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‘AIDS is not over:’ Behind a local group’s awareness campaign for HIV funding
Walk through East Liberty, Bloomfield or other Pittsburgh neighborhoods and you might see flyers with black-and-pink block print and an urgent message:...
Google testing iPhone feature to add to Pixel smartphones
Google might soon expand on a current Pixel capability matching the iOS version of it.
Scratching that bug bite might feel good at first but science explains why it's a bad idea
You've likely heard it since childhood: Don't scratch that bug bite or rash, you'll make it worse. But why would something that feels so good be bad?
Scratching that bug bite might feel good at first but science explains why it's a bad idea
Now researchers better understand why even a mildly annoying itch could put you on an itch-and-scratch cycle if you give in.
Scratching that bug bite might feel good at first but science explains why it's a bad idea
For the summer itchiness of bug bites and poison ivy, dermatologists recommend hydrocortisone cream, calamine lotion or oatmeal baths.
Scientists finally reveal the shocking truth behind England’s dolphin deaths - Futura-Sciences
A Drastic Decline Along English Shores The dolphin populations along the English coastline have been disappearing at an alarming rate for several decades. Contrary to what many might hope, these aren’t dolphins simply seeking warmer waters elsewhere. The brutal reality is that this is not migration—it’s mass mortality. English researchers...
This might be the new best smart speaker
In this week’s Installer: Gemini, Star Fox, and The Last Airbender.
Scratching that bug bite might feel good at first but science explains why it's a bad idea
You scratched that bug bite and now it's a big itchier bump. Some science explains why scratching really can make things worse, even if you don't break the skin. University of Pittsburgh researchers put tiny “cones of shame” onto mice to help unravel what happens on a cellular level when an itch ...
Scratching that bug bite might feel good at first but science explains why it’s a bad idea
You scratched that bug bite and now it's a big itchier bump.
Scratching that bug bite might feel good at first but science explains why it’s a bad idea
WASHINGTON (AP) — You’ve likely heard it since childhood: Don’t scratch that bug bite or rash, you’ll make it worse. But why would something that feels so good be bad? A lot of things can cause itc…
Scratching that bug bite might feel good at first but science explains why it's a bad idea
You scratched that bug bite and now it's a big itchier bump. Some science explains why scratching really can make things worse, even if you don't break the skin. University
Scratching that bug bite might feel good at first but science explains why it's a bad idea
You’ve likely heard it since childhood: Don’t scratch that bug bite or rash, you’ll make it worse. A lot of things can cause itchiness, sometimes serious diseases. Whatever the cause, doctors have l...
The Extremely Online Senate Race Testing Democrats’ Midterm Strategy
In 2026, winning an election might mean winning the most attention online.
If a Lyme disease vaccine gets approved, how would it go over? We asked hunters
Drugmakers are working on a potential new shot to prevent the tick-borne illness. How might it fare in the era of vaccine skepticism?
Scientists mull controversial plan to steer hurricanes
It might sound like science fiction, but some scientists say we have tools that could be used to disrupt natural disasters. Others are skeptical.
New iPhone 18 specs report raises big question of iOS 27 limitations
Apple’s base model iPhone 18 might not get as much RAM as expected—raising questions about support for iOS 27’s most demanding features.
Science Can Fully Explain Consciousness? By 326 Theories…
It’s supposed to be a given that machines or brain organoids might be conscious despite not knowing what that is or how it works — but preborn children can’t be…
New iPhone Ultra report has new pricing and release timing info
Apple’s highly anticipated foldable iPhone is launching this fall, but a new report today claims you might have to wait...
Grand Theft Auto 6 demand may be so high it will "likely outstrip supply" this holiday season, a video game retailer warns
Speaking to The Game Business, a major senior games buyer has warned that stocks might not survive the spending frenzy this fall.
JD Vance claims Diddy is 'not a Christian' after federal conviction
Sean "Diddy" Combs might have scored a reference in Vice President JD Vance's new book, but the politician is not eager to associate himself with the imprisoned music mogul.
