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Honolulu kids explore science and art at YMCA's STEAM Camp
The YMCA of Honolulu's fall break "Mission to Mars" camp at St. Patrick's School gave students a hands-on way to explore science, technology, engineering, arts, and math — with the
BiomEdit receives conditional licensure for first commercial product
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Gov. Moore insists Maryland’s budget is balanced; watchdog calls it 'political math'
WBFF Fox45 provides local news, weather forecasts, traffic updates, notices of events and items of interest in the community, sports and entertainment programming for Baltimore and nearby towns and communities in Maryland, including Annapolis, Crofton, Glen Burnie, Linthicum, Pasadena, Severn, Cockeysville, Dundalk, Essex, Parkville, Owings Mills, Towson, White Marsh, Woodlawn, Hampstead, Sykesville, Taneytown, Westminster, Elkton, Perryville, Pt. Deposit, Rising Sun, Aberdeen, Abingdon, Bel Air, Edgewood, Fallston, Havre De Grace, Columbia, Elkridge, Ellicott City, Fulton, and Ocean City.
Marlins Option on Alcantara Only Requires Elementary Math
The ace righthander is highly likely to receive a club option, despite letting him go costing considerably less
Former NFL player John Urschel makes breakthrough in mathematics research
Former Baltimore Ravens player John Urschel draws attention for mathematics research after transitioning from the NFL to MIT.
Music & Meditation Concert Features Versatile Tones of Clarinet Family
Nancy Mathison and Antonio Artese. Music & Meditation Santa Barbara continues its Fall 2026 season with a performance by clarinetist Nancy Mathison,
APSU’s Science on Tap to explore backyard ecosystems on Oct. 6
Austin Peay State University's College of Science, Technology, Engineering and Mathematics will host Science on Tap.
Boyle Heights community calls for more public health testing post-Lineage Fire
The Boyle Heights community gathered to discuss health concerns following the aftermath of the Lineage Fire. Keenan Willard reports for the NBC4 News at 8:30 p.m. on Wednesday, Sept. 30, 2026.
Twenty Hours a Week at an Amazon Warehouse for Health Insurance at 60? Here Is the Math a Retired Paralegal Will Run
A 60-year-old paralegal with $350,000 saved and five years until Medicare faces a decision that could quietly reshape her entire retirement, and the answer turns on a warehouse job that pays less than half what she used to earn.
Louisiana School for Math, Science, and the Arts to host Exploration Day on Nov. 9
NATCHITOCHES, La. — The Louisiana School for Math, Science, and the Arts will hold an Exploration Day on Nov. 9 from 8:30 a.m. to 2 p.m. where prospective students and
Diamondbacks bringing back hitting coach Joe Mather
Joe Mather has been the Diamondbacks' hitting coach since the start of the 2022 season.
Buy A Shop, Earn Rent, Retire Early? We Did The Math Behind The Dream
Buying a shop can require a large upfront investment, and the rent may not be enough to cover the loan EMI.
‘Breathtaking,’ ‘Devastating’: Mathematics Reels After New OpenAI Release
Hundreds of new A.I.-generated findings moved the frontiers of higher math in a single day — dispelling any doubt that the field is forever changed.
Mammoth Solar projects begin commercial operation in northwest Indiana
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Science fair at UCCS sparks kids’ interest in science, tech
Kids learned about different sciences, technology, engineering, art and math at the Cool Science Carnival Day, now in its 17th year.
Can math stay human in the age of AI?
As AI churns out mathematical proofs, a DeepMind study raises a bigger question: What do we lose when we focus only on the answers?
Mike Rowe on America’s great tradesperson shortage: ‘I don’t care what your politics are. Math doesn’t care, either’
At Ford Pro Accelerate, Rowe sparred with Education Secretary Linda McMahon and Defense's Mike Duffy about the divide between college and the trades.
California students post gains in math, science and English in 2025-26
SACRAMENTO, Calif. — California students made gains across key academic and success measures during the 2025-26 school year, with test scores rising in English, math and science while the statewide graduation rate reached a record high of 87.9 percent. For the first time, the state released assessment results and California School Dashboard data together to give families and educators a more complete picture of student performance.English language arts scores increased 0.5 percentage points to 49.3 percent of students scoring proficient or advanced, while math scores rose 1.1 percentage points to 38.4 percent. Science showed the strongest improvement, jumping 3.3 percentage points to 36 percent of students scoring proficient or advanced.The statewide chronic absenteeism rate dropped 1.1 percentage points to 18.3 percent. The suspension rate declined to 2.8 percent, marking the third consecutive year of improvement."At a time when California continues to focus on accelerating learning and closing opportunity gaps, these results provide important evidence that our sustained investments are making a difference," Gov. Gavin Newsom said. "The continued progress we are seeing is a testament to the hard work of students, families and educators across the state—and we will continue working to build on these gains."The percentage of students prepared for college and career increased to 55.2 percent, up 2 percentage points from the previous year. More than 54 percent of graduates completed coursework required for admission to University of California and California State University systems, an increase of 0.9 percentage points."California has made important strides in expanding student supports and opportunities, but the pace of academic improvement reminds us that our work is far from finished," State Superintendent of Public Instruction Tony Thurmond said.Eighth grade students posted the strongest score trends in both English language arts and math. Science performance improved across all tested grades, with the strongest gains at the high school level.African American students demonstrated larger-than-average improvements across English, math and science. Hispanic or Latino students showed larger-than-average improvements in English and math, with positive math score trends across all grades.Students who were socioeconomically disadvantaged made gains across all 3 subjects, including larger-than-average improvements in English and math. Students who were homeless continued to demonstrate gains across grades, with particularly strong improvements in math."We are encouraged to see students making gains across all 3 major assessments and are especially encouraged by the progress of African American students, Hispanic or Latino students, socioeconomically disadvantaged students and homeless youth," said Linda Darling-Hammond, president of the California State Board of Education.The progress comes as California's public school system undergoes significant demographic change. Statewide school enrollment declined to approximately 5.73 million students in 2025-26, nearly 500-thousand fewer students over the past decade.The number of students with disabilities increased approximately 25 percent over the same period, from about 680-thousand to more than 850-thousand students. Graduation rates increased across nearly all student groups, including notable increases for American Indian or Alaskan Native students at 4.3 percentage points and students with disabilities at 3.7 percentage points.In Downey Unified School District, the rate of students demonstrating proficient or advanced grade-level knowledge increased by 2.1 percentage points in English, 2.8 percentage points in math and 4.7 percentage points in science. The district aligned graduation requirements with college admission expectations and invested in inclusive special education practices, expanded career technical education and expanded learning opportunities."The gains Downey Unified is seeing in student assessment results and on the Dashboard are the result of years of focused, consistent work across the district," Superintendent Roger Brossmer said.In Exeter Unified School District, proficiency rates increased by 4.6 percentage points in English, 2.3 percentage points in math and 3.6 percentage points in science. The district placed a strong focus on phonics and the science of reading, particularly in kindergarten through second grade."The gains Exeter Unified is seeing in student assessment results and on the Dashboard are the result of having great people who are focused on the right work, and great students who want to do well," Superintendent Melanie Dorough said.Union Elementary School District saw proficiency rates increase by 1.5 percentage points in English, 2 percentage points in math and 4 percentage points in science. Teachers regularly analyze state and local assessment data to guide instruction, supported by strong core instruction and early literacy strategies."Union Elementary School District's achievement gains reflect a focused, data-driven approach to meeting the needs of every student while fostering a strong sense of belonging, safety and connection," Superintendent Carrie Andrews said.The California Department of Education is examining local results to better understand strategies and conditions associated with stronger student outcomes. The state has made significant investments in recent years, including literacy and math coaching, tutoring, expanded learning opportunities, community schools and universal transitional kindergarten.
