, but the work surrounding them has expanded. Professionals now move from cleaning data and testing hypotheses into predictive modeling, recommendation systems, deep learning, Generative AI, and applications that can retrieve information or act through external tools. That progression matters when comparing programs. A curriculum […]
What Does the Science of Climate Change Really Look Like?
Editor’s Note: This is the second in a three-part series on how the Right should think about environmental and climate policy. Read Chris Barnard on Reclaiming Environmental Policy from the Left. More than a decade ago, the world’s governments negotiated and signed the Paris Climate Agreement, committing to hold global warming well below 2 degrees Celsius. In the following years, climate had immense cultural power. Greta Thunberg emerged. Fortune 500 companies established net-zero plans. The U.S. got the Inflation Reduction Act. Then the pendulum swung back. President Donald Trump returned to the Oval Office, speaking of green energy as a “scam” and carbon footprints as a “hoax” and on all matters of international policy emphasizing bilateral engagement and power over globalism and cooperation. The technology companies that had been at the forefront of net-zero pledges and green leadership discovered the importance of all-of-the-above power to their visions for artificial intelligence and began quietly postponing or removing their targets. On the left side of the political spectrum, rising concerns about inflation and cost pushed climate lower down in the stated priorities of voters and stump speeches of politicians. The moment is ripe for a reset on the politics of climate change and for conservatives especially to chart a course that acknowledges and addresses the real challenges in plausible ways. So what should we think about climate change as we enter a post-peak climate era? If it is neither an apocalypse nor a hoax, what will hold up as a durable view of climate change? And how does it project onto the concerns and priorities of the right-of-center’s emerging coalition? New to Commonplace_? Subscribe below to get the magazine in your inbox._ Subscribe A Solid Foundation As the politics and economics of climate careened over the past ten years, the physics carried on regardless. In 2015, the year the Paris Climate Agreement was adopted, the world emitted 41.2 gigatonnes of carbon dioxide. By 2025, emissions had risen only 2.4%, to 42.2 gigatonnes, because falling emissions from land use offset increasing emissions from fossil fuels. But slow growth in annual emissions is not rapid decline, let alone net-zero, so carbon dioxide kept accumulating in the atmosphere, going from 399 to 426 parts per million. Global temperature kept rising too, from about 1.1 degrees Celsius above the 1850–1900 average in 2015, to 1.4 degrees above it in 2025. None of this should surprise anyone. Even most prominent skeptics, like the authors of the 2025 report commissioned by the Trump administration’s Department of Energy, accept the basic physics. Carbon dioxide added to the atmosphere at industrial volumes accumulates and traps heat that would otherwise escape to space. That heat warms the upper ocean and increases temperatures at the surface. Increased ocean heat and melting of land ice cause sea levels to rise. Changes in the climate become noticeable to us as subtle shifts in temperature and precipitation, seasons arriving early or late, changes in the surrounding ecosystem, and weather extremes. The much more meaningful debate is not about whether these things are happening, but about what it all means for human societies. (While it is a proxy measure of overall risk, no one experiences global surface temperature.) This debate invokes at least three interesting questions: how much today’s warming is showing up in regional climate trends, how much more warming we should anticipate, and how extreme weather events can be understood in the context of climate change. Recent scientific advances give us new insights into each of these, but there is still much to learn. This is the context in which the next generation of conservative leaders will be the first to deal with significant climate change as a fact, not a forecast. It will be with them for their entire careers. Our evolving scientific picture of climate change will come from weather and climate records stretching further into the past, new kinds of observations in the present, better modeling tools for the future, and simply more time to observe the emergence of climate signals and their tangible effects on ecosystems and communities in the United States and around the world. The policy landscape will be shaped by not only the impacts of changes in the climate, but also by the ways in which societies respond and by the emergence of new technologies for both altering the trajectory of emissions and adapting to new climate realities. Global climate records now show the fingerprints of warming in different phenomena around much of the world. The IPCC, in its most recent assessment report, documents warming trends over all land regions, even as natural variability adds substantial variance to local trends. And the highest temperature extremes have become more intense and frequent, almost everywhere, since the 1950s. Scientists are highly confident that these changes are attributable to human influence. But confidence in detecting trends and attributing