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Scotty James & Nigel Sylvester: the big business of extreme sports
Yahoo Finance Executive Editor Brian Sozzi chats with professional snowboarder and Olympic silver medalist Scotty James and professional BMX athlete Nigel Sylvester about the evolution of their extreme sports and how it's driving big business for their brands.
TSU HOF Class of 2026: Johnny "Ballpark" Franks
Throughout the summer months leading up to the Tennessee State University Athletics Hall of Fame Banquet on Aug. 27, Tennessee State Athletics will highlight the members of the Hall of Fame Class of 2026. This distinguished group of former student-athletes, coaches, administrators and contributors helped shape the proud tradition of Tiger athletics through championship performances, individual achievements and lasting contributions to the university. Their stories reflect the excellence, perseverance and impact that have defined Tennessee State Athletics across generations.
Why AI Doesn’t Think, Cannot Reason, Isn’t Intelligent and Will Never Achieve Consciousness
Recent public comments made about AI suggest that Americans have difficulty with the implications of linear time. This is odd given that its conception is largely Western and is centered on the clock time used to coordinate capitalist employment. The conceptual difficulty regards sequencing, or plans for future actions. But it also involves the distribution of profits. 100% of the capital equipment used in Western economic production was produced by workers. So, why does the resulting product belong to financiers rather than those who produced it?
How lawfare is strangling politics in Africa
Politicians are using the courts to shut down dissent.
B.I.D. Academy demonstrates how artificial intelligence can transform small businesses
BELTON — For years, artificial intelligence has largely been associated with chatbots, writing assistance and internet searches. But during the second session of Prairie View A&M University Extension’s Business Innovation
Detroit Tigers Land Three American League All-Stars
Dillon Dingler, Kevin McGonigle and Riley Greene have been selected to represent the Detroit Tigers in the 2026 MLB All-Star Game.
Scientists find the “Achilles’ heel” of deadly gut bacteria
Researchers discovered an “Achilles’ heel” shared by E. coli, Shigella, and other diarrhea-causing bacteria: enzymes they use to breach the gut’s protective mucus layer. By targeting this common vulnerability, scientists may be able to develop a single vaccine that prevents several major diarrheal infections before they even begin.
Superintelligent AI in space could explain the Fermi Paradox
Why haven't we found evidence of advanced aliens? It could be that they've outsourced cosmic exploration to superintelligent AI, a new paper suggests.
Tribes hope Farm Bill can feed more people, preserve Indigenous culture
HOPKINTON, R.I. — At Ashawaug Farm in southwest Rhode Island, Dawn and Cassius Spears preserve their Indigenous knowledge of agriculture through the cultivation and keeping of three Narragansett heritage crops:
Overnight News Digest: Star Spangled Science Saturday, 7/4/26
Welcome to the Overnight News Digest with a crew consisting of founder Magnifico, regular editors side pocket, maggiejean, Chitown Kev, eeff, Magnifico, annetteboardman, Besame, jck, JeremyBloom…
Thomas Jefferson’s Embrace of Intelligent Design
Today on Independence Day, it is appropriate to review the sources of our rights as citizens.
Are the ‘MANGOS’ Stocks Already Turning Soft?
Wall Street loves an acronym. The latest one stands for Meta, Anthropic, Nvidia and three other companies at the center of the artificial intelligence boom.
‘Who Should I Vote for?’ Voters Turn to A.I. Before Casting Their Ballots
It takes effort to be an informed citizen. Artificial intelligence tools offer an alluring shortcut — but they’re not without risk.
AI is already reshaping US politics at every level
Artificial intelligence is barging into the workplace and transforming the battlefield. Now, it’s coming to the ballot box, promising to rewrite the DNA of politics. Driven by a flood of
iPhone 18 With 9GB RAM Still Won't Support Two New iOS 27 Features
The lower-end iPhone 18 and iPhone 18e will be equipped with 9GB of RAM, up from 8GB in the iPhone 17 and iPhone 17e, according to supply chain analyst Ming-Chi Kuo. In a social media post, Kuo said the 1GB increase in RAM will ensure that Apple Intelligence features continue to run smoothly on the pair of devices. The higher-end iPhone 18 Pro, iPhone 18 Pro Max, and foldable "iPhone Ultra" will each have the same 12GB of RAM as the iPhone 17 Pro models, added Kuo.
