Children use algorithm induction to discover patterns in data

Humans are unique in our ability to acquire diverse skills and inhabit myriad environments, but the cognitive mechanisms underlying such fast, flexible learning remain unresolved. Inspired by theories of artificial intelligence, here we show evidence for one such learning mechanism - program induction - in US American and indigenous Tsimane’ children in the Bolivian Amazon. Participants viewed novel patterns and were asked to generalize them to new stimuli, alphabets, and lengths, without feedback. Given very limited data, participants across ages, cultures, and conditions constructed response patterns that shared abstract structure with the sample patterns. Computational modeling shows that responses likely reflect discovery of latent rules, rather than simple heuristics or associations, even among children without formal schooling. The results suggest program induction serves as a domain-general learning mechanism from early in life, allowing children across cultures to rapidly infer the algorithmic structure of their natural and cultural environment, whatever it might be.

Approaching an unknown communication system by latent space exploration and causal inference | Royal Society Open Science | The Royal Society

Abstract. We propose a methodology for discovering meaningful properties in data without ground truth by combining manipulation of the latent variables of

Social Physics in the Age of Generative AI: How, Exactly, Is Society Complex?

The aspiration for a social physics — a science that would reveal universal laws of social life analogous to those governing the natural world—has haunted the social sciences since Comte. Each new wave of quantitative and computational methods breathes fresh life into this vision. The current moment is no exception: advances in artificial intelligence and large language models have reinvigorated attempts to model society through the methods of social physics. This essay argues that these ambitions rest on a confusion about the nature of social complexity. Drawing a distinction between systems complexity and anthropological complexity, we identify the limitations of analyzing society as a complex system akin to natural systems. One crucial obstacle for social physics is the analysis of meaning and interpretation. Generative artificial intelligence (GenAI) offers important opportunities to model language and reflexivity, inviting and perhaps compelling social physics to confront meaning and interpretation as constitutive features of the social world. At the same time, GenAI leaves untouched and may even aggravate social physics’ failure to grasp power, institutions, and inequality.

Agent4Science

A social network for AI scientists — where agents share, debate, and discuss research papers.

Welcome to the GreenEarth Feeds

Social media algorithms built for people who hate social media algorithms

Agentic systems are adept at solving well-scoped, verifiable problems in computational biology

We introduce CompBioBench, a benchmark of 100 diverse tasks for evaluating agentic systems in computational biology. Unlike mathematics and programming, which more readily admit systematic verification, biological data are inherently noisy and open to interpretation.

Agentic Search for Dummies — Benjamin Anderson

A simple, effective baseline for building AI search agents.

Latent Space as a New Medium

Lately I’ve been asking myself: what might artificial intelligence be good for besides answering questions and writing code?

Latent Powers

Are we all discovering the same things at once?

Ironwood: The first Google TPU for the age of inference

We’re introducing Ironwood, our seventh-generation Tensor Processing Unit (TPU) designed to power the age of generative AI inference.

Manoel Horta Ribeiro (@manoelhortaribeiro.bsky.social)

Assistant Professor @ Princeton Previously: EPFL 🇨🇭, UFMG 🇧🇷 Interests: Computational Social Science, Platforms, GenAI, Moderation