them to human influence degrades across heavy precipitation events; drought trends are heterogeneous and not attributable to human activity with great confidence. For hurricanes, tornadoes, and other severe storms there is even less confidence in trends or their relationship to climate change. In general, a fair summary of the evidence is that for well-observed phenomena, like surface temperature or heavy rainfall, with a clear relationship to warming, our multidecadal observations are consistent with human influence overcoming natural variability over long time periods and extremes increasing. For regions that are sampled more sparsely, or phenomena with a higher noise-to-signal ratio, we will need to observe them longer to understand the magnitude of the climate signal or improve dynamical understanding using models and observations together. But for Climate For those who can’t wait, we also have new techniques that attempt to understand the role climate change has in influencing particular extreme events. When the climate is changing and disaster strikes, it is natural to ask whether that disaster was somehow “caused” by climate change. For scientists and policymakers acting in good faith, answering that question helps develop a more accurate picture of the problem, how it may be getting worse, and what preparations we should be making to respond to it. It may, at some point, give some weight to how liability is assigned by courts or adaptation funding is distributed by society. But how we ask this question is extremely important. It is easy to get the analysis wrong by discounting the role that meteorology plays. As meteorologist Theodore Shepherd explained in a clear 2016 review of climate attribution methods, “if a weather or climate event is truly extreme in the present climate, then perforce it requires unusual meteorological conditions, which means that climate change is at most a contributing factor.” Share Scientists have two ways to probe climate as a contributing factor, both of which have entered media coverage of extreme weather events. One asks a probabilistic question: How much more likely is a particular event (e.g., a temperature record over a particular area) amid global warming? The answer involves using historical records and computer simulations to estimate the likelihood of such an event in both a changed and a preindustrial climate. The findings are less about the specific event, and more about events of that nature. The other question is: How has the changed climate affected the specific event in question? Here, scientists try to understand what a similar event would have looked like in the preindustrial climate. This is sometimes called the storyline approach, where the story is the specific meteorological details of the event. These methods offer ways to test the intuition of scientists about real weather extremes. In late June and early July of 2021, a persistent high-pressure ridge, or heat dome, set up over the Pacific Northwest and an extraordinary heat wave affected the area from Oregon to British Columbia. For six days, it shattered temperature records across the region and hundreds died from heat-related causes, in an area where such high, and persistently high, temperatures were well outside of experience and many live without air conditioning. Scientists have studied its connection to climate change using multiple approaches. This specific event was created by a rare combination of meteorological factors, a strong high-pressure ridge created the conditions for extreme heat, which occurred on top of higher average temperatures in the region from global warming. Probabilistic analyses showed that climate change increased the likelihood of such an event by at least 8-fold to more than 100-fold. One standout example found that such an event had effectively zero probability of occurring in the preindustrial climate. The enormous range in these assessments reflects how hard it is to estimate probabilities of events at the tail of the historical record. Estimates of the effect on the temperature of the event are more clustered. A recent review paper documents how multiple methodologies have found a positive influence, roughly 1-2 degrees Celsius of an anomaly that exceeded 15 degrees, of climate change on the temperature magnitude of the event. Multiple studies have investigated and found some positive influence of climate change in other extreme events. Attribution studies found climate likely increased the heavy rainfall that accompanied Hurricane Helene in North Carolina and surrounding regions in 2024. The extensive fires that struck Los Angeles in 2025 illustrate how the causal chain can become messy, though. Studies do detect a positive influence of climate on the event’s likelihood, but while climate change likely contributed to underlying aridity, it would not have played a role in the heavy winds or land practices that preceded disaster. These event attribution studies will become more common for extreme, or damaging, weather events. They can be produced quickly, and often are reported before peer review. As the Pacific Northwest heatwave example shows, when multiple methods converge on a positive attribution, the finding should probably carry some weight even if you have to be careful about accepting the results from a single study. I expect that as climate change proceeds and the climate thus departs further from a preindustrial