Making Our Own Luck
We are cross-posting a piece that former NIH official Kris Willis wrote for Macroscience. Kris is now the President and Founder of the Woodley Park Institute, for which I’m a board member. Picking winners or hedging bets? Federal science agencies have long wrestled with the question of how to ensure their billions of dollars of grants and contracts result in maximum benefits for Americans. On the surface, it might seem that a goal-oriented, interventionist style of management would be the best policy: prioritize our most pressing problems and distribute funding accordingly.This top-down strategy creates a unique hazard, though: bad choices can send resources in the wrong direction, resulting in slow progress that impedes the delivery of tangible benefits to citizens. Moreover, if decision makers pursue an applied advance when the underlying scientific principles remain poorly understood, they risk costly failures and a loss of support for the research enterprise at large. Examples include Nixon’s 1971 War on Cancer, undertaken on the premise that a cure could be achieved in less than a decade, the drive to build an operational commercial fusion plant by the mid-1990s, and the National Plan to Address Alzheimer’s Disease, which failed to produce the effective treatments and prevention strategies promised by 2025. The contrasting laissez-faire approach is to hedge your bets. If the path to an advance is impossible to predict, then the wisest choice might be to distribute funding to as widely as possible. The main risk of this strategy is that spreading funding thinly may limit the resources available to the most promising research, resulting in the same negative outcomes as the interventionist approach: limited support for the most promising topics, slow progress, and delayed returns. In reality, at science agencies like the National Science Foundation and National Institutes of Health, program managers navigate between these two extremes, setting some funds aside to target high-priority areas and using the rest to cover the widest possible range of meritorious proposals. This middle path seems like the most rational way to proceed, but it still requires making choices. How much should be set aside for high priority topics? What should those topics be? How should we define merit? Expert opinion can provide a helpful guide, but leading experts can have remarkably different opinions about what matters and what should be prioritized. Moreover, experts and administrators alike can be risk averse, giving an edge to high-profile, well-established concepts. What’s really needed is a data-driven framework to help guide decision-making, one that doesn’t simply repackage prestige or incumbency. Recognizing breakthroughs years in advance Many breakthroughs fail to attract funding or recognition at the earliest stages of development. Katalin Karikó, for example, spent years struggling to win grants for work on mRNA that would eventually win her and Drew Weissman a Nobel Prize. Similar stories can be told about Jim Allison’s groundbreaking studies leading to the development of cancer immunotherapy, Stan Pruisner’s demonstration that prions can self-replicate without DNA, or Robin Warren and Barry Marshall’s proof that Helicobacter pylori, not stress, causes ulcers. Yet at some point, the scientific community recognizes and responds to transformative breakthroughs like these. How does that change happen? And what if science funders could identify the areas that are on the cusp of a breakthrough? Those questions led my collaborators and me to study how scientists responded to past breakthroughs. Based on patterns in that data, we developed a means of predicting future breakthroughs, using biomedicine as a proof of concept. It’s worth taking a moment to describe how our approach works. Briefly, our prediction process begins by grouping the 18 million or so peer-reviewed papers in PubMed, the authoritative database of biomedical research, into roughly 50,000 unique topics. A small subset of these topics are so widely acclaimed as breakthroughs that they have been recognized with a major prize like a Nobel or a Lasker Award. We theorized that studying the development of these outliers over time, especially before they gained acclaim, was the key to identifying future instances of them. After defining topics, we chose 21 representative breakthroughs and asked what they looked like before they won prestigious prizes. To answer that question, we re-ran our topic-mapping algorithm repeatedly, winding back the clock one year each time, stringing together the years to follow the progress of each field as it grew and advanced. We found that in each case, the years leading up to a discovery followed a pattern: a burst of papers on a new, rapidly evolving topic, many of which quickly became influential. These characteristics—the percent of papers that are new, the percentage of papers that are brought in from other topics, and the influence of each individual paper—can all be measured separately for any given topic. Together, they act as a predictive signal that is detectable an average of five years before a transformative discovery is made, and up to thirty years before the discovery receives a major prize. Scanning the current research landscape for topics that display this signal allows us to predict what work will likely produce a future breakthrough. Each year we analyzed includes four or five signals, a number that appears to have remained constant across a twenty year time frame. In their classic work on the sociology of science, Bruno Latour and Steve Woolgar established that the desire to participate in a major discovery is an important motivator for scientists as they consider whether they should take up (or abandon) a research problem. Although further work is needed, the simplest explanation for our results is that this behavior is widespread enough to detect at scale. A foundational advance, or something like it, draws the attention of scientists and causes them to change the direction of their research. They vote with their feet, staking their own reputation and