Nando de Freitas

somebody made a huggingface model visualizer!! just plug in the url and explore at any granularity

Ilya Sutskever just told the AI industry why scaling is finished. One word built it. One word is about to break it. Sutskever: “Scaling is just one word, but it’s such a powerful word because it informs people what to do.” For five years, that single word replaced an entire research culture. Nobody needed breakthroughs. They needed bigger checks. Sutskever: “If you mix some compute with some data into a neural net of a certain size, you will get results, and you will know that it will be better if you just scale the recipe up.” That’s not science. That’s a recipe. Sutskever: “Companies love this because it gives you a very low risk way of investing your resources.” The most transformative technology in human history ran on the same logic used to franchise a restaurant chain. More locations. More ingredients. Same recipe. Predictable returns. You didn’t need researchers who could see around corners. You needed accountants who could approve purchase orders. But recipes expire. Sutskever: “At some point though, pre-training will run out of data. The data is very clearly finite.” Five years of infrastructure. Five years of hiring. Five years of investor decks. All built on top of something temporary. Sutskever: “I don’t think that’s true.” The co-founder of OpenAI. The mind behind the breakthroughs that made this entire era possible. Saying more money won’t solve it. Sutskever: “In some sense we are back to the age of research.” Most of the companies racing to build AGI were never research companies. They were scaling companies. They hired for execution. Not discovery. They optimized for throughput. Not insight. The talent pipelines. The investor pitches. The board decks. All built around one assumption. That the recipe would never expire. It’s expiring. And the companies that spent five years perfecting the art of spending money are about to discover something. The next era demands what capital can’t purchase. An original idea.

Update on Erdős Problem 1196: In joint work, we refined and adapted the proof method from GPT-5.4 Pro to give proofs of several additional problems. This includes another 60 year old conjecture by Erdős, Sárközy, and Szemerédi. A proof is valued not just by the problem it solves, but by what new avenues it opens up. This is perhaps one of the first examples of an AI-generated proof having downstream impacts, which we are still exploring. We are announcing the result today at the Future of Mathematics Symposium (see links below)

BREAKING: Sequoia and Lightspeed co-lead Europe's largest seed funding round with $1.1B at $5.1B post for ex-Deepmind David Silver's Ineffable Intelligence. David is committing to giving away 100% of the money he makes from his Ineffable equity via Founders Pledge - the biggest pledge in their history and it is likely to amount to multiple billions.

GPT 2 is totally insane… 🙀⚡️ I asked for a prehistoric predator and it built an entire museum around it. This is not just an image. It feels like discovering history.🤯 Prompt Drop ⤵️

AI tutorial. Self-training can be made possible by inverting models (input becomes output) and alternating. For example, want to teach your vision model to count? Easy, take advantage of coding models and image editing to create a dataset for counting! Step 1. Use a coding model to generate a diagram. Step 2. Convert the diagram to a real image. I’ve illustrated this with one example. Any of the models out there can be used to achieve this. This is well known. An important ingredient to train good image gen models is to have a VLM that is good at creating good descriptive captions. This in turn can be used to train better VLM models.