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Tree Fresno offers free trees to South Central Fresno businesses
Tree Fresno offers free shade trees to eligible South Central Fresno businesses through a $3 million grant. Learn about the Oct. 21 meeting.
Experts Reveal How MS Can Be Misdiagnosed Amid Christina Applegate’s Surprising Health Update
Christina Applegate is searching for answers after learning that she was misdiagnosed with multiple sclerosis (MS) five years ago — and medical experts say several conditions can be mistaken for MS, m...
Nonpartisan coalition of religious leaders plans to counter voter intimidation on Election Day
The diverse, multifaith group is training all the way through the end of October online and in person learning de-escalation techniques and equipping themselves with knowledge of local laws and resources.
Dear Tim Sweeney, You Could Learn A Thing Or Two From The Xbox Exec Who Posted So Bad That He Had To Delete His Account
A better world is possible
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.
Kenny Atkinson Makes Candid Admission About Mario Hezonja’s Cavaliers Role
Kenny Atkinson says Cleveland is still learning how Mario Hezonja fits and does not want to put the returning NBA veteran in a box.
Online student expands domain from sports blogosphere to city hall
Grand Canyon University online student Jon Zaghloul learned about communications from the blogosphere. He started "Sports Talk Chicago," a popular sports blog, and that was […]
Pirates Learn Projected Price Tag for Potential Target Seiya Suzuki
The Pittsburgh Pirates need to add another power bat this offseason.
More Than Athletes: How UT Health Sciences Is Preparing the Next Generation of Sports Pharmacists
As the sports pharmacy field grows, UT Health Sciences College of Pharmacy students are learning to explore this career path.
More Than Athletes: How UT Health Sciences Is Preparing the Next Generation of Sports Pharmacists
As the sports pharmacy field grows, UT Health Sciences College of Pharmacy students are learning to explore this career path.
EXCLUSIVE: SANTOS BRAVOS to return to NYC for one-night-only event at The Paley Museum
SANTOS BRAVOS will appear at The Paley Museum on Nov. 4 for a live music and storytelling event, amNewYork has exclusively learned.
Head of the class: URI quantum physics students work as teaching assistants for Brookhaven National Laboratory
KINGSTON, R.I. – Oct. 20, 2026 – As a sophomore at the University of Rhode Island three years ago, Morgan Beck ’26 began diving deep into the world of quantum physics, wanting to learn more about the branch of science that studies matter and energy at the most fundamental level. At that time, Beck—a 2025 […]
Mississippi Gulf Coast Community College students use AI to solve business problems
Students at the Harrison County campus are learning ways to solve real-world problems using artificial intelligence.
Christina Applegate Says She Was Misdiagnosed With MS After Yearslong Battle
Years after Christina Applegate shared that she was battling multiple sclerosis, the actress said she is "back at square one" after learning she had been misdiagnosed.
Gentle Brain Ultrasound May Help Ease Delusions in Schizophrenia
NYU Langone pilot study shows that transcranial ultrasound stimulation may safely treat delusions in schizophrenia and schizoaffective disorder. Learn more.
‘SIX’ Provides Lively History Lesson
Anyone who has seen “Hamilton” has doubtless thought, “Why can’t we learn all of our history from theater performances?”
AI is Changing Science: Here’s What Journalists Need to Know
Researchers are rapidly adopting AI for everything from data analysis to planning and executing entire research studies. Journalists will need to keep up. That means learning the basic principles, strengths, and weaknesses of the diverse technologies labeled as “AI”—and how to tell when using AI in science is actually noteworthy. Luckily for science reporters, knowing your beat well can help you cut through the hype.
Scientists discover learning and memory formation in model membranes
A decades-long collaboration between two Department of Energy Oak Ridge National Laboratory scientists is reshaping how we understand
A note to readers: Science, accountability and elections
Dear readers, As early voting begins, I wanted to share some news — and why it connects to the decisions we make at the ballot box. I’ve been selected for the inaugural cohort of the South Atlantic Climate Journalism Hub mentorship program, hosted by Covering Climate Now. I’m excited to join this group, learn from scientists and fellow journalists, and expand my science reporting. Science directly ties to governance. Local officials make decisions about where development goes, how infrastructure is maintained, what emergency preparations receive funding, and how communities rebuild after disasters. Understanding the evidence behind those decisions helps us evaluate whether our government is serving us well. Here in western North Carolina, those connections are all around us, in Lake Lure and beyond. Flood maps inform decisions about property and development. Engineering assessments help identify risks at dams and bridges. Rainfall forecasts guide emergency preparations. Geological information helps explain landslide hazards. Water testing gives communities information they need to assess conditions. Meteorologists’ knowledge and public perception of weather events shape public safety. Each involves scientific or technical knowledge. Each also raises questions about public responsibility: What did officials know? How did they use that information? What did they tell the public? What did taxpayers receive for their money? A study can identify a problem. Elected officials still have to decide what to do about it, how to pay for it, and how to explain their choices. Reporting should examine both the evidence and the response. That is where I want to strengthen my work. My reporting on Helene recovery, infrastructure, flooding, public safety, and public spending has repeatedly brought me to questions that require scientific expertise alongside public records. This mentorship may help me better understand the research, ask more precise questions, and explain the answers clearly. I also bring the perspective of someone living with these concerns. I live downstream of Lake Lure Dam, and landslides affected my property during Helene. Those experiences bring urgency to my questions. Careful reporting helps establish the answers. Readers deserve to know what the evidence shows, where uncertainty remains, and whether officials accurately describe the information