Man arrested after unlawfully entering Alabama School of Math and Science Property, police say
A 44-year-old man was arrested Monday afternoon after unlawfully entering the grounds and a building at the Alabama School of Math and Science, according to the Mobile Police Department.
MSSM to Host Public STEM Night
The Maine School of Science and Mathematics in Limestone is hosting a STEM Discovery Night tonight.
Weekend reads: Computer science conferences impose caps; Lancet strikes to continue; Is this the ‘mathocalypse’?
If your week flew by — we know ours did — catch up here with what you might have missed. The week at Retraction Watch featured: A company named Springer that isn’t the publisher is offering authors…
2026 BFI London Film Festival Preview: Political Messages, Animation
Kristy Matheson previews key highlights and stars featured in the 70th edition and why she doesn't agree with people having "a grumble about how it doesn't seem like a big or a good year for cinema."
Math Professor Secures National Science Foundation Grant to Advance Math Education
Professor of Mathematics, Dr. Dana Clahane, was recently awarded a federal grant of $499,893 by the National Science Foundation (NSF). A prestigious award, the grant will elevate undergraduate mathematics education in a project titled “Building Mathematics Research Communities at Two-Year Colleges” (BMRCTYC). Over the next three years, BMRCTYC will establish local Mathematics Research Communities at eight... Read more »
Using Math and Science to Tell a Story, Engineering Student Adds in Some Magic Too
‘The Enchanted Book’ combines elements of art and engineering to create an automated mechanism that flips pages, emits light, and offers sound
World Food Championship returns this week
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How Democrats Might – or Might Not
Just a few months ago, it didn’t look feasible for Democrats to flip control of the U.S. Senate. Now it’s a real possibility – and this is how the party could get there. It’s all about the math. Today, Republicans control the chamber, 53-47. If Democrats can hold all four of their vulnerable seats,
Dodgers NLDS notes: Yoshinobu Yamamoto, Dave Roberts
The Dodgers beat the Braves in four games to win the National League Division Series, and have advanced to their eighth National League Championship Series in the last 11 years with Dave Roberts at the helm. But before we dig into the celebration and aftermath from Game 4, here are a few leftover notes from […]
NC School of Science and Math didn't get 'nerd' world record, but set a food-drive one instead
As ABC11 prepares for our 40th annual ABC11 Together Food Drive, we are highlighting people and efforts that have made a difference during the past four decades.
Informanté on Instagram: "Namibia urged to harness growing space science opportunities Staff Reporter NAMIBIA is positioning itself to benefit from the growing space economy through new satellite infrastructure, proposed legislation and expanded education and training opportunities, with young people urged to pursue science, technology, engineering and mathematics (STEM) careers. This was said by Dr Lisho Mundia, Deputy Executive Director for the Department of Higher Education, Training, Research and Innovation, who represented Education Minister Sanet Steenkamp at the official opening of World Space Week Namibia 2026 at the Windhoek High School Stadium on Monday. This year’s World Spac
1 likes, 0 comments - informante_news on October 10, 2026: "Namibia urged to harness growing space science opportunities Staff Reporter NAMIBIA is positioning itself to benefit from the growing space economy through new satellite infrastructure, proposed legislation and expanded education and training opportunities, with young people urged to pursue science, technology, engineering and mathematics (STEM) careers. This was said by Dr Lisho Mundia, Deputy Executive Director for the Department of Higher Education, Training, Research and Innovation, who represented Education Minister Sanet Steenkamp at the official opening of World Space Week Namibia 2026 at the Windhoek High School Stadium on Monday. This year’s World Space Week is being held under the theme “Rocket Revolution”, reflecting the transformation of space technology from the first human-made satellite launched in 1957 to modern spacecraft, satellites, space stations and missions beyond Earth. Full story: https://informante.web.na/?p=401904 Photo: National Commission on Research, Science and Technology - (NCRST)".
Science Spooktacular At Elkhart County 4-H Fairgrounds Oct. 31
Science, technology, engineering and math are getting a spooky twist this Halloween.
Simple math formula predicts when AI chatbots will go rogue
Most of us now carry in our pockets devices capable of running small AI chatbots. These chatbots have little safety oversight to ensure they don't provide information and answers with the potential to ...
OpenAI's math dump rattles researchers who saw programs wiped out
OpenAI's release of hundreds of AI-generated math papers raised questions over its training data and what it would do to the academic norms in math.
FULL SHOW: Inside INdiana Business 10/18/26
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A $100,000 Science Prize Could Leave His Social Security Checks Intact, but Still Raise His Tax Bill
A $100,000 honorary science prize sounds like a clean windfall, but for a retiree on Social Security, the tax math that follows can turn a moment of recognition into a costly surprise.
Simple math can predict when AI is about to go rogue, physicists say
A new mathematical formula tracks an AI's internal "compass needle" to flag undesirable outputs before they happen.
The Surprising Reason Some Children Struggle With Math, According to Science
Language and reasoning skills help predict math performance, offering clues to the learning difficulties that a test score alone may miss. Why do some students grasp mathematical concepts with ease while others struggle, even after repeated instruction? The answer may lie in abilities that extend far beyond working with numbers. Mathematics requires students to recognize
Studying Science in an Anti-Scientific Time
This Chronicle Review Deep Cut was first published in 2025. By Julianne Werlin Thanks for reading The Chronicle Review! Subscribe for free to receive new posts and support my work. Subscribe The last two decades have not been kind to science studies. Already bruised and battered by the “science wars” of the 1990s, by the 2000s sociologists of science — who had long argued that science had to be understood in its human context — were encountering the funhouse-mirror version of their own views in right-wing critiques of climate scientists. It was enough to give some prominent scholars pause. “Was I wrong to participate in the invention of this field known as science studies?” asked Bruno Latour, the most famous sociologist of science in the world, in 2003. “Should we apologize for having been wrong all along? Or should we rather bring the sword of criticism to criticism itself and do a bit of soul-searching here: What were we really after when we were so intent on showing the social construction of scientific facts?” In response, Latour proposed a change of method and attitude. Science studies, now chastened and contrite, would no longer seek to strip facts of their authority, but to enrich and enhance them. What this meant in practice was never fully clear. But his diagnosis of problems resonated, even if his solutions did not. Latour’s exercise in public hand-wringing signaled a shift in mood. By 2016, with the first election of Donald Trump, the idea that skepticism of science was a right-wing position, not a left-wing one, had become a commonplace, discussed eagerly in The New York Times. For liberals, vaccine denial during the Covid era and Trump’s second-term attacks on the funding of scientific research, covered extensively in these pages, added a new urgency to the defense of science on both practical and principled grounds. There was little appetite for a sociology or philosophy of science that emphasized the messiness and missteps of the scientific process rather than its integrity and triumphs. The distinguished historian of science Peter Dear’s new book, The World as We Know It: From Natural Philosophy to Modern Science (Princeton University Press, 2025), which seeks to tell the story of 18th- and 19th-century science for a general public, may seem comfortably remote from these bitter contests. In its 16 short chapters, Dear draws swift, sure sketches of major discoveries from Newton to Einstein, ably guiding the reader through debates on stellar nebulae, animal taxonomy, atomic chemistry, evolution, and other flashpoints in the history of science. But though a work of history, Dear’s book must still