AI

Are you worried your AI chatbot is trying to build a bomb or leak personal information about you? There’s a website for that.

As enterprises deploy AI agents across teams, vendors, and infrastructure, managing agent-to-agent communication becomes a growing operational burden. Without a centralized layer, each new agent integration adds point-to-point connections, separate credentials, and custom routing logic. Teams spend engineering cycles wiring up connectivity instead of building agent capabilities. Access control becomes fragmented, with no single place

Let’s say your customer support agent asks for “billing issues”, and gets back technical support tickets, sales conversations with receipt issues, and billing disputes all mixed. This is the retrieval precision wall that teams hit once their agents accumulate weeks of interaction history: similarity search finds everything that’s semantically close for this customer but does

Large language models (LLMs) have transformed how we process and generate information, but they still struggle with effectively integrating knowledge across multiple sources. Standard Retrieval Augmented Generation (RAG) methods, although helpful, often fall short when tackling multi-hop reasoning tasks that require connecting information from separate documents. To address these limitations, we explore HippoRAG, a novel

This post is co-written with Conor Burke, CTO and Co-Founder at Inscribe Fraud now appears in 1 of every 16 documents, and AI-generated forgeries grew 5x from April to December 2025 (Inscribe’s 2026 State of Document Fraud Report). For financial institutions processing thousands of applications daily, this scale of deception creates an impossible challenge. Traditional

Generative AI adoption is accelerating across industries, and Amazon Bedrock provides a managed service for building production-ready AI applications. With access to more than 100 foundation models from providers such as Anthropic, OpenAI, Meta, Mistral AI, Cohere, and Amazon, teams have the flexibility to choose the right model for each use case. But choice comes

BoltzGen on Amazon SageMaker AI accelerates protein binder design by managing GPU compute infrastructure end to end. BoltzGen is a diffusion-based generative model that designs proteins and peptides capable of binding to specific biomolecular targets. A typical design campaign involves multiple GPU-intensive steps: backbone generation, inverse folding, structural validation, and candidate ranking. Running these steps

The government has removed restrictions on Anthropic’s Fable 5 and Mythos 5 AI models—but there were strings attached.

A researcher found that using Anthropic’s Claude Opus 4.7, he could break into the website of Front Gate—used by every festival from Lollapalooza to Bonnaroo—and freely issue any ticket he chose.

Congratulations to the Berkeley Artificial Intelligence Research (BAIR) Lab class of 2026! This year, BAIR celebrates another remarkable group of Ph.D. graduates whose curiosity, creativity, and perseverance have pushed the frontiers of artificial intelligence and machine learning. Their work spans the breadth of modern AI — robotics and embodied intelligence, large language models and reasoning,