Medline warehouse fire: Lawsuit filed after residents report health issues negligence
Neighbors living near the site of last month's massive warehouse fire in Tracy say they are still dealing with health concerns and the environmental aftermath.
Meta's Watermelon AI model has caught up to GPT-5.5, Alexandr Wang says
Meta's superintelligence chief says its upcoming Watermelon model now matches GPT-5.5 on key AI benchmarks.
Game 88 Game Day Thread – Detroit Tigers @ Texas Rangers
Once more into the consecutive games breach
Artificial intelligence: Yann LeCun works on more flexible AI
Leading AI researcher Yan LeCun has a start-up which is developing a more flexible AI system.
Zuckerberg said AI agent progress has been slower than expected
Superintelligence will take time, Zuckerberg tells Meta staff, as the company makes its AI training program opt-in after a data leak.
Report: Small Business AI Adoption Grows » CBIA
Artificial intelligence is moving from experimental technology to an everyday business tool for small businesses, according to a new report.
Tufts University and Bigelow Laboratory Partner to Expand Marine Science Training
Tufts University and Bigelow Laboratory for Ocean Sciences have launched a partnership offering graduate students a Marine Science specialization through the Sea Change Semester, combining coursework with hands-on research voyages and laboratory training in Maine.
Microsoft Frontier Company: AI engineering that amplifies and protects your intelligence
The pace of AI adoption is moving incredibly fast. Customers have moved well beyond experimentation and understand the importance of adopting AI to transform their business. They are now concentrating on delivering measurable business outcomes and demonstrating a return on their AI investments, while ensuring their intelligence is amplified and their IP is protected. Today...
The White House goes all in on aliens with new UAP Science Advisory Council
This new group, which is led by Harvard professor Avi Loeb, aims to advise the Trump administration and the U.S. intelligence community, as well as to publish its findings in peer-reviewed journals
Nigel Mansell auctions F1 memorabilia for charity
Motorsport fans will be able to buy pieces of Formula 1 history at the auction.
Bowles Extremely Impressed With 1 Quality of Rueben Bain Jr.
Defying concerns over his physical reach, the rookie edge rusher is wielding veteran-level intelligence to ignite Tampa Bay’s pass rush and solve a long-standing defensive drought.
The Next Chapter for Founder-Led Beauty Brands
At The Business of Beauty Global Forum 2026, Unilever Prestige CEO Mary Carmen Gasco-Buisson joined K18 founder and CEO Suveen Sahib and Tatcha founder Vicky Tsai to discuss scaling founder-led beauty brands.
New Collaboration between Tufts University and Bigelow Laboratory for Ocean Sciences Boosts Experiential Opportunities for Graduate Students
Tufts University is advancing this mission through a new collaboration with Bigelow Laboratory for Ocean Sciences in East Boothbay, Maine, giving graduate students direct access to immersive real-world ocean research.
ESPN reportedly favorite to renew TGL media rights
TGL, the tech-infused simulator golf league co-founded by Tiger Woods and Rory McIlroy’s TMRW Sports, is in the market for a new media rights deal after having completed its second season earlier this year. The league spent its first two seasons on the ESPN family of networks, posting strong and stable viewership during a portion…
Frankie Muniz and wife Paige Price split after 7 years of marriage
Frankie Muniz announced he and wife Paige Price are getting divorced after seven years of marriage: 'Closing one chapter with gratitude,' the 'Malcolm in the Middle' actor said.
‘The science is here’: UN chief welcomes first global AI assessment
Fresh scientific evidence and options for harnessing artificial intelligence (AI) were unveiled Wednesday following the launch of the first global, independent scientific assessment of opportunities, risks and impacts by a pioneering UN expert panel.
Experity acquires AI firm to automate urgent care revenue management
The move by the Machesney Park-based Experity is meant to accelerate revenue cycle management automation through artificial intelligence.