careers by publishing on and citing the new idea. That upswell of interest is the foundation of our breakthrough signal. What kind of research does our model identify as a breakthrough? After building a model based on the patterns in our 21 preselected breakthroughs, we tested it by identifying all early signals of discovery from 1994 through 1997. Of the 18 signals we found in that time period, 17 can be traced forward in time to a breakthrough. Examples include super-resolution fluorescence microscopy (see the image above), the directed evolution of proteins and enzymes (Chemistry Nobel, 2018), and sequencing the human genome (National Medal of Science, 2008). The discovery of the role of leptin signaling in obesity is a particularly interesting case study. Around 1950, scientists noticed the existence of a type of lab mouse that suffered from a unique inherited form of obesity. When given the freedom to choose their own diet, these mice ate more than normal and preferred food that was high in fat. Researchers had few clues as to why the animals overate; early experiments pointed to the existence of a soluble ob factor found circulating in the bloodstream that regulated appetite, but its identity was unknown. In 1994, Jeffrey Friedman and colleagues identified and sequenced the ob gene, noting that the protein it encoded appeared to fit the profile of a circulating factor. A year later, a trio of high-profile publications demonstrated that ob was a hormone that regulated body weight and fat deposition by regulating appetite. One of these was co-authored by Friedman, who christened it leptin from the ancient Greek word for thin. Using our model, the data from 1996 make it clear this advance would become a breakthrough. Scientists had flocked to study the biology of the new hormone, generating the flurry of new papers and citations required to produce a signal. One of these demonstrated that serum leptin concentrations reflected the amount of adipose tissue in the human body, providing clinical validation of the earlier mouse studies. Before the identification of leptin, scientists were unable to point to a specific molecule that controlled appetite and adiposity, so that it was impossible to rule out non-physiological causes of obesity; its characterization fundamentally changed our concept of weight gain. In 2010, Friedman and fellow pioneer Douglas Coleman were awarded a Lasker for the discovery. Our work also identifies breakthroughs in clinical practice and behavioral and social sciences, even though these areas are less likely to draw the attention of major prize committees. One example that falls into this category is the introduction of endovascular aneurysm repair, a minimally invasive surgical procedure for the treatment of abdominal aortic aneurysm, a leading cause of death among Americans over the age of 65. Relative to the previous standard of care, it reduces in-hospital mortality by almost four-fold. In 2017, Timothy Chuter received the American College of Surgeons’ prestigious Jacobsen Innovation award for his multiple refinements to the technique. Another under-recognized breakthrough identified by our model is the development of standardized survey instruments to assess the quality of life for HIV+ patients. The antiretroviral cocktails introduced in the mid-1990s reduced AIDS mortality, but treatment came with serious side effects. The new evaluations showed that patients whose disease progressed had worse physical functioning than Americans with other chronic diseases, while those who were asymptomatic maintained a health-related quality of life on par with the overall US population. This evidence made it possible for patients and activists to argue that even in the absence of a total cure, and in spite of the side effects, alleviation of symptoms was a worthwhile priority for clinicians. My colleagues and I have failed to identify any significant recognition for the physicians and health policy experts who pioneered this advance, although in 2016, researchers called for the World Health Organization to set targets for good health-related quality of life as part of its framework to end the AIDS pandemic. From forecasting to funding How should science funders use our predictions? Any serious discussion of what we should do with this new capability needs to begin with an understanding of its limitations. We can’t (and shouldn’t) invest all of our resources in the small number of topics predicted to produce breakthroughs. Innovative new fields are born from existing ones; they rarely arise de novo. If funders don’t maintain a diverse portfolio, the breakthroughs of tomorrow will have no antecedents. Think of this as eating the seed corn, or killing the goose that lays the golden egg. There are also multiple goals of funding beyond basic scientific discovery, including translating discoveries into clinical practice, developing new technologies, supporting economic development, and training new scientists. The investments needed to accomplish these goals are likely different from those required to support emerging breakthroughs. Finally, we must be aware that we may not capture every instance of a breakthrough. The concept is fuzzy, and not every significant discovery is recognized with a major award. Further, breakthroughs are rare, meaning that our dataset is, by necessity, small. This makes formal estimates of accuracy challenging. Although our overall success rate appears to be high, any one signal may turn out to be a false positive. We found one apparent example: the development of the third generation COX-2 inhibitors, Vioxx and Celebrex, which were much celebrated in the early 2000s as effective but non-addictive pain relievers, meets all the algorithmic criteria of a breakthrough. Five years after it received FDA approval, researchers demonstrated that Vioxx was associated with serious cardiovascular complications, and the manufacturer voluntarily pulled the drug from the market. Celebrex remains available, although like other NSAIDs, it carries an FDA warning for increased cardiovascular risk. Decision makers who were overly focused on the