guiding their decisions. You also deserve scrutiny of proposed solutions: what they cost, what they are expected to accomplish, and what risks remain. Access to that information matters. As a mostly deaf journalist who relies on written communication, I will continue pressing for written public warnings, records, and explanations that people can read and use in real time. As you consider the candidates seeking your vote, consider how they approach evidence, preparedness, and transparency. Do they support local journalism, even when it’s not parroting a press release? Do they explain weather events clearly in plain English and explain their decisions after them? Make supporting records available? Acknowledge uncertainty? Follow through when a report identifies a risk? Those are practical questions about governing. Thank you for supporting Cops & Congress and the work that makes these questions part of the public conversation. I’m grateful to David Dickson and the CC Now team for this opportunity, and I look forward to bringing what I learn back to you. Annie DanceEditor & Publisher, Cops & Congress — Learn more about this newsletter, ethics policy, how you can help shape this work, and support it. Follow on X and Facebook. Send constructive criticism, fan mail, and tips with public documents for future stories: CopsandCongress@gmail.com ICYMI: View the Cops & Congress archive — Annie Dance is the publisher of Cops & Congress, a newsletter that analyzes what happens when crime, courts, disaster, democracy, and small-town policies collide. Views expressed here are covered by the First Amendment. Dance has a Bachelor of Arts from Manhattan University in Communication with a focus in Journalism and Government. She has been a journalist for over 20 years. Share Upgrade now or give a gift subscription
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Israel prepares for a high-stakes election
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Here is a list of events this week. Monday/Tonight! SF Politics 101 with Joel Engardio. Learn how local politics shaped San Francisco. Monday/tonight, October 19, 5:30-6:45 p.m. SPUR Urban Center, 654 Mission Street, S.F. Wednesday 20th Street Protected Bikeway Ribbon Cutting. The Oakland Department of Transportation, city officials, Bike East Bay and other community partners will celebrate the completion of the 19th Street BART to Lake Merritt Urban Greenway. Wednesday, October 21, 1:30 p.m. 400 Thomas L Berkley Way, Oakland. Thursday 2026 Silver SPUR Awards. This SPUR event brings together elected officials and business, civic, and philanthropic leaders to celebrate four individuals and their contributions. Thursday, October 22, 4-7:30 p.m. Blue Shield of California Theater at YBCA, 700 Howard Street, S.F. Get tickets here. Friday Woman and Non-Binary Morning Ride. Join the San Francisco Bicycle Coalition for a socially distanced bike ride for women and nonbinary cyclists. Friday, October 23, 7:15-8:45 a.m. McLaren Lodge, 501 Stanyan Street, S.F. Friday Muni Trivia Night. Bring your friends or come solo and make some new ones. Friday, October 23, 7:30 p.m. Standard Deviant Brewing, Pier 70, suite 195, 1070 Maryland Street, S.F. RSVP here. Saturday Fifth Annual Firefighter Family Ride. Join SFFD, SF Safe Routes to School, and the SF Bicycle Coalition for a fun, low-stress bike ride through one of the city’s most beautiful routes. Saturday, October 24, 10:30 a.m.-1 p.m. Panhandle Park Blacktop (Fell & Ashbury) to JFK Promenade (Whale’s Tail). Sunday Canvas for No on Prop G. Join the San Francisco Bicycle Coalition to canvas for No on ‘G’ to save Sunset Dunes Park. Sunday, October 25, 10 a.m.-12 p.m. Marina Blvd & Laguna Street, S.F. Got an event we should know about? Drop us a line.
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Can AI solve our mental health access problem?
Last week I asked you what worries you most about AI in health. It was close, but your top answer was “AI giving bad health advice.” Last week’s poll results: You’re in good company. This concern was top of mind at a conference I joined at Stanford last week, where researchers, clinicians, and people who build AI systems spent a day debating trust and safety in health AI. Stepping back, I’m also hearing your emerging concerns about AI’s impact on our well-being generally. I see this as a matter of public health, and we’ll take it in pieces as we learn together. Today we’ll focus on AI chatbots used for therapy for mental health. Early this month, Governor Newsom vetoed Senate Bill 903, California’s most ambitious attempt to regulate AI use for mental health. It’s at the center of active debate, and raises the questions patients, family members, citizens, and voters will face as AI moves deeper into health care. Let’s work through it. What is “AI-based therapy”? It’s really two things: purpose-built mental health apps designed to deliver structured support, and general chatbots like ChatGPT, Claude, and Gemini that people increasingly use for emotional support—even if they weren’t built for it. Spoiler alert: I don’t know whether the shift toward AI therapy is good or bad. The evidence isn’t there yet. But I do know it’s already happening, and I have some ideas about how we can roll it out with safety in mind. The therapy dilemma, in a nutshell Mental health challenges are common, and strain health and well-being across populations. Therapy (interactive dialogue with a trained, empathetic practitioner) helps many people heal. The demand for licensed therapists far exceeds the supply in California, and AI-based therapy can narrow that gap. AI-based therapy remains novel, unproven, and not well tested. Right off the bat, we can see we’re dealing with trade-offs here. Since the main driver of demand for therapy from a robot is lack of access to therapy from a human, we’ll start there. It’s too hard to find and afford a human therapist California is short roughly 55,000 licensed, non-prescribing therapists and counselors, about 40% of what we need. Psychiatrists, who rely on talk therapy as a complement to prescribed medications, are even more scarce. Eight counties have none, and density ranges from 1.7 per 100,000 residents in San Benito County, to 68.1 in Marin County. My wife is a psychiatrist in Marin, and even here access is tight. If it’s hard in the best-supplied county, imagine rural Sierra County, or the San Joaquin Valley. In 2024, more than half of Californians who tried to make a mental health appointment struggled to find a provider who took their insurance, compared with 14% for physical health care. When Senate staff posed as “secret