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A heat dome with extreme temps threatens Fourth of July weekend
The heat dome arrives just before the Fourth of July weekend, when AAA predicted 72.2 million Americans would travel.
ESPN boldly predicts Dak Prescott to join exclusive NFL club
Do you think Dak Prescott can throw for 5K?
Scientists Thought Ravens Followed Wolves for Food. They Were Wrong. Ravens Predict Them
New GPS tracking suggests ravens navigate Yellowstone using a mental map of wolf kills.
Team's prediction task compares GPT-4o with classic machine learning
Large language models have been functionally opaque. Seeking transparency, a team undertook a comparison with traditional machine learning.
MLB Predictions and Moneyline Picks for Thursday, July 2
Colby Marchio’s MLB moneyline leans and insights for Thursday, July 2, featuring the Los Angeles Angels.
PwC predicts: Providers, payers, pharma, medtech—all will have new to-do lists after the midterm elections. And that’s regardless of results.
With Election Day only four months away, healthcare is again emerging as a major source of idealistic campaign promises. Many of these are driven by widespread public angst. What sorts of changes can U.S. healthcare stakeholders realistically expect once the results are in?
UFC champ Sean Strickland predicts unlikely future title matchup
UFC middleweight champion Sean Strickland believes a fight between him and a training partner at Xtreme Couture is inevitable.
Landlocked Colorado’s sea-change primary
Colorado’s primary elections held in the doldrums of early summer tend to be routine affairs, most often predictable and by the book. Tuesday’s battlefield was anything but. It is no exaggeration to call it a sea change. Its ripples will shape our state’s politics for many years to come. Writing on this morning after, let’s […]
Apple’s iPhone 18 Pro Max eSIM Model To Pack A Mammoth 5,425mAh Battery, While Physical SIM Buyers Get Predictably Shortchanged On Battery Capacity
In keeping with the prevailing divulgatory trend, the latest tidbit has detailed the battery capacity within Apple's iPhone 18 Pro Max.
USA vs Bosnia-Herzegovina – Predicted lineup and team news
San Francisco Bay Area Stadium hosts the World Cup last 32 on Thursday morning as USA take on Bosnia-Herzegovina, with a place in the round of 16 against Belgium or Senegal at stake.Mauricio Pochettin...
Claude Science is Here, Antibiotics Designed by Text Prompt Among Applications
Basecamp Research’s antibiotic design and vaccine prediction AI models are now available through Anthropic’s Claude Science.
CitrixBleed To Infinity And Beyond (Citrix NetScaler Pre-Auth Memory Overread CVE-2026-8451)
Well, well, well - once again, the cat has dragged us in, and spat us out. Today, we find ourselves questioning the reality we sit within. Must it be so predictable, and why us? “But watchTowr, what do you mean?” Well, if you’re here, you likely fit into one of
USA vs. Bosnia And Herzegovina: Storylines, Projected Lineup, Predictions
Everything to know about USA vs. Bosnia and Herzegovina in their group-stage finale.
Basecamp Research brings EDEN's antibiotic and vaccine design models to Claude Science
/PRNewswire/ -- Basecamp Research today announced that its antibiotic design and vaccine target prediction EDEN models are now available through Claude,...
GTA VI May Be Limited to 30 FPS on Current-Gen Consoles
With the launch of Grand Theft Auto VI rapidly approaching, rumors surrounding the game's launch have been circulating, with some of those predicting that the game's launch will result in a console hardware shortage, more recently, however, Digital Foundry predicted that the new GTA game may not be ...
Predicting winners for NFL's remaining QB competitions: Who wins starting gigs?
Five NFL teams are set to enter training camp with quarterback competitions lingering. Who will emerge as QB1 in each battle?
Steelers reporter predicts Pittsburgh will cut Will Howard
Will the Steelers cut Will Howard?
Netflix and Superheroes Are Having a Rough Stretch on Streaming....
