We’ve developed sophisticated safety and security measures to prevent the misuse of our AI models. But cybercriminals and other malicious actors are actively attempting to find ways around them. Today, we’re releasing a report that details how.
Our Threat Intelligence report discusses several recent examples of Claude being misused, including a large-scale extortion operation using Claude Code, a fraudulent employment scheme from North Korea, and the sale of AI-generated ransomware by a cybercriminal with only basic coding skills. We also cover the steps we’ve taken to detect and counter these abuses. — Read More
Daily Archives: September 4, 2025
Open Global Investment as a Governance Model for AGI
This paper introduces the “open global investment” (OGI) model, a proposed governance framework for artificial general intelligence (AGI) development. The core idea is that AGI development could proceed within one or more corporations in a context that (a) encourages wide international shareholding, (b) reduces the risk of expropriation, (c) implements strengthened corporate governance processes, (d) operates within a government-defined framework for responsible AI development (and/or a public-private partnership), and (e) includes additional international agreements and governance measures to whatever extent is desirable and feasible. We argue that this model, while very imperfect, offers advantages in terms of inclusiveness, incentive compatibility, and practicality compared to prominent alternatives—such as proposals modelled on the Manhattan project, CERN, or Intelsat—especially in scenarios with short AGI timelines. — Read More
The Landscape of Agentic Reinforcement Learning for LLMs: A Survey
The emergence of agentic reinforcement learning (Agentic RL) marks a paradigm shift from conventional reinforcement learning applied to large language models (LLM RL), reframing LLMs from passive sequence generators into autonomous, decision-making agents embedded in complex, dynamic worlds. This survey formalizes this conceptual shift by contrasting the degenerate single-step Markov Decision Processes (MDPs) of LLM-RL with the temporally extended, partially observable Markov decision processes (POMDPs) that define Agentic RL. Building on this foundation, we propose a comprehensive twofold taxonomy: one organized around core agentic capabilities, including planning, tool use, memory, reasoning, self-improvement, and perception, and the other around their applications across diverse task domains. Central to our thesis is that reinforcement learning serves as the critical mechanism for transforming these capabilities from static, heuristic modules into adaptive, robust agentic behavior. To support and accelerate future research, we consolidate the landscape of open-source environments, benchmarks, and frameworks into a practical compendium. By synthesizing over five hundred recent works, this survey charts the contours of this rapidly evolving field and highlights the opportunities and challenges that will shape the development of scalable, general-purpose AI agents. — Read More
GitHub Repo
In a first, scientists map complete brain activity during decision-making
Mice moving tiny steering wheels to control shapes on a screen have given scientists an unprecedented view of how decisions unfold across the brain.
For the first time, researchers have mapped decision-making at single-cell resolution across an entire mammalian brain. — Read More
Read the Paper
Meta’s Data Scientist’s Framework for Navigating Product Strategy as Data Leaders
One question that I often get is what makes Product Data Scientist special at Meta. My answer has always been “You are by default a product leader, navigating product directions with data”. This is true across all levels, from new grads to directors. Data scientists at Meta don’t just analyze data — they transform business questions into data-driven product visions that help building better human connections.
The challenge? Product strategy development exists across a spectrum of conditions. Here I’ll explore how data scientists at Meta can drive product strategies across four distinct scenarios defined by data availability (low to high) and problem clarity (broad to concrete). — Read More
Rick's Cafe AI 