counterfactual, the influence will become more detectable across a variety of extreme events and more easily identified. This will be used to cast blame, but can also be used to inform how communities and society adapt to ongoing change. We May Still Be Surprised One positive development, insofar as less climate change is better, has been that our central estimates for future climate change should probably be revised downward. Mostly, that is because the high-end warmings that scientists regularly analyzed about ten to 20 years ago, driven by high emissions throughout the twenty-first century, now appear to be somewhere between unlikely and impossible. At one time, the upper end of mainstream climate projections extended well into 4 to 6 degrees Celsius of warming by the end of this century, which, models suggest, would have wrought enormous real-world damage. Current energy and policy trends now point toward 2.5 or 3 degrees. Damages and risks are commonly modeled as increasing steeply with more warming, so this is already a better-than-previously-expected outcome for the climate (which is independent of the physical response to emissions). But we should maintain a wide range for plausible outcomes and prepare for the possibility of being surprised. Emissions trajectories are subject to deep uncertainty, the physical climate response is still developing, and whatever change occurs in the climate will then be mediated through unpredictable economic, social, and political institutions. Over the past decade, the pace of warming surprised some keen observers and appeared to accelerate, though not yet outside the range of expectation provided by climate models. The reasons for that apparent acceleration are being actively studied (as was the apparent pause in warming from 1998 to 2012), but no single driver has emerged. Some blame it on the El Niño, variability which would have no bearing on climate. Some think it is a result of factories in East Asia and global shipping fleets cutting aerosol emissions, which in the strongest version of the argument would indicate higher climate sensitivity to carbon-dioxide emissions. Others are more measured, not yet ready to draw solid conclusions as to whether climate projections require revision. We will have to see. Long-Term Thinking Climate change asks us to think over long time scales. But the pace of human-driven warming is compressing a large global change into a century. Changing weather patterns and extreme events are already causing adjustment costs and damages and will do more. Adaptation will help, but it is not free. For some communities, these costs will erode livelihoods and well-being, and may force migration. Are we prepared for large-scale managed retreats in the United States? Thankfully, American wealth, geographical diversity, and moderate climates may leave us better prepared than many around the world. But wealthy countries can still suffer serious disruption from narrowly concentrated costs, even if they appear entirely manageable in aggregate. Readers of Commonplace are familiar with the challenges of rapidly adapting to economic forces. The displacement that came from the China Shock is barely perceptible in aggregate GDP and employment data, but it affected millions of people and those people were far less mobile than economic models tended to assume. Deindustrialization rippled through the nation with enormous effects for not only our economic vitality, but also our national security. The forces surrounding climate change may not be so different; the world gets richer and must accept some diffuse costs. Those may be modest overall, but cause all manner of unpredictable effects with which policymakers must cope. In the case of the China Shock, our faith in a growing pie served us poorly. We’ll need to do better on climate. Leave a comment
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“AI polls” are fake polls
A few weeks after Donald Trump’s second presidential win, I took the train up from London (where I was living at the time) to Oxford to attend a conference on polls and forecasts of the 2024 election. Most of the attendees were pollsters or academics, but I also watched presentations from Aaru and Electric Twin, two companies that do what is interchangeably called synthetic sampling, silicon sampling, or synthetic audiences. Stripped of startup jargon, that means they use large language models (LLMs) to simulate responses to public opinion polls by having AI agents take on the role of survey respondents. I had already heard of Aaru thanks to some articles with eye-catching headlines like “No people, no problem: AI chatbots predict elections better than humans” in the months leading up to Election Day. The founders were making some big, some might even say far-fetched claims, such as: “within two years, we will simulate the entire globe — from the way crops are grown in Ukraine to how that impacts production of oil in Iraq, trade through the strait of Malacca, and elections for the mayor of Baltimore.” When Semafor asked Aaru’s cofounders — Cameron Fink and Ned Koh — about my boss, they said “we respect all those who came before us.” Nate (as he so often does) shared his thoughts on Twitter: Fink and Koh were relatively good-natured about this back-and-forth when we spoke at Oxford. They even offered to mail me one of the t-shirts featuring Nate’s quote they apparently had made. I never took them up on the