(Welcome to my weekly streaming ratings report, the single best guide to what’s popular in streaming TV and what isn’t. I’m the Entertainment Strategy Guy, a former streaming executive who now analyzes business strategy in the entertainment industry. If you were forwarded this email, please subscribe to get these insights each week.) I have a quick prediction before we start today’s article: You might see this data in future news articles. Not my specific charts or analysis, but the core trend I’ve been monitoring—Netflix is having a lackluster Q2—will become inescapable. Often, folks want to know “What does the data say?” Well, that’s what it says. I haven’t seen this analysis hit the trades really yet, but I have a feeling some other outlets are going to start making this case soon. Remember, you read it here first. You probably have read about the problems facing superhero films at the box office this weekend. Well, this week, I explore whether superheroes are dying on TV as well! And the data says: definitely maybe! We have a lot to cover in today’s Streaming Ratings Report, which covers the two weeks of 18-May and 25-May. Today, I’m just looking at TV, like Netflix’s disappointing Q2, superhero shows underwhelming, a few TV shows dropping off the charts, The Pitt finally ending its epic run on the viewership charts (and whether “view counts” change its status as one of the biggest shows of all time), all the flops, bombs and misses, and a whole lot more. Tomorrow, I’ve got a great rundown of the film side of things. Let’s dive right in! (Reminder: The streaming ratings report focuses on the U.S. market and compiles data from Nielsen’s weekly top ten viewership ranks, Luminate’s Top Ten Data, Showlabs, TV Time trend data, Samba TV household viewership, company datecdotes, Netflix hours viewed data, Google Trends, and IMDb to determine the most popular content. While most data points are current, Nielsen’s data covers the weeks of May 18th to May 31st, 2026. You can find a link to my terminology here.) Subscribe Television - Netflix’s Q2 Slump Continues Having analyzed streaming ratings for going on half a decade, I have learned to trust my gut, but I’m always relieved when I can find actual data to back it up. For example, after a string of very popular shows like Stranger Things, Bridgerton, The Lincoln Lawyer and The Night Agent came out, week after week, in January and February, my gut wondered if Netflix had enough new and returning TV shows to sustain them throughout the year. Since then, Netflix’s post-March slate of shows has not impressed me. This week seemed to feature a potential new hit, but then, like a classic Duffer Bros. TV show, there was a twist. I speak of The Boroughs, Netflix’s latest (and last) Duffer Bros. produced show. The pitch is “Stranger Things with older actors”. And it topped the Nielsen charts: With just two weeks of viewership, The Boroughs would have been a top 25 debut show last year: But here’s the twist: Netflix has already cancelled The Boroughs! What happened? Likely a few things. First, the show didn’t do as well globally as it did in the US. As What’s on Netflix showed, it lagged behind several other shows that got cancelled. Second, its completion rate was likely low; after an initial burst of interest, it quickly dropped off on the Samba TV and Luminate charts. Third, the Duffer Bros. left Netflix for Paramount, and that may have factored into Netflix’s decision here. (Though I tend to believe that if a show is doing well, Netflix would have kept greenlighting seasons.) Still, just because I can explain why Netflix may have cancelled this show doesn’t mean I’m justifying it. In particular, as I’ve noted before, Netflix is losing a lot of their bigger shows as they reach their natural ends, and they need to replace those shows. This show had the hallmarks of a big new genre show, and it’s their biggest hit of the last few months (in the US at least), but now it’s done. Netflix’s other show this week—The Four Seasons, the second season of the “hit” show from last year—definitely disappointed. Its sophomore season had about half the viewership of the first, only 12.7 million hours. It also only had a two week run on Samba TV, and its numbers were all down on Luminate too. In other words, even with two successes, the Q2 slump for Netflix looks real: I could add to the list of disappointments: Both Lord of the Files and Man on Fire had short runs on the Nielsen charts. Running Point’s sophomore season also opened to about half its viewership, similar to The Four Seasons. A Good Girl’s Guide to Murder’s second season—see below—didn’t even make any of the three viewership charts I track, unlike the first season. Netflix is absolutely having a dismal second three months of 2026. If this Nielsen data is any indication, and I think it is, I think this will show up in Netflix’s engagement report, and maybe even their financial outcomes. And the rest of June doesn’t look much better. We have two hit shows coming back—Sweet Magnolias, one of their quietest big shows, and Avatar: The Last Airbender, a big hit for them in 2024—but the other big swings are limited series crime thrillers i.e. no future season potential. Like I said above, sometimes when I uncover a juicy nugget like this, other outlets end up writing very similar articles mimicking the data without credit. We’ll see if that happens this time! Share Television - The State of Superhero Shows on Streaming When I first heard the pitch for Spider-Noir—a Sony TV show on Prime Video that binge-released eight episodes first on MGM+ then on Prime Video two days later—I had my doubts. It’s a live-action Spider-Man show set in the 1930s, starring Nicholas Cage, based on a cameo of a character in Spider-Man: Into the Spider-Verse. No, seriously, that’s the pitch. That could either go terrifically well or horribly wrong. Great because, Spider-Man. He’s the biggest superhero we have. I’ve seen tracking numbers that put the new _Spider-Man: Brand New Da_y as the blockbuster of the year and initial pre-sales back that up. And yet this Spider-Noir pitch feels very esoteric. (Also, the eight-episode binge release feels very “Amazon copying Netflix” again.) So what’s the data say? And what does this say about the future of superhero shows?