negotiate our polarized present. In a blurb on its back cover, the historian of biology Lynn K. Nyhart alludes to the “present moment, when scientific knowledge is being swamped by misinformation and its institutions are under siege.” Dear himself, in his conclusion, refers to the “yard signs” and “bumper stickers” that proclaim “Science Is Real.” One antidote to both anti-science propaganda and to reductive slogans in science’s defense is a robust history and philosophy of science: exactly what Dear’s scholarship has long offered. But his new book illustrates how difficult such work has become. Dear, an emeritus professor of history at Cornell University, is best known as a creative and philosophically minded historian of the Scientific Revolution. His edited volume The Literary Structure of Scientific Argument (1991) analyzed the rhetoric of Galileo’s thought experiments to brilliant effect. Discipline and Experience: The Mathematical Way in the Scientific Revolution (1995) was a major contribution to the analysis of scientific experiments in early modern Europe and beyond, while Revolutionizing the Sciences: European Knowledge in Transition, 1500-1700 (2001), now in its third edition, remains one of the best overviews of the profound epistemological upheavals of 16th- and 17th-century science. But Dear has also ventured into later periods. Nearly two decades ago, in The Intelligibility of Nature: How Science Makes Sense of the World (2006), he turned to the 18th and 19th centuries to sketch a history and theory of scientific knowledge. Aimed at a generalist audience, Dear’s simple prose belied serious philosophical ambitions. Science, Dear argued, has two aspects: “natural philosophy,” the quest to understand the true nature of the universe, and “instrumentality,” the applied skills and technologies that allow us to manipulate the world for human advantage. Both strands are ancient, but the innovation of the phenomenon we call “science” was to yoke them together. As Francis Bacon wrote, “Human knowledge and human power meet in one; for where the cause is not known, the effect cannot be produced,” an insight typically abridged to “knowledge is power.” Or, in Bertrand Russell’s characteristically blunt phrase, science has two functions, “1. to enable us to know things, and 2., to enable us to do things.” So far, so conventional. But Dear took issue with this familiar story in one respect. Bacon’s formula implied that theory and practice could be fused in a single method. In fact, Dear argued, the quest to understand nature and the desire to manipulate it were coupled only loosely. They could come together, but they could also pull apart. Focusing on natural philosophy, the “knowing” half of the scientific circle, he used a series of case studies from cosmology, taxonomy, chemistry, electromagnetism, and quantum theory to show how theories changed over time, in dialogue with practice, but never reducible to it. Take the case of matter, the basic substrate of the physical world. In the 18th and 19th centuries, scientists not only disagreed about what it was, but also about how well it could be known. In 1789, the chemist Antoine Lavoisier dismissed speculation about the atomic composition of chemicals as “discussions entirely of a metaphysical nature,” as he sought to orient chemistry away from philosophical abstraction and toward laboratory results. But in the 19th century, atoms returned with a vengeance in the atomic chemistry of John Dalton, who wanted to explain the underlying structure of nature. It was not just that Dalton and Lavoisier had a different set of experimental results, or even a different theory, Dear showed. What counted as an explanation had changed. In arguing that scientific theories depended on their intelligibility to human beings with complex arrays of beliefs and commitments, Dear was in sympathy with science studies. Key figures such as the sociologist Steven Shapin emphasized the importance of psychological mechanisms like trust in the formation and acceptance of scientific ideas. But in focusing on the scientific pursuit of knowledge, not its politics or economics, Dear resisted the more cynical perspective of some of his colleagues. The Intelligibility of Nature strikes a very different note than Shapin’s collection of a few years later, Never Pure: Historical Studies of Science as If It Was Produced by People With Bodies, Situated in Time, Space, Culture, and Society, and Struggling for Credibility and Authority (2010), to name just one example. Walking a very fine line, Dear depicted science as driven at once by complex social dynamics and by the intellectual power of real discoveries. It was a testament not only to his own acumen but also to the vitality of the field that he was able to do so. Nearly two decades later, The World as We Know It returns to the questions broached in The Intelligibility of Nature. Like Dear’s earlier study, it begins with the Newtonian cosmos and ends with debates between Einstein and Bohr on quantum physics. Beyond its terminal figures, many of the same characters appear in both studies, including the naturalists John Ray and the Comte de Buffon; the chemists Étienne François Geoffroy, Antoine Lavoisier, and John Dalton; as well as Charles Darwin and Michael Faraday, among others. Its subject is nearly identical: As in The Intelligibility of Nature, The World as We Know It considers natural philosophy as opposed to instrumentality, here defined as the “desire to create a picture of what the world is really like — the world as God knows it — rather than simply having instrumental or operational control over it.” The two books also overlap in insights — and sometimes even in language. The reader who has learned in The Intelligibility of Nature that Buffon’s method of classifying animals “reflected his belief in the importance of the senses and empiricism in learning about nature (a doctrine associated with Newton and the philosopher John Locke)” will experience a sense of déjà vu on reading, in The World as We Know It, that the classification “reflects Buffon’s commitment to the epistemological stance of Newton and his philosophical underlaborer John Locke, who stressed the role of the senses in creating natural knowledge.” Later in the passage, we read that, “because of its stress on understanding the ways of life of animals in their environments, Buffon’s approach could almost be labeled (anachronistically) as ‘ecological.’” And again, “an anachronistic way of putting it would be to say that Buffon recommends an ecological mode of understanding.” Likewise, in The Intelligibility of Nature we read of Dalton that he was initially drawn to meteorology, and that: He did a bit of experimental work on these things, especially having to do with the water-holding capacity of gases, but his real interest lay in understanding what, at an underlying natural-philosophical level, was really going on physically.” Whereas, in The World as We Know It: He did experimental work on these things, especially concerning the water-holding capacity of gases — although not at a very refined level quantitatively — but he was continually trying to develop a theoretical conception of what was happening physically. The final full chapter of each book closes on the same resigned note. Intelligibility: These days, most serious work on the natural philosophical underpinnings and implications of quantum mechanics is performed not by physicists but by philosophers of science. Among scientific practitioners themselves, Bohr’s attempt to use instrumentality as sufficient grounds for a respectable science has met with a high degree of success. And again, in The World as We Know It: Bohr’s and Einstein’s concerns are now questions that are mostly discussed by philosophers of science rather than by physicists themselves. The marginalization of these sorts of questions amounts to the demise of natural philosophy, at least in physics, in the 20th century. Quantum mechanics “works,” and that’s good enough for most scientists who encounter it. Examples could be multiplied. When handling the same subject matter, some echoes are inevitable. Scholars should not feel that because they have written “Isaac Newton died in 1727” in one book, in the next they must describe him as perishing, expiring, or meeting his maker. But the repetitions of language and arguments (not to mention six reproduced images) threaded throughout The World as We Know It are more extensive than that. They should have been caught by Princeton University Press or its reviewers, and revised. Because the two books have so much in common, it is easy to see where they diverge. The World as We Know It is a less theoretical work than its predecessor. The sociology of knowledge that organized The Intelligibility of Nature has been reduced in scale and folded into