EE Times published a video interview with AMD executive Mark Papermaster on the future of AI and semiconductor innovation. This is video interview content; the article provides no further technical details or specific figures.
AWS released its Trust in Depth methodology, which lets agents operate safely without per-step human approval through four layers: Scope (limiting capabilities and access), Know (Agent Registry and independent identities), Bound (AgentCore Policy enforced at the Gateway, Guardrails, and the open-source sandbox Strands Box), and Observe (CloudWatch Omni tracking and evaluation).
In a Microsoft Research podcast, partner research manager Jennifer Neville speaks with applied scientist Chad Atalla about how evaluation drives AI systems past performance limits to meet user needs, and about the "unexpected failures" models show when tested beyond standard benchmarks. She shares practical advice on using existing AI systems and stresses the importance of carefully checking data when results don't match expectations.
As AI workloads shift toward inference and agentic AI, system bottlenecks are moving from compute to data movement. The article notes that under processor-centric architectures, fetching data once from DRAM can consume 150 to 2,000 times the energy of a simple arithmetic operation, and over 90% of system energy in large machine learning models may be spent on memory access and data movement. It leads into SK hynix's vision of building a next-generation AI memory ecosystem, arguing that GPUs/NPUs/TPUs, memory, storage and networking should be co-designed as a single system.
GitHub Copilot authors argue that chat is not the best interface for collaborating with AI, introducing the canvas feature in the GitHub Copilot app—a small full-stack app that communicates bidirectionally with agents, can call third-party APIs, and runs code locally.
OpenAI CEO Sam Altman addressed the United Nations Security Council, discussing AI safety, human control, and international cooperation.
OpenAI's Chris Lehane argues that stronger AI capabilities require stronger safety evidence, shared standards, and durable policy action, and calls for action while the policy window remains open.