shopper” patients and called 120 mental health providers in Medicare Advantage networks, they landed an appointment less than once for every five tries. Insurers pay behavioral health clinicians about 22% less than medical and surgical clinicians for comparable office visits. On top of that, billing denials and prior authorizations hit hardest at the solo and small practices common in mental health. Psychiatrists opt out of Medicare at the highest rate of any specialty. In many places, a cash practice for therapy fills quickly with patients paying $250 or more an hour, with no insurance paperwork at all. Families who can pay cash find providers more easily. Those who rely on insurance, including Medi-Cal enrollees, older adults, rural residents, and non-English speakers, compete for what’s left. So we can’t pretend the human system works, and we can’t blame people for turning to AI for support when they’re in distress. AI is filling the gap It’s estimated that roughly a quarter of AI users use it for mental health support. If you have internet, it’s available 24/7, it’s cheap, and it speaks your language. Uninsured adults are about twice as likely as insured adults to do it (30% vs. 14%). It feels anonymous in ways that may enable people to ask things they’re unsure or embarrassed about. The largely hidden, personal use makes it hard to study whether it’s actually helping people with their mental health issues, but researchers are trying. Does AI therapy work? A 2026 analysis of nearly 40 randomized trials found chatbots showed a real but small reduction in depression symptoms. But most of these trials were compared to an alternative of no therapy, not a human therapist. So the benefit of AI therapy is probably real, but modest, and mostly untested against the alternative of human contact. We need more studies. Serious concerns OpenAI’s own estimate is that more than a million ChatGPT users each week show signs of suicidal planning or intent. These are high stakes encounters, and AI doesn’t always get it right. That’s partly because a chatbot can sound like a therapist, but it has no license, no duty of care, and it’s not clear who to hold accountable when it gets things wrong. AI is also designed to help users in whatever task they’re seeking to accomplish. People may feel like they’re getting therapy, but they’re mainly getting a convincing conversation. It’s easy for an AI companion to wander into gray areas, like playing along with “creative writing” that may be about the user’s own suicidal intent. Courts are driving safety conversations A wave of lawsuits have been filed on behalf of families claiming that chatbots pushed people they loved toward self-harm, including teens who died by suicide. The first settlements came this year. OpenAI is headquartered in San Francisco, and dozens of cases alleging that ChatGPT drove users to suicide or self-harm are now before a San Francisco judge. Character.ai has filed for bankruptcy after several states brought lawsuits accusing the chatbot of pushing dangerous content to kids. But the technology seems to be improving. Last month, an independent evaluation found that newer AI models “almost never endorse suicide.” That’s progress, but our societal appetite for even one preventable death is very low. So how do we move forward? Finding a middle ground Earlier this month the governor vetoed Senate Bill 903, a bill designed to place guardrails on the use of AI for therapy. The bill would have prohibited any AI-based platform from being advertised as psychotherapy, and would have required a licensed professional to review and approve each encounter where AI was used to support therapeutic decisions. Newsom called it overly broad, since even routine screening questions would have needed clinician sign-off. Knowing we’re already short on mental health providers, requiring human review of AI encounters may not relieve our access problems. Still, the answer isn’t no oversight. Here’s one feasible short term solution. Rather than reviewing each encounter, a licensed clinician can design the guardrails: what counts as a crisis, what triggers an immediate handoff to a human, and what gets sampled for audit each month. The AI runs inside that structure. Conversations get checked, failures get reported, and a real clinician owns the framework. Medicine already has models to learn from. The FDA process for rolling out newly approved medications is an example. After a product reaches patients, we keep watching for adverse events. Patient-facing AI needs the same “digital pharmacovigilance”: systematic tracking of harms, near misses, and the types of groups that get hurt and why. It’s also critical that users know that they can reach a human if they’re in crisis. Most chatbots now include messages like these: Bottom line The rush to use unproven AI tools for mental health support is mostly a workaround, a sign of a health system people can’t get into. We need better access to proven care from licensed clinicians. That takes reimbursement that makes accepting insurance viable, and a bigger workforce. But, built and tested well, AI could be part of the answer. It could be the 2 a.m. check-in when no one else is awake, or a way for patients to practice skills their therapist taught them. It could be the first step to get someone the right level of care, and take paperwork off clinicians’ plates so they spend more time with patients. None of this is simple. The SB 903 veto doesn’t end the debate, and the legislature will be back. In the meantime, those of us using these tools need to stay informed, ask hard questions, and hold the companies building them accountable. I’ll try to do my part, and we’ll keep learning together. Love,Matt P.S. If you or someone you know is struggling with mental health challenges, emotional distress, substance use problems, or just needs to talk, call or text 988- you’ll reach a real person— or chat at 988lifeline.org 24/7. Dr. Matt Willis is the author of Your Local Epidemiologist in California. A California native, he’s served as a primary care physician, CDC epidemiologist, and public health officer for Marin County, where he guided the pandemic response. He lives in Marin with his family and their dogs, Teddy and Ramona. Thank you Cynthia He, MD, PhD, and Parnika Saxena, MD, for their guidance in preparing this article. Cynthia works in an outpatient mental health clinic, treating patients who have Medi-Cal or, sometimes, no insurance. Parnika works in an inpatient psychiatric unit, treating patients with public or private insurance or self-pay ability. All opinions are their own and do not represent their employers.