Eagles 53-man roster prediction: Updated after spring practices
Here’s a look at how Philadelphia’s 2026 team could be shaping up.
At 99, this Pasadena ballet teacher has no plans to stop dancing
Helga Monson de Kansky was once predicted to become ballet's next star. Instead, she chose a life with her family and a career as a dance instructor.
Winning Before Kickoff: The science behind success at the 2026 FIFA World Cup
The World Cup is usually remembered for what happens after the whistle: the impossible assist, the heroic save, the deflection that turns a nation’s hope into heartbreak. But in 2026, some of the most important contests may be decided before kickoff, in training rooms, recovery suites, flight schedules, hydration plans, and cooling protocols. Science cannot eliminate soccer’s glorious unpredictability, but it can help teams survive it.
Baker Mayfield Drops Clear Message on Kyler Murray
As Kyler Murray seeks a fresh start in Minnesota, former teammate Baker Mayfield defends the polarizing quarterback, predicting a dominant resurgence fueled by a much-needed change of scenery.
Prediction market Kalshi sues Illinois over its push to regulate sports bets
The most recent move in the ongoing legal battle between the state and prediction market operators came after the state enacted new sports wagering taxes to support the $55.9 billion budget it passed this spring.
Efzimfotase alfa demonstrated improvements in bone health in treatment-naïve pediatric patients with hypophosphatasia achieving median difference of 1.67 vs placebo in RGI-C Score at week 25 in MULBERRY Phase III trial
Positive results from the MULBERRY Phase III trial showed that efzimfotase alfa (ALXN1850), an investigational enzyme replacement therapy, demonstrated a statistically significant and clinically meaningful improvement in bone health in children (2 to
PlayStation Plus Free Games July 2026: Predictions, Rumors, Leaks And More
PlayStation Plus free games for July 2026: predictions, rumors and leaks for PS Plus Essential, including possible PS5 and PS4 titles ahead of the official reveal.
Brain activity under anesthesia challenges what we know about consciousness
The unconscious brain appears to be far more capable than scientists once believed. Researchers found that patients under general anesthesia could still process language at a sophisticated level, distinguishing nouns, verbs, and adjectives while listening to stories. Even more remarkably, neural activity showed signs of predicting upcoming words before they were heard. The results challenge traditional ideas about consciousness and hint at new possibilities for brain-computer interfaces.
Science says these 7 parenting beliefs may be quietly making life harder for parents and children
Every parent wants to give their child the best possible upbringing. But in trying to be the “perfect parent,” many may be putting unnecessary pressure on themselves. A 2023 survey of more than 700 parents by researchers at Ohio State University found that 57% reported experiencing parental burnout. Surprisingly, the biggest predictor wasn't the number of children they had or how many hours they worked- it was the pressure to live up to an impossible standard of parenting. Child development experts say many of the beliefs parents consider signs of “good parenting” may actually be making life harder for both parents and children. Here's what science says.Here are seven common parenting beliefs that science says may be working against both parents and children.
UNC, NC State to get millions from sports betting; gambling deductions allowed in budget proposal
Not only would the tax on sports betting operators rise to 23%, but North Carolina would levy taxes on prediction market operators as well under proposals expected to be included in the state budget.
Phillies vs Mets Prediction, Picks & Odds for Today's MLB Game
The Phillies have been hot, and our expert MLB predictions expect their winning ways to continue against the lowly Mets.