readings and examples. The taut interplay between history and theory, which gave the earlier study so much interest, is there, but it is subtle. In the earlier book, the case studies demonstrated theoretical claims about the character and development of science. In the later work, each narrative of discovery is an end in itself. Insights emerge from the history, but they do not control the organization of the study. With the theory muted, it can be hard to understand the logic uniting Dear’s sprawling history of more than two centuries of scientific knowledge. The 16 brisk chapters cover an enormous amount of material. But the closer the volume edges to comprehensiveness, the more obvious the missing elements become. A chapter on “Institutions and Pedagogy,” describing the birth of the research university, illustrates the problem. Inserted roughly halfway through the book, the unnumbered chapter is designed as an “entr’acte,” in acknowledgement that it does not fit the study’s organization around scientific discoveries. Yet as Dear is well aware, no general history of the development of modern science can bracket its 19th-century institutionalization and professionalization. It was the 19th-century university, after all, that gave us the “scientist,” a term coined by the Cambridge philosopher William Whewell. Dear’s history of scientific discoveries implies a wider social and institutional history, but it remains largely subterranean, only occasionally extruded under the pressure of the narrative. It must be said that the balance is not all on the side of the earlier study. There are some advantages to the more historical, less theoretical organization of The World as We Know It. Dear’s accounts of paradigm shifts are remarkably lucid, no small accomplishment given that they span fields and centuries. He makes excellent use of the wealth of new scholarship on Darwin and his influences, to which he has himself contributed. It is satisfying to see puzzle pieces from Buffon’s natural history, Georges Cuvier’s animal taxonomy, Thomas Robert Malthus’s demography, and Charles Lyell’s geology recombined seamlessly in the Darwinian jigsaw. Likewise, Dear’s discussion of stellar nebulae, divided between chapters two and 15, shows vividly how Newton, Kant, Herschel, and Hubble reimagined insights and observations as they sought to discover whether the diffuse light that filtered through early telescopes was a glowing, gaseous substance or discrete but distant stars. Yet despite such pleasures, the book remains a more modest work than The Intelligibility of Nature. Dear has clearly not changed his views about the history of science. But in the two decades that have elapsed, the discipline and the world around it have changed, making it harder to write a sociologically and philosophically ambitious history of science for the general reader. That is a shame, because in the age of Silicon Valley and biotechnology, both the reciprocity and the tensions Dear identified between the epistemological and instrumental ambitions of science are more evident than ever. In recent years, some of the most exciting new books on the history of science in early modernity have focused precisely on this pressure point. Important studies such as Pamela H. Smith’s From Lived Experience to the Written Word (2022), James Poskett’s Horizons: The Global Origins of Modern Science (2022), and Vera Keller’s The Interlopers: Early Stuart Projects and the Undisciplining of Knowledge (2023) all shed new light on how early modern science negotiated the relationship between its instrumental and epistemological ambitions, or in Russell’s phrase, knowing and doing. There is every opportunity for a philosophically informed approach to draw new insights from this wealth of material. Dear, unfortunately, has not done so in The World as We Know It. But for future scholars of the history of science, his body of work over nearly four decades may provide just the model. Julianne Werlin is an associate professor of English at Duke University. 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Powerhouse UCSB team awarded big grant to investigate mathematical foundations of AI
Researchers Nina Miolane, Haewon Jeong, Sanjuka Krishnagopal and Yao Qin win $1 million from NSF for their collaborative investigation
Elanco, Purdue planning research institute at One Health Innovation District
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Chris Koza album release concert Oct. 24
Lanesboro Arts is thrilled to celebrate the release of Chris Koza’s latest album, “Millsmoke on the Skyline,” with a concert at the St. Mane Theatre at 7 p.m. on October 24. Chris Koza is a composer and performer based in Denver and Minneapolis. In his writing, Koza both honors and builds on the vast genre of Americana music. For every song that sounds like it could be strummed from a boxcar clacking slowly across a grassy midwestern plain, there’s a tune sailing from a city rooftop, echoing across a valley of streets and traffic, or a melody twisting and shimmering like a dim frequency deep into the starry night.Both as a solo artist and with his band Rogue Valley, Koza has supported and shared the stage with artists such as Brandi Carlile, Gordon Lightfoot, Ingrid Michaelson, The Jayhawks, Neko Case, Kathleen Edwards, Dan Wilson, and many more. His music regularly appears on ABC’s “General Hospital,” “60 Minutes,” and “Good Morning America.” Koza’s song, “Be Wild,” was also featured in a state of Minnesota tourism campaign.#placement_932485_0_i{width:100%;max-width:550px;margin:0 auto;}var rnd = window.rnd || Math.floor(Math.random()*10e6);var pid932485 = window.pid932485 || rnd;var plc932485 = window.plc932485 || 0;var abkw = window.abkw || '';var absrc = 'https://ads.empowerlocal.co/adserve/;ID=181918;size=0x0;setID=932485;type=js;sw='+screen.width+';sh='+screen.height+';spr='+window.devicePixelRatio+';kw='+abkw+';pid='+pid932485+';place='+(plc932485++)+';rnd='+rnd+';click=CLICK_MACRO_PLACEHOLDER';var _absrc = absrc.split("type=js"); absrc = _absrc[0] + 'type=js;referrer=' + encodeURIComponent(document.location.href) + _absrc[1];document.write('');Koza’s latest offering, “Millsmoke on the Skyline,” grants the newfound outsider perspective of the rich life and golden fleeting days throughout the rural Midwest. Weaving melody and lyrics into vignettes and narratives set in places where love is powerful, dreams are vast, and life is beautiful.The St. Mane Theatre box office will open at 6 p.m. on the day of the concert. Tickets will be sold in person at the door or can be purchased online at lanesboroarts.org. Seating at the St. Mane theatre is first come, first served. Tickets are on sale now for $25 for general admission or $22 for Lanesboro Arts members.The 2026 performing arts season at Lanesboro Arts is made possible by our season sponsor, Poet Refinery, as well as our concert level sponsors, High Court Pub, Krage Insurance Agency & Preble Farmers Mutual, Stone Mill Clothing & Gifts, E2 Boutique/Amish Experience, and The Cottage House Inn, and community sponsors, VIS Plumbing, Granny’s Liquor, Kingsley Mercantile, Batstone CPA, Clara’s Eatery, IGA, Niagara Cave, and Huntington Electric. This event is made possible by the voters of Minnesota through a Minnesota State Arts Board grant, thanks to a legislative appropriation from the Arts and Cultural Heritage Fund. #placement_932489_0_i{width:100%;max-width:550px;margin:0 auto;}var rnd = window.rnd || Math.floor(Math.random()*10e6);var pid932489 = window.pid932489 || rnd;var plc932489 = window.plc932489 || 0;var abkw = window.abkw || '';var absrc = 'https://ads.empowerlocal.co/adserve/;ID=181918;size=0x0;setID=932489;type=js;sw='+screen.width+';sh='+screen.height+';spr='+window.devicePixelRatio+';kw='+abkw+';pid='+pid932489+';place='+(plc932489++)+';rnd='+rnd+';click=CLICK_MACRO_PLACEHOLDER';var _absrc = absrc.split("type=js"); absrc = _absrc[0] + 'type=js;referrer=' + encodeURIComponent(document.location.href) + _absrc[1];document.write('');
Steve Hilton faces steep odds in bid for California governor after 20-year GOP drought
Republican Steve Hilton's biggest obstacle to becoming California's next governor is simple math.
Leading Chinese mathematicians unite behind use of AI in research
Fields medallist Shing-Tung Yau, who is leading the project, warned that the country is lagging behind the US in using the technology.
YMCA camp in Honolulu turns fall break into a science adventure
The YMCA of Honolulu wrapped up "STEAM Camp" over fall break, teaching students important science skills. The camp is a place where Science, Technology, Engineering, Arts and Math engage students.
‘I’m on a War Footing’: Terry Tao and the Crisis in Math
The Fields Medalist and celebrated mathematician is on the front lines of a debate about artificial intelligence and the future of his field.
