Nature

Towards provably efficient quantum algorithms for large-scale machine-learning models

We corroborate the intuition developed here by a number of theorems, as well as extensive numerical experiments. The formal definition of dissipation, sparsity, and quantum speedups are rigorously ...
Nature

Deep learning large-scale drug discovery and repurposing

Large-scale drug discovery and repurposing is challenging. Identifying the mechanism of action (MOA) is crucial, yet current approaches are costly and low-throughput. Here we present an approach for ...
Wired

This AI Model Never Stops Learning

Modern large language models (LLMs) might write beautiful sonnets and elegant code, but they lack even a rudimentary ability to learn from experience. Researchers at Massachusetts Institute of ...
Psychology Today

Rethinking Learning Theory—the Value of LLMs

Effective learning isn't just about finding the easiest path—it's about the right kind of challenge. Two prominent theories—Desirable Difficulties (DDF) and Cognitive Load Theory (CLT)—offer valuable ...
Geeky Gadgets

AI transfer learning from large language models explained

Transfer learning has emerged as a pivotal strategy, particularly in the realm of large language models (LLMs). But what exactly is this concept, and how does it revolutionize the way AI systems learn ...