Protecting Science. Advancing HIV Prevention. Preparing for What's Next
Global health is being reshaped by rapid geopolitical shifts, scientific breakthroughs and the growing role of artificial intelligence — creating both new opportunities and new challenges. Translating scientific breakthroughs into impact requires protecting research, advancing equitable access and ensuring communities are at the center. Explore AVAC’s latest resources to help navigate and shape what comes […]
People moves: Lucy Fato departs from Seaport Entertainment Group
Seaport Entertainment Group has confirmed that Lucy Fato, its EVP, general counsel and corporate secretary, h
LG LIGHTS UP FOURTH OF JULY SAVINGS ACROSS HOME ENTERTAINMENT AND APPLIANCES
Limited-Time Fourth of July Deals Available on Select LG OLED TVs, Gaming Monitors, Refrigerators, Dishwashers, Vacuums, Laptops and Audio Products News...
AI, Data Centers, and the New Politics of Power Demand
Artificial intelligence (AI) has overturned the long-stable trajectory of U.S. electricity demand. After decades of flat growth, planners and regulators now face a surge driven by hyperscale data
AI-Driven Restructuring Accelerates Layoffs Across U.S. Technology Sector
Artificial intelligence is rapidly reshaping the U.S. technology industry, with major companies accelerating workforce reductions.
Employers who laid off workers citing AI are already starting to regret it
Companies are realizing artificial intelligence can't do everything after all, prompting them to rehire employees to grow their businesses
Cam Schlittler's odyssey: Tigers ambush Yankees' starting pitcher
New York's starter yielded three homers in the same inning Tuesday for the first time in his two-year career.
2026 John Deere Classic tee times, TV coverage, viewer's guide
Tee times, TV coverage and viewer's guide for the John Deere Classic at TPC Deere Run in Silvis, Ill.
Nearly 1 in 5 U.S. teens have turned to AI chatbots for mental health advice, study finds
A growing number of adolescents and young adults in the United States are turning to artificial intelligence chatbots for help with their mental health.
What 124 Future Business Leaders Really Think About AI and Work
Insights from 124 career assessments written by UMD-Smith graduate students show 98% expect artificial intelligence to transform, not eliminate, their careers. The analysis also found AI raises performance expectations while increasing demand for strategic thinking, AI literacy, ethical judgment and critical evaluation of AI-generated work.
Anthropic launches AI drug discovery program, joining tech giants in betting on healthcare
Anthropic will start an internal drug discovery program, part of a new push to sell artificial intelligence tools to drugmakers.
Anthropic launches AI drug discovery program, joining tech giants in betting on healthcare
Anthropic will start an internal drug discovery program, part of a new push to sell artificial intelligence tools to drugmakers.
Bailey campaign embraces artificial intelligence in new era of politics
State lawmakers consider requiring campaigns to disclose AI-generated ads
Bailey campaign embraces artificial intelligence in new era of politics
Article Summary Republican Darren Bailey’s campaign for governor is embracing images made with artificial intelligence in social media posts. The
Coach Cal to be Inducted into Memphis Sports Hall of Fame
Razorback basketball coach John Calipari, who was head coach of the Memphis Tigers from 2000-09, will headline the 2026 Memphis Sports Hall of Fame class. The induction ceremony is Sept. 16.
Jon and Mindy Gray Bet on AI Science Research to Prevent Cancer
Blackstone Inc. President Jon Gray has steered his firm to more artificial-intelligence investments, and the same is true with his science philanthropy.
Sequins! Dancing queens! ABBA! Backstage for 'Mamma Mia!’
Twenty-five years after its Broadway debut, the veteran BTS creatives of “Mamma Mia!” helps turn the Ahmanson Theatre into an ABBA-fueled, multigenerational dance party.
Use of artificial intelligence for mental health splits opinion
Two-thirds of those aged 25 to 34 have asked chatbots for wellbeing support
Jazz Chisholm leaves with injury, Yankees lose odiously to Tigers
The Yankee second baseman entered concussion protocol; the Tigers dealt New York another nasty loss.
New York Yankees’ Jazz Chisholm Jr. Exits Game Against Tigers
The New York Yankees have been struggling in recent games, and their ballclub is continuing to spiral. In the fourth inning, Jazz Chisholm Jr. collided with Jasson Domínguez, ultimately knocking Chisholm to the ground. Status alert: Jazz Chisholm Jr. leaves game Monday.pic.twitter.com/5XyTh98wIX — Underdog MLB (@UnderdogMLB) June 30, 2026 Chisholm exited the game following the […] The post New York Yankees’ Jazz Chisholm Jr. Exits Game Against Tigers appeared first on HEAVY.