Former Eagles WR A.J. Brown Predicted to Threaten NFL Record
NFL.com's Bucky Brooks predicts former Eagles WR A.J. Brown could break the 2,000-yard receiving mark with the Patriots in 2026. The post Former Eagles WR A.J. Brown Predicted to Threaten NFL Record appeared first on HEAVY.
Prediction market Kalshi sues Illinois over its push to regulate sports bets
Prediction market Kalshi — which offers trades on everything from the World Cup top goalscorers to the winners of the reality show Love Island and the 2028 Democratic primary — sued Illinois on Tuesday over its latest push to regulate sports bets on the platform.
Scalpers List Steam Machine Reservations at $1,700+
Following a brief reservation period, prospective buyers of the Valve Steam Machine started reporting on June 26 that they had begun to receive reservation confirmation emails for the new hybrid console. However, on the same day, and almost predictably, apparent scalpers listed Steam Machine units o...
Prediction market Kalshi sues Illinois over its push to regulate sports bets
Prediction market Kalshi sued Illinois on Tuesday, seeking to block new taxes and license requirements set to take effect July 1.
Prediction market Kalshi sues Illinois over its push to regulate sports bets
Suit seeks to block new taxes and licensing requirements set for July 1
Prediction market Kalshi sues Illinois over its push to regulate sports bets
Article Summary Prediction market Kalshi sued Illinois on Tuesday, seeking to block new taxes and license requirements set to take
iPhone 18 Pro and Pro Max May See $200 Price Increase
Apple's iPhone 18 Pro models could be up to $200 more expensive, according to a prediction from analytics firm IDC. IDC expected Apple to raise iPhone 18 prices, but prior to yesterday's Mac and iPad price hike, the prediction was a $100 increase for the 18 Pro and Pro Max, and a $50 increase for the base models. IDC Senior Director of Data & Analytics Nabila Popal says the magnitude of the Mac and iPad price increase points to even higher iPhone 18 prices.
‘Under the Radar’ Cowboys Free Agent Predicted for Major Payday
Bleacher Report predicts Dallas Cowboys CB Cobie Durant could land a major payday in 2027 with a standout season. The post ‘Under the Radar’ Cowboys Free Agent Predicted for Major Payday appeared first on HEAVY.
iPhone 18 Pro and Foldable iPhone Tipped to Get Price Hikes Too
Apple's iPhone 18 Pro lineup and its first foldable iPhone will launch at higher prices than originally predicted this September, according to several known Chinese leakers.
2026 SEC football predictions: Game-by-game picks, projecting every team's final win-loss record
Continuing our early-summer projection series for all 16 teams in the SEC and where they'll end up in the postseason
Sweden and WHO launch new long-term initiative to transform Ukraine’s health system
WHO and the Swedish International Development Cooperation Agency (Sida) have launched a new multi-year cooperation project to support the long-term recovery and transformation of Ukraine’s health system. The new project, “Ukraine’s Health System Transformation Initiative: Strengthening Health Governance, Health Financing and Service Delivery for Long-Term Recovery”, was launched at the Ukraine Recovery Conference 2026 in Gdansk, Poland on 26 June, and will run from June 2026 to 2029. Sweden will contribute approximately US$ 26 million (240 million Swedish krona) to support this project. This contribution forms part of Sweden’s broader support to Ukraine through the Team Europe approach, which brings together the European Union, its Member States, and key partners such as the European Commission’s Directorate-General for Enlargement and Eastern Neighbourhood and Enabel, and WHO as lead health agency, to support Ukraine’s recovery, reform agenda and European integration.“Ukraine’s health recovery cannot be measured only by what is rebuilt, but by whether people can count on the health system when they need it most. The scale of the task is immense, with health reconstruction and modernization needs estimated at US$ 19.4 billion over the next decade. Sweden’s US$ 26 million contribution to Ukraine’s Health System Transformation Initiative is a timely investment in the institutions, financing and services that will support Ukraine’s long-term recovery and European integration. WHO remains a trusted partner of the Government of Ukraine in advancing this process. I am deeply grateful to Sweden for its consistent support and partnership,” said Dr Hans Henri P. Kluge, WHO Regional Director for Europe. More than 4 years of war have placed unprecedented pressure on Ukraine’s health system, creating 3 interconnected challenges. The first is responding to the immediate consequences of the war and the urgent health needs of the population. The second is recovering and modernizing health services today. The third is continuing reforms and aligning the health system with European standards in the context of Ukraine’s future accession to the European Union. The war has led to large-scale