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ENTREFest announces Cedar Rapids as host city for 2027 event
ENTREFest will bring entrepreneurs, startup founders and small business owners from across Iowa and the Midwest to Cedar Rapids June 9-11, 2027, with programming at multiple locations throughout the NewBo District and Kingston Yard. Produced by NewBoCo, the conference will feature multiple pitch competitions, keynote presentations, hands-on workshops and sessions covering artificial intelligence, marketing, venture…
A golden age of science is coming
It’s almost hard to remember, but just a few years ago, a lot of very smart people thought that science was in terminal decline. Respected researchers were writing papers with titles like “Are Ideas Getting Harder to Find?”, “Papers and patents are becoming less disruptive over time”, and “Slowed canonical progress in large fields of science”. In 1996, science writer John Horgan even published a book called The End Of Science: Facing The Limits Of Knowledge In The Twilight Of The Scientific Age. The fundamental idea was that the Universe only has so many insights to discover, and that we’ve picked the low-hanging fruit. It was the idea that Joseph-Louis Lagrange expressed when he said that “we cannot find more than once a system of the world to establish.” As the big ideas and the simple ideas get mined out, we’re forced to throw ever more brains at the mysteries of nature just to keep scientific progress going at a fraction of its old pace: It looked like science was hitting a wall. Then along came AI. It’s far too early to tell how much AI will eventually speed up the growth of total factor productivity. But already we can see signs of a speedup in science. Anthropic Founder Jack Clark assembled some of the hopeful data points in a recent lecture: [ Dror Poleg@drorpoleg "If you pick different parts of science, you see the same pattern show up: Progress is accelerating, and it has started to accelerate in the last two years." Jack Clark @jackclarkSF I’m speaking at the IMF in an hour about the singularity and why I believe it has begun and the choices we'll need to make. Watch live here: https://t.co/Hj9HzcXx5h 6:08 PM · Oct 18, 2026 · 21.2K Views 2 Replies · 10 Reposts · 123 Likes ](https://x.com/drorpoleg/status/2111882070376980529) Anthropic is accelerating its investment into drug discovery, and is already announcing some impressive preliminary results: [ BioTender@Junioryu136689 BREAKING: Anthropic unveils early results from six Claude-powered drug development collaborations. • 70% shorter CAR-T signaling domain • ~35,000 antibody designs • >10× tighter antibody binding • 89% less batch-record review time Still largely preclinical. Anthropic @AnthropicAI Over the past few months, Anthropic scientists have engaged in deep research partnerships with six organizations developing new medicines. We've also begun drug discovery efforts of our own. In doing so, we’ve learned much more about how Claude can support the many stages of 9:43 PM · Oct 27, 2026 · 11.2K Views 3 Replies · 24 Reposts · 205 Likes ](https://x.com/Junioryu136689/status/2115197821150495150) Claude also appears to be enabling rapid progress toward a treatment for food allergies. OpenAI, meanwhile, announced a possible breakthrough treatment for neurodegenerative disease. Meanwhile, Google Cloud Chief Scientist Pushmeet Kohli has a list of recent scientific advances that have been enabled by its new Gemini model, including breakthroughs in nuclear fusion reactor design, rare disease research, and quantum computing: [ Pushmeet Kohli@pushmeet Gemini 4 Argon is a step change in model performance. In recent weeks, it has had a transformative impact on the Science projects in our team at @GoogleDeepMind and our external collaborators, and helped make a number of remarkable advances across scientific disciplines. 🧵👇 4:47 PM · Oct 13, 2026 · 36.5K Views 8 Replies · 41 Reposts · 497 Likes ](https://x.com/pushmeet/status/2110049807385702623) But as cool as these stories sound, they’re just very minor, preliminary efforts. There are a number of reasons to think that AI is going to propel us into a golden age of scientific discovery. That age might not last forever — AI might still run into diminishing returns eventually. But the next few decades are probably going to be an absolutely amazing time for human science. The number of “effective researchers” is no longer limited The most important input into science is scientists themselves. Until this year, that meant human brains — talented, highly trained human brains. The number of trained human brains could be increased by sending more people to grad school, but only to a point — the smartest people tend to be sent into the hardest research fields first, and if you 10x the number of researchers, the average quality will go down. This constraint was already starting to bite, as Ekerdt and Wu argued in 2024: We argue that a substantial portion of the downward historical trend in U.S. research productivity is a composition effect resulting from self-selection in researcher ability and the dramatic expansion of the research sector…[W]e find that this composition effect has lowered average researcher ability by between 37% and 53%[.] And of course, low fertility was making the problem worse, because fewer children meant fewer people who were able to become future scientists. AI just blows up this constraint completely. If an AI agent can function as an autonomous researcher, there’s no constraint on the number of researchers in the world — you can just build more of them, by building more computer chips and data centers that enable the running of more AI agents. Whether or not massive hordes of agents can collaborate to solve individual problems much faster, increasing the number of agents allows them to tackle many different research questions in parallel. What’s more, these AI “researchers” can be trained incredibly quickly compared to human beings. A human scientist takes decades to train, and more years to reach the frontier of their research field by diligent reading and conversation. This is the “burden of knowledge”, and it’s commonly believed to be one reason ideas have been getting harder to find — by the time human scientists can get up to speed, they’re often so old that their ideas are no longer novel. AI just annihilates this constraint — it can assimilate the entire literature and reach the frontier of research extremely quickly, and it can also tell human scientists where the frontier lies without forcing them to discover that for themselves. It can also pivot to new research directions more easily than humans can. And there’s also a self-reinforcing economic cycle here. Because scientific advances enable higher GDP, they enable more resources to be invested into the creation of yet more electronic “scientists”. Now that doesn’t mean the amount of scientific research goes to infinity — there may still be some essential bottleneck task that human researchers are still needed to perform. There are some indications that AI’s progress on open-ended conceptual research tasks is slower than in other dimensions, and it may be that humans are still needed to provide some of this long-term conceptual thinking. I’d bet against this being true for much longer, but it might be true for a long time. Even in this most “pessimistic” case, though, the amount of effective capital that scientists can use to leverage their work just went up by a lot. AI eliminates the theory bottleneck Science proceeds by an iterative process of theory and experiment. The theory part of that cycle is typically short, but non-trivial — it took a couple of years after the blackbody radiation spectrum was observed for Max Planck to come up with the quantum hypothesis to explain it, and decades longer before quantum theory as we now know it was created. It took about a year for Watson and Crick to figure out the shape of DNA after the requisite data was gathered. AI will basically eliminate the theory bottleneck entirely — or at least shorten the turnaround time between experiment and theory by an order of magnitude. AI is an absolute beast at math. Recently, an OpenAI model solved some of the most important open problems in computer science, math, and theoretical physics, and no one even batted an eyebrow because that sort of triumph is becoming increasingly commonplace. One of those solutions was a proof that the Standard Model of Particle Physics could be mathematically