AI

Professor Jesse Thaler has been named director of the MIT Laboratory for Nuclear Science (LNS), effective Aug. 1. He succeeds Professor Bolek Wyslouch, who directed LNS for the past decade. Thaler is a theoretical particle physicist who combines techniques from quantum field theory and machine learning to address outstanding questions in fundamental physics.  “In his […]

You can clip a cover over the cameras, which could be a double-edged sword.

Norwegian striker Erling Haaland isn’t just a footballer anymore. He’s become an internet character perpetuated by fans and AI.

The lab will beam back data to train AI models to predict how proteins behind age-related diseases like Alzheimer’s and certain cancers behave.

Today, we’re excited to announce a deep-link integration between Hugging Face and Amazon SageMaker AI. Developers can now go from model discovery to hands-on experimentation in SageMaker Studio with a single selection. Whether you fine-tune a foundation model (FM) from Amazon SageMaker JumpStart or deploy it to an Amazon SageMaker Inference endpoint, you can now

Organizations deploying foundation models (FMs) often encounter a common challenge: model safeguards designed for content moderation can also prevent legitimate, business-critical use cases. A media company summarizing scripts with mature language, a cyber security firm simulating real-world threats, or a legal team processing sensitive evidence may all find that default content moderation controls deflect the

As advanced medical technology gets closer to hitting consumer markets, the need for guardrails on protected usage should increase. What might begin as a neural implant to aid in communication could become a device used to police one’s innermost thoughts. Intrigued by the far-reaching benefits and risks of neural implants, Rachel Sava, a PhD candidate

Organizations are increasingly adopting open-weight foundation models (FMs) to power production AI workloads, from agentic coding assistants to long-context document analysis. As these workloads move from experimentation to enterprise deployment, two requirements shape every model selection decision: the model must deliver the capabilities the workload demands, and the inference environment must support the organization’s security

When you build enterprise agents that execute multi-step workflows, you face a fundamental training challenge. These agents query databases, call APIs, cross-reference results, and recover from mid-process failures. The quality of any single action depends on what happens several steps later. Standard reinforcement learning from human feedback (RLHF) optimizes single responses in isolation. This approach

Sharing data internally across teams, externally with partners, or using it for workloads such as machine learning (ML) model training is fundamental to modern business operations. However, when that data contains Personally Identifiable Information (PII), organizations face significant legal and compliance obligations under regulations such as the General Data Protection Regulation (GDPR) and the Payment