breakthrough potential of this research might have wasted substantial resources nurturing an area that ultimately proved to be a failure—but so would those who relied on expert opinion at the time. That said, the potential benefits of funding the next mRNA vaccine or cancer immunotherapy years earlier than we might have are large. When we can’t assume the outcome of an action, it’s worth conducting an experiment. Can targeted support for areas that are poised to produce a breakthrough increase the return on scientific investment without cannibalizing future advances? With the necessary administrative infrastructure in place, conducting this experiment would be straightforward. First, identify all the breakthrough signals between 2018 and 2023, select half at random, and commit to a decade of investment. For biomedicine, a six year window should yield about 30 topics with the potential to produce a breakthrough. Providing strong support to half of these might be accomplished for $300 million a year. That’s roughly half of the recent annual budget of the NIH Common Fund or around 20% of the fiscal year 2025 budget for ARPA-H. After five years, then again at ten, compare to see which group — the one that was actively managed, or the control — produced more breakthroughs, and on what timescale. Although we used biomedicine as an example, the same experiment could just as well be run for physics, materials science, or any other discipline. Any attempt to undertake such an experiment should keep three points top of mind. First, we can predict the topic of future breakthroughs, but the people and vision for how to move the work forward are still important. In practice, this means success requires active program managers with the expertise to develop the science and the authority to do so, including by recruiting investigators. Second, we would be wise to learn from the failures of past attempts to fund transformative research. High among these are funding modestly repackaged work, confusing the genuinely novel with the merely unfamiliar, and relying too much on incumbent investigators. All of these risks can be mitigated by thoroughly and intelligently integrating high quality data into portfolio management. Finally, we need to have patience. Even with strong funding support, producing a breakthrough takes time. If supporting breakthroughs is the best way to nurture the birth of new fields, we also need to accurately describe and properly manage the other two stages, so that we invest wisely throughout the productive lifetime of ideas and divest once they reach the point of diminishing returns. We would benefit from more study of all of these phases, especially the transition from one to another. More research could answer questions like: When does a breakthrough become an established field? How do the availability of funding, the size and characteristics of the available workforce, and the accumulation of evidence that disagrees with prevailing models affect a field’s decline? Exactly how do old ideas give birth to new ones, and can we speed up the process? The more we understand about the dynamics of scientific progress, the better we can maximize the odds that our curiosity leads to results. I’d call that finding a way to make our own luck.
How open are Americans to socialism, after all?
Democratic socialists might be having their best year ever.
Can your smartwatch detect sleep apnea?
No smartwatch can actually diagnose sleep apnea, but some can track certain signs that might indicate you have the condition.
David P. O'Brien
About five years ago, David P. O'Brien had a comfortable position at Marsh & McLennan Cos., where he held the top post at the financial giant's Louisville, Ky., office. Despite the lofty rank, Mr. O'Brien found himself longing to be at a smaller company that had to work a little harder to land business. That desire led him away from Kentucky five years ago and to Cleveland, where he serves as executive vice president at business insurance agency Oswald Cos. "I left Marsh before it was en vogue," he said, referring to layoffs and defections at Marsh over the course of this year that followed accusations of bid rigging at the big firm. Above all, Mr. O'Brien, 38, wanted to work at a company that relied on its inventiveness, rather than its name, for business. He said he has found that at Oswald. "It causes you to be more creative, to be more resourceful," Mr. O'Brien said. For instance, an Oswald client seeking to do business internationally might be directed to the network of offices abroad run by competitors such as Marsh and Aon Risk Services Inc. "We're going to use whatever access into the market that makes the most sense for our clients," he said. Of course, using such a strategy can be dangerous for Oswald if its client decides to further a business relationship with one of the firms Oswald recommended. "That's the obvious question and why it's nontraditional," he said. "I think if we're good, and if we're bringing value to the client, then there's no risk. I'm not threatened by it." Imaginative business solutions of that type made his career move to Oswald from Marsh worthwhile. "It's very much a challenge," he said. "It helps you keep your edge. I'm all for the competition." John Jacunski, currently vice president and corporate controller at York, Pa., paper company PH Gladfelter Co., met Mr. O'Brien four years ago when Mr. Jacunski was at WCI Steel Inc. in Warren. So impressed was he with Mr. O'Brien's work for WCI that Mr. Jacunski has retained the Oswald executive at PH Gladfelter. "He's a young guy for the position he has, but he's done a great job for us," Mr. Jacunski said. Among his roles at Oswald, Mr. O'Brien leads the firm's client retention efforts and also serves as director of its property and casualty group. "You don't have the same human resources," he said of the job jump to Oswald. "You don't have, obviously, the safety net a big company has." But sometimes, it can be difficult to ascertain individual contributions when working at a big company. "I wanted to be able to see the end results of my work and my efforts," Mr. O'Brien said.