offer, which I now somewhat regret. These synthetic sampling companies fell off my radar for a while, but they do still exist. In fact, Aaru recently received a $1 billion valuation. Is what they’re doing anywhere close to the most important frontier in AI development? Not by a longshot, especially when Anthropic just developed a model so adept at exploiting software vulnerabilities that it’s only being released to 40 companies. Still, silicon sampling is increasingly finding its way into public polling. Axios reported in March that “a majority of people trust their own doctors and nurses” based on findings from Aaru — without mentioning that the “people” in that sentence were actually LLMs. Around the same time, the Public Sentiment Institute “boosted” their online sample of 373 real survey respondents with 114 AI agents.1 (Spoiler alert: even the co-founder of Electric Twin doesn’t think that’s a particularly defensible approach.) Polling companies like Qualtrics and Ipsos are also developing synthetic data panels. So, what should we make of these … “polls”? Let’s get one thing out of the way: whatever they are, they’re not polls in the way that term is usually defined. Subscribe You can’t replace polls with AI On one hand, using LLMs to essentially make up fake survey respondents sort of sounds like the dumbest idea ever, one that will at best imperfectly replicate real polls while introducing all sorts of biases. On the other hand, with LLMs improving at a remarkable, perhaps even alarming rate, maybe that means I’m a dinosaur at the ripe old age of 24 because I still want to rely on polls that talk to actual people. I’m not going to argue that synthetic samples are completely useless. In fact, as I’ll return to later, there is evidence that some techniques can replicate topline survey results quickly and cheaply. But the marketing from certain companies can be slightly optimistic. “No traditional poll will exist by the time the next general election occurs,” said Fink in 2024. We’re just 206 days away from the midterms, and based on the fact that I still have to collect a bunch of polls every day, I’d say he should have run that prediction by a sample of AI agents before the interview.2 To see why synthetic samples can’t replace polls, here’s a quick primer on how they work. The simplest version of these models involves taking a LLM (like ChatGPT or Claude), giving it a demographic profile (e.g., a white, college-educated woman who lives in Utah and makes $70k a year), and then asking it to respond to a survey question. You repeat that process a few thousand times using different demographic profiles and end up with a sample of synthetic survey responses. The actual models used by private companies are more sophisticated than this, usually because they incorporate more hypothesized demographic characteristics for each agent and provide them with extra information. Aaru, for example, feeds agents a diet of news and information they’d be likely to consume, while Electric Twin incorporates their customers’ proprietary data about the audience they’re trying to replicate. The way Ben Warner, the co-founder of Electric Twin, explained it to me was “we have a large amount of data on […] for instance, 5,000 people. Can we make an accurate prediction of how they would respond to another question?” Still, it should be obvious why synthetic samples can’t replace polls. Polling is fundamentally a data collection process. We might use surveys to make predictions by feeding them into election forecasts, but the main purpose of a poll isn’t prediction, it’s gathering new data about what people think and how they feel. Silicon sampling, on the other hand, produces no new data. It’s simply a model: you input LLM training data, demographic prompts, and a bunch of other information, and it spits out a prediction for what a poll would say. We love models here, but models aren’t polls. That difference is an important philosophical sticking point for most pollsters I talk to. “I think politics should stay away from [synthetic sampling], because we’re trying to […] represent the voice of the people,” said Natalie Jackson, a vice president at GQR Insights. Democratic pollster John Hagner told me: “I think I’m just incredibly skeptical of this idea. I don’t think it’s research. At that point, you’re asking the machine to tell you what you already believe.” Hagner has seen some presentations of early synthetic sampling experiments, but so far, “if it’s being used in a campaign, people are keeping it incredibly quiet.”3 But Eli, I hear you saying, aren’t polls themselves increasingly governed by modeling decisions? Indeed they are: pollsters’ choices on which sampling method to use, how to define their likely voter models, and how to weight their samples can and do lead to dramatic differences in the results they publish. Aaru even referenced these limitations in the methodology statement included with that maternal mortality “poll” — although I’m using the term “methodology statement” loosely here, because it doesn’t really explain how the model works at all. We can ignore the (frankly preposterous) implication that synthetic sampling isn’t subject to a separate set of biases. The important point is that there’s still a meaningful difference between using weighting and other statistical techniques on actual polling data and using a model to predict what a poll would say. The latter is far closer to