Topic: causality

This Perimeter Institute conference brings together researchers studying quantum reference frames, quantum gravity, quantum foundations and related experiments. Topics include relational observables, quantum information in dynamical spacetime, locality, causality, entanglement, and the operational meaning of observers and subsystems. The meeting runs from 26 to 30 October 2026 in Waterloo, Canada. The organizer provides information for virtual participation.

Daphne Koller of insitro discusses the differences between finding predictive patterns and identifying interventions that improve disease outcomes. The conversation examines biological data scarcity, structure and causality, failures in drug development and the limits of speeding up wet-lab work with AI agents. It also considers foundation models for biology, generalization beyond observed data, human evidence and scientific discovery. Hosted by Ravid Shwartz Ziv and Allen Roush. Watch the full conversation on the publisher’s YouTube channel.

In autonomous navigation, an agent's knowledge of its environment is represented as sensory inputs (states) linked together by actions. A popular approach for acquiring this knowledge is reinforcement learning (RL), where learning is stereotyped, i.e. an agent learns to react to a state with the action associated with maximum reward. Model training requires many trials and occurs offline, where the agent aims to learn generalisable reflexes rather than to reason about its surroundings actively. Thus, RL agents are inefficient to train and can exhibit inflexible behaviour. Indeed, behavioural f