Sundar Pichai: Google’s Frontier Imperative, Compute Scarcity Dilemmas, Future AI Products and Businesses
Alphabet CEO Sundar Pichai has little patience for doubts about Google’s willingness or capacity to compete at the frontier of AI. His company’s latest Gemini 4 Argon model — long-promised and recently delivered — beats OpenAI and Anthropic’s latest public models on coding, math, science, and knowledge-work benchmarks and has placed Google’s AI back among the world’s best. Amid chatter that Google was fine with good-enough lightweight ‘Flash’ models, Pichai pushed back hard against any suggestion that company is limiting its research ambitions. “We won’t do it any other way,” he told me in a Big Technology Podcast interview recorded this week at Google’s Mountain View headquarters. Pursuing cutting-edge research is costly for Google, requiring compute and chips that otherwise could be sold at a high markup by its cloud business. But Pichai said allocating resources to frontier AI development is “ground zero” for Google. “Our cloud business is at any given moment in time forgoing short-term revenues because we are making compute trade-offs to support frontier AI development,” Pichai told me. In our conversation, we discussed Argon (which Pichai says processed three times as many tokens as Gemini 3 in the 72 hours after launch), AI’s role in developing better models, the AI interface of the future, competition with OpenAI, Google’s partnerships with Anthropic and Apple, data centers in space, and the nature of his optimism. You can read the full conversation, lightly edited for length and clarity, below, or listen to it on Apple Podcasts, Spotify, YouTube, or your podcast app of choice. Alex Kantrowitz: Google is back at the frontier with Gemini 4 Argon, which beats OpenAI’s and Anthropic’s public models on benchmarks spanning coding, math, science, and knowledge work. The conventional wisdom was that Google had given up on the frontier to focus on fast, good-enough models for its products. That was wrong? Sundar Pichai: Very wrong. Look, we are so committed to being at the frontier. It’s in our DNA. We won’t do it any other way. We’ve been thinking about the AI frontier for well over a decade now. The frontier is very dynamic. It’s very competitive. It’s very exciting. I couldn’t be more pleased with how Argon has come together. We took our time with it. We made sure we tested it safely and deployed it carefully. We took a bit longer than our other models, given its capabilities. Across the board, be it for agentic work, knowledge work, coding, creative tasks, Argon is one of the really good frontier models. The first 72 hours since we launched, it’s almost up to 25 trillion tokens used, which is about three times what Gemini 3.0 had in the first 72 hours. And we are capacity constrained as we are serving it. Excited, but there are always areas….. You know, the frontier is very dynamic, and you have to keep evolving the models. There are areas which we can see we need to improve as well. We’ll be at work on all of that as well. Which areas? On any given model, Argon would be across the board better at everything if you just look even two months back. But the space is so evolving. Even on coding, for example, there are pockets where we know we need to do better, and there’s clear feedback. We’ll make it better. The argument for standard models is that they’re already good enough to build great products, with the frontier valuable only for certain tasks. Meta built Muse with good models rather than the best, and Google could put standard models into Maps, Gmail, and Search. Why isn’t that sufficient? It’s definitely not how I have thought about it at all. We are deeply committed to driving the state of the art and actually pushing where this technology can go, all the way from transformers. That’s a journey we’ve been on. You will see us continue to do that. When you’re at the frontier, you get an earlier look into the emerging capabilities, earlier than others, which helps you better prepare for the future. We are fortunate to have a wide set of opportunities across consumer and enterprise. Some of our cloud customers, for sure, want the model at the frontier. The frontier models will be better at developing the next set of models in an era of recursive self-improvement. I would argue it matters there. Internally, we have definitely seen a steep point of inflection by deploying Argon to our developers. It definitely tackles problems which our previous models could not do. Being at the frontier puts you at the innovation frontier and helps you drive that as well. There is room for models of all sizes and capabilities. That’s why we did Flash, Flash-Lite, etc. I agree with you that you can develop very good product experiences with not necessarily the most frontier model, because they tend to be slower and more expensive too. But we care about the Pareto frontier, and we’re committed to doing all of that. You said developing frontier models gives you a look at what’s coming. What do you see on the horizon? What makes this moment so exciting is you build something which you think is a culmination of months of work, and you put it out. You already see clear headroom ahead. We’ve all been working on these agentic, long-horizon capabilities, but clearly the frontier is going to have multiple of these agents working together to do things. That’s an example of an exciting paradigm underway. It’s also clear to me that users, both in consumer and enterprise, will have access to some multi-agent framework for them, working all the time doing things. It feels like a moment of infinite possibilities. Sergey Brin said one of Argon’s core innovations was relying more on the model to help develop the model. Yet incidents involving OpenAI’s bots breaking containment and Google’s bots accessing a site they shouldn’t have suggest these systems still aren’t perfectly aligned. Isn’t there reasonable cause for concern about accelerating toward recursive self-improvement? There are three parts to your question. Let’s first talk about RSI itself. You’re right, Sergey mentioned RSI being a critical contributor as we are developing Argon. But to be very clear, today, in a continuum, we are pretty far from the end state of RSI or something like that. The right way to describe it is you have RSI loops along with AI researchers in various aspects of model development. You almost have agents helping AI researchers in some automated way work together to build these models. It’s good to at least understand where we are today, and it’s super exciting. I expect that continuum to keep making progress as we go into next year. The second question is around safety. Security comes from defense in depth. You’re talking about alignment, obviously. We all are doing a lot of work to make sure the models are as aligned as possible. But it’s an active area of research, and there are clear gaps. That’s not the only thing we rely on. You have the work you do on alignment, but beyond that, we put a lot of effort into real-time monitoring as these models are working. Making sure you have good telemetry into what’s happening so that you can intervene in real time if you see unexpected behaviors. How you do that, the architecture with which you do that, how you make sure it’s all sandboxed and isolated in a way that it can continue doing its job during real-time monitoring — these are all areas of investment. Obviously, when we deploy these models, giving customers, enterprises, control, both with real-time monitoring, but their ability to decide where they deploy, what type of data which models ever have access to, etc. All that work is very, very important, and we are doing a lot of that. As an industry, we need to constantly up our game and take the time to do all this stuff, including dedicating the compute required to make sure you can safely deploy these models. The third part is: Is it all enough? This is why there are a lot of conversations in the industry, and I think it’s reasonable to be worried. The right way to approach something you worry about is early on thinking about what more we can do. As an industry, you’re definitely seeing increased levels of work on alignment, testing for safety, etc., as we deploy these models. The next step would be developing better standards so there are cross-industry standards of how we do these things, including the ability to evaluate using independent evaluators. If you look at areas like financial controls, we have accounting standards, we have independent auditors. Those are all the next natural steps for us as an industry to work toward, and regulation will end up playing a role as we go through all of that as well. Google pursues both language models and world models, while some other AI companies like OpenAI have focused more heavily on LLMs as the path to AGI. Why is world-model research important to you? As humans, you obviously perceive the world around you, and you’re taking all those inputs. Making sure these models are able to understand the laws of physics, simulate the world in high fidelity. All that, I think, are key enabling blocks for intelligence. We are working across all possible applications of this. Take an area like robotics. It’s not an accident: If you look at embodied reasoning benchmarks, Argon is state of the art. That all comes from our experience building world models. Inherently, I would expect there will be architectures which will converge all of this in the future. But for us, we view this as, today, this jagged frontier