The government cuts key data used in hurricane forecasting, and experts sound an alarm
Weather experts are warning that hurricane forecasts will be severely hampered by the upcoming cutoff of key data from U.S. Department of Defense satellites, the latest Trump administration move with potential consequences for the quality of forecasting.
The National Oceanic and Atmospheric Administration said it would discontinue the 'ingest, processing and distribution' of data collected by three weather satellites that the agency jointly runs with the Defense Department. The data is used by scientists, researchers and forecasters, including at the National Hurricane Center.
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Forbes
27 minutes ago
- Forbes
Quantum, Moore's Law, And AI's Future
microchip integrated on motherboard In the game of AI acceleration, there are several key moving parts. One of them is hardware: what do the chips look like? And this is a very interesting question. Another is quantum computing: what role will it play? Another is scaling. Everyone from CEOs and investors to engineers is scrambling to figure out what the future looks like, but we got a few ideas from a recent panel at Imagination in Action that assembled some of the best minds on the matter. WSE and the Dinner Plate of Reasoning Not too long ago, I wrote about the Cerebras WSE chip, a mammoth piece of silicon about the size of a dinner plate, that is allowing the centralization of large language model efforts. This is an impressive piece of hardware by any standard, and has a role in coalescing the vanguard of what we are doing with AI hardware. In the aforementioned panel discussion, Julie Choi from Cerebras started by showing off the company's WSE superchip, noting that some call it the 'caviar of inference.' (I thought that was funny.) 'I think that as we evolve, we're just going to see even more innovative, novel approaches at the hardware architecture level,' she said. 'The optimization space is extremely large,' said Dinesh Maheshwari, discussing architecture and compute units. 'So I encourage everyone to look at it.' Panelist Caleb Sirak, also of MIT, talked about ownership of hardware. 'As the models themselves start to change, how can businesses themselves integrate them directly and get them for a fair price, but also convert that AI, and the energy involved, into a productive utility?' 'What is a computer, and what can a computer do?' asked Alexander Keesling, explaining his company's work on hardware. 'We took the fundamental unit of matter, a single atom, and turned it into the fundamental unit of information, which is a quantum bit … a quantum computer is the first time in human history where we can take advantage of the fundamental properties of nature to do something that is different and more powerful.' Jeremy Kepner of MIT's Lincoln Lab had some thoughts on the singularity of computing – not the race toward AGI, but a myopic centralization of an overarching 'operation.' 'Every single computer in the high end that we built for the last many decades has only done one operation,' he said. 'So there's a lot to unpack there, but it's for very deep mathematical and physics reasons: that's the only operation we've ever been able to figure out how to accelerate over many decades. And so what I often tell the users is, the computer picks the application. AI happens to be acceleratable by that operation.' He urged the audience to move forward in a particular way. 'Think about whatever you want to do, and if you can accelerate it with that kind of mathematical operation, you know the sky is the limit on what you can do,' he said. 'And someone in your field will figure it out, and they will move ahead dramatically.' Engineering Challenges and AI Opportunities The panel also mentioned some of the headwinds that innovators must contend with. On the other hand, Jeff Grover noted the near-term ability of systems to evolve. 'We're actually quite excited about this,' he said. The Software End Panelists discussed the relevance of software and the directions that coding is going in. 'Programming languages are built for people,' Sirak said. 'How do you actually change that to build languages and tools that AI can use?' Choi mentioned benchmarks like inference rates of 2900 tokens per second for Llama 4. 'Open source models are rich for developers,' she said. 'What that's doing is building a bridge between the bravest developers. I would say the early adopters tend to be very courageous, and they're willing to code on things that they've never seen before.' The Fast Car Several panelists talked about a particular metaphor to a Ferrari, with Choi referencing 'Ferrari-level' speeds for the Cerebras chip. Maheshwari talked about 'exotic' chips, and design from an architecture paradigm, comparing certain builds to 'picking up groceries in a Ferrari.' He also mentioned the imperative of keeping the technology 'street legal.' Moore's Law and Progress Kepner talked about being surprised by what computers can do, and the size of investment in the industry. Moore's law, he said, implied an upper limit for spending. He predicted another decade of efficiencies, and cited the Ozaki scheme, a matrix method for preserving precision in calculations. What About Quantum? 'I think that the first area where we're going to see quantum computing impact is going to be in research,' Keesling said. 'These problems, at their core, are (about) trying to answer what happens when atoms and electrons interact with one another and develop these emergent behaviors … how we think about chemistry, how we think about drug interactions, how we think about material properties, all comes from electrons and atoms moving.' There was a lot to unpack in this panel discussion, including details on how we're going to achieve progress in the next few years. The Ozaki Scheme Going back to this matrix idea, I was not familiar with this term, so I looked it up and asked ChatGPT to describe it in basic English. 'It's named after Makoto Ozaki, the person who came up with the idea,' the model told me. 'He found a smart way to do very accurate math (like multiplying big grids of numbers) using fast but less accurate tools (like low-precision numbers). His method splits the work into small, simple steps and then carefully puts the pieces back together to get the exact right answer.' Going further, ChatGPT, just to be nice, even gave me a medieval storyline to show how the Ozaki scheme works, and to contrast it to other alternatives. I'm just going to print that here, because it's interesting. The Tale of the Kingdom of Matrixland In the kingdom of Matrixland, the royal court has a big job: multiplying giant tables of numbers (called matrices). But the royal calculator is slow when it uses fancy, high-precision numbers. So the King holds a contest: 'Who can multiply big matrices both quickly and accurately?' Sir Ozaki's Clever Trick Sir Ozaki, a wise mathematician, enters the contest. He says: 'I'll break each matrix into small, easy pieces that the royal calculator can handle quickly. Then I'll multiply those simple parts and put them back together perfectly.' The crowd gasps! His method is fast and still gives the exact right answer. The King declares it the Ozaki Scheme. The Other Contestants But other knights have tricks too: Lady Refina (Iterative Refinement) She does the quick math first, then checks her work. If it's off, she fixes it — again and again — until it's just right. She's very accurate, but takes more time. Sir Compenso (Compensated Summation) He notices small errors that get dropped during math and catches them before they vanish. He's good at adding accurately, but can't handle full matrix multiplication like Ozaki. Lady Mixie (Mixed Precision) She charges in with super speed, using tiny fast numbers (like FP8 or FP16). Her answers aren't perfect, but they're 'good enough' for training the kingdom's magical beasts (AI models). Baron TensorFloat (TF32) He uses a special number format invented by the kingdom's engineers. Faster than full precision, but not as sharp as Ozaki. A favorite of the castle's GPU-powered wizard lab. The Ending Sir Ozaki's method is the most exact while still using fast tools. Others are faster or simpler, but not always perfect. The King declares: 'All of these knights are useful, depending on the task. But if you want both speed and the exact answer, follow Sir Ozaki's path!' Anyway, you have a range of ideas here about quantum computing, information precision, and acceleration in the years to come. Let me know what you think about what all of these experts have said about the future of AI.