well-defined — a problem that human theorists had so far been unable to tackle. Not all research relies equally on theory, but understanding novel experimental results almost always involves some important component of either mathematical or spatial reasoning. That part of the task has now been made much, much faster. And on top of that, science is now open to a greater number of humans who aren’t naturally good at theorizing. AI can read the whole literature Much of the work of human scientists involves reading and understanding endless amounts of papers and going to endless conferences and seminars. The purpose of that work is to understand the existing scientific literature. Understanding the literature allows scientists to do the following things: Come up with new ideas for hypotheses to test Avoid doing research that someone else has already done Synthesize approaches from different areas or fields AI can reduce this effort to almost zero. Consider this excerpt from OpenAI’s blog post about their new research into neurodegenerative disease: The team divided the analysis among thousands of AI agents powered by GPT‑Rosalind, an approach we call distributed biological reasoning. The agents examined a whole-brain CRISPR atlas…a dataset showing how gene disruptions affect different types of cells across the mouse brain. Each agent investigated a gene disruption, analyzed cellular responses, searched scientific literature, and proposed explanations for the results. Their reports were combined into a reasoning atlas linking genes, cellular effects, relevant publications, and possible biological mechanisms. The research team estimated that comparable human analysis and literature review, conducted one perturbation at a time, would take months to a year. The agents reproduced findings from human analyses while uncovering additional leads. Their assessments of novelty and scientific value also aligned with judgments from blinded human experts. These findings help prioritize further experiments needed to test the proposed biological mechanisms. Some of these insights point to new leads for understanding brain disease. [emphasis mine] This is one case in which parallel work by a large number of agents really does make a huge difference: [ Haider.@haider1 this is actually insane openai deployed thousands of GPT-Rosalind agents to analyze millions of mouse brain cells in 32 minutes, work that could take human researchers months to a year also the agents uncovered new leads for brain diseases like Alzheimer's and Parkinson's OpenAI @OpenAI GPT-Rosalind surfaced a new lead for drug discovery: could restoring cholesterol transport protect neurons from degeneration? DCTN1 is a gene linked to Perry syndrome, a rare neurodegenerative disorder. In mouse-brain data, disrupting it increased expression of genes involved in 2:41 PM · Oct 21, 2026 · 51.5K Views 26 Replies · 95 Reposts · 1.26K Likes ](https://x.com/haider1/status/2112917197903540516) Not only does instantaneous literature review vastly speed up the research process, but it allows AI to make novel conceptual leaps from combining approaches from wildly different fields of research. AI regularly does this in math research, combining results, approaches, and theorems from far-flung places that no human mathematician would have thought to combine. It can do it in science, too. Much of scientific innovation, historically, has been “recombinant” innovation — scientists applying insights from one field to another. With AI, essentially all research is now cross-disciplinary — AI can read not just the literature on a particular subject, but the literature on a million other subjects that might have produced useful techniques or insights for the problem at hand. Superscience So far, all of the factors I’ve listed are ways that AI can speed up science as it’s already done. I’ve basically described how AI can send gigantic hordes of superhumanly fast, superhumanly well-read, superhumanly mathematically gifted researchers to re-accelerate scientific discovery. But it’s also possible that AI will enable types of discovery — and ways of understanding the natural world — that no network of humans, no matter how large or how brilliant it was or how long it had to work, would ever be able to discover. In a post back in July, I wrote about how that might work: What if there are lots of…aspects of the Universe that [are] too complex to understand in terms of simple laws, but not so complex that they just dissolve into unknowable chaos? It’s possible that we can reliably control these complex phenomena with AI, even if we never reduce them to the kind of principles that we can teach a grad student in a textbook… Another way of saying this is that there may be laws of the universe that humans can’t understand but AI can. I call these “cloud laws” — causal regularities that can be exploited by technology, but which are too diffuse and complex for an individual human being to either intuit or communicate… Perhaps social sciences like economics, sociology, and political science obey similarly complex regularities, and AI can help us find them. Perhaps there are physical processes — plasma, or topological materials, or aerial turbulence, etc. — that obey cloud laws instead of chaos?… [H]uman collaboration is bottlenecked — it’s limited by our ability to intuit patterns at an individual level, and to communicate these patterns from one individual to another. AI, being a computer, just doesn’t have this sort of limitation; it can work with vast, diffuse patterns without having to break them into pieces or simplify them in order to compress them into the tiny pipelines of person-to-person explanation… If much of the Universe turns out to be ruled by cloud laws, we could be on the precipice of a scientific renaissance. In order to discover cloud laws, AI will need huge amounts of data about the natural world. AI figured out human speech by assimilating essentially all of the text humans have ever written. If it’s going to figure out hidden patterns in biology, neuroscience, materials science, etc., it’s going to need similarly massive amounts of data from physical instruments. That data will be far, far more than humans — individually or collectively — would be able to parse, label, characterize, or model. But AI was built to do this. If scientists simply build huge arrays of sensors to measure complex phenomena like the brain, the cell, the economy, etc., they may be able to feed this flood of data to AI directly and bypass human cognitive intermediation entirely. AI would then learn about the world directly, and it might learn things that humans never could. This “superscience”, if you want to call it that, would actually go beyond the kind of science humans have done heretofore. Many of the technologies it yielded would be beyond human comprehension — indistinguishable from magic. But to the AIs, they would make perfect scientific sense. In any case, it seems clear that the scientific slowdown that threatened human progress will now be delayed — at least for a while. Unless humans have somehow found most of the actual exploitable regularities in our Universe, we’re about to enter a new golden age of science. Subscribe Share
Intel Readies "Moon Lake" and "Wildcat Lake Refresh" Built on 18A Node
Intel is reportedly preparing two new processor SKUs focused on extremely low-power computing. The first, called "Moon Lake," is designed as a pure low-power efficiency (LPE) core SoC. The "Moon Lake" generation features no P-Cores and up to 4 "Darkmont" LPE-Cores on the Intel 18A die. Unlike...
Meet Bowen Wang, the San Carlos seventh grader who built an AI tool to predict earthquake aftershocks and won a $3,500 first-place national technology award
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Jackson leaders urged to ignore data center misinformation
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Amazon debuts the Alexa Photo Frame and Echo Time smart clock, plus other upgraded devices
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Why Anthropic's IPO is risky business
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Zoho Brings Trusted Business Intelligence to AI Agents in Zoho Analytics Platform Evolution, Connecting Data, Business Context, Insights and Action
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The best photos from BVB's DFB-Pokal party
This article was translated into English by Artificial Intelligence. You can read the original version in 🇩🇪 here.