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California students post gains in math, science and English in 2025-26
SACRAMENTO, Calif. — California students made gains across key academic and success measures during the 2025-26 school year, with test scores rising in English, math and science while the statewide graduation rate reached a record high of 87.9 percent. For the first time, the state released assessment results and California School Dashboard data together to give families and educators a more complete picture of student performance.English language arts scores increased 0.5 percentage points to 49.3 percent of students scoring proficient or advanced, while math scores rose 1.1 percentage points to 38.4 percent. Science showed the strongest improvement, jumping 3.3 percentage points to 36 percent of students scoring proficient or advanced.The statewide chronic absenteeism rate dropped 1.1 percentage points to 18.3 percent. The suspension rate declined to 2.8 percent, marking the third consecutive year of improvement."At a time when California continues to focus on accelerating learning and closing opportunity gaps, these results provide important evidence that our sustained investments are making a difference," Gov. Gavin Newsom said. "The continued progress we are seeing is a testament to the hard work of students, families and educators across the state—and we will continue working to build on these gains."The percentage of students prepared for college and career increased to 55.2 percent, up 2 percentage points from the previous year. More than 54 percent of graduates completed coursework required for admission to University of California and California State University systems, an increase of 0.9 percentage points."California has made important strides in expanding student supports and opportunities, but the pace of academic improvement reminds us that our work is far from finished," State Superintendent of Public Instruction Tony Thurmond said.Eighth grade students posted the strongest score trends in both English language arts and math. Science performance improved across all tested grades, with the strongest gains at the high school level.African American students demonstrated larger-than-average improvements across English, math and science. Hispanic or Latino students showed larger-than-average improvements in English and math, with positive math score trends across all grades.Students who were socioeconomically disadvantaged made gains across all 3 subjects, including larger-than-average improvements in English and math. Students who were homeless continued to demonstrate gains across grades, with particularly strong improvements in math."We are encouraged to see students making gains across all 3 major assessments and are especially encouraged by the progress of African American students, Hispanic or Latino students, socioeconomically disadvantaged students and homeless youth," said Linda Darling-Hammond, president of the California State Board of Education.The progress comes as California's public school system undergoes significant demographic change. Statewide school enrollment declined to approximately 5.73 million students in 2025-26, nearly 500-thousand fewer students over the past decade.The number of students with disabilities increased approximately 25 percent over the same period, from about 680-thousand to more than 850-thousand students. Graduation rates increased across nearly all student groups, including notable increases for American Indian or Alaskan Native students at 4.3 percentage points and students with disabilities at 3.7 percentage points.In Downey Unified School District, the rate of students demonstrating proficient or advanced grade-level knowledge increased by 2.1 percentage points in English, 2.8 percentage points in math and 4.7 percentage points in science. The district aligned graduation requirements with college admission expectations and invested in inclusive special education practices, expanded career technical education and expanded learning opportunities."The gains Downey Unified is seeing in student assessment results and on the Dashboard are the result of years of focused, consistent work across the district," Superintendent Roger Brossmer said.In Exeter Unified School District, proficiency rates increased by 4.6 percentage points in English, 2.3 percentage points in math and 3.6 percentage points in science. The district placed a strong focus on phonics and the science of reading, particularly in kindergarten through second grade."The gains Exeter Unified is seeing in student assessment results and on the Dashboard are the result of having great people who are focused on the right work, and great students who want to do well," Superintendent Melanie Dorough said.Union Elementary School District saw proficiency rates increase by 1.5 percentage points in English, 2 percentage points in math and 4 percentage points in science. Teachers regularly analyze state and local assessment data to guide instruction, supported by strong core instruction and early literacy strategies."Union Elementary School District's achievement gains reflect a focused, data-driven approach to meeting the needs of every student while fostering a strong sense of belonging, safety and connection," Superintendent Carrie Andrews said.The California Department of Education is examining local results to better understand strategies and conditions associated with stronger student outcomes. The state has made significant investments in recent years, including literacy and math coaching, tutoring, expanded learning opportunities, community schools and universal transitional kindergarten.