Yankees Promote Cody Bellinger’s All-Star Candidacy Before Tigers Game
The New York Yankees made a social media post concerning left fielder Cody Bellinger.
AI is rewriting the political competitive landscape
Artificial intelligence is rapidly becoming a central infrastructure layer in political campaigns, reshaping how elections are financed, contested and influenced, The New York Times reports. Campaigns and affiliated political groups are increasingly investing in AI-driven tools for voter targeting, message optimization and large-scale content generation, turning modern elections into capital-intensive, technology-enabled competition. This shift is […]
Newsom's office touts Anthropic 'partnership,' 50% discount on Claude AI for California agencies, localities
Golden State Gov. Gavin Newsom's office indicated that state agencies and local governments may access Anthropic's Claude artificial intelligence platform at a 50% discount.
Healthcare AI's next big leap: Removing friction from clinical workflows
Rather than just adding more applications, one CMIO says, organizations should focus on using automation and intelligence to eliminate unnecessary steps, reduce clinician burden and streamline care delivery.
Taylor Launches New Bachelor's Degree in AI & Machine Learning
Taylor University is launching a Bachelor of Science in Artificial Intelligence and Machine Learning, building on four decades of AI expertise.
Google Cloud to Offer Specialist AI Models for Science Research
Alphabet Inc.’s Google will start offering specialist artificial intelligence models from software company SandboxAQ through its cloud service, expanding enterprise and research access to technology designed to accelerate drug discovery, materials science and semiconductor manufacturing.
South Korean tech giants to build a $518 billion chipmaking hub to serve soaring AI demand
South Korean tech giants Samsung Electronics and SK Hynix plan to invest a combined $518 billion in a new computer chip manufacturing hub, capitalizing on surging artificial intelligence-driven demand.
Detroit Tigers Release 3-Year MLB Player Before Yankees Series
On Monday night, the Detroit Tigers will open up a series with the New York Yankees in the Bronx.They are coming off a 7-5 loss to the Houston Astros on Sunday (at home).Detroit Tigers Release 3-Year MLB PlayerEarlier this week, news came out that the Tigers had released Konnor Pilkington from their organization (h/t MLB […] The post Detroit Tigers Release 3-Year MLB Player Before Yankees Series appeared first on HEAVY.
Indian Health Center’s grief vigil offers space for remembrance
Gerald L. Ignace Indian Health Center gathers local Indigenous community to find solace in remembrance and grief.
EXCLUSIVE: Three Candidates Vying For Global Fund Leadership In Contentious Race
As the Global Fund heads into a secretive but highly contentious election of its new Executive Director, the names of three likely US candidates have surfaced - former Global Fund Executive Director Mark Dybul, former Trump appointee William Steiger and a former NYC public health official, Ashwin Vasan.
Family visited Water Park Capital of the World; wouldn't do it again
Wisconsin Dells, "Water Park Capital of the World" had many family-friendly activities for our multigenerational travel group, but the cons added up.
6 Ways Your Body Can Change After Starting a High-Protein Diet
Adding more protein to your diet can cause several changes in your body, such as better muscle repair and potential digestive issues.
The AI Politics of 2028 Are Starting Now
Gina Raimondo and Eric Holcomb are not running for president. They might drive the 2028 agenda on artificial intelligence anyway.
Private Selection chicken breast product target of health alert
The U.S. Department of Agriculture wants people who may have purchased a variety of Private Selection raw boneless chicken breasts to check their freezers or refrigerators in case they still have them.
Private Selection chicken breast product target of health alert
The U.S. Department of Agriculture wants people who may have purchased a variety of Private Selection raw boneless chicken breasts to check their freezers or refrigerators in case they still have them.
GameThread: Detroit Tigers vs. Houston Astros, 6:40 p.m.