that is intelligence. We want to be at the frontier of all the possible approaches, and that will help us both drive better research, better insights, and develop a much better AGI model, is how we think about it. Some people argue Google should focus on selling its computing resources rather than using them for AI research. Cloud wants to sell compute at a high markup, while researchers need a very big computer to build the best models. How do you balance those demands? Ground zero for us is making sure we are investing in the compute that’s needed to be at the AI frontier. In 2015, I set the company to be AI-first. AI is that key horizontal ingredient across everything we do, all the way from Search to Cloud to Waymo. That is ground zero for us, making sure both for research and developing the models that we have enough compute needed. Beyond that, we have these independent businesses which each have their own long-range plans. They’re using those long-range plans to project forward, and we plan for compute for all of them. The cloud team, they have a business and they have a projection and they have demand signals. They have committed contracts. With that help, we can plan on a long-term compute trajectory for them. We’re fortunate to have this wide set of opportunities. There’s no company in the world which isn’t making trade-offs at any given moment in time. But the first principles we operate on is making sure we have enough compute to invest in the frontier, and that’s non-negotiable, and everything else works on top. Remember, we are building our own compute, our own data centers. There are long lead times. The compute we have today is a function of decisions I made in 2024. Even if you think back to a year from today, last year the agentic coding takeoff hadn’t happened. Today, obviously, when you sit and look at anything, everyone is short—not just us, other companies—and everyone wishes you had two times what you had planned for. That comes with the territory. At any given moment in time, we are planning for what we would need two to three years out in the future. You’re going to find some decisions you make may look suboptimal two years out, depending on where the state of the art is. I don’t spend too much time thinking about it. The way I think about compute allocation is making sure Google DeepMind, both for research and frontier model development, is cutting edge in terms of compute access. After that, we plan for all our businesses. It’s great we can provide our compute to other people too, like we have done. It allows us to scale that up, get real tests for it in the widest application set possible, test it in the real world, and make sure we are at the cutting edge of technology at every layer of our AI stack as well. Ben Horowitz relayed on a podcast that Andy Jassy had said compute was sold out until 2028. Are you seeing the same thing? If today a customer is coming out and asking us for compute in ’27, that is incredibly difficult to do. Even last year we went out and leased some short-term compute to accommodate some customers and help them out. But yes, we are all dealing with similar timelines. Every major technology shift seems to involve overbuilding, from railroads to fiber. Do you think that’s happening here? It depends on the time frames you’re looking at. This level of build, if I take a long enough time frame, it makes complete sense to me. We are at early stages of the most consequential technology ever, and this is not like fiber. It’s not dark. We are using it. We are capacity constrained on a daily basis. The applications, the underlying technology is improving rapidly. But running a company, the way I work through the moment is you look at your demand signals. You look at your committed revenue opportunities that you’re getting in the form of contracts. You balance that with the underlying technical capability, or internally you balance how we are using it internally, the model trajectories. Based on all that, you take a disciplined approach—a confident, aggressive, but disciplined approach—to plan out compute. But when you talk about the broader economy as a whole, you’re having people operate with various levels of risk. I focus on what I can control as a company and plan through it. If your question is, can there be an overbuild? Yes, because you can have some constrained components. Remember, what matters is a set of everything you need to build out that compute. Memory, electricity, electricians…. Land. Like 2,000 components or more. You could get constrained at some point. There could be things which cause bottlenecks, and then you could have overbuilds. There could be external shocks to the economy. There are macro risks. All that is possible at any given time. But am I confident that the compute we are planning for, we will have uses for it given enough time? Absolutely, yes. I was in the room when you said AI would be as profound as fire and electricity. If you believe that, shouldn’t you give researchers the biggest possible computer, even at the expense of today’s cloud profits? We’re all doing some form of that. Our cloud business is at any given moment in time forgoing short-term revenues because we are making compute trade-offs to support frontier AI development. What you’re seeing is true already. But you want to be thoughtful. Even our AI researchers have some constraints, which are also good. That’s what inspires creativity. That’s what helps you come up with a more efficient algorithmic breakthrough, maybe to use less memory. What we are committed to is to invest what’s needed to stay at the frontier. OpenAI President Greg Brockman told me a few months ago that the ideal AI product might have no interface at all—just as natural as conversation. What do you think the interface looks like in the age of AI? Computing historically has been humans adapting to computing. With AI, it’ll be the other way around. The technology will adapt seamlessly to how humans live and go about their day-to-day life. You want to be able to speak to that AI when you want to. It has context. It can be persistently running somewhere, give you continuity. You’re not dealing with a lot of complexity. It’s natural. All that makes sense to me. That doesn’t mean it won’t have any interface, however. Nowadays, even on my phone, Gemini voice models are so good. I can just press the microphone button. I can just ramble. That’s why we call it Rambler. I can ramble. It intelligently corrects with my personal context. Even my friends’ names, weird names, it gets it right, even if it first guesses it wrong. It’s amazing. I’m not going in, backspacing, editing once I finish saying it. It’s remarkable how good it’s gotten. But there are going to be moments you will need an interface. You may want to entertain yourself. You may want to watch a video. You may ask your AI to create something, and you want to watch it, consume it. The way I think about it is, you want it to be as seamless and as natural as possible, and that you are not doing the hard work adapting to whatever the interface is. The technology is adapting to how you’re working at any given moment in time. When I asked Sam Altman about competition from Google, he argued that OpenAI was building AI-native products and implied Google was bolting AI onto existing ones. Do you accept that framing, and can a “bolt-on” compete with an AI-native product? First of all, it’s a very dynamic moment. We are all pushing each other. OpenAI has pushed us as a company too. This is not just we are doing something to them. Far from it. Yes, Google went into its own version of a code red too. In the AI moment, all of us have to respond with varying levels of urgency at any given moment in time. We are constantly building new products as well. Gemini was built from the ground up as a product—the Gemini app I’m talking about. Or tomorrow, as all of us are adapting to agents, are you bolting agents onto ChatGPT, or are they building it natively? How would you think about that? You can always put these questions back. I don’t think of it that way. I think of it as you want to build world-class product experiences that are AI-native and they are winning. Some of it is adapting products, some of it is rethinking new experiences. They will all be part of how you approach this moment. How do you expect to make money from these AI interfaces? If an agent recommends a product and I buy it, should it take a cut of the sale, even if that’s disclosed? I’m going to put enterprise aside. Consumers are going to increasingly value this technology. Take software developers today. Developers are willing to pay for these coding models because it really helps make them more productive. Nobody would want to code the way they were coding three years ago, or even the same time last year. When you’re delivering value, there’s value to be had. I’ve never seen examples where consumer products really scale to billions of users, where people are using it intensely, and haven’t had monetization to go along with it. If you’re providing value to people, there is inherent value to be had. The nature of it may change. You’re asking a scenario in which agents are doing stuff for you. At the end of the day, you will have commercial needs. The agents need to fulfill those commercial needs. The providers on the other side are going to see value in that. There’s going to be some mechanism by which it gets fleshed out. That’s what an efficient system does. But what matters is that you’re building those world-class consumer experiences. I