Laboratory of Evolutionary Design

AI That Evolves in the Wild | Edge.org

I’m interested not in domesticated AI—the stuff that people are trying to sell. I'm interested in wild AI—AI that evolves in the wild. I’m a naturalist, so that’s the interesting thing to me. Thirty-four years ago there was a meeting just like this in which Stanislaw Ulam said to everybody in the room—they’re all mathematicians—"What makes you so sure that mathematical logic corresponds to the way we think?" It’s a higher-level symptom. It’s not how the brain works. All those guys knew fully well that the brain was not fundamentally logical.

AI agents team up in Agent Laboratory to speed scientific research

Johns Hopkins University and AMD have developed Agent Laboratory, a new open-source framework that pairs human creativity with AI-powered workflows.

AI: A Guide for Thinking Humans | Melanie Mitchell | Substack

I write about interesting new developments in AI. Click to read AI: A Guide for Thinking Humans, by Melanie Mitchell, a Substack publication with tens of thousands of subscribers.

On AI as model organism for human learning

I've a pretty taste for paradox

Import AI 455: AI systems are about to start building themselves.

The first step towards recursive self improvement

The ecology of AI risk

Understanding the risk from applications of artificial intelligence (AI) is a critical part of creating AI governance strategies. Building on the idea of studying AI using ecological and evolutionary perspectives, we propose a novel approach for assessing risk from AI using indicators derived from theoretical ecology models. We illustrate our methods by deriving 3 indicators from population and ecosystem models originating from theoretical ecology. We conclude with a discussion of limitations of our analysis and considerations for improving AI governance policy.

Togelius: Please, don't automate science!

I was at an event on AI for science yesterday, a panel discussion here at NeurIPS. The panelists discussed how they plan to replace humans a...

Meet Foundry: An AI Startup that Builds, Evaluates, and Improves AI Agents

Accelerating Science with Human+AI Review

This issue of NEJM AI features the first two articles published through our accelerated human+AI review process. In this editorial, we describe the invitation-only “Fast Track” process used to revi...

Introducing LM Studio Bionic: the AI agent for open models

The AI agent made for open models, built to get things done.

Jenny Zhang on Twitter / X

Introducing Hyperagents: an AI system that not only improves at solving tasks, but also improves how it improves itself.The Darwin Gödel Machine (DGM) demonstrated that open-ended self-improvement is possible by iteratively generating and evaluating improved agents, yet it… pic.twitter.com/YJPFTJ51SO— Jenny Zhang (@jennyzhangzt) March 23, 2026

AI should help us produce better code - Agentic Engineering Patterns

AI should help us produce better code - Agentic Engineering Patterns

The Umwelt Representation Hypothesis: rethinking Universality

Recent studies reveal striking representational alignment between artificial neural networks (ANNs) and biological brains, leading to proposals that all sufficiently capable systems converge on universal representations of reality. We argue that this claim of Universality is premature. We introduce the Umwelt Representation Hypothesis, which proposes that alignment arises not from convergence toward a single global optimum but from overlap in the ecological constraints under which systems develop. We review empirical evidence showing that representational differences between species, individuals, and ANNs are systematic and adaptive, which is difficult to reconcile with Universality. Finally, we reframe ANN model comparison as a method for mapping clusters of alignment in the ecological constraint space rather than as a search for a single optimal world model.

wharton-generative-ai-labs/AIBO

An open-source tool for running controlled behavioral experiments on AI systems at scale.