Animals Starves while the Kardashians Miss the Charts Again
(Welcome to my weekly streaming ratings report, the single best guide to what’s popular in streaming TV and what isn’t. I’m the Entertainment Strategy Guy, a former streaming executive who now analyzes business strategy in the entertainment industry. If you were forwarded this email, please subscribe to get these insights each week.) Before we get going, I’d like to offer congratulations to Jonathan Funke, whose suggestion “midflicks” won the poll for what I should call straight-to-streaming films which aren’t big or cinematic enough to go to theaters but seem too expensive for a TV movie. It’s so useful a term, I just used it last week! Now I just need to add this term to my data dictionary! Okay, on to this week’s issue. We’re starting out looking at Netflix/Ben Affleck’s latest crime thriller, Animals. I’ll also look at Disclosure Day heading to Peacock (it did better than you’d remember in theaters...) and a new movie based on toys. Plus, a long-time streaming flop returns for its eighth season...will it finally chart? (No.) Will anyone at Disney care? Probably not. All that, plus another look at new shows landing on streaming on 1-Oct from Reelgood, what streamer hasn’t had a film make the Luminate charts since August, Reacher’s long run on the charts, M_onster: The Lizzie Borden Story’s_ short run, the top shows in 2026 according to Samba TV, all the flops, bombs and misses, and a whole lot more. And I mean that last part: putting this week’s issue together, I assembled OVER 25 images, most of which are bespoke looks I made just for this report. Really, publishing articles like this takes the work of writing two articles: one is the narrative and the other is making charts/graphs. Let’s dive right in! (Reminder: The streaming ratings report focuses on the U.S. market and compiles data from Nielsen’s weekly top ten viewership ranks, Luminate’s Top Ten Data, JustWatch and Reelgood interest data, Samba TV household viewership, company datecdotes, Netflix hours viewed data, Google Trends, and IMDb to determine the most popular content. While most data points are current, Nielsen’s data covers the week of October 5th to October 11th, 2026. You can find a link to my terminology here.) Subscribe Film - Netflix’s Latest Star-Studded Thriller Goes For Glory The tempting angle for anyone writing about streaming film this month, like yours truly, is to ask, “Is this why Dan Lin lost his job at Netflix?” Take Animals, the Ben Affleck-starring-and-directing, Artists Equity-produced crime thriller that opened on 9-Oct. (Kerry Washington and Gillian Anderson are also in it.) If it did really well, then one could argue that Lin has another success on his resume. If it underperformed, then it’s another example of a Netflix underperformance this year. So where did Animals fall? Kinda in the middle, but that means it’s more “not a hit” than “big hit”. On Nielsen, it opened to 12.0 million hours, and here’s how that stacks up for 2026 films so far this year: Obviously, that’s not great. It’s both under my 15 million opening bar for “fine” for a Netflix film and 20 million bar for “great” opening. It did a bit better on Samba TV, getting to first place. That’s very, very good, though again, looking at the Nielsen data, it probably means that it was a light week overall. Luminate’s data is a bit closer to Samba TV’s, tied for 17th all time for single weeks since May of 2024: To top it off, no one seemed to like it. It has a 33 on Metacritic and only a 5.7 on IMDb. Both of these numbers could have been impacted by the stories about Affleck’s comments about using AI to make the film. Given the controversy, the film might not have been as bad as the ratings suggest, but it’s also not a bullish sign. All to say…yeah, on net this film isn’t a point in Dan Lin’s favor. At this point, Netflix’s slide has been long enough—going on since February—that I’m almost tempted to lower the bar for what a “hit” is on streaming. Post-February of 2026 or so, it seems like straight-to-streaming films just haven’t hit the same heights as they did from 2022 to 2025. Before we head onto the “Quick Hits” for film, I want to shout out a fellow newsletter whose articles I’ve been loving this year, LA Material, a new LA-based news outlet. For just a highlight of my favorite articles since July... I loved this article on Ghost Town, a group on Venice that clears homeless encampments. As a Los Angeleno, obviously I adore their articles on LA parking issues. Their article on illegal parking tickets got the City Council to address the issue! Also check out their article on people parking in medians. Check out their deep dive on what went wrong with the LA Jazz Festival and how it fell apart, or what’s wrong with USC’s finances. Finally, they wrote up a voter guide on voter guides for the LA General Election. So meta! In search of a city? L.A. Material is an independent news outlet covering the people, politics, culture and glorious chaos shaping Los Angeles. L.A. Material brings you intel on the city you can’t get anywhere else. From exploring how Reddit short stories became Hollywood’s hottest commodity to reporting on the survival jobs keeping entertainment workers afloat. And answering, once and for all, why the AMC Burbank escalator is always broken. Through their newsletters, podcast, videos, events, and member community, L.A. Material is designed to help Angelenos connect to Los Angeles and each other. Sign up now for the free weekday newsletter. Quick Notes on Film Steven Spielberg’s latest film, Disclosure Day, came to Peacock on 9-Oct, and I’ll admit that, after pulling its stats, I did a pinch of a double take. As I wrote throughout my deep, deep, deep dive on summer box office/Obsession/Backrooms/IP/superheroes, oftentimes the film media discusses films as “flops” or “misses” or “hits” without clarifying what they specifically mean. Since Disclosure Day didn’t make $250 million in the US or $500 million globally, it was definitely a disappointment by Spielbergian standards, who is the highest-grossing director of all time, and arguably even more ahead in adjusted grosses. But it did make $245 million globally on a $115 million budget, so it isn’t as big of a miss as I remember. That’s still probably not enough to break even in the theatrical window, when you factor in marketing costs…but it’s close. And it grossed a lot more even in the US than some titles with about ten times more coverage. Anyways, lots of preamble to say this one only did 4.5 million hours according to Nielsen, so it did make the charts, but on the low end. Here’s how it stacks up to other Peacock titles: Apple TV (née +) followed up their “success for them” Mayday with Matchbox The Movie. This one doesn’t look like a success even for them, but at least it’s not a miss. Apple/Skydance clearly tried to take advantage of the success of Barbie at the box office—let’s greenlight all the toy properties!!!