How should European scientists respond to the US AI kill switch?
Earlier in June, Europe’s worst fears of technological dependence were confirmed when the White House ordered the AI firm Anthropic to stop non-US citizens using its leading models. This kill-switch moment crystalised anxieties in Brussels that the continent, lagging the US on the creation of leading-edge AI models, is now a technological of vassal of Washington. The shock has specific implications for European scientists and universities. Some governments are now worried that with AI being integrated into labs, Donald Trump now has his finger hovering over an off switch for European science. “Having restrictions on this tool, it can have an impact on our own research,” said France’s research minister, Philippe Baptiste, at the VivaTech conference in Paris on June 18.Baptiste, himself a former academic, said that colleagues in mathematics had recently warned him they were now cut off from leading-edge US models, risking their work. “This model is not available anymore now because of the US restriction, and for the first time this had an impact on mathematics,” a field where large language models (LLMs) are making a significant contribution, said Baptiste. So, should European scientists avoid relying on US models in the future? Or would this be a self-harming overreaction? And how is AI being used in science, anyway? What happened?On June 12, it emerged that Washington, citing cybersecurity concerns, had ordered Anthropic to stop non-US citizens using two models: Claude Mythos 5, and Claude Fable 5, a version of Mythos but with guardrails to prevent abuse. Anthropic said Washington’s concerns were overblown. But forced to follow the order, yet with no way to check who was using the models, the company suddenly shut down access for everyone, US citizens included. There’s still no agreement on how to restore broad access. But last week, Bloomberg reported that select US companies, and the US government, still had access to Mythos despite the ban, but the EU’s cybersecurity agency Enisa had been shut out. Class division of researchersIn the longer term, access outside the US should be restored, said Marcel Salathé, co-director of the AI centre at the Federal Polytechnic School of Lausanne (EPFL), because a permanent restriction would be financially ruinous to US AI firms. “Even Trump will eventually budge to economic pressure,” he said.However, US AI firms might end up operating rather like banks, doing due diligence on users of their leading-edge AI models, he went on.European scientists are unlikely to fall foul of such checks, he said. But having a Chinese postgraduate in their lab, or doing a type of research the US disapproved of, could potentially result in access being denied. “It will create a sort of class division of researchers,” he said. More than frontier modelsHowever, successfully deploying AI in science is about far more than just having access to leading LLMs such as Mythos.Instead, many science-specific tools, most famously Alphafold, which predicts protein folding structures, require “far less” computing power to train than frontier models, well within the capabilities of European supercomputers, according to a recent analysis by the Arq Foundation, a Brussel-based AI think tank. Instead, tools such as Alphafold are possible because certain fields, like protein folding, weather prediction or astronomy, have large, clean, standardised datasets that can be used to train predictive models. Rather than compute, “data is the central constraint for AI for science,” the report says. For example, a less powerful physics model trained on CERN’s data “will be more valuable than the best model without certain data,” said Bengüsu Özcan, author of the report. European advantage?Europe could therefore steal a march by using its scientific data and infrastructure to create models US digital giants cannot. “They might actually match or go even beyond what these big [US] companies are doing,” said Özcan.Indeed, some European start-ups hope to create automated testing labs to create the kind of standardised data that could speed up the discovery of new materials. Materials scientists say there’s currently too little data to train models. AI scientistsHowever, even if the bottleneck for AI in science is often data, rather than compute, being at the mercy of the US for LLM access could still become a problem for European scientists. A new class of “AI scientists” based on leading LLMs is also emerging, Özcan’s report says.Unlike domain-specific tools like Alphafold, these try to automate day-to-day scientific work, by providing an overarching tool that coordinates specific models to synthesise existing literature, generate hypotheses or run analysis of data. The problem for Europe, however, is that these tools are sometimes based on US leading models. Google Deepmind’s AI Co-Scientist, for example, is built on Google’s Gemini LLM. Systematic failureThese scientific automation tools are still in their early stages, and still exhibit “systematic failures on scientific tasks,” Özcan’s report says. One of the problems