Tokenization has been the final barrier to truly end-to-end language models. We developed the H-Net: a hierarchical network that replaces tokenization with a dynamic chunking process directly inside the model, automatically discovering and operating over meaningful units of data

There’s a really pronounced divide between people who are on the AI side of things and people who are on the biology side of things. Even among people working in AI, the divide persists based on where they’re focused. The AI side, adherents of scaling laws and the bitter lesson, believe that AI getting better means AI being more capable of novel discoveries in medicine (that go beyond discovering new drugs in well understood targets or speeding up dry lab work etc). The biology people think the idea of a bitter lesson in biology is kind of ridiculous and that regardless of AI’s capabilities it’s going to be at least 5-10 years until you could hope to reliably simulate human toxicology in silico (right now AI/ML can’t even reliably simulate in vivo studies like binding affinities or functional assays). Even the most AI pilled of them, who will list all the areas that AI can reduce the time spent on various areas, don’t think the concept of AI discovering novel targets or generating new understanding of causal biology is realistic in the near term. Most, in fact, will readily characterize the assumption that even an “ASI” level model could find cures hidden in our current understanding (without expanding that understanding with significantly more biological data) as patently false. This was a big part of why we wanted to experience it ourselves by using AI for a (very) small portion of what it can do in biology. It was both mind blowing and revealed some poignant insights into how different the problem of making AI better at something like coding is to the problems of biology. Even if it unlocked the ability to generate new molecules at a stunning pace, its simulation (in silico) of binding only had 49% accuracy. And if we wanted to improve that, we’d need to feed it back the data from the lab - data that took about 30x as long to get as it took to generate the molecules. And these were tests in a test tube (turn of phrase), imagine how long it would take if we wanted to improve the model with data that required recruiting humans and monitoring their reactions. Human biology is very complex, and adding a drug - especially a novel drug and doubly so for a novel drug acting on a novel mechanism - means mapping interactions that have nth-order-effects that we don’t have the data to predict. Virtual cells have made advancements enabled by AI, but the perturbations equate to a small fraction of the possible. This is before you even get into curing disease, especially uniquely human diseases. Brain function involves cellular identities that are unique to humans, meaning animal studies are of little use. How long does it take to understand what the side effects of chronic administration of a drug in humans are? AI can speed up analyzing the data, or designing the trial or even getting it approved…but it can’t make a 6 month trial complete in 3 months. I think its important to point out that significant advancements can be made in AI that contribute to biology, while also being realistic about how much it speeds up something as complex as curing a specific cancer or diseases of the brain.

My theory is biotech is about to turn into a national security thing and the west doesn't really get a vote Beijing just floated blocking foreign buyouts of china's cell and gene therapy and siRNA platforms And what's interesting is what they're NOT blocking. Licensing individual drugs is still totally fine So you can sell the west a medicine, just not the platform that makes the next 100 of them And the west has been buying like crazy lol. Foreign deals for chinese biotech assets are at $120B through august, up 36% Astrazeneca's $1B gracell deal, that CAR-T platform, probably doesn't happen under this draft Same update hits AI tech too btw we spent years fighting over who gets the chips. Feels like the next fight is over who owns the platforms that spit out the next generation of medicines Once biology is programmable it's not really a healthcare business anymore, it's infrastructure Still just a draft tho, comments open till nov 15 bio/acc.

11 years ago today, Apple over-engineered the iPhone 6s so thoroughly that millions of users simply refused to upgrade for the next six years. Panicked by the iPhone 6 "Bendgate" backlash, Apple didn’t just iterate in 2015. They accidentally over-engineered an immortal tank: • 7000-series aerospace alloy that flat-out refused to bend • MacBook-grade NVMe storage that smoked speed benchmarks for years • 3D Touch & the Taptic Engine, embedding 96 capacitive sensors under glass to give flat software actual physical depth • The A9 chip, which held on through 7 full generations of iOS (iOS 9 through iOS 15) Apple sold 13 million units in the opening weekend, and then customers simply refused to upgrade for half a decade. Do you remember the kind of hype Rose Gold color created back then?

New: FBI and Coast Guard investigators have found evidence that hackers accessed the propulsion system of an oil supertanker as it was approaching the Texas coast this summer, US officials familiar with the matter told @business. W/@ruth_on_LNG bloomberg.com/news/articles/…

Introducing Gemini 4 Argon – our new frontier model. It’s built for complex workflows across coding, enterprise knowledge work, and cybersecurity defense – rolling out today to a set of trusted testers through our Fairwind Program.

Green hydrogen currently runs on iridium — one of Earth's rarest metals, a few tonnes mined per year. Our AI-directed lab screened 2,942 catalysts in 3 months and surfaced a palladium-based lead the field had written off, now past 1,000 hours of testing in acid. lila.ai/news/how-an-ai-run-l…