—but of course, this didn’t make it to theaters. And it stars John Cena, who, as I argued earlier this summer, NEEDS to get his movies back in theaters. Anyways, this one has mixed-to-bad numbers. It missed the Samba TV and Nielsen charts in its first week, but did make Luminate with 7.1 million hours. That’s just enough to not be an outright miss, and about half what Mayday did earlier this summer. Speaking of Apple Studio’s Mayday, it ended up lasting five weeks on Luminate, which isn’t bad for Apple TV. On Nielsen, it had a three-week run, racking up 13.5 million hours total. Again, like Matchbox, the issue is the budget; I doubt that this viewership earned enough to justify that budget. Also, to be clear, neither of these films are “midflicks”; both should have gone to theaters. Toy Story 5 is not hanging around on the streaming charts on Disney+, so it’s not as leggy as either Zootopia 2 or Moana 2, and definitely not in the same class as Encanto. (It did make all the money at the box office, so Disney is going to be fine…) The Breadwinner on Netflix (from Sony) is down to 3.9 million hours in its third week. It only lasted for one week on the Samba TV charts. Netflix’s Doing Life had a smaller-than-normal binge release curve bounce, only going to 7.1 million hours from 6.0 to start, but it made Samba TV at fifth place in its second week. The Netflix documentary, The Widower: Til’ Death Do Us Part, also had a small binge release curve bounce to 6.5 million hours, but did make a second week on Samba TV. Meanwhile, Netflix’s Unabomber fell off after two weeks on Nielsen—which is not great at all—and Demon Slayer fell off after just one week. Over on Luminate, the two smaller titles The Swan: Behind the Mirror and Kill. Bury. Print both fell out of the Luminate top ten charts. The story of library film at the first of the month is the arrival of the Ice Age films on Netflix. Now, here’s a small example of optimizing your library that even Netflix could do better. Say you want to rent/license a new film series from Disney**.** A streamer could pay a big paycheck and then drop each Ice Age film on the exact same day of the month, the fourth of October. That’s one choice. In contrast, the streamer could have released each film one week apart, and make each one a mini-event for folks watching Ice Age films. Or even roll them out monthly. Netflix opted for the former strategy, and as a result, only three of the films made the Nielsen charts this week. The “Miss of the Week” goes to Tubi’s YA-ish horror film_, R.L. Stine’s Pumpkinhead 2_, which didn’t even make the Luminate charts like Tubi films usually do, or, should I say, “used to”? Tubi films, most of which are straight-to-streaming, used to reliably make the Luminate film charts (which only covers straight-to-streaming films), but a Tubi film hasn’t made it since August. The first Pumpkinhead got 1.4 million hours last year according to Luminate. As I wrote about this film before, it seems to be a bit muddled in terms of age: too scary for young kids but probably not scary enough for older kids. Speaking of Luminate, Netflix’s latest doc about a comic (expect more docs about comedians all month), Who Is Martin Mull?, flopped, getting just 200K and 700K hours according to Luminate. Three other film/special misses belonged to Hulu, including a standup special, Jamie Foxx: Take Care of Yourself, Neon’s latest horror film, Hokum, which made $17 million in the US, and a Family Guy Halloween special. (That last one was only 22 minutes, so it’s not a surprise it missed.) Finally, another Netflix standup special missed the charts, Mojo Brookzz: I Know You Lying missed too. I can confirm last week’s misses, most notably Paramount+’s Jackass: Best and Last, MUBI’s Teenage Sex and Death at Camp Miasma and HBO Max’s Evil Dead Burn. Television - I Can’t Visualize Nothing: The Kardashians Story Normally, in the mini-dive essays that open the Streaming Ratings Report, I focus on shows that do well or, at the very least, make the charts. Why? Because as I’ve said time and again, I can’t visualize nothing like, say, the Kardashians’ TV shows. Here’s the TV debuts chart for this week with a missing spot for The Kardashians, whose eighth season just landed on Hulu:
AI Solutions & Smarter Business Workflows with Luis Martinez
Getting your Trinity Audio player ready... On this episode of See Beyond Technology, host Humberto welcomes Luis Martinez from PatchNet to discuss how artificial intelligence is transforming the way businesses operate. From AI-driven solutions to smarter, more seamless workflows, they explore how technology can help businesses improve efficiency, streamline operations, and adapt to an increasingly […]
Watch the newest commercials from Ninja, Meta Muse, AT&T Business and more
Ninja shows off its HydraSense Intelligent Water Filtration System in a spot starring Tom Brady.
Amazon Business Debuts New Procurement Tools
Amazon Business has rolled out a pair of tools designed for procurement professionals. First up is Amazon Business Value Intelligence, which “quantifies
Vertical IQ Will Expand Industry Intelligence Platform with Small Business Financial Exchange (SBFE) Small Business Credit Data
Small Business Financial Exchange (SBFE), the trusted source for small business lending data and Vertical IQ, a nationally recognized provider of Industry Intelligence, today announced their partnership. Through the partnership, Vertical IQ will incorporate SBFE small business credit insights into its platform, providing financial institutions with deeper context to support commercial lending decisions.
URI to launch new Interdisciplinary and Applied AI bachelor’s degree
KINGSTON, R.I. – Oct. 27, 2026 – Citing the increasing need for professionals trained in artificial intelligence and the significant push for implementing ethical practices in utilizing the advanced technology, the University of Rhode Island is launching a new Interdisciplinary and Applied AI bachelor’s degree program. The new program, which will have a soft launch […]
URI to launch new Interdisciplinary and Applied AI bachelor’s degree
KINGSTON, R.I. – Oct. 27, 2026 – Citing the increasing need for professionals trained in artificial intelligence and the significant push for implementing ethical practices in utilizing the advanced technology, the University of Rhode Island is launching a new Interdisciplinary and Applied AI bachelor’s degree program. The new program, which will have a soft launch […]
Apple is adding an AI chatbot to Pages, Keynote, and Numbers
The new AI assistant can draft and restructure content in Pages, while organizing data in Numbers.
Vera Health secures $28.5M series A for AI-driven clinical search engine
Clinical artificial intelligence search engine Vera Health secured $28.5 million in series A funding to accelerate and expand its platform. | Vera Health’s platform scans more than 60 million peer-reviewed publications, clinical guidelines, consensus statements and drug labels, executives say.
From Rice Fields to Accelerating Vegetable Breeding
Kashish Grover, a doctoral student in horticulture, combines artificial intelligence, advanced genetics and computer simulations to streamline vegetable breeding, helping to develop climate-resilient varieties faster.
Anthropic's IPO is risky business
Anthropic's much-anticipated IPO is giving potential investors some perspective on the risky business of artificial intelligence.
Acer's Arc G3 Extreme handheld costs $500 less than MSI Claw in US
The Acer Predator Atlas 8 is available in the US with Intel Arc G3 Extreme and an 8-inch 120Hz VRR display. Early tests show 6,607 points in 3DMark Time Spy.
More Madison area businesses are adopting AI, survey finds
About 83% of surveyed businesses used artificial intelligence this year, up from 51% two years ago, the 2026 Next Normal Regional employer survey found.