is that the messy, day-to-day process of scientific discovery is largely not documented, meaning LLMs don’t have a good source of data to learn how scientists actually work. Meanwhile, some tasks, such as literature synthesis, don’t typically require access to leading edge models.However, the performance of AI scientific assistants could improve as companies such as Google pour resources into developing them. Rely on US“Unfortunately, for scientific assistants, there is not much we can do right now except rely on US frontier AI,” said Antoine Bosselut, a natural language processing expert based at EPFL. Chinese-built open models, which can run on local computers, lessening the risk of a shutdown, are touted as one alternative to US dependence. There’s also Mistral, once seen as Europe’s main AI hope, but the Paris-based company has seen its recent models fall behind in performance. But the Chinese alternatives are generally not as capable as US frontier AI models, said Bosselut, unless paired with fiddly, custom tools that allows them to act as independent agents. “Most scientists do not have the time,” he said. Don’t jump ship yetAlthough the kill switch moment is seen a warning shot for European scientists, it doesn’t mean labs should abandon US AI tools immediately. “I do not think the right response is simply to abandon US-based models and systems under the banner of sovereignty or independence,” said Mattias Björnmalm, secretary general of the Cesaer university association. “That would be an overreaction.”Related articlesEuropean sovereignty in AI requires ‘ugly trade-offs,’ say experts Robotic lab start-up makes case for €500M EU-funded materials testing facilityAI in science: is it useful? Instead, European researchers need to ask themselves whether they’re becoming dependent on technology that could be turned off in Washington, said Özcan. “They might not see this today, but they should really start asking this question,” she said. European researchers need a plan B, said Salathé. “I would recommend to every researcher, very proactively explore alternatives.”Own toolsIn addition, Björnmalm and others want European research institutions to develop their own scientific AI tools.Instead of signing AI deals with US digital giants, European universities should be using this money to create their own models, which may end up being superior for research, said Sabine Leonelli, an expert on the use of AI in science at the Technical University of Munich. “Universities in Europe seem to be happy to sign commercial contracts with US companies on the assumption [their AI tools] are better,” she said. But US frontier LLMs are typically opaque, and have been developed without independent scientific input, she said. “This is not good enough for science.”Last year, Swiss researchers released Apertus, an LLM designed to be completely transparent, from its development methods to its training data. The idea is that this makes scientific tools built on top of the model far more reproducible than with closed, commercial alternatives.“Europe needs more of this: serious capacity-building that gives researchers, universities and companies credible options,” said Björnmalm.
Zohran Mamdani Tightened His Grip on New York Politics
His opponents thought Tuesday night might offer an opening. It very much did not.
You Might Find Soccer Boring but American Sports Media Are All In
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Cavaliers named landing spot for ex-$206 million Lakers disaster
The Cleveland Cavaliers were linked to a former Los Angeles Lakers guard who struggled mightily with the franchise years ago.
Grand Theft Auto VI will cost $80 without a physical disc
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Valve clarifies: all Steam Machines, for now, will ship with one 16GB stick of RAM.
In an interview with Gamers Nexus, Valve had said that the PCs would ship with either one 16GB stick of RAM or two 8GB sticks of RAM. “We misspoke here - all units will actually have one 16GB stick of RAM,” the company says in a follow-up statement to the channel. “It’s possible that this might change in future builds, but the units that customers are currently signing up for will all come with one 16GB stick of RAM.” [Media: https://youtu.be/glXA3ObwSwQ?si=QGmDWmVfbMiONxLd&t=109]
5 players who might be playing themselves out of a trade, from Freddy Peralta to Willy Adames
'ANTM' cycle 25 might cast 'an AI contestant,' Tyra Banks rep says (exclusive)
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Water might secretly be a mix of 2 different liquids, scientists say
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Scientists just found the hidden 'lid' keeping Yellowstone's supervolcano from erupting - Futura-Sciences
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Zohran Mamdani has lost none of his political magic
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Circulating metabolites at the crossroads of lifestyle and brain health
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2026 World Cup Bracket, Standings, Projections: USA in Round of 32, Who Else Might Advance?