election forecasts or techniques like MRP — potentially useful models, but not a replacement for polls.4 To be fair, other synthetic sampling companies are perfectly happy with the distinction between polls and models. Warner compared polling and synthetic sampling to different tools in a toolbox. “The mistake I think we make is we think that these new tools should either work in exactly the same way or somehow replace these old tools,” he said. “Rather than thinking of it as, okay, so we’ve always had the hammer, we’ve always had the screwdriver, now we’ve got a saw. But don’t use a saw to try [to] do the job of a hammer.” A quick comment from Nate Eli didn’t ask me for a comment — rather rude of him, don’t you think? But since I’m editing this story, I figured I’d add a few quick thoughts rather than putting words in his mouth. Beyond the frequently misleading marketing, what bothers me about the AI “poll” hype is that as AI tools make statistical inference cheaper and/or better (note that these are not synonyms) that actually increases the comparative value of collecting original data. You might be able to train a model to make a reasonable estimate of what some hard-to-reach poll respondent would say — say, a young Black man who voted for Trump. (Such a person checks a number of boxes for a voter who is usually hard to reach in surveys.) Indeed, this is closely related to what models like the Silver Bulletin forecast already do. They essentially smooth out the kinks in noisy survey data by making inferences based on past voting patterns or national polls or surveys of other states. But you don’t actually know what these voters think unless you’re reaching them directly. If there’s a shift in opinion among this subgroup, you’re not going to detect it. So if I were running a campaign, I’d invest more in going the extra mile to find a representative sample of those voters. And then I’d hire some smart quants — or Claude? — to figure out the implications for campaign strategy based on proprietary data that my competitors didn’t have access to. -Nate Silver Are these models any good? If synthetic surveys are just a new type of model, the next obvious question is whether the models are at least accurate. The answer very much depends on who you ask. On one end of the spectrum, you have the maximalist argument that synthetic sampling is more accurate than actual polls. “It’s an incredibly challenging problem to go to someone and say ‘hey, we’re going to be more accurate at predicting human behavior than you, even when you talk to your customers directly’,” Koh recently told CNBC. In his view, synthetic sampling isn’t a saw to polling’s hammer, it’s “magic.” There’s certainly evidence that synthetic samples can replicate certain survey toplines. But if Aaru does have any examples of their approach outperforming the polls, they’re keeping those to themselves.5 Aaru’s 2024 election model, for example, had Kamala Harris leading in Michigan, Nevada, Pennsylvania, and Wisconsin on November 4th. And although they’ve since taken down their forecast page, they gave Harris a 50.5 percent chance of winning the race on November 2nd.6 After the election, Fink told Semafor he was happy enough with those results because they were “within margin of error,” a term that is completely meaningless when applied to a “sample” of AI agents. And of course, Aaru says their models have improved since 2024, so supposedly now they’d be more accurate than the polls? Still, their stronger argument is on cost: “We are significantly faster and cheaper than traditional polling, and still more accurate,” said Fink. The first two claims are undeniably true, but the third brings us to the opposite end of the spectrum. Both Jackson and Hagner are skeptical that these models are reliable for anything beyond replicating common survey toplines. “I just […] don’t think the machines are what we want when we’re looking for nuanced views. My example on this is people in Arizona and Nevada in 2024 who voted for Trump and voted for expanding abortion in their states on ballot initiatives,” said Jackson. Hagner identified another issue. Maybe the synthetic respondents, like sycophantic LLMs, are inhuman in one important way: they’re too nice. “The reports that have come through at the meetings that I’ve been at are that the early experiments on this, they cannot get respondents to be as racist or sexist or, frankly, as negative as human respondents,” he said. Academic research mostly agrees on this point. While there are some papers that show promising results when using LLMs to replicate polling data, most show that LLMs suffer from various quirks like producing too few “don’t know” responses and can seriously overpredict the favorability of politicians like Donald Trump and Kamala Harris. They also seem to struggle with too little variation between demographic subgroups, so the difference in predicted opinion between Democrats and Republicans, for example, is too small. When I asked Warner about these studies, his response to these papers was that just because academics can’t get synthetic sampling to work doesn’t mean that the technique doesn’t work in general. “Actually, the argument is, okay, yours does not [work]. That does not mean […] for this complex set of machinery, which uses a lot of investment, a lot of time, a lot of money, you can’t get it to work.” Cards on the table, I’m somewhat sympathetic to this argument because