192,987 research outputs found
Learning Universal Predictors
Meta-learning has emerged as a powerful approach to train neural networks to
learn new tasks quickly from limited data. Broad exposure to different tasks
leads to versatile representations enabling general problem solving. But, what
are the limits of meta-learning? In this work, we explore the potential of
amortizing the most powerful universal predictor, namely Solomonoff Induction
(SI), into neural networks via leveraging meta-learning to its limits. We use
Universal Turing Machines (UTMs) to generate training data used to expose
networks to a broad range of patterns. We provide theoretical analysis of the
UTM data generation processes and meta-training protocols. We conduct
comprehensive experiments with neural architectures (e.g. LSTMs, Transformers)
and algorithmic data generators of varying complexity and universality. Our
results suggest that UTM data is a valuable resource for meta-learning, and
that it can be used to train neural networks capable of learning universal
prediction strategies.Comment: 32 pages, 11 figure
Universal Intelligence: A Definition of Machine Intelligence
A fundamental problem in artificial intelligence is that nobody really knows
what intelligence is. The problem is especially acute when we need to consider
artificial systems which are significantly different to humans. In this paper
we approach this problem in the following way: We take a number of well known
informal definitions of human intelligence that have been given by experts, and
extract their essential features. These are then mathematically formalised to
produce a general measure of intelligence for arbitrary machines. We believe
that this equation formally captures the concept of machine intelligence in the
broadest reasonable sense. We then show how this formal definition is related
to the theory of universal optimal learning agents. Finally, we survey the many
other tests and definitions of intelligence that have been proposed for
machines.Comment: 50 gentle page
- …