AI Breaks the Traditional Relationship Between Business Growth and Hiring
Artificial intelligence is creating a new competitive question for small businesses. What happens when revenue can grow without payroll growing alongside
Delaware wants to let AI run entire companies: what it means for insurers
A new proposed Artificial Intelligence Company raises a multitude of questions for anyone underwriting D&O, cyber or E&O
Clarivate's Web of Science Research Intelligence Is Now in Use by 100 Institutions
Clarivate reached a milestone of more than 100 institutions across 25 countries and regions adopting Web of Science Research Intelligence, an AI-native research intelligence platform designed to support institutional decision-making across funding, strategy and impact.
iPhone AI Images and Edits Now Have a Hidden Watermark
Images generated with Image Playground and some photos edited with Apple Intelligence will now include a SynthID watermark. Apple announced plans to add SynthID support back in June, and the feature is live in iOS 27.2. SynthID is a Google-created tool that identifies AI-generated content. It watermarks images and videos at the pixel level instead of adding metadata. Google says it does not affect image quality and is meant to survive cropping, filters, and compression.
FF EAI Robotics Ecosystem (FFR) Completes Acquisition of FFAI Robotics Business, Establishing a Nasdaq-Listed Humanoid and Bionic Robotics Platform October Robot Sales Expected to Exceed 300 Units; Unveils FF OmniMind, a Unified AI Brain for All Robots
FF EAI Robotics Ecosystem Inc. (NASDAQ: FFR) ("FFR" or the "Company") a U.S.-based Embodied AI robotics company, today announced at the deal closing and its first Investor Day event in New York that it has completed its acquisition of the robotics business and related assets of Faraday Future Intelligent Electric Inc. (NASDAQ: FFAI) ("FFAI" or "Faraday Future") on October 23, 2026. The related business operations and assets have been transferred to FFR, and FFR has issued and delivered common sh
Fort Bend County judge candidate Daniel Wong uses AI to create Black 'supporters' in political ad
Fort Bend County Judge Daniel Wong is facing heat for posting a campaign video generated by artificial intelligence that depicts Black people voicing support for his campaign.
ABI keynote: AI should expand human potential, maintain vitality
Organizations should use artificial intelligence to automate routine tasks and stop unnecessary work while protecting human judgment, creativity and experience, Seth Mattison said during his keynote address at the Iowa Association of Business and Industry’s Manufacturing Conference. Mattison is the founder of FutureSight Labs, where he consults on talent management, change and innovation, leadership and…
Fort Bend judge candidate uses AI to create Black 'supporters' in ad
Fort Bend County Judge Daniel Wong is facing heat for posting a campaign video generated by artificial intelligence that depicts Black people voicing support for his campaign.
PAHO and Special Olympics International sign framework agreement to advance inclusive health for people with intellectual and developmental disabilities
Washington, D.C., 26 October 2026 (PAHO) — The Pan American Health Organization (PAHO) and Special Olympics International (SOI) have signed a Framework Agreement to strengthen cooperation on health systems that are inclusive of people with intellectual and developmental disabilities (IDD) across the Region of the Americas.
Intel Core Ultra 4000 Nova Lake-S leak: 28-core CPU beats Core Ultra 9 285K in single-thread test
Early Nova Lake-S benchmark results suggest improved single-thread performance over Arrow Lake. However, the 28-core sample still trails Core Ultra 9 285K in the overall CPU Mark test.
Protecting Life Sciences Trade Secrets: From Development to the AI Era
For life sciences companies, some of their most valuable information may exist outside the protections afforded by patents and other intellectual property rights. From product development and clinical data to manufacturing processes, customer information, pricing strategies, and competitive intelligence, protecting sensitive information as a trade secret requires companies to identify what is truly valuable and take reasonable measures to maintain its secrecy.
📈 Monday data: The bottleneck isn’t intelligence
In the AI Investment Brief today: CapEx updates and lease commitment breakdowns Anthropic co-founder Jack Clark recently said in a speech at the IMF that the singularity has begun: For many years, researchers and science fiction authors have forecast a future in which AI systems drastically increase the pace of overall scientific and technical progress, making things move so quickly that it is hard for humans to keep up. They call this the singularity. I believe that this year we have gained convincing evidence that it has now begun. This follows Anthropic’s claim that in August, 26% of its AI R&D was led by Claude, with a human supervising its work. In October, OpenAI posted 719 math manuscripts1 made by its internal model. Math and coding are two fields where a research loop can close without leaving the desk. But can the loop close in the rest of science? Subscribe The loop is real The idea is not new. Mathematician Irving John Good, who had worked with Alan Turing at Bletchley Park, argued that a machine smarter than us “could design even better machines; there would then unquestionably be an ‘intelligence explosion,’ and the intelligence of man would be left far behind.” The Gödel machine, a self-improving computer that can rewrite its own code better than its prior, was first proposed in 2003. In 2025, researchers at Sakana AI and the University of British Columbia set out to show that this was possible. Their agents iteratively improved their own code through a feedback loop, resulting in gains of 30 pp on SWE-bench and more than 15 pp on Polyglot, testing coding skills beyond Python. The idea has since been tested in other labs. A Gemini-powered agent called AlphaEvolve found a faster matrix multiplication kernel used for training Gemini, speeding up the kernel by 23% and cutting Gemini’s training time by 1%. In China, MiniMax reported that its M2.7 model worked on improving its own agent’s harness, skills, sampling and memory. Over 100 rounds of rewriting led to a 30% gain, per its internal evaluations. In each case, what was being rewritten was not the model itself but everything around it. Is it better than human work? In September this year, in what seemed like a milestone, an AI-built agent proved as good at improving other AI agents as a human-built one. It did that in what appeared to be half the steps. But the result was inconclusive: the differences between runs were greater than the difference between the AI- and human-built agent results. Nonetheless, this is increasing the chatter around recursive self-improvement (RSI) in AI. Google’s Sundar Pichai said that “you have RSI loops along with AI researchers in various aspects of model development.” Sakana AI published a paper on RSI through multi-agent collaboration, finding that after two cycles its system scored 1.2–1.6x better per output token than the base model across four research benchmarks. Ramez Naam challenges that view. He splits AI self-improvement into five overlapping types. The first four describe how autonomous the AI is; so far, only types 1 and 2 have been observed. The fifth type describes accelerating returns, and he notes that greater autonomy doesn’t guarantee a takeoff scenario. Instead, each round of self-improvement would need to deliver at least as much as the last. The results from Sakana, AlphaEvolve and MiniMax above might look like type 3 or type 4 autonomy. But in each case, the AI wasn’t updating its own weights directly, only the ancillary parts — the harness, the memory. And where Claude leads its own R&D, for now a human still supervises.
Emotional intelligence is the edge in an AI-fueled world, J.P. Morgan’s behavioral science head says
J.P. Morgan's Jeff Kreisler told DC West 2026 that emotional intelligence — empathy, trust-building and intrinsic motivation — is the advantage AI cannot replicate for financial professionals.