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Clinton Policy Institute outlines maternal health solutions for states
Health Care Clinton Policy Institute outlines maternal health solutions for states It’s the first major report from the project, which launched in July to foster nonpartisan policies to improve the wellbeing of children and families. Stephanie Akin 10:00 AM, Oct 15, 2026 Dr. Jacob Lenz performs an ultrasound at the Oklahoma State University obstetrics and gynecology clinic in Tulsa, Okla., on Tuesday, July 16, 2024. (AP Photo/Mary Conlon) The Clinton Policy Institute is calling on states to lead efforts to curb maternal mortality, releasing a 61-page playbook Thursday highlighting successful policies that could be adopted nationwide. The playbook is the first major report from the Clinton Foundation’s project, which launched in July to foster nonpartisan state and local policies to improve the wellbeing of children and families. “Eighty percent of maternal deaths are preventable, and we know what works,” CPI Chair Jennifer Klein said. “The solutions are well established and evidence-based.” CPI’s mission is to convene political leaders from both parties, policy experts, state officials and other stakeholders to explore solutions to longstanding problems. “What we aim to do is bring together people who are deeply engaged, but may not be meeting together in the same room,” Klein said, noting that state policymakers might not always be aware of what their peers are doing in other states. In May, CPI brought together lawmakers from Virginia and Pennsylvania, two of the 14 states that have passed bipartisan maternal health “Momnibus” packages modeled after a comprehensive federal proposal that has repeatedly stalled, called the Black Maternal Health Momnibus Act. Other participants included Dr. Mona Hanna, founder of Rx Kids, a Michigan program that provides direct cash allowances to pregnant women and infants. Pennsylvania Rep. Morgan Cephas, who was part of the group, said she learned about funding opportunities she hadn’t come across during her decade in the Legislature. “As legislators we can issue grants, we can pass legislation, we can move policy, but we want to ensure that funding opportunities and programs that are tackling this issue are sustainable,” Cephas said. The playbook outlines broad areas where state policy can make a difference, such as allowing Medicaid to pay for doulas and remote patient monitoring, and using federal and private grant funding to pay for home visits to pregnant and postpartum women. For each category, it highlights innovative programs that can serve as models, with examples of specific actions state lawmakers and governors’ offices can take. Those include the Rx Kids model, which gets funding from the Michigan state budget; Pennsylvania’s Healthy Moms Vibrant Futures strategic action plan, emphasizing equity-centered maternal care, regional collaboration, Medicaid innovation, and workforce development that was launched this year with more than 160 stakeholders; and Kansas’s Bridging Plan, which reserves three slots and provides at least $26,000 in loan forgiveness for obstetric residents who commit to practicing in rural or underserved communities for 36 months. CPI emphasizes that its work is nonpartisan. Klein, the director of the White House Gender Policy Council under former President Joe Biden, acknowledged that CPI also takes positions on abortion, Medicaid funding, and the need to address the economic and social drivers of poor health, which might rankle some conservatives. “We don’t shy away from talking about those things that we think are part and parcel of the maternal health crisis in this country,” Klein said. “That said, there are so many policies that are both evidence-based and have been tried successfully in both red and blue states, and people can see that they can have real impact.” She pointed to the federal option to extend postpartum Medicaid coverage from 60 days to 12 months that was made available during the Biden administration and has since been adopted by every state but Arkansas. Other initiatives embraced across party lines include Medicaid coverage for midwives and doulas, as well as “bundled” payments for prenatal and postpartum care. That alternative payment model, pioneered in states such as Arkansas, Colorado and Tennessee, incentivizes fixed fees to cover all healthcare services associated with pregnancy and childbirth. Advocates say it promotes more coordination among caregivers, discourages unnecessary interventions, and expands postpartum support. In addition to maternal health, the initiative is also focused on addressing family economic security, children’s well-being, and democracy and civic resilience.
What Chinese viewers are learning from America’s political chaos
What many Chinese viewers see is not a nation falling apart, but a freedom they have never been allowed to have at home.
Precision weather radar visualization requires better science, not just new tech
Learn how multi-source atmospheric science, GRAF modeling, and precision weather radar visualization create a more accurate representation of surface weather.
Computational Models as Liminal Models in Neuroscience, a Cognitive Science Speaker Series Presentation | Events
Speaker: Danielle Williams, Ph.D. Title: Computational Models as Liminal Models in Neuroscience Short Bio: Danielle is a philosopher of mind and science whose research examines the role of computation in our theories and explanations of the mind and brain. Her work brings together philosophy of mind, cognitive science, neuroscience, and artificial intelligence, with particular interests in computational theories of mind, computational modeling, and the relationship between different explanatory and modeling strategies in the mind sciences. She also investigates philosophical questions raised by contemporary AI, including the nature of opacity in computational models and its implications for explanation in cognitive science. Alongside this work, she is a historian of cognitive science and AI, examining how the development of computer technology has shaped our conceptions and models of the mind and brain. Abstract: Scientists rely on various types of models to make sense of their phenomena of interest. This has led philosophers to take up a range of semantic, ontological, and epistemological questions about how models represent, what kinds of things models are, what we learn from models, and how they explain. One type of model that has been of particular interest to philosophers is the computational model. Computational models are commonplace in the mind sciences in general and neuroscience in particular, with many asking how computational modeling can help us understand the nervous system and its contribution to cognition and behavior. These questions are particularly pressing in neuroscience, as computational models have become a dominant framework for making sense of cognition. Thus, old questions about model-based science have re-emerged within the context of neuroscience, with many philosophers looking to established frameworks to understand how computational models represent, explain, and contribute to theory development in neuroscience. These established frameworks, however, characterize computational models as a homogeneous type which cannot capture actual modeling practices in neuroscience. In this talk, I argue that computational models in neuroscience are best typed as a kind of liminal model, a type I introduce to capture how the status of a computational model is determined by the epistemic context in which it is deployed. Indeed, the category to which a computational model belongs shifts according to the role it plays within a given context. ASL-English interpreters have been requested. Light refreshments will be provided.
Science Wizard David Hagerman to perform extreme science at Knollwood School
Renowned science wizard David Hagerman brings magic to learning at Knollwood School
Companies pledge free software and computing for AI in science
The European Commission has announced that a group of companies and foundations will offer free services, such as software or access to supercomputers, to European scientists using AI in their research.Unveiled on October 15 at a conference in Dublin, the initiative is the latest step by the Commission to try to help researchers use the technology. Scientists have used forms of AI, such as machine learning, for decades. But advances in deep learning in the past decade, and the wider interest in large language models since the release of ChatGPT in 2022, have spurred governments to invest in new AI tools that could help the scientific process.In July, the US announced it is spending $5 billion on the Genesis Mission, which it describes as a “national effort to harness AI for science,” funding 278 projects across areas such as autonomous labs or new AI tools in astronomy. The Commission has also identified AI in science as a priority, but the €107 million it pledged from Horizon Europe in 2025 does not come close to US spending levels. That said, the vast majority of public research spending in the EU comes from national funds. Today’s announcement at the European AI in Science Summit doesn’t commit any new public money. Instead, it launches the AI in Science Enablers Initiative, a set of voluntary contributions from companies and foundations. The idea is that the companies also benefit by getting access to scientific ecosystems, and feedback on how their tools work. Google, for example, has put up €3 million of cloud computing credits to European researchers. The Novo Nordisk Foundation intends to make its supercomputer, Gefion, available to European researchers, according to the Commission statement. The chipmaker Nvidia said it would provide access to tools, compute, training and collaborative projects.The publisher Elsevier also offered complementary licenses to some of its AI tools, plus AI training for PhD, postdoctoral and master’s students. The French AI firm Mistral will provide the European research community with access to its open-weight models, Vibe seats and application programming interface credits, according to the Commission’s statement. Vibe is one of the firm’s chatbot models. The idea is that more firms and foundations will come on board in the future as the initiative grows.
