Video world models, which predict future frames conditioned on actions, hold immense promise for artificial intelligence, enabling agents to plan and reason in dynamic environments. Recent ...

A newly released 14-page technical paper from the team behind DeepSeek-V3, with DeepSeek CEO Wenfeng Liang as a co-author, sheds light on the “Scaling Challenges and Reflections on Hardware for AI ...

DeepSeek AI has announced the release of DeepSeek-Prover-V2, a groundbreaking open-source large language model specifically designed for formal theorem proving within the Lean 4 environment. This ...

The quality and fluency of AI bots’ natural language generation are unquestionable, but how well can such agents mimic other human behaviours? Researchers and practitioners have long considered the ...

A pair of groundbreaking research initiatives from Meta AI in late 2024 is challenging the fundamental “next-token prediction” paradigm that underpins most of today’s large language models (LLMs). The ...

The recent Nobel Prize for groundbreaking advancements in protein discovery underscores the transformative potential of foundation models (FMs) in exploring vast combinatorial spaces. These models are ...

Recent advancements in training large multimodal models have been driven by efforts to eliminate modeling constraints and unify architectures across domains. Despite these strides, many existing ...

Researchers from Google DeepMind introduce the concept of "Socratic learning." This refers to a form of recursive self-improvement in artificial intelligence that significantly enhances performance ...

Days after Facebook F8 wrapped up in San Jose, the Microsoft Developer Conference Build 2018 opened in Seattle. The conference runs May 7-9 at the Washington Convention Center, and is an opportunity ...

Large language models (LLMs) have proven to be versatile tools across various domains, including natural language processing, biological studies, and chemical research. In a new paper Autonomous ...

In recent developments within the realm of large-scale models, a tantalizing prospect has emerged: the potential to enhance pre-training efficacy by scaling up data, model size, and training duration.