Yahoo
31 minutes ago
- Yahoo
Merck's (MRK) WINREVAIR Shows Strong Results in HYPERION Trial
Merck & Co., Inc. (NYSE:MRK) is one of the 12 stocks that will make you rich in 10 years. On June 23, the company shared encouraging results from its Phase 3 HYPERION trial. The study tested a drug called WINREVAIR (sotatercept-csrk) against a placebo, both combined with standard treatment, in adults recently diagnosed with pulmonary arterial hypertension (PAH)—a serious lung condition. A lab technician in a biopharmaceutical laboratory, surrounded by technology and equipment necessary for advanced research. The trial involved around 320 people from different countries. Most participants were already on two background treatments, unlike earlier trials where most were on three. WINREVAIR showed it could significantly slow down the worsening of PAH compared to the placebo. However, the company didn't release the exact numbers yet, only saying the results were both statistically and medically meaningful. Merck & Co., Inc. (NYSE:MRK) is a global biopharmaceutical company. It discovers, develops, manufactures, and markets prescription medicines, vaccines, biologic therapies, and animal health products. Its key products include Keytruda (cancer), Gardasil (HPV vaccine), Januvia (diabetes), and Bridion (anesthesia reversal). While we acknowledge the potential of MRK as an investment, we believe certain AI stocks offer greater upside potential and carry less downside risk. If you're looking for an extremely undervalued AI stock that also stands to benefit significantly from Trump-era tariffs and the onshoring trend, see our free report on the best short-term AI stock. READ NEXT: and . Disclosure: None. Error in retrieving data Sign in to access your portfolio Error in retrieving data Error in retrieving data Error in retrieving data Error in retrieving data
Fast Company
36 minutes ago
- Fast Company
This accuracy-obsessed weather app does one thing oh so well
Whether weather is always on your radar or merely a passing front of occasional interest, having an on-demand eye on the world around you is one of the most powerful slices of sorcery you can set your sights on today. And this week, I want to introduce you to a worthwhile new weather app I recently encountered that's decidedly different from the others. It isn't meant to replace whatever weather app you're already using, whether that's the one that came preinstalled on your phone or another favorite you've found over time (maybe even one that serves up the forecast with a hilarious side of sass?). Rather, it serves one super-specific purpose—and serves it impressively well. And it might just be worth your while to keep around as a complement to whatever other weather wesource resource you're using. Be the first to find all sorts of little-known tech treasures with my free Cool Tools newsletter from The Intelligence Your personal precipitation station Most weather apps aim to do it all—to be your one-stop spot for every manner of weather info you could possibly wonder about. This week's Cool Tool is more of a specialist. It does just one extremely specific weather-related thing, but good golly, does it do it well. ➜ The app is called Precip. Any guesses about its purpose? If you said it knits you sweaters whilst preparing delightfully steamy stews, (a) excellent guess—but (b) unfortunately, that isn't correct. Precip, as perhaps might've been your next guess following sweaters 'n' stew, measures the rain and other precipitation around you. It isn't offering up a forecast like most weather apps, in other words. It's actually telling you how much moisture came down in any given area—over any time period you want. And it lets you zoom into a narrow area for supremely detailed and precise info, too, even going down as far as to a specific address. There's just one asterisk: Precip's data is limited to the U.S. and Canada, at least for the time being. (Sorry, international pals!) ⌚ If you're in one of those areas, though, it'll take you all of a single minute to start using: For the best and most robust experience, you'll want to snag the mobile app—available on Android as well as on iOS. You can also check rainfall totals by zip code on the Precip website, but you won't be able to zoom in any further to exact locations or to access any of the more advanced data. In the apps, you can search by city, zip code, or street address, and you can see rain totals from the past 12, 24, or 48 hours. You can also save specific locations and then easily track their rain totals over time. Precip offers even more info as a part of its premium subscription, for 20 bucks a year and up. But the app's free version is plenty powerful, and odds are, it'll be all you need. (That version also doesn't even seem to have any ads present, as far as I've seen so far.)