population displacement, an increasing need for trauma care and rehabilitation, and shortages of health workers. High out-of-pocket payments in Ukraine make access to health services more difficult. Around 46% of total health expenditure is paid directly by individuals, placing a significant financial burden on many households and underscoring the need for reforms in health financing. Older people, persons with disabilities, women, children and veterans continue to face the greatest difficulties in accessing health services. “Ukraine is demonstrating that health system transformation can continue even during war. Ukraine is also continuing to align its health system with European norms and standards. WHO is committed to supporting Ukraine on this journey. We provide technical support, help coordinate partners’ efforts and support the institutional changes needed to strengthen the system. We are grateful to Sweden for its support. Through this partnership, we can help Ukraine build a more resilient, accessible and people-centred health system,” said Dr Jarno Habicht, WHO Representative in Ukraine. About the Health System Transformation InitiativeThe new initiative will support Ukraine through 3 interconnected areas: strengthening governance and coordination of health sector reforms through more inclusive coordination platforms and structured policy dialogue; transforming service delivery by reinforcing a primary health care-centred model that integrates family medicine, nursing and midwifery; and improving financial sustainability by supporting reforms that ensure the long-term viability of the national health system and reduce out-of-pocket payments for households. Overall, these reforms are expected to make the health system more coherent and easier to navigate, improve the quality and continuity of care, and ensure more equitable access to services, particularly for those who need them most. They will also support Ukraine’s progress towards a more modern health system aligned with European standards. “We are convinced that long-term recovery is not possible without strong, high-quality, person-centred health services that are accessible to everyone. Our partnership with WHO is designed to support deep and lasting systemic changes. Investments in human capital especially autonomous midwives, whose role for maternal, newborn and adolescent sexual and reproductive health cannot be understated, stronger governance, digital transformation and the financial sustainability of the health system are investments in Ukraine’s future. We are proud to support this process,” said Malin Perhult, the Head of the Ukraine Unit at Sida. Focus throughout implementation will be given to the most vulnerable population groups, including older people, women and children, and persons with disabilities, who have been affected by the war and face greater health-care needs. The initiative will be implemented in close cooperation with the Ministry of Health of Ukraine, the National Health Service of Ukraine, the Public Health Centre of Ukraine, regional authorities and international partners. This project will help Ukraine move towards a system where everyone can access the health care they need. It will strengthen primary care, make the health finance system more stable and predictable, and reduce the out-of-pocket costs that Ukrainians face when using health services. As a result, the Ukrainian health system will become more aligned with European standards and more sustainable in the long run.
Egypt vs Iran at World Cup 2026 – Salah, prediction, lineups, how to watch
Mohamed Salah's Egypt and Mehdi Taremi's Iran are both in the hunt for the FIFA World Cup knockouts in Group G.
2026 NHL Mock Draft
Ahead of tonight’s NHL draft, let’s take a stab at how the first round could play out tonight. First, a moment of silence for our near miss last year – when we tried to predict that Kyle Dubas would go ‘off the board’ with a high pick on a WHL center that no one saw […]
Serious statin side effects on muscles are extremely rare, new research confirms
Scientists created a new tool to help doctors predict a patient’s risk of severe muscle problems from taking a statin.
Scientists develop predictive roadmap to boost performance in next-gen spintronics
Chiral 2D metal halide perovskites (MHPs) are among the most promising materials for future technologies that exploit the spin of electrons in spin-based optoelectronics, or spintronics, but getting them ...
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.
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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.
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Chaos Cubed has landed in Minecraft! Explore a striking cave biome filled with bubbling sulfur pools, build with sulfur and cinnabar, and discover the sulfur cube’s curious appetite for blocks! From bouncing and sliding to explosive surprises, this unpredictable mob unlocks exciting new ways to build, compete, and survive. Play Chaos Cubed today in Minecraft!