#llm-compression

#llm-compression

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).

By neoclouds, I'm referring to GPU-centric, purpose-built cloud services that focus primarily on AI training and inference rather than on the sprawling catalog of general-purpose services that hyperscalers offer. In many cases, these platforms deliver better price-performance for AI workloads because they're engineered for specific goals: keeping expensive accelerators highly utilized, minimizing platform overhead, and providing a clean path from model development to deployment.

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.

"To enable the next generation of foundation models, we must solve the problem of continual learning: enabling AI systems to keep learning and improving over time, similar to how humans accumulate knowledge and refine skills throughout their lives," the researchers noted. Reinforcement learning offers a way to train on data generated by the model's own policy, which reduces forgetting. However, it typically requires explicit reward functions, which are not easy in every situation.

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.

A major difference between LLMs and LTMs is the type of data they're able to synthesize and use. LLMs use unstructured data-think text, social media posts, emails, etc. LTMs, on the other hand, can extract information or insights from structured data, which could be contained in tables, for instance. Since many enterprises rely on structured data, often contained in spreadsheets, to run their operations, LTMs could have an immediate use case for many organizations.