Absolutely everything I learned while testing RTX Spark for a week: It's a beast, but not at everything
The first RTX Spark PCs launch tomorrow, and I've spent the last week testing what this new chipset from NVIDIA is good at.
NVIDIA Expands Advanced Technology Access for Europe’s Scientific Community Through Open Learning Resources, Collaborations and Compute
NVIDIA’s participation in the European Commission’s AI in Science Enablers Initiative deepens its commitment to supporting the pursuit of scientific progress across the region.
Lakeland receives $1 million to boost science, health care learning
A $1 million estate gift to Lakeland University will fund science scholarships and expand science and health care programs.
The secret to learning from failure may be what you do next
The timing and framing of failure can be critical for future success. A setback, says one researcher, can actually propel a person forward.
Heart doctor: Healthy school meals are heart health policy
Right now, far too many Virginia children lack consistent access to the food they need to grow, learn and thrive. The 2026 Virginia Hunger Study found that one in two (51%) Virginia households with…
Different measures of what learning looks like
Colorado voters face some big propositions — Amendment 87, Proposition 136 and Proposition NN — about money. Nothing like a fight over dollars to get everyone’s hackles up. If Amendment 87 and Proposition NN pass and Proposition 136 fails, a chunk of money will go to our public schools to help make up for years […]
How did political campaigns get so expensive?
The 2026 midterm elections are fast approaching, and it’s gearing up to be the most expensive election in American history. So how did we get here, where every election feels more expensive than the last? Today on the show, we’re exploring what’s causing the flood of money into our politics. Fact checking by Sierra Juarez.Your Next Listen –Bad economics, smart politics –Over 2,000 episodes in our archive on the NPR AppConnect with The Indicator from Planet Money–The Indicator newsletter — a weekly roundup of news, ideas and staff picks. –Instagram, TikTok and YouTube –Find the Planet Money book –Find the Planet Money game, Sell Me A Sasquatch, and other merch at the NPR Shop!Support public media by subscribing to NPR+ and enjoy perks for over 25 podcasts like this one. This show’s perks include sponsor-free listening. Learn more at plus.npr.org.
Campus Creamery’s new owners are learning business in real time
Campus Creamery is a South 40 favorite, a classic midnight snack for WashU’s underclassmen. Three sophomores took over the well-loved ice cream shop last January as first-years.
Your Business Probably Has More Data Than Insight by Nathan Tayloe
Most businesses know more about themselves than they ever have. Leaders have access to financial reports, sales pipelines, customer data, employee metrics, website analytics, and dashboards for almost every part of the organization. CRMs track every stage of the sales process. Marketing platforms report results in real time. AI can analyze and summarize enormous amounts of information in seconds. Yet I've sat in plenty of meetings where all of that information is available, and some of the most important questions are still surprisingly difficult to answer. What's actually working? Why are customers choosing us? Where are we losing opportunities? What should we do differently? Having the data and understanding what it means are two very different things. More Information Doesn't Always Create More Clarity Over the past decade, businesses have invested heavily in visibility, and that's a good thing. We can understand performance with a level of detail that would have been unimaginable not long ago. But there's an unintended consequence. The easier something becomes to measure, the easier it is to assume it must be important. Website traffic goes up, so that looks positive. Leads increase, so marketing appears to be working. Sales activity climbs, so the pipeline looks healthy. Maybe. The numbers tell us what happened. Understanding why it happened, whether it matters, and what we should do next still requires judgment. Not Everything That Matters Fits on a Dashboard Consider a customer who ultimately contacts your company through Google. The data may attribute that lead to search. What it may not show is that the customer heard about you from a colleague six months earlier, saw your company mentioned in an industry publication, followed one of your employees on LinkedIn, and read several reviews before finally searching for your company. Google may have produced the click. It didn't necessarily create the decision. This happens throughout a business. Some of the things that matter most are difficult to reduce to a single metric: Why your best customers stay Why talented employees choose your company Why a prospect trusts one provider over another Why one service is gaining momentum while another isn't Why the market perceives your business the way it does Data can help answer those questions, but it needs context. Better Decisions Start With Better Questions One of the easiest traps for leaders is starting with the information in front of them rather than the decision they're trying to make. Instead of opening a dashboard and asking, "What does the data show?" it can be more useful to start with a different set of questions: What are we trying to understand? What decision are we trying to make? What would we need to know to make that decision confidently? That changes the role of data. Instead of looking at every number available and searching for meaning, you're using information to understand something specific about the business. Information Still Requires Judgment More sophisticated analytics and AI will continue to give leaders access to better information, faster. That's valuable, but I don't think it reduces the importance of experience and judgment. If anything, it increases it. Someone still has to decide which numbers deserve attention, which patterns matter, what context might be missing, and when the data isn't telling the whole story. The goal isn't to know more about your business simply because you can. It's to understand your business well enough to make better decisions. Good data can tell you what happened. Good leadership has to decide what it means. — Learn more about how we help organizations turn business, customer, and marketing insights into smarter strategies at tayloegray.com, or reach out directly at nathan@tayloegray.com.