Track the 2026 FIFA World Cup bracket with live standings and Round of 32 projections. See where Team USA stands and who's on track to advance.
MSI Claw 8 EX AI+ review: Big money for big performance
Intel and MSI have teamed up to create what might be the most powerful handheld gaming PC on the market.
"The 'AI Stigma' is real and severely punishes developers": A new study shows how much using AI in games hurts sales, and the numbers are hard to believe
Amateur developers might be able to make a quick buck, but established studios are seeing serious declines when using AI during development.
Like New PlayStation 5 Slim Disc Edition Consoles Are Going for $499 for Amazon Prime Day. Blink and They Might Be Sold Out.
Blink and they might be sold out.
Finnish scientist Wickström awarded Körber Prize for cell research
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2026 MCWS finals: Sneaky Sooners claim another national title
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South Carolina small business summit connects owners with government resources
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How This Common Painkiller Is Stopping Cancer From Spreading, Scientists Reveal - Futura-Sciences
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Best Buy launches massive anti-Prime Day sale — see 25+ Android deals that might make Amazon look bad
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Register now for even bigger Run Dollywood race weekend this fall
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Halo: Campaign Evolved Main Menu Video Sparks Fan Debate Over Ring Detail
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Read this before you vibe-code another app
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"I Think I Have Made History With My Remake" - Fan Remake Of Zelda: Ocarina Of Time "Officially Stopped" After 10 Years
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San Antonio health officials urging gun owners to secure firearms
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Phillies on the Pharm: 6/21/2026
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Appreciation: Sitcom mastermind James Burrows made TV feel like family
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Taking supplements every day? You might be doing more harm than good
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This month's Windows 11 update broke the Recycle Bin, OneDrive, and possibly your PC's stability
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The next step in your career might be becoming your own boss
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AMC Entertainment Holdings (AMC) Stock Might Be 31.1% Overvalued After Record May Attendance
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Valve is so behind on Steam Controller orders that some won’t ship until 2027
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SF Giants Opinion: Time to clean house
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Google Health returns 'Hourly Activity' in June, tosses in extra for Android
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Ebola's spread fueled by cuts in humanitarian aid | Harvard T.H. Chan School of Public Health
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From The Sports Desk: Harry Kane scores two goals, spurs England’s strong World Cup start
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No Law's Dense Cybergrunge City Might Just Be the Most Alive Open World Ever Created
No Law interview with developer Neon Giant.
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YouTube is testing another Android redesign, and your muscle memory might hate it
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Cold water changes your cells, and science says it only takes one week - Futura-Sciences
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Why Elon Musk might want to merge Tesla and SpaceX
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A Wild Rumor Linked To Sundar Pichai Suggests Google Is “Evaluating” The Procurement Of Memory Chips From China’s CXMT
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Democratic socialists are on the rise in Trump-era mayoral races
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Vampire Survivors Fortnite Crossover Might Not Happen Due To GenAI
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Earth Might Have Been Sprinkling Europa With Life Since Before Animals Existed
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War, sports collide as Iran's World Cup kicks off in Los Angeles
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Merchants of Doubt and the attacks on Extreme Event Attribution science
Last week, I attended a meeting at Columbia University on attribution science and climate law, hosted by the Sabin Center. It was a fantastic event, bringing together scientists and legal experts working at the intersection of extreme event attribution and climate law. Subscribe For those unfamiliar with it, extreme event attribution attempts to quantify the contribution of climate change to an extreme event. For example, several groups analyzed the impact of climate change on Hurricane Harvey’s enormous rainfall totals over Houston, Texas and they found that climate change increased rainfall by 15 to 38%. One thing that came up again and again was how terrified fossil-fuel interests are of extreme event attribution science. They are acutely aware that this research could land them in court. And losing those cases would leave them legally liable for billions of dollars in climate damages. Because the legal stakes are so high, the blowback has turned ugly. I spoke with several scientists at the meeting who are facing ongoing harassment over their work. This blowback is a coordinated campaign to make the entire field look suspect. The goal is to create the impression that attribution science is too uncertain, too political, or too