academics aren’t exactly great at making election forecasts. Usually, the people with skin in the game are the most accurate. Warner’s argument is that the approach Electric Twin takes — which includes, for example, making multiple predictions for each synthetic respondent using different models and prompts and subsequently averaging those to get a final prediction in a sort of ensemble forecast — produces better results than the simpler academic models. Warner shared a comparison between his method and the method from a recent academic paper with me, and Electric Twin was indeed able to get more accurate replication. But even still, he acknowledged that synthetic sampling “is not a crystal ball.” “If you asked me, do I think using other data sources will be more accurate than asking somebody who they will vote for, I would probably say no. But if you asked me ‘would your system be useful for our turnout modeling today?’ I would say yes.” For better or worse, it looks like the method is already getting more popular in the market research world. Most of the clients Aaru touts these days are businesses like EY and McDonald’s. And AI will probably begin to pop up in other parts of the political polling process. Pollsters are already using it to code open-ended survey responses, and some firms, like YouGov, are testing using LLMs to ask survey respondents questions. More worryingly, one danger to actual polls is that AI agents can be used to infiltrate online surveys. Most online polls use various checks to prevent that from happening, but there’s conflicting evidence on how effective those filters are and how prevalent AI agents currently are in online panels. If those agents ever become impossible to detect, it might spell the end of online polling, but the solution isn’t to replace all of your respondents with ChatGPT. Silver Bulletin is a reader-supported publication. To receive new posts and support our work, consider becoming a subscriber. Subscribe 1 That particular poll obviously doesn’t meet Silver Bulletin standards for aggregation. But we exclude all Public Sentiment Institute polls from our averages because we classify them as an amateur polling firm. 2 You could argue that Fink meant the next presidential election, but (a) I’m also confident we’ll still have real polls in 2028 and (b) in that case he should have asked an LLM to define “general election.” 3 Quick caveat: that’s reporting from a Democratic pollster. It’s possible that Republicans are more willing to use AI in political campaigns. 4 Indeed, Silver Bulletin does not include “polls” produced by MRP in our forecasts or averages, and we think it’s extremely misleading when their practitioners describe them in a way that suggests original data had been collected among a large number of states or Congressional districts. 5 A recent report from Aaru and EY did show two examples of a synthetic estimate being closer than a survey to a real-world benchmark — but I’d take those findings with a grain of salt because the report reads more like an ad and didn’t involve any sort of prediction being made ahead of time. 6 For comparison, our odds for Harris on the same day were 48.2 percent.
There's a startling new plastic surgery trend. It's called 'rib remodeling.'
'Rib remodeling' is a relatively new plastic surgery trend that's picking up steam. How did we get here?
Quantum Computing Is on Its Way to Transforming Science. Inside IBM’s Latest Breakthrough.
IBM teams up with researchers to show a quantum processor’s capabilities in materials modeling.
Scientists See Converging Evidence of Antarctic Ice Retreat
A sediment core spanning millions of years and new modeling studies point to significant ice retreat under temperatures similar to today.
Nordic Semiconductor introduces precise, adaptive battery health-monitoring to enable smarter, longer-lasting IoT devices
Nordic Semiconductor, a global leader in low-power wireless connectivity solutions, today announces Nordic Fuel Gauge v2.0, a major upgrade of its highly accurate software-based fuel gauge solution for the award-winning nPM1300, and nPM1304 Power Management ICS, at Embedded World 2026. The release adds sophisticated State-of-Health estimation, adaptive battery modeling, and long-term fleet analytics capabilities, extending advanced battery management to a wide range of power-constrained IoT prod
Quad Cortex mini: Insane amp modeling power for the discerning guitarist
A warehouse of guitar gear in the palm of your hand.
The first animals on Earth had no skeletons and that changes everything
Sponges may be ancient, but their timeline has been murky. New research suggests the earliest sponges were soft and skeleton-free, explaining why their fossils don’t appear until much later. By analyzing hundreds of genes and modeling how skeletons evolved, scientists found that mineralized spicules arose separately in different sponge lineages. The discovery rewrites the story of how the first reef-building animals—and possibly the first animals of all—emerged.
Scientists Tracked Particles Floating Through the Air—And Solved a 100-Year-Old Mystery
Accurately modeling irregularly-shaped particles isn’t easy, but a new study found a way to improve a century-old idea.
Scientists Tracked Particles Floating Through the Air—And Solved a 100-Year-Old Mystery
Accurately modeling irregularly-shaped particles isn’t easy, but a new study found a way to improve a century-old idea.
