#softmaxxing

#softmaxxing

PolarQuant is doing most of the compression, but the second step cleans up the rough spots. Google proposes smoothing that out with a technique called Quantized Johnson-Lindenstrauss (QJL).

Since its release in 2021, this repository has become a bedrock in discovery and a first port of call for research projects that try to understand life at the molecular level. But previous iterations of the database lacked predictions of how proteins form complexes, which can be indispensable for their function.

Last November, OpenAI investor Brad Gerstner pressed Sam Altman on a podcast about how a company with $13 billion in revenue could commit to $1.4 trillion in spending. Altman bristled. "If you want to sell your shares, I'll find you a buyer," he said. "Enough." Three months later, OpenAI is aiming to raise $100 billion in its latest funding round - a sign that, even amid mounting questions, Altman can find buyers.

OpenAI's GPT-5.2 Pro does better at solving sophisticated math problems than older versions of the company's top large language model, according to a new study by Epoch AI, a non-profit research institute.

What happens under the hood? How is the search engine able to take that simple query, look for images in the billions, trillions of images that are available online? How is it able to find this one or similar photos from all that? Usually, there is an embedding model that is doing this work behind the hood.

Each of these achievements would have been a remarkable breakthrough on its own. Solving them all with a single technique is like discovering a master key that unlocks every door at once. Why now? Three pieces converged: algorithms, computing power, and massive amounts of data. We can even put faces to them, because behind each element is a person who took a gamble.

What we are beginning to see is that many open-source and other models are becoming compact enough and sufficiently optimized to run very efficiently on consumer GPUs,

Since AlexNet5, deep learning has replaced heuristic hand-crafted features by unifying feature learning with deep neural networks. Later, Transformers6 and GPT-3 (ref. 1) further advanced sequence learning at scale, unifying structured tasks such as natural language processing. However, multimodal learning, spanning modalities such as images, video and text, has remained fragmented, relying on separate diffusion-based generation or compositional vision-language pipelines with many hand-crafted designs.