Introducing ChatGPT Team
We’re launching a new ChatGPT plan for teams of all sizes, which provides a secure, collaborative workspace to get the most out of ChatGPT at work.
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Can Large Language Models Understand Context? This AI Paper from Apple and Georgetown University Introduces a Context Understanding Benchmark to Suit the Evaluation of Generative Models
In the ever-evolving landscape of natural language processing (NLP), the quest to bridge the gap between machine interpretation and the nuanced complexity of human language continues to present formidable challenges. Central to this endeavor is the development of large language models (LLMs) capable of parsing and fully understanding the contextual nuances underpinning human communication. This…
Enhancing Low-Level Visual Skills in Language Models: Qualcomm AI Research Proposes the Look, Remember, and Reason (LRR) Multi-Modal Language Model
Current multi-modal language models (LMs) face limitations in performing complex visual reasoning tasks. These tasks, such as compositional action recognition in videos, demand an intricate blend of low-level object motion and interaction analysis with high-level causal and compositional spatiotemporal reasoning. While these models excel in various areas, their effectiveness in tasks requiring detailed attention to…
This AI Paper from China Proposes SGGRL: A Novel Molecular Representation Learning Model based on the Multi-Modals of Molecules for Molecular Property Prediction
Molecular property prediction stands at the forefront of drug discovery and design, which has grown increasingly dependent on advancements in artificial intelligence and machine learning. Traditional methods, while foundational, often need to catch up in their scope, unable to encapsulate the vast and intricate details of molecular characteristics. This gap in capability highlights the need…
AI Weekly Issue #485: When AI teaches AI, it teaches in secret
ByWeb Dev
Watch & Listen First Jensen Huang on Dwarkesh: The $4 Trillion Company · Listen on Spotify -> Huang on TPU competition, why Anthropic drove “100% of TPU growth”, and why Nvidia’s supply-chain moat is harder to copy than any benchmark. The best single piece of context for everything else below. Simon Willison on Lenny’s Podcast…
AI Weekly Issue #493: Meta hired $145B in capex and fired 8,000 people
ByWeb Dev
Six days after we called $725B a bet on what no one wanted, the receipts started landing. Meta committed $145B to AI infrastructure the same week it began firing 8,000 people. Standard Chartered described its own cuts as replacing “lower-value human capital.” Pope Leo XIV announced he’d co-launch his first AI encyclical with Anthropic’s Christopher…
AI News Weekly – 100 years from now : Future lost in transation – Mar 15th 2026
ByWeb Dev
Welcome What happens when we can’t understand the machines anymore? You were a lot to write back last week about the Museum of Human Effort piece, thanks for that feedback and happy to continue the conversation on this 2nd iteration. Alexis This is 100 Years From Now, a weekly series. Once a week, we skip…