‘America’s Next Top Model’ is messier than we ever knew
A new Netflix docuseries has reopened the wounds of “America’s Next Top Model,” the popular reality show that began by promising young women a chance at a real modeling career
Five Berkeley Lab Scientists Receive DOE Early Career Research Awards
Five scientists from Lawrence Berkeley National Laboratory (Berkeley Lab) have received Early Career Research Program (ECRP) awards from the Department of Energy. The ECRP program, now in its 16th year, was established to bolster the nation’s scientific workforce by supporting exceptional researchers at the outset of their careers, when many scientists do their most formative work. Recipients use the funding to pursue projects that have the potential to solve scientific challenges and advance the country’s STEM capabilities. To be eligible for the program, a researcher must be an untenured, tenure-track assistant or associate professor at a U.S. academic institution, or a full-time employee at a DOE national laboratory or Office of Science user facility, who is within 10 years of having earned their doctorate. Awards to an institution of higher education will be approximately $875,000 over five years, and awards to a DOE national laboratory or Office of Science user facility will be approximately $2,750,000 over five years. This year’s Berkeley Lab awardees and their projects are listed below: Timon Heim: Better detectors for more powerful particle colliders Timon Heim is a staff scientist in the Physics Division. His ECRP project will develop a new type of detector that can track the particles generated by collisions in particle colliders with high spatial and temporal resolution. Current detectors in use at high-energy colliders, like the ATLAS detector at the Large Hadron Collider (LHC) at CERN, can track the trajectory and measure momentum of particles, but they can’t track a particle’s time of arrival with sufficient precision to discern from which of the many particle interactions they originate. Heim’s proposed technology — called a 4D tracking detector, because it captures data about particles’ positions in space and time — aims to solve this challenge, allowing researchers to glean more precise data from collision experiments to uncover new insights into fundamental forces and potentially discover new particles. The enhanced capabilities of 4D detectors will be a necessity for experiments at future colliders, such as the Muon Collider or Future Circular Collider. The project will develop custom microelectronics that process the signal of a pixelated silicon sensor that can register charged particles passing through it in an extreme radiation environment that these colliders produce during operation. The goal is to measure individual particles’ position with 50-micro-meter spatial and 50 pico-second (one trillionth of a second) temporal precision, for an incoming flux of around 1 billion particles per second per square centimeter. Harrison Lisabeth: Studying underground processes to unleash abundant energy Harrison Lisabeth is a research scientist in the Energy Geoscience Division focused on combining real-world experiments and AI to guide development of geothermal energy systems. His project will explore how deep underground rock formations – where geothermal wells are dug – are stressed by chemical reactions of the minerals and mechanical strain from movements in the Earth. A better understanding of subsurface stresses is essential to engineer safe and reliable geothermal wells that access the heat of deep rocks to produce electricity. It is estimated that the U.S. could generate ten times the amount of energy needed to power the grid using geothermal sources. Lisabeth plans to simulate how minerals grow, dissolve, and influence the behavior of surrounding rock in geothermal reservoirs by tracking chemical changes in rock samples under pressure in the lab. The project will also develop machine learning models to predict geologic strain based on field measurements and refine existing models that describe how common rock types deform under stress. Combining these approaches, Lisabeth hopes to develop a framework to predict stress to improve stability and longevity of geothermal wells. Daniel Carney: Pushing the quantum boundaries of particle measurement Daniel Carney is a staff scientist in the Physics Division who uses quantum information science to study particle physics and gravity. At very small scales, “quantum noise” — random fluctuations from the act of measurement — can drown out faint signals from rare processes. Carney’s ECRP project, “Fundamental Physics at the Quantum Limits of Measurement,” aims to build a new mechanical quantum sensor that can measure extremely small particle interactions below the limits of that quantum noise. The approach uses lasers to hold a tiny bead containing radioactive atoms; when one decays, the system measures the bead’s motion and the energy of any particles emitted. The experiment’s initial goal is to search for heavy sterile neutrinos, hypothetical particles predicted by some extensions of the Standard Model. The system could also help measure the mass of the neutrino, study the way particles interact through the electromagnetic and weak forces, and hunt for low-mass dark matter — all