Purdue and Accenture: A model for applied partnership
Artificial Intelligence is reshaping every industry. Find out how Purdue and Accenture are working together to prepare Indiana’s workforce and students in Purdue’s Mitch Daniels School of Business for an AI-powered future.
Leadership in SMEs and business transformation: how to lead teams in the age of AI
Javier Vizcaino reflects on leadership in SMEs, transformation, artificial intelligence, trust, customers and how to build committed teams.
OpEd: World Needs U.N. Convention on AI
When world leaders gathered last General Assembly, artificial intelligence was high on their agenda. And rightfully so. Climate change has taught us, at
Students and artificial intelligence industry leaders connect at inaugural Michigan AI Business Conference
More than 250 students, professors and industry leaders gathered Friday for the first Michigan Artificial Intelligence Business Conference at the Ross School of Business.
A New Level of Threat and Fear Risks Pushing the U.S. and Iran Back to Full War
With new intelligence and the possibility of all sides seeking pretexts for action, the war in Iran is entering a dangerous phase ahead of elections in the United States and Israel.
U.S. is reducing staffing at 4 Middle East embassies over increased threats from Iran
In a memo sent Friday to Secretary of State Marco Rubio, Director of National Intelligence Jay Clayton said his teams “have received intelligence that Iran has substantially increased efforts to target U.S. personnel.”
AMD FSR 4.1.2 now runs on Intel Arc XMX cores, matching XeSS performance with better image quality
Intel Arc B580 runs AMD FSR 4.1.2 Lite with a 0.59 ms upscaling time. That's nearly identical to XeSS at 0.58 ms in community testing.
Rethinking science museums in a future shaped by AI
As artificial intelligence rapidly transforms human life, how should science museums tell the story of science in the future?
Rethinking science museums in a future shaped by AI
As artificial intelligence rapidly transforms human life, how should science museums tell the story of science in the future?
Updated Google privacy policy says your Gmail might be used for advertising
Turning on Personal Intelligence gives the company more data for ad targeting.
Catholic social scientists hold conference blending disciplines and faith
Scholars will deliver talks on Catholic teaching in relation to American history, political science, and hot-button issues like artificial intelligence, immigration, and euthanasia.
2 ex-FBI officials charged with making false statements about Hunter Biden's laptop
Two former FBI intelligence officials who specialized in foreign election interference were charged Friday with making false statements about their roles in investigating the origins of Hunter Biden’s laptop in the run-up to the 2020 presidential election.
AMD FSR 4.1.2 Now Runs On Intel Arc XMX Cores, Delivering Better Image Quality Than XeSS Upscaling With Similar Performance
Modding community brings AMD FSR 4.1.2 to Intel Arc GPUs, showing superior image quality and efficiency with upscaling times as low as 0.59 ms.
(US)AI becomes GSA's first direct OneGov contract with a small business
The Maryland firm known for its Intelligent Computing Platform will offer significant discounts to government customers on software licenses and pilots through September 30, 2028.
Texas Athletics and McCombs School of Business Launch New Bevo Lab, Bringing Together Sports, AI and Analytics
Dell Technologies, Google Cloud, and SHI International join Texas Athletics to power a first-of-its-kind hub for sports intelligence, data analytics, researc...
Oral Health Disparities and Parental/Caregiver Role in Dental Care Among Children With Special Healthcare Needs and Children Without Special Healthcare Needs in Sriganganagar, Rajasthan: A Cross-Sectional Study
Background: Children with special needs often face greater challenges in maintaining oral health due to physical, intellectual, or behavioral limitations. Their oral hygiene depends largely on c...
As AI transforms patient experience, clinicians see changes too
Panelists at the HIMSS AI in Healthcare Forum explored both sides of a new paradigm where artificial intelligence tools have the potential to both streamline and complexify care delivery, depending who's using them and how.
Gemini app expands Google Maps Personal Intelligence, replacing Deep Think
Besides coming to Android Auto this week, Personal Intelligence in the Gemini app is also expanding its Google Maps integration.
Microsoft’s Mustafa Suleyman says its AI outperforms doctors on hard cases as he chases ‘medical superintelligence’
Microsoft’s new AI diagnostic system beat generalist doctors on hard cases, according to a study in Science. But most of the improvement seems to come from the AI models underneath, not from Microsoft’s system itself.
Apple Pulls Keynote, Pages and Numbers From App Store
Apple has temporarily removed Keynote, Pages, and Numbers from the App Store, just after releasing an update that added iPhone Duo support and a new Intelligent Assistant. It is not clear if the apps were removed intentionally, or if it is a bug. It is not clear if the apps were removed intentionally, or if it is a bug.
Statistician Susan Murphy to present 2026 Chemerda Lectures in Science Nov. 4, 5
Susan Murphy, Mallinckrodt Professor of Statistics and of Computer Science, and associate faculty in the Kempner Institute for the Study of Natural and Artificial Intelligence at Harvard University, will present the 2026 John M. Chemerda Lectures in Science on Nov. 4 and 5 at Penn State University Park. The free public lectures are sponsored by the Penn State Eberly College of Science.
Applications Open for County College of Morris Artificial Intelligence and Data Science Apprenticeship
MORRIS COUNTY — County College of Morris (CCM) is accepting applications through Monday, November 2, for the next cohort of its Artificial Intelligence (AI) and Data Science Apprenticeship, offering CCM students and adult learners living in New Jersey an opportunity to gain paid, hands-on experience while preparing for a career in the fast-growing fields of […]
"The Diplomat" reminds us that political couples used to be smart
Netflix’s hit drama recalls eras when presidents and their spouses were intellectual equals
Scientists Found a Surprising Link Between Intelligence and How Attractive You Think You Are
New research found that people who scored higher on intelligence tests tended to rate their own looks slightly lower, but the difference was small.
Call for Abstracts — 2027 Petrie-Flom Center Annual Conference: Generative AI in Healthcare: Law & Regulation - Petrie-Flom Center
The Petrie-Flom Center for Health Law Policy, Biotechnology, and Bioethics at Harvard Law School is pleased to announce our 2027 Annual Conference, Generative AI in Healthcare: Law and Regulation. Generative AI –- artificial intelligence designed to produce output, such as text or images, that normally require human intelligence — has become a…
1Password Surpasses $500 Million in Revenue as AI Agents Drive Growth
As businesses and consumers increasingly adopt artificial intelligence agents, 1Password is aiming to position itself to deliver solutions for growing challenges around security, access and trust.
FAU Receives NSF Grant to Prepare Future AI, Robotics Workforce
FAU engineering researchers have received a $500,000 NSF grant to integrate AI, digital twins and robotics experimentation, advancing research and training in intelligent systems.