Talarico criticizes Israel’s actions in Gaza, backs two-state solution
James Talarico, the Democratic candidate for Texas’ Senate seat, addressed the nuances of a voter’s question about rising antisemitism and his stance on Israel. “I have deep love and respect for the Jewish people, for the Jewish scriptures,” noting that he learned about them from a local rabbi and not from a Christian perspective.
Delta County students immerse themselves in the sciences for Mind Trekkers
Students turned the U.P. State Fairgrounds into a hands-on science lab on Wednesday with an interactive learning experience.
Ubisoft plans cuts at German studios, adding to wave of workforce reductions
Ubisoft is planning to reduce its workforce in Germany, where it operates a trio of studios, by about 100 workers, Game File has learned. The company wants to offer employees buyouts to, should enough people accept, avoid having to lay anyone off. “In Germany, we initiated discussions with the Works Council last week regarding a proposed voluntary departure plan across Ubisoft Dusseldorf, Ubisoft Berlin & Ubisoft Mainz,” a company rep told Game File. A “works council” is a union-like group representing employees. “The voluntary plan is the primary measure being proposed to support the intended organizational changes,” the Ubisoft rep added. The company declined to confirm the scale of the potential headcount cut. Of the impacted studios, Mainz might be the best known to the public. It leads development of the Anno series and shipped the well-reviewed strategy game Anno 117: Pax Romana in November 2025. Berlin and Dusseldorf operate as support studios within Ubisoft’s creative network. Berlin recently supported the development of Rainbow Six Siege and the Rayman Legends Retold remake. Dusseldorf also contributed to those projects, as well as The Settlers and Ubisoft-branded escape rooms. Dusseldorf is the biggest of the three studios, with some 400+ workers, compared to just over 100 at the other two, a source familiar with the reductions and the teams told Game File. More than half of these new reductions of about 100 people are expected to happen at Dusseldorf. The planned cuts were announced at a town hall for the German studios on Tuesday of last week. In an email follow-up, the studio group’s managing director, Benedikt Grindel, said the cuts were intended to “best position our German studios for the future... with a clearer focus on their role, capabilities and organizational setup needed for the work ahead.” The email was an echo of the past. In January 2025, Grindel had emailed the Ubisoft developers in his region (or studio “zone”) to explain that a round of 185 cuts at the time, including about 65 at Dusseldorf, were “essential to improve our operational efficiency and ensure the long-term sustainability of our studios, the zone and the company.” A half-decade of continued job cuts Ubisoft has been aggressively cutting its headcount for years. At the end of September 2022, it had 20,729 workers. By the end of March 2026, that total was down to 16,590. Studio management is trying to trim costs as it grapples with a changing industry and a drought of releases, let alone hits. The company announced earlier this year that it had reduced its costs since 2022 by “nearly €325 million” ($362m) and planned to lower them through 2028 by another €180 million ($200m). In June the company said it was closing down its Winnipeg and Belgrade studios and cutting roles in its Barcelona team. Even commercial and creative success hasn’t spared studios from this ongoing company-wide contraction. As Game File was nailing down details around the proposed cuts in Germany, Ubisoft management was informing workers of cuts in its Singapore studio, which shipped a hit remake of Assassin’s Creed IV: Black Flag this past July. Some 70 positions in Singapore are being eliminated “to align the studio with upcoming production needs,” per a company rep’s statement to Game File. In early 2024, a large team in the Singapore studio launched the multiplayer pirate game Skull & Bones. It performed poorly but has been supported with regular content updates since. Many of Ubisoft’s studios around the world are heavily subsidized via regional grants and tax breaks. Singapore had been among them for years, but received no such local assistance in the 12 months ending this past March. News of the Singapore cuts broke today alongside word that Ubisoft is also proposing the closure of RedLynx, pending negotiations with officials. The Finnish studio behind the motorbike series Trials was founded in 2000 and sold to Ubisoft in 2011. As reported by VGC (and corroborated by Game File), Ubisoft management told employees it could not come up with a new, sustainable assignment for RedLynx. The 2025 workforce at Ubisoft Dusseldorf were straight-up cuts. This time, Ubisoft is hoping the reductions at its trio of German studios will largely come from workers taking a payout to leave. The company offered such voluntary dismissals at its Massive Entertainment studio (The Division) a year ago. For the German studios’ buyouts this time around, Ubisoft and the Works Council had discussed a formula that’d pay a worker’s monthly salary multiplied by 1.5 and by the number of years they’ve worked at the company, according to Game File sources. The formula is still subject to negotiations with the Works Council and has not yet been part of an official offer to workers. It could be adjusted up or down, and it could be supplemented with additional bonuses. Asked about the floated payout formula, a Ubisoft rep told Game File, “The terms of a potential voluntary departure program remain subject to negotiation with employee representatives. No final agreement has been reached, and we won’t comment on specific proposals while discussions are ongoing.” Game File is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber. Subscribe Item 2: Epic’s AI adventures Epic Games (Fortnite, the Unreal engine) is all-in on using AI to try to transform the way it makes video games and game-making tools. It’s leaders appear to be undeterred by any resistance among gamers and some game developers around generative AI—and instead emboldened by advances, particularly in AI-assisted programming. A recap of the last three days: On Monday, Bloomberg reported that Epic management told workers last week that development of an upcoming game combining Star Wars and Marvel characters would be “AI first.” Employees would be expected to use AI models such as Claude to code the game. A manager told Epic’s developers there would be “[n]o more hand-written code” and that art for the game