unsolved mysteries in particle physics. Callum Wilkinson: Using machine learning to demystify neutrinos Callum Wilkinson is a staff scientist in the Physics Division and part of the international Deep Underground Neutrino Experiment (DUNE) hosted by Fermi National Accelerator Laboratory. DUNE will create an intense beam of neutrinos that interact in detectors in Illinois and, after an 800-mile journey through the earth, in massive detectors in South Dakota. By precisely measuring neutrinos and how they change, researchers will get a better understanding of their fundamental properties and whether they are responsible for why there is so much more matter than antimatter in our universe. Wilkinson’s ECRP project will apply two machine learning techniques to improve the analysis of DUNE’s data and enhance the experiment’s precision. One approach will look for hidden patterns in how neutrinos interact and produce low-energy particles, while the other will help correct for distortions in what the detectors record. The methods will be tested on one of DUNE’s prototype detectors and could be used in other neutrino experiments, improving their measurements and helping scientists build better models. Aditi Krishnapriyan: Machine learning methods that scale as datasets grow Aditi Krishnapriyan is a faculty scientist in Berkeley Lab’s Applied Mathematics and Computational Research Division (AMCR) and assistant professor at UC Berkeley. Her project, awarded through UC Berkeley, will develop machine learning methods that scale with dataset size for modeling complex, multi-scale phenomena. The resulting tools can be applied in a wide range of research, including the study of new electrolytes for batteries or the examination of molecular interactions. ### Lawrence Berkeley National Laboratory (Berkeley Lab) is committed to groundbreaking research focused on discovery science and solutions for abundant and reliable energy supplies. The lab’s expertise spans materials, chemistry, physics, biology, earth and environmental science, mathematics, and computing. Researchers from around the world rely on the lab’s world-class scientific facilities for their own pioneering research. Founded in 1931 on the belief that the biggest problems are best addressed by teams, Berkeley Lab and its scientists have been recognized with 17 Nobel Prizes. Berkeley Lab is a multiprogram national laboratory managed by the University of California for the U.S. Department of Energy’s Office of Science. DOE’s Office of Science is the single largest supporter of basic research in the physical sciences in the United States, and is working to address some of the most pressing challenges of our time. For more information, please visit energy.gov/science.
Advancing AI for science: extracting and fusing cross-disciplinary expert knowledge with data to accelerate alloy discovery
High-entropy alloys are promising advanced materials for demanding applications, but discovering useful compositions is difficult and expensive due to the vast number of possible element combinations. Now, researchers have developed a novel AI-driven framework that integrates experimental data, computational modeling, and cross-disciplinary expert knowledge extracted from scientific literature. By combining these sources in a way that accounts for uncertainty, their approach can make reliable predictions even for poorly studied alloy compositions, outperforming conventional data-driven machine learning methods that rely on training data alone.
RISR teams with $34B Allworth to scale business-owner planning
Partnership embeds RISR’s capabilities for modeling valuations, exits, and post-liquidity outcomes into Allworth’s national advisor tech platform.
How Do Climate Scientists Use Artificial Intelligence?
The AI used in climate research right now is much less energy intensive than the large language models like ChatGPT.
Ashton Kutcher says Tom Ford fired him from Gucci modeling job for looking 'too fat' in pink Speedo
Kutcher alleged that he lost a Gucci gig early in his modeling career after the famous designer wasn't impressed by his body at the time.
Ashton Kutcher says designer fired him for being 'too fat' in pink Speedo
Ashton Kutcher alleged that Tom Ford fired him from a Gucci modeling job for looking 'too fat' in a pink Speedo: 'I was 178 pounds, what are you talking about?'
Scientists Solve Mars Water Mystery With a Thin Layer of Ice
New climate modeling suggests that small lakes on ancient Mars could have stayed liquid for decades, even in a generally cold environment. Small lakes on early Mars may have stayed in liquid form for many years, despite average temperatures that were far below freezing. Researchers at Rice Uni
Blender 5.0 Benchmarks Since Blender 3.0 For CPU Rendering Performance
As part of the many different year-end benchmarks on Phoronix, over the holidays I was curious about how far the Blender 3D modeling software's performance has evolved over the past few years
The Bear River Family Entertainment Center Will Shut Down for Remodeling at the Beginning of the Year
The Imperfect Science of Home Climate Risk Scores
Zillow’s climate risk scores have disappeared from home listings, but the risks themselves are very real. Modeling those threats down to the home level, though, is challenging.