promised myself one product feature request: Can you make Gemini-powered search in Gmail as good as searching Gmail through ChatGPT? What’s going on here? One of the exciting things we announced recently is the unified Gemini agent for enterprise, and we’re bringing that to consumers. These are persistent agents which run 24/7 in the cloud. We’re spinning up a micro VM effectively for every query, and from the runtime, the harness, the model—it’s all a headless service. It’ll work everywhere, and it has your context with you. You can address this agent whether you’re in Workspace, in Search, in Gemini. Agents are going to drive this unification from a customer experience, which people are looking for. When you have a broad product portfolio and you’re moving forward fast, there are always areas where you’re moving forward faster than some other areas. That’s valid feedback. But we are putting a lot of time and effort into making sure, as we build out our agentic experience, that unification and that capability works well across all these services. People have asked where Google’s equivalent of Muse or Dots is. Do you anticipate creating a new product to compete with them? Our goal with Gemini is to be a personal, proactive, and powerful assistant. That’s what we are working toward. You saw us describe the early versions of it at Google I/O this year with Spark. Effectively, already, if you open the Gemini app as a Pro or an Ultra user, you see Chat and Task, and they start giving you that agentic experience. With Argon now, that capability is dramatically accelerated. What we announced recently in our enterprise event, we are bringing that to consumers as well. Soon in Gemini, you will be able to ask something, and it’s a continuum. You don’t need to think about whether you’re asking a chat or an agentic task. It’ll just automatically do the right thing, and it can work persistently in the background securely and get things done for you. Already, empirically, when I look at what’s coming into Gemini in our app, last year you would have tagged them and most of them would have been chats. But a steadily growing percentage is already agentic tasks in Gemini. Yes, we are going to bring personal agents to our consumers. It’ll work in the Gemini app. It’ll work in Google Search. It’ll work in Gmail. You can address it, call it, etc. We want to make sure we’re getting it right. It’ll be powered by Argon, and we are very excited to bring it to our users. Google has invested and committed tens of billions of dollars to Anthropic. What do you see in Dario Amodei that makes you want such a deep investment and partnership? Very early on, when we looked at the founding team of Anthropic, it was very clear to us this was a world-class team which was involved in some of the earliest GPT models. We were quickly able to assess that they would be at the frontier. We’ve always tried to identify and partner with companies which would be at the cutting edge of technology. We’ve done that with SpaceX. We’ve done that with Anthropic. That’s what we saw, and we had a chance to partner. Is your partnership with Apple to license Gemini for Siri expanding, contracting, or standing still? For both us and Apple, that partnership was about translating the capabilities of AI into better experiences for users. I thought Apple has done a phenomenal job of incorporating—they customize these models and build it into Siri—and it’s been great to see the reception. We are committed to doing more. You’re going to do more with them? There’s nothing specific to comment, but it’s a partnership. It’s working well. What have you learned so far about the feasibility of data centers in space? It’s a very exciting project. Projects like this remind me—I still recall that moment in the desert for the DARPA Grand Challenge, watching the Stanley car compete in it, which ended up being the basis for Waymo. With all these technologies, you’re at a moment in time where it looks a bit like sci-fi, but 10 to 20 years down the line, it’s actually working in a meaningful way. I feel the same in space. We’ve always believed in betting big on these technologies. Quantum has been a great example of it. We’re probably 10 years into the journey. Waymo, we are almost 20 years into the journey. Space, we are two to three years into the journey. It’s clear to me that it’s an approach which will make a lot of sense in the future. Being in sub-synchronous orbits with 24/7 access to the most abundant form of energy we have access to will make a lot of sense for this exponential increase in compute that we are going to need. Having said that, this is a challenging project. The team has disciplined milestones. It was a super exciting milestone to send the TPUs up to space. Sometime, as we are speaking, we are expecting a “hello world” moment from them. I look forward to seeing that happen, and we’ll continue investing here too. You previously said 25% of the code written within Google was written by AI. What’s the number today? That number in our world today is meaningless in the sense that every part of what we now touch is influenced by AI deeply. What we are tracking with Argon, for example, it’s been a point of inflection. People are using Argon to really go tackle complex projects, including projects which affect millions of lines of code. In the four weeks since we have launched Argon, we have seen a 30% increase in CLs, and more importantly, almost a similar increase—a one-third decrease in rollbacks. People are writing code more productively and better code with it. We are tracking it with newer metrics now. How are you dealing with opposition to data centers? If you can’t build in the U.S. the way you want to, where else do you go? We’re very committed to building our data centers very responsibly. When I look at our largest data centers in some of the communities where we have been the longest, I visit them, I talk to people. There are people who come and tell us their kids have gone to college because of our investments in these communities, and we’ve grown in those communities. To me, this is about doing it right. Engaging with the communities early, doing it transparently. We are committed to making sure we are bringing incremental power, and we cover 100% of the cost of our power needs for these projects. It’s understanding the impact on neighborhoods and making sure you’re working thoroughly to address them. Done rightly, you can continue making progress. But we are constantly taking feedback into account. I do worry not everyone operates the same way. People have been surprised by data centers showing up in their backyard. Those are all important. As an industry, we should continue to do better. But part of the reason, in a 20-year time frame, we are thinking about things like data centers in space is making sure we are able to put these data centers where maybe the trade-offs will be different. What unproven area of AI is Google working hardest to crack? Is it continual learning—getting models to keep updating after release—or something else? There are multiple parallel paths. You saw with Argon we have a million output tokens. With our Gemini models early on, we had a one-million context window on the input side. These are all examples of breakthroughs we are constantly working on. How do you think about synthetic data? How do you think about continual learning? How do you think about more efficient architectures? We do dedicate a meaningful percentage of our compute to these research bets too. It always goes hand in hand, including our world models. Those are all bets we are placing. Sometimes, as part of these bets, there are ideas that emerge which just make their way into the mainline models. Maybe we don’t fully talk about it, but we have multiple steps in these areas. Working in technology requires optimism that building something will improve the world, but those bets can leave real consequences when they fail. I recently returned to a town in El Salvador where enthusiasm for Bitcoin had given way to unfinished construction and something resembling a ghost town. How do you maintain the optimism you spoke about in your Stanford commencement address while accounting for those risks? I genuinely have a deep conviction that improving technology and improving access to technology makes people’s lives better. It’s been my personal experience in my life, out of which comes that deep conviction, optimism. But with that, I’ve always felt humanity’s journey is always about working hard to harness that technology to make it work well for society. The optimism is not an optimism that you just build whatever you want, whenever. You work hard to make sure you’re building technology in the service of humanity, to make the human condition better. That’s got to be the defining goal as we are approaching it. With that framework, I’m very, very optimistic. We are in very early stages of the kind of progress that we are going to see on many areas. We’re all talking about areas like healthcare. A lot of the progress is ahead. The ability to make things in a fundamentally cheaper way on many things where things have been getting expensive for a long, long time, discovering new materials, etc., which really bend the curve—it’s tough to step today and understand the impact of all that, but they’re going to be very profound. I’m very optimistic, but we have to be rightfully concerned about all the ways this technology can go wrong. Sundar, I know you don’t do many of these. Thank you. Thanks, Alex. Appreciate it.