Argonne welcomes Chicagoland mayors to campus for meeting and tours of user facilities
Argonne hosted the Metropolitan Mayors Caucus for its quarterly meeting, where dozens of Chicagoland leaders learned about the lab’s work in super intelligence and resilience planning and toured the Advanced Photon Source and Aurora supercomputer.
Experts: How are climate scientists using artificial intelligence?
Carbon Brief spoke to 10 experts working across a range of climate-related fields about how AI is being used
From Animal Camouflage to AI: The Evolving Science of Staying Unseen
Nature’s game of hide and seek is entering the age of intelligent machines. A new article in National Science Review traces how animal camouflage, advanced materials and artificial intelligence are converging to reshape the contest between detecting an object and keeping it concealed.
Vaisala Viewlinc Cloud Suite supports life science innovation from laboratory to regulated operations
Vaisala Viewlinc Cloud Suite Vaisala Viewlinc Cloud Suite and Care for Viewlinc enable professional-quality monitoring as critical operations evolve. Vaisala, a global leader in measurement instruments and intelligence, introduces Viewlinc Cloud Suite, a cloud-based environmental monitoring offering designed to help organizations establish professional-quality monitoring from day one and build on the same platform as their requirements evolve. The launch is complemented by Vaisala Care for Viewl
On Main Street, Goldman Sachs makes a big bet on AI
The Wall Street giant says it’s teaming up with OpenAI, Google, Meta and Anthropic to help small businesses embrace artificial intelligence.
Five Women Shaping Hawai‘i’s Tech Future
From telecommunications and artificial intelligence, five women are challenging and reshaping how technology is built, used and taught in Hawai‘i.
Clarivate Demonstrates Global Demand For AI-native Web Of Science Research Intelligence
Clarivate Plc (NYSE: CLVT), a leading global provider of transformative intelligence, today announced that more than 100 institutions across 25 countries and regions have adopted Web of Science Research Intelligence, highlighting growing global customer demand since the platform launched in May 2026.
Clarivate Demonstrates Global Demand For AI-native Web Of Science Research Intelligence
/PRNewswire/ -- Clarivate Plc (NYSE: CLVT), a leading global provider of transformative intelligence, today announced that more than 100 institutions across 25...
Clarivate Demonstrates Global Demand For AI-native Web Of Science Research Intelligence
More than 100 institutions across 25 countries and regions are using the AI-native research intelligence platform.
Independent filmmakers finding ways to protect work from AI
As the use of artificial intelligence in the entertainment industry continues to grow, indie filmmakers are looking for ways to protect their work
5 classic movies that saw our AI future coming
An MSU Denver film professor explores how science fiction anticipated today's biggest questions about artificial intelligence.
Forge Nano to Trade on NASDAQ as NANO After Completing Business Combination with Archimedes Tech SPAC Partners II Co.
Forge Nano common stock and warrants expected to begin trading on NASDAQ on October 22, 2026 under the symbols “NANO” and “NANOW,” respectivelyPublic listing positions Forge Nano to accelerate growth across semiconductor manufacturing equipment and advanced battery technologies DENVER, Oct. 21, 2026 (GLOBE NEWSWIRE) -- Forge Nano, Inc., a leading U.S.-based semiconductor equipment and advanced materials company pioneering Atomic Layer Deposition (“ALD”) technology for artificial intelligence (“A
BTA Sports Expands into Prediction Markets With AI-Powered Analytics
The fourth quarter rollout pairs independent probability modeling with “Ask BTA” to help users assess sports event contractsHORSHAM, Pa., Oct. 21, 2026 (GLOBE NEWSWIRE) -- BTA Sports, a subscription-based sports analytics and decision-intelligence platform, today announced its expansion into sports prediction markets, bringing the data and insights used by its sports betting subscribers to users evaluating sports event contracts. The offering, available in the fourth quarter as part of Version 2
Baba nabs $40M series A, backed by FirstMark Capital and CVS Health Ventures, for AI-powered Medicare patient navigation
Applied artificial intelligence company Baba
Visier Debuts Talent Agents, Closing the Gap Between HR Programs and Business Impact
/PRNewswire/ -- Visier today announced Talent Agents at HR Tech 2026. Built on the Visier Workforce Intelligence Platform, these new AI agents guides every...
Special Olympics Chief Health Officer Dr. Dimitri Christakis Elected into the National Academy of Medicine
The National Academy of Medicine (NAM) announced the election of 90 regular members and 10 international members during its annual meeting on 19 October 2026. Among them was Special Olympics Chief Health Officer, Dr. Dimitri Christakis. One of the highest honors in the field, Dr. Christakis's election reflects both his work advancing health for people with intellectual and developmental disabilities at Special Olympics and his expertise in child development, particularly related to digital media and its effects on children’s social, emotional, and cognitive health.
Shader Compilation Stutter Begone: Nvidia's Latest GPU Drivers Finally Add Advanced Shader Delivery
Now AMD, Qualcomm and Nvidia all support the tech - with Intel coming soon.
CSC Science Team Asks: What Should We Expect to Find in Nature?
The junk DNA paradigm shift is a good example of how an intelligent design framework can serve as a productive guide to scientific research.
UTC computer science team’s AI research paper accepted to NeurIPS 2026
A research team in the University of Tennessee at Chattanooga Department of Computer Science and Engineering has had a paper accepted to the main track of NeurIPS 2026, one of the world’s leading conferences in artificial intelligence and machine learning.
This week in Santa Cruz County business: Cruz Foam ceases operations; West Coast Community Bank celebrates Nasdaq listing
Santa Cruz-based Cruz Foam, which developed a biodegradable packaging made from shrimp shells and other natural materials, has closed and sold off its intellectual property, Jessica M. Pasko reports in her weekly look at local business. Meanwhile, Santa Cruz-based West Coast Community Bancorp has moved its stock listing to the Nasdaq Stock Market, a milestone executives say is more than two decades in the making.
Local nonprofit empowers individuals with disabilities through pet treat business
The Ella and Bella Pet Company, a nonprofit founded by Kristine Flora, creates unique opportunities for individuals with intellectual and developmental disabilities. This innovative initiative not …
Guns, drugs, AI, budget cuts join traditional maladies at public health forum
A daylong public health forum in Baltimore on Tuesday touched on health challenges that likely aren't found in any medical textbook: gun violence, artificial intelligence, social media and "the ravages of Washington" under the Trump administration.