Detroit Tigers (35-46) vs. Houston Astros (40-44) Time/Place: 6:40 p.m., Comerica ParkSB Nation Site: The Crawfish BoxesMedia: Detroit SportsNet, MLB.TV, Tigers Radio NetworkPitching Matchup: LHP Framber Valdez (4-5, 3.91 ERA) vs. RHP Kai-Wei Teng (4-6, 4.03 ERA)
Egor Shilov Drafted 43rd Overall By Colorado Avalanche
Penn State hockey commit Egor Shilov was drafted 43rd overall by the Colorado Avalanche in the 2026 NHL Entry Draft: Shilov quickly flew up the NHL Draft radar after an outstanding 2025-26 season with the Victoriaville Tigers of the QMJHL. He had 82 points (32 goals, 50 assists) in the regular season, good for 7th […]
4 iconic American CEOs are making way for nuts-and-bolts leaders
Warren Buffett, Tim Cook, Bob Iger, and Doug McMillon are making way for nuts-and-bolts leaders, but success will require more than an eye for detail.
Nigel Farage’s anti-WHO campaign moves to US with allies added to board
Relocation of Action on World Health raises questions over why Reform UK leader is involved in a US pressure group
Nigel Farage’s anti-WHO campaign moves to US with allies added to board
Relocation of Action on World Health raises questions over why Reform UK leader is involved in a US pressure group
Gilbert Chamber Foundation brings AI basics to local businesses
Business owners in Gilbert and the East Valley are getting a hands-on look at how artificial intelligence could keep them competitive.
Are You Falling Into These 5 AI Traps? Here's How Smart Leaders Avoid Them.
Artificial intelligence is making founders fall into the same leadership traps. Here's how to make sure you avoid them.
Sports Intelligence Project explores high-end decision making in multifaceted industry
A new co-curricular offering tasks students with combining data, AI and the human element of athletics.
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…
Four bad habits tournament golf exposed within my game
What one writer learned from his second tournament round of the year.
Taylor Launches New Bachelor's Degree in AI and Machine Learning
Taylor University is launching a Bachelor of Science in Artificial Intelligence and Machine Learning, building on four decades of AI expertise.
Federal AI security center measure advanced by House Science
A measure that would put into law a key federal office focused on artificial intelligence advanced Thursday out of the House Science, Space and Technology Committee, as part of a slate of AI-focused and data center bills. The committee advanced the bills on a bipartisan basis, but put off potential changes regarding funding levels for […]
How Outward Intelligence founders bootstrapped to seven-figure revenue
Three friends with résumés spanning Google, Palantir, and Harris Poll skipped venture capital and bootstrapped their startup instead.
Tigers host the Astros on home losing streak
The Detroit Tigers are looking to stop their three-game home slide with a victory against the Houston Astros
‘Disgusting and vile’: Locals react to arrest of Tampa business owner on AI child porn charges
A Tampa business owner is behind bars facing more than 100 felony charges. He’s accused of making child pornography using Artificial Intelligence.
Astros Share Hunter Brown Decision Ahead of Tigers Series
A key roster situation develops as the season continues.
Troy Melton magnificent but Tigers’ offence falters in loss
Troy Melton had a perfect game into the sixth. Unfortunately, baseball games are nine innings and runs are required.
Generative artificial intelligence creates delicious, sustainable, and nutritious burgers
npj Science of Food - Generative artificial intelligence creates delicious, sustainable, and nutritious burgers
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.
Apple to Skip High-End M6 Mac Chips in Favor of AI-Focused M7 Line
Apple Inc. is making one of the biggest-ever changes to its Mac silicon strategy, preparing to jump ahead to a new artificial intelligence-focused chip generation for its next top-end processors.
Tech: House Science advances AI bills
The House Science Committee advanced a bipartisan bill that would codify increasingly prominent artificial intelligence oversight work being done by the Commerce Department.
How Elifin is reshaping the commercial real estate playbook
Elifin is leveraging a decade's worth of proprietary commercial real estate data as well as artificial intelligence to reshape how properties are marketed, analyzed and sold. The Baton Rouge-based … Already an INSIDER? Sign in. Already an INSIDER? Sign in
Moody Center announces exhibition exploring math, science and tech rooted in Indigenous knowledge systems
Through a framing of North, Central and South America as interconnected regions, “Radiant Geometries: Vectors of Knowledge from the Indigenous Americas” brings together contemporary Indigenous and Latin American artists whose practices illuminate knowledge as a living technology, through which the mathematical, metaphysical and artistic realms converge.
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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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