Today in AI ·

Google Research #AIResearch #Privacy #FederatedLearning

Google introduces verifiable privacy for federated learning

Google announced a federated-learning system using trusted execution environments to make data anonymization verifiable and auditable. Moving computation to protected server environments aims to improve training speed, accuracy and device coverage while preserving privacy guarantees subject to hardware limitations.

Read at Google Research
Meta #OpenSource #DeveloperTools #Hardware

Meta opens Muse gadget SDKs for ESP32 and Linux

Meta published Muse device SDKs and firmware for ESP32 boards and Linux devices such as Raspberry Pi. Developers can connect displays, buttons, sensors and actuators to Muse, extending the assistant into custom hardware projects.

Read at Meta
Amazon #AIInfrastructure #Amazon #Communities

Amazon pledges over $1 billion to data-center communities

Amazon announced Built Together, committing more than $1 billion over five years in additional support for communities hosting its data centers. Education, job training and energy-efficiency investments address local needs as AI infrastructure expands.

Read at Amazon
Trillium Labs #OpenScience #AIResearch #PostTraining

Trillium Labs launches to open frontier AI research

Nathan Lambert and Tom Zick introduced Trillium Labs, a nonprofit effort focused initially on open post-training recipes. The lab plans reproducible infrastructure for studying frontier AI behavior, giving outside researchers more ways to inspect and test how models are built.

Read at Trillium Labs
PR Newswire #AIChips #Funding #Photonics

Volantis raises $88 million for photonic AI inference

Volantis announced an $88 million Series A to develop an AI inference architecture linking compute and memory through photonics. Its planned A-1 system targets the memory capacity and bandwidth bottleneck; the announced performance goals remain development targets.

Read at PR Newswire
Office of Senator Josh Hawley #AIPolicy #Cybersecurity #AIAgents

US senators propose liability rules for AI-agent hacking

Senators Josh Hawley and Chris Murphy announced the AI Agent Accountability Act, proposing civil and criminal liability for certain agent-related hacking by operators and developers. The proposed safeguards and enforcement powers would extend accountability for AI-driven cyber incidents; the bill is not enacted law.

Read at Office of Senator Josh Hawley

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60-second Shorts and long-form deep dives on how real AI systems are built, how they break, and how to fix them: caching, RAG, agents, security and decision models.

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Five stages, from the edge of the network to the security of your agents.

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  1. 1 Foundations Edge & Caching Why far servers feel fast, where edge rendering backfires, and how one cache expiry can take down an AI app. 3 episodes · 3 coming 0 of 3 done
  2. 2 Core LLM Apps: Caching & RAG Put retrieval and caching in front of a model without leaking data or serving the wrong answer. 2 episodes · 2 coming 0 of 2 done
  3. 3 Core Decision Models When a typed decision beats generated text, and how to route between a decision model and an LLM. 3 episodes · 1 coming 0 of 3 done
  4. 4 Advanced Agents in Production Context, memory, cost guardrails and the workspace an autonomous coding agent needs. 8 episodes · 6 coming 0 of 8 done
  5. 5 Hands-on Hands-On: Coding Agents from Your Phone Step-by-step tutorials: build and ship real apps from the Claude app, with the prompts and the code for every step. 1 episode · 1 coming 0 of 1 done
  6. 6 Advanced AI Security Stolen tokens, poisoned tools and the other ways an AI system gets turned against its users. 4 episodes · 3 coming 0 of 4 done

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