New Richland Lab Could Transform How Scientists Study Microbes and Boost Regional Jobs
The BriefGroundbreaking held Tuesday: PNNL officially began construction on the M2PC lab, a 30,800-square-foot facility funded by the U.S. Department of Energy's Office of Science that will study soil microbes to improve crop productivity and develop new biotechnology applications. The facility will be open to researchers from around the nation and the world.Research timelines could shrink dramatically: The facility's automation and AI capabilities could reduce experiments that currently take six months to just 72 hours, allowing scientists to explore more possibilities faster than ever before.New jobs coming to the Tri-Cities: The project is expected to create positions for automation engineers, AI experts, roboticists, technicians, and skilled craftspeople, with the facility opening in 2029.RICHLAND, Wash. — Pacific Northwest National Laboratory broke ground Tuesday on a first-of-its-kind facility that could accelerate scientific discoveries about the microscopic organisms living in soil—organisms that may hold the key to growing more food and developing new energy technologies.The Microbial Molecular Phenotyping Capability, or M2PC, represents a significant investment in automation and artificial intelligence to speed up research that traditionally takes years to complete. The 30,800-square-foot facility will house more than 100 connected automated instruments designed to study microbes at a pace and scale never before possible.Reshaping Agriculture's FutureThe M2PC facility will not put a new tool directly into a farmer's hands tomorrow morning, but it is actively reshaping the commercial pipeline to solve agriculture's biggest headache with biologicals: inconsistent field performance. For years, PNNL researchers have explored how microbes work and how they might help produce useful materials, advance energy technologies, and address challenges involving critical materials and minerals. But those experiments have been limited by what researchers could accomplish by hand."They can be used to provide nutrients to a plant so that the plant would grow more. So, like increasing your crop productivity, for example," said Douglas Mans, PNNL's Associate Laboratory Director for Integrated Discovery Sciences.PNNL's own research shows soil microbes can help plants access nutrients and withstand drought—benefits that could prove crucial as farmers face growing environmental pressures. The M2PC facility will allow scientists to identify and optimize useful microbes faster than ever before, accelerating the development of biological solutions that actually work consistently in the field.What Makes M2PC UniqueThe Microbial Molecular Phenotyping Capability differs from traditional research labs because it operates as a first-of-its-kind, AI-enabled autonomous laboratory designed to shrink experimental timelines from years down to months or weeks. The M2PC lab will connect more than 100 automated instruments to grow microbes, prepare samples, measure their behavior, and link observations to the molecular processes behind them. Artificial intelligence will help analyze results and guide the next experiments, allowing scientists to explore more questions faster.Deb Gracio, PNNL Director, emphasized the transformative potential during Tuesday's groundbreaking ceremony: "Research that has taken years will become possible in months and hopefully in weeks."That acceleration is already being demonstrated. PNNL's smaller automated lab, called the Anaerobic Microbial Phenotyping Platform or AMP2, completed three experimental cycles in just 72 hours—a process that normally would have taken about six months."That normally would have taken us about six months … and we were able to do that in 72 hours," Mans said.The AMP2 laboratory is not being closed or replaced. Instead, the current 1,800-square-foot laboratory serves as an active prototype and precursor for the much larger M2PC facility. AMP2 will continue operating as a separate lab designed to run anaerobic experiments—those conducted in the absence of oxygen—while M2PC will focus on aerobic research.Broader Applications Beyond AgricultureWhile improving crop productivity is one important application, the M2PC facility is designed to support research into multiple areas critical to national priorities. The lab will advance research into biofuels, manufacturing materials, and energy technologies. It will also help researchers understand how microbes respond to environmental changes and how to design biological systems for biotechnology applications.As a flagship component of the Department of Energy's Genesis Mission, M2PC represents a significant investment in using artificial intelligence and automation to accelerate U.S. scientific discovery. Dorothy Koch, Associate Director of the DOE Office of Science Biological and Environmental Research program, highlighted the facility's importance during the groundbreaking."M2PC will accelerate biological discovery and design," Koch said, noting that the facility is expected to conduct a minimum of 500 strain experiments per week, 100 microbiome experiments per week, and take a million measurements per month.Economic Opportunity for the RegionBeyond the scientific breakthroughs, the M2PC project represents significant economic opportunity for the Tri-Cities region. The facility will require hiring automation engineers, AI experts, technicians skilled in operating robotics, and specialized craftspeople to build and maintain the sophisticated equipment."We're anticipating that type of hiring," Mans said when asked about job creation.PNNL has already begun recruiting talent nationally and internationally for positions related to the smaller AMP2 lab, bringing automation engineers from cities like Boston and San Francisco to work in the region. The M2PC facility is expected to accelerate that trend."This capability won't really exist anywhere else in the world," Mans said, suggesting the facility could become a draw for scientific talent seeking to work on cutting-edge biotechnology research.Timeline and Next StepsThe M2PC facility is expected to open in 2029. Between now and then, construction crews will level and prepare the ground, while the building takes shape. Simultaneously, Ginkgo Bio Works, the automation contractor, will build the facility's automation components off-site so they can be installed as the building is completed.In the interim, PNNL researchers and the broader scientific community will begin planning the first experiments to be conducted in the facility, ensuring research is lined up and ready to begin as soon as the doors open.The groundbreaking ceremony included representatives from the Department of Energy, PNNL leadership, and elected officials at the federal, state, and local levels, underscoring the project's significance to the region and the nation's biotechnology future. News The U.S. Department of Energy Secretary tours Pacific Northwest National Laboratory Dan Hanson, Apple Valley News Now News Beyond DNA: Scientists Explore New Frontiers in Biology Research Dan Hanson, Apple Valley News Now News TRIDEC optimistic that Hanford, PNNL will see funding in national budget Robert Desaulniers News Big picture science: monitoring the Yakima River Basin News PNNL research could shape the future of fighting fentanyl COPYRIGHT 2026 BY APPLE VALLEY NEWS NOW. ALL RIGHTS RESERVED. THIS MATERIAL MAY NOT BE PUBLISHED, BROADCAST, REWRITTEN OR REDISTRIBUTED.
Google’s more personalized Gemini AI is heading to your car
Personal Intelligence is available in Android Auto starting today.
SpaceX Swings to a Profit, Even as A.I. Spending Soars
Elon Musk’s rocket company also said its revenue had more than doubled, driven by its Starlink internet satellite service and artificial intelligence products.
Regional Banks Put AI Into Core Operations as Business Lending Grows
Third-quarter results from PNC, U.S. Bancorp, Truist, Fifth Third, Regions and KeyCorp show that regional banks are putting artificial intelligence into
Gemini's Personal Intelligence is coming to cars
It's probably time to audit your Gemini settings.
Google is bringing a more personal version of its Gemini AI assistant to your car
Google is bringing Gemini's Personal Intelligence feature to cars, allowing its AI assistant to draw on information from Gmail, Calendar, Maps, YouTube, and other Google services to deliver more personalized responses while driving.
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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