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Donaldson’s Finer Chocolates celebrates 60 years
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When 1 + 1 = 1: The Miserable Math Behind Many Media Mergers
(Welcome to the “Most Important Story of the Week”, my bi-weekly strategy column analyzing the most important (but often not buzziest) news story of the last two weeks. I’m the Entertainment Strategy Guy, a former streaming executive who now analyzes business strategy in the entertainment industry. Please subscribe.) The last few issues of this newsletter have circled two topics: September’s streaming viewership and all the Paramount/Skydance/Warner Bros./Discovery merger news. I’m happy to say on the Streaming Ratings Report front, we’ll be all caught up by early next week, then the SRR will come out on a tight weekly schedule. Then, after today, I’ll have slayed my PSWbD dragon. It’s such a big story, it demanded multiple issues. With the announcement of a name (just Skydance) and executive team, though, I think we can almost close the book on this story. In terms of topics, it becomes just another studio to cover. So today, I have one last set of takes on that merger. As I’ve sat with it, reading about who was staying and who was going, a big worry crystallized in my brain, and I gotta get that take out. Plus, I still owe you a host of “ugliness” that didn’t fit in my previous “The Good, The Bad and The Ugly” takes. Let’s dive in! Subscribe Most Important Story of the Week - As Paramount Shuffles Deck Chairs, Will They Build or Consolidate? On social media—I try to avoid most social media platforms, but sometimes I end up checking in—the best joke I saw about the announcement of the new name of Skydance was from fellow reporter Claire Atkinson who pointed out… Yeah, I laughed out loud at that one. Later, though, I saw another Twitter response (that later got deleted, so I can’t shout the person out) that did give me pause. The person noted that the Titanic film made the better analogy: a super expensive project everyone in the town doubted that went on to conquer the globe. So yeah: on one hand, the image of the Titanic recalls the actual ship and its infamous end, but that clip is from a movie which proved the doubters wrong. So for all the skepticism of the latest massive media merger, one could wonder which way this one goes. Does it crash like the Titanic or rise to glory like Titanic, the movie? Either way, this deal is “Titanic”, looking at the massive debt required to close the deal, which is not going well when it comes to borrowing costs for Paramount… …or borrowing costs for Oracle. But I’ve started to get worried about another outcome: the sum of this merger will be smaller and weaker and less profitable than its former parts. In other words, one plus one won’t equal three or two, but an even smaller number. Skydance paid to take a traditional studio off the board, but they won’t really capture the upside. Let me explain what I mean, but first, let’s look at the series of news that inspired this thought. Skydance Sets Its Leadership Team As soon as the Paramount-Skydance-Warner Bros.-Discovery merger closed, the new leadership team announced a new name—Skydance, which I’ll use going forward—and a new executive leadership team. You’ve probably read through all the executive shuffling, but if you haven’t, here’s a quick, incomplete summary, in rough order of importance: Mattel’s CEO Ynon Kreiz—who previously ran Maker Studios—joined as co-CEO with David Ellison. HBO/HBO Max’s Casey Bloys will stay and run streaming. Former head of streaming for Paramount-Skydance Cindy Holland—who was also at Netflix—will depart. The former heads of Warner Bros.—Pam Abdy and Mike De Luca—are out. The film studios will be run by Dana Goldstein and Josh Greenstein. George Cheeks of Paramount will run Skydance TV, controlling all the TV production units. In comic book news, the DC films will still be run by Peter Gunn and Peter Safran. Lastly, the head of CNN, Mark Thompson, will stay in place. Let’s quickly opine on the particulars before diving into the broad ramifications. We’re just getting started with this issue, but the rest is for paid subscribers of the Entertainment Strategy Guy, so if you’d like to find out… Who I like and dislike in their next gigs… Why I think this consolidation will likely leave Skydance worse off… The math behind which mergers succeed and which don’t… A lot more ugliness in this merger… And a whole lot more... ...please subscribe! We can only keep doing this great work with your support.
Opinion: In D.C. Schools, How the Science of Reading Offers a Blueprint for Math
Students in the nation’s capital posted gains in math and reading for the fourth straight year, according to the results of statewide accountability tests released last month. It was the latest piece ...
Hillary Clinton and Kara Swisher to release weekly midterms podcast
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25% Of Middle Schoolers Proficient In ELA, Science; 18% In Math
According to a recent presentation by district leaders, who described those numbers as “steady progress” and a small improvement from previous years.
The Math Singularity Is Here, and Science May Be Next
OpenAI has released an extraordinary body of mathematical work produced by an unreleased frontier model: 722 manuscripts spanning 372 research families across fields including number theory, complexity theory, and mathematical physics. Each result required, on average, the equivalent of roughly three hours of ChatGPT Pro reasoning. A New Era of Discovery We may be witnessing the beginnings of a “math singularity”: a point at which machine-generated discovery accelerates beyond humanity’s ability to independently reproduce, verify, or fully absorb it. If frontier models can generate hundreds of meaningful results in the time it takes a human researcher to investigate one, the constraint will no longer be producing new mathematics. It will be our capacity to validate, interpret, and integrate it. This does not make mathematicians obsolete. It changes their role. They may increasingly serve as guides, critics, and meaning-makers. Increasingly, they will decide which questions matter, determining which results can be trusted, and explaining why they are important. Mathematical insight may become abundant, while human judgment becomes more valuable than ever. Mathematics May Be Only the Beginning Every scientific discipline could eventually experience a similar acceleration. AI systems could generate and test hypotheses, connect findings across specialized fields, and explore possibilities at a scale no human research community could match. In medicine, imagine models identifying overlooked disease mechanisms, designing drugs and clinical trials, personalizing treatments, or discovering cures in months rather than decades. In materials science, AI could help create better batteries and cleaner industrial processes. In climate science, it could accelerate advances in energy, forecasting, and carbon removal. In biology, it could uncover relationships hidden across enormous genomic and molecular datasets. The result would not simply be faster research. It could expand the frontier of what is scientifically possible. The New Bottleneck Scientific discovery, however, is not the same as scientific progress. A medical breakthrough must still survive laboratory testing, clinical trials, regulatory scrutiny, manufacturing constraints, and real-world deployment. As machine-generated ideas multiply, the bottleneck may shift from producing breakthroughs to validating them safely and translating them into practice. We may soon have more hypotheses than laboratories can test, more drugs than clinical trials can evaluate, and more mathematical results than experts can review. That imbalance will require better systems for automated verification, reproducible research, prioritization, peer review, and collaboration between AI and domain experts. The importance of OpenAI’s work is not merely the number of manuscripts produced. It offers a glimpse of a world in which intelligence can explore thousands of intellectual paths in parallel. If ideas cease to be scarce, humanity’s defining challenge will be deciding which are true, which matter, and how to turn them into progress. The math singularity is not the end of human inquiry. It is the beginning of a new kind—one in which machines expand the frontier of knowledge and humans decide where it should lead. Subscribe Share Leave a comment
Mathematician Who Waited 50 Years for Proper Credit for Her Work Has Died
At the Los Alamos National Laboratory, Mary Tsingou Menzel’s computer programming helped set the stage for chaos theory and other fields.
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Taft ISD hosts Math, Science STEM family night
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OSSM schedules Fall Preview Day for prospective students Oct. 24
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FULL SHOW: Inside INdiana Business 10/11/26
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What Are the Remaining Millennium Prize Problems?
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UNCP Computer Science Program earns ABET accreditation
Photo by UNCPUNC Pembroke’s Bachelor of Science in Computer Science program has earned initial accreditation from ABET’s Computing Accreditation Commission, providing independent recognition of the quality and rigor of the program as it prepares students for careers and advanced study in computing.The accreditation, which extends through Sept. 30, 2032, comes as UNCP continues to expand its computing programs amid rapid growth in artificial intelligence, cybersecurity, data science and other emerging technologies.ABET accreditation is a voluntary, peer-reviewed process that evaluates academic programs against established standards for quality. The review examines curriculum, student outcomes, faculty qualifications, assessment practices, student support and continuous improvement.“Earning initial ABET accreditation is a significant milestone for our Computer Science program because it provides external recognition of the quality of our program and validates our commitment to continuous improvement,” said Dr. Haitao Zhao, assistant professor in the Department of Mathematics and Computer Science, in a press release. “For our department, this achievement reflects the hard work and dedication of our faculty and staff and gives our students greater confidence in the quality of their education and preparation for careers and graduate study.”For prospective students, the accreditation provides independent assurance that UNCP’s program meets standards established for computing education and is preparing graduates to enter technical fields in the global workforce.Earning the accreditation required faculty and staff to conduct a comprehensive review of the program, including its curriculum, educational objectives, student learning outcomes and assessment processes. The review included a detailed self-study, documentation and an on-site evaluation.The accreditation arrives as UNCP looks toward the next phase of growth in computing. The department plans to continue strengthening its computer science curriculum while expanding hands-on learning and research opportunities for students. It also plans to pursue ABET accreditation for its information technology and cybersecurity programs.Future plans include developing new opportunities in artificial intelligence and graduate education, including an AI track and a Master of Science in Computer Science with a focus on AI and cybersecurity.
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The hidden math of Alzheimer's could point to new treatments
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Explore Backyard Ecosystems at APSU’s Science on Tap October 6th - Clarksville Online - Clarksville News, Sports, Events and Information
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