Questions to Guide the Future of Artificial Intelligence Research

TL;DR

This paper proposes guiding questions for future AI research, emphasizing the integration of insights from neuroscience and machine learning to identify fundamental computational principles amid biological complexity.

Contribution

It introduces a set of guiding questions to steer AI research towards combining algorithmic and observational insights from neuroscience and machine learning.

Findings

Highlighting the importance of interdisciplinary approaches

Identifying the need for computational principles in brain function

Suggesting that biological constraints can inform AI development

Abstract

The field of machine learning has focused, primarily, on discretized sub-problems (i.e. vision, speech, natural language) of intelligence. While neuroscience tends to be observation heavy, providing few guiding theories. It is unlikely that artificial intelligence will emerge through only one of these disciplines. Instead, it is likely to be some amalgamation of their algorithmic and observational findings. As a result, there are a number of problems that should be addressed in order to select the beneficial aspects of both fields. In this article, we propose leading questions to guide the future of artificial intelligence research. There are clear computational principles on which the brain operates. The problem is finding these computational needles in a haystack of biological complexity. Biology has clear constraints but by not using it as a guide we are constraining ourselves.