would be “generated with full AI workflows,” per the outlet. Just ahead of that story running, Epic founder Tim Sweeney tweeted: “We have a huge pivot underway here at Epic, moving developers around to retool for maximum AI acceleration. The aim isn’t to do the same stuff with fewer people, but to enable everyone here to build MORE and BETTER stuff, and to bring the benefits to all teams using Unreal.” In a follow-up, Sweeney predicted that most game development processes would be at least three times faster a year from now than they were in January 2026, thanks to AI. To a person on X who told him he was failing to read the room, Sweeney said: “Much of the room wants it to be 2016 again, but it’s not. Our job, as the provider of tools and technology to much of the industry, is to design for the present that is and the future that will soon be.” On Wednesday, Epic announced it was releasing a faster launcher for its Epic Games Store marketplace. It did so by stating in a blog post as clear as could be that the launcher is an AI-enabled, vibe-coded project: Programming is hard. So, instead of doing that, we vibe coded a new Epic Games Launcher that’s way faster and more responsive than the current one, and has a new look. Programming is hard? That was unexpected phrasing from a company founded by a legendarily great programmer. Back in 2011, Sweeney told me that he’d picked up programming quickly as a kid and rejoiced at the power it gave him. “I could look at any program that anybody else had written and, with enough effort, build something cooler.” Item 3: The Xbox Labs experiment (and one of the people behind it) Microsoft’s Xbox division, which is flooding the zone with announcements lately, yesterday revealed a new internal tech-tinkering team called Xbox Labs. In the words of Xbox boss Asha Sharma Labs is “a small group of game makers, engineers and scientists whose job is to turn emerging ideas into things our players and developers can experience.” Conventional wisdom coming out of the announcement is that Labs is a team that’ll focus on ideas driven by generative AI. In her announcement, Sharma cited some of the dizzying achievements in tech and gaming over the past month as AI agents and other AI tools have been deployed to swiftly figure out how popular games work, then rebuild them and mix them up. She mentioned seeing ”games rapidly decompiled and rebuilt to run on hardware they were never originally designed to support” and said “[p]erhaps more surprisingly, we’ve seen many of the biggest games in the world have been remixed into the source code of another.” (Minecraft plus Elden Ring, for example, or Fortnite + Super Monkey Ball + Overwatch + Diablo + others.) Sharma also cited the work of Windows PC games being ported to run in browsers, a seeming reference to a recent AI-assisted fan port of Halo. Or perhaps Call of Duty or GTA. Sharma didn’t use the specific term “AI” in her memo, but she came close, citing AI “agents” and their potential for game-making tools that Labs could help craft: I think XBOX has an important role to play here. Coding became the first major workload for agents because they can write, run, and test software. Games share many of those same properties, and people spend more than 72 billion hours a year playing XBOX games, roughly five times the estimated time spent writing code worldwide. We’ve spent 25 years learning how to make great games, and I want our game makers to help shape what these new technologies make possible. As we learn, we’ll share the tools we build with developers across the industry, just as we always have. When I asked an Xbox rep if Labs was an AI-centric team, they said genAI was not the focus, describing it as having a broader mandate for using new tech in new ways in gaming. As an example, they pointed to Xbox’s recently launched disc-to-digital gaming usage rights program. Microsoft didn’t specifically name anyone involved full-time in Xbox Labs yesterday, but on social media and LinkedIn, I spotted Adrian Mato IDing himself as leading the creatio of tools at Labs and talking up the launch. Mato first caught my attention in late September, when he said on X that he was “starting to build a new team at Xbox focused on products and tools for how games get built and distributed as agentic tools become more capable.” At the time, he framed AI-based tools as a supplement to traditional game dev tools. “I don’t think we need to replace them,” he said. Since then, he’s publicly shown interest in AI and in games. He’s recently spent some of his X feed marveling at the power of AI agents and praising the lighting in the latest Gears of War. Following the Labs announcement yesterday, he enthused about the Xbox Labs’ stylish landing page. He wrote: “We built some fun tools to art-direct shaders in real time, experimented with interactive ASCII art, and sprinkled in lots of tiny details. “Move your mouse to leave a colorful trail that reveals the original footage, or interact with the video to trigger bursts of particles. “A lot of love went into making it all feel just right. “More to come”
GTA 6 Reviews Will Be Released Before Launch
Insider Gaming has learned that Grand Theft Auto 6's reviews will be released ahead of the game's release. Get the full story here.
Faculty in the College of Behavioral, Social and Health Sciences receive funding to develop industry-embedded experiential learning programs
Two faculty members in the College of Behavioral, Social and Health Sciences (CBSHS) have received funding from Clemson University to develop experiential
Faculty in the College of Behavioral, Social and Health Sciences receive funding to develop industry-embedded experiential learning programs
Two faculty members in the College of Behavioral, Social and Health Sciences (CBSHS) have received funding from Clemson University to develop experiential
Teradata Brings Elastic Python Compute to AI Studio, Unifying Data, Governance, and AI
New distributed Python capability accelerates large-scale AI operations while keeping data and compute within the Teradata platform SAN DIEGO, Oct. 14, 2026 — Teradata today announced the general availability of Elastic Python Compute within AI Studio. The capability gives enterprise data science teams a managed, distributed Python environment for scaling AI compute and machine learning […]
Hazle Township Early Learning Center enjoy science program
On Oct. 5, second-grade students at the Hazle Township Early Learning Center participated in a Title I federally funded parent engagement program, Joe Romano’s Is it Magic or Science? This event was organized to promote literacy within s…