Help Me Grow: Artificial Cognitive Development via Human-Agent Interactions Supported by New Interactive, Intrinsically Motivated Program Synthesis Methods.

Projektbeschreibung

Neue Wege, über künstliche kognitive Systeme zu denken

Der künstlichen Intelligenz (KI) gehört die Zukunft. Maschinen, die mit der Welt interagieren und sich Fähigkeiten aneignen können, sind ein ehrgeiziges Ziel in der KI. Doch diese Zukunft hängt davon ab, wie gut wir autonome Agenten bauen und ob wir sie auf Problemlösung programmieren können. In diesem Zusammenhang wird das über die Marie-Skłodowska-Curie-Maßnahmen finanzierte Projekt INTERACT künstliche kognitive Systeme erforschen, um die Grenzen aktueller KI-Systeme zu verstehen. Dabei wird es insbesondere auf diesen drei Methoden aufbauen: der bayesschen Programminduktion, dem intrinsisch motivierten Lernen und linguistischen Mensch-Maschine-Interaktionen. Es sollen neue Forschungswege für interpretierbare, leistungsfähige und adaptive KI-Systeme aufgezeigt werden. Die Arbeit wird wissenschaftliche (Ansätze zur interaktiven Programmsynthese), gesellschaftliche (demokratisiertes KI-Training) und wirtschaftliche (adaptive KI-Assistenten) Auswirkungen haben.

Ziel

Building machines that interact with their world, discover interesting interactions and learn open-ended repertoires of skills is a long-standing goal in AI. This project aims at tackling the limits of current AI systems by building on three families of methods: Bayesian program induction, intrinsically motivated learning and human-machine linguistic interactions. It targets three objectives: 1) building autonomous agents that learn to generate programs to solve problems with occasional human guidance; 2) studying linguistic interactions between humans and machines via web-based experiments (e.g. properties of human guidance, its impact on learning, human subjective evaluations); and 3) scaling the approach to the generation of constructions in Minecraft, guided by real players. The researcher will collaborate with scientific pioneers and experts in the key fields and methods supporting the project. This includes supervisors Joshua Tenenbaum (program synthesis, MIT) and Pierre-Yves Oudeyer (autonomous learning, Inria); diverse collaborators, and an advisory board composed of an entrepreneur and leading scientists in developmental psychology and human-robot interactions. The 3rd objective will be pursued via a secondment with Thomas Wolf (CSO) at HuggingFace, a world-leading company in the open source development of natural language processing methods and their transfer to the industry. By enabling users to participate in the training of artificial agents, the project aims to open research avenues for more interpretable, performant and adaptive AI systems. This will result in scientific (e.g. interactive program synthesis approaches), societal (e.g. democratized AI training) and economic impacts (e.g. adaptive AI assistants). The dissemination, communication and exploitation plans support these objectives by targeting scientific (AI, cognitive science), industrial (video games, smart homes) and larger communities (gamers, software engineers, large public).