conflicted to be useful in court or in public policy. The strategy is not based on actual science or evidence of misconduct, but on the generation of doubt. The new Merchants of Doubt We’ve seen this before. In fact, not that long ago: We only have to go back a year to the Department of Energy (DOE) Climate Working Group (CWG) report to see an example of using doubt as the tool to push back against well-established science. This strategy is laid out in an email from a member of the CWG, Dr. Roy Spencer, that was released during litigation over the Climate Working Group process. The key quote is: About all I can hope is that what we write will provide sufficient “reasonable scientific doubt” regarding the science claims in the 2009 TSD [technical support document], based upon almost 2 decades of new science, to call into question the original reasoning for the EPA Administrator’s decision that CO2 presents a threat to human health and welfare. This statement is strong evidence that at least some members of the committee were working to support a particular policy outcome: revoking the Endangerment Finding. The email also explains how they planned to do it: by attempting to generate “reasonable doubt”. This is going to be hard, Spencer implies. Despite falsely claiming that “2 decades of new science” weakens the case, Spencer explicitly acknowledges that the actual peer-reviewed science of climate change overwhelmingly rejects his position: But if the science argument is decided upon by a vote, or by the number of published citations, we lose the science argument. We can go back even further: This CWG email shares unmistakable DNA with the infamous 1969 tobacco memo that declared: “Doubt is our product, since it is the best means of competing with the ‘body of fact’ that exists in the mind of the general public. It is also the means of establishing a controversy.” The tobacco memo also acknowledges the limit of this strategy: Like the CWG, they knew the science was not on their side. Share The new new Merchants of Doubt The people attacking the IPCC chapter on extreme event attribution are the newest iteration of the Merchants of Doubt. Their goal, like all Merchants before them, is to introduce doubt into the process. Because the report is not even out yet, they cannot attack its conclusions. So they are attacking the authors instead. Here is a press release from the House Science, Space, and Technology Committee: In the letter, the Chairmen express concerns about potential conflicts of interest involving members of the Attribution Committee, stating that “publicly available information suggests a troubling pattern” in which committee members are affiliated with nonprofits that support climate accountability lawsuits, “raising the appearance of impropriety and member bias.” To be clear, this is just innuendo. There is no actual evidence of bias. And given the robust process that these reports go through, including multiple lines of peer review, it seems very unlikely that significant bias can survive into the report. When the report comes out, critics will have the opportunity to make legitimate criticisms of the report — if any exist. If none do, however, they’ll still make criticisms, but they’ll be bogus, simply designed to generate doubt. We’ll see. A note to the press: Fix your frame To any journalists reading this: The public debate over extreme event attribution science is not going away. The science is simply too dangerous to fossil-fuel interests for them to stop fighting it. You very well might be assigned to write an article about this area of research in the future. When you do, do not automatically adopt the framing that climate misinformers want you to use. They want you to frame the story around questions like: Are climate scientists trying to put their thumb on the scale to achieve a predetermined, politically motivated result? Are climate scientists improperly letting their politics invade the science of the IPCC? That frame is a trap. Instead, you need to view this through the historical lens of the Merchants of Doubt. How does the ecosystem of doubt operate? Who funds it? What methods do they use to misrepresent science and slime researchers? What scientific results are they trying to keep people from understanding are legitimate? Ultimately, you instead need to focus your article on the generation of doubt as a way to maintain the fossil fuel industry’s social and legal license to keep burning oil, gas, and coal. If you treat the misinformers’ frame as a legitimate, good-faith scientific critique, you are helping them produce doubt. Don’t do it. Don’t be a Merchant of Doubt. Thanks for reading The Climate Brink! Subscribe for free to receive new posts and support our work. Subscribe other stuff The Staying Curious Substack has an interesting post about the different flavors of climate sensitivity. This is crucial information if you want to understand how scientists think about how much warming the Earth will experience. I’d be grateful if you could hit the like button ❤️ below! It helps more people discover these ideas and lets me know what’s connecting with readers.
Apple’s First 1.4nm Chipset, The A22 Pro, Could Be Powering iPhones In Just A Couple Of Years
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Michigan health department issues warning about recalled squeeze toys
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Your medical provider might be recording your mental health care visits
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This exercise ‘sweet spot’ is linked to greater longevity
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What to watch in Tuesday’s primaries in Georgia, Alabama, Oklahoma and Washington, DC
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What This Iran Deal Might Mean for American Consumers | WSJ Politics Newsletter for June 15
Plus, Kevin Warsh wants the Fed to say less, and AI deepfakes muddle the midterms