Animal-AI 3: What's New & Why You Should Care

The Animal-AI Environment is a unique game-based research platform designed to serve both the artificial intelligence and cognitive science research communities. In this paper, we present Animal-AI 3, the latest version of the environment, outlining several major new features that make the game more engaging for humans and more complex for AI systems. New features include interactive buttons, reward dispensers, and player notifications, as well as an overhaul of the environment's graphics and processing for significant increases in agent training time and quality of the human player experience. We provide detailed guidance on how to build computational and behavioural experiments with Animal-AI 3. We present results from a series of agents, including the state-of-the-art Deep Reinforcement Learning agent (dreamer-v3), on newly designed tests and the Animal-AI Testbed of 900 tasks inspired by research in comparative psychology. Animal-AI 3 is designed to facilitate collaboration between the cognitive sciences and artificial intelligence. This paper serves as a stand-alone document that motivates, describes, and demonstrates Animal-AI 3 for the end user.

Predictable Artificial Intelligence

We introduce the fundamental ideas and challenges of Predictable AI, a nascent research area that explores the ways in which we can anticipate key indicators of present and future AI ecosystems. We argue that achieving predictability is crucial for fostering trust, liability, control, alignment and safety of AI ecosystems, and thus should be prioritised over performance. While distinctive from other areas of technical and non-technical AI research, the questions, hypotheses and challenges relevant to Predictable AI were yet to be clearly described. This paper aims to elucidate them, calls for identifying paths towards AI predictability and outlines the potential impact of this emergent field.

Inferring Capabilities from Task Performance with Bayesian Triangulation

As machine learning models become more general, we need to characterise them in richer, more meaningful ways. We describe a method to infer the cognitive profile of a system from diverse experimental data. To do so, we introduce measurement layouts that model how task-instance features interact with system capabilities to affect performance. These features must be triangulated in complex ways to be able to infer capabilities from non-populational data -- a challenge for traditional psychometric and inferential tools. Using the Bayesian probabilistic programming library PyMC, we infer different cognitive profiles for agents in two scenarios: 68 actual contestants in the AnimalAI Olympics and 30 synthetic agents for O-PIAAGETS, an object permanence battery. We showcase the potential for capability-oriented evaluation.