Questions to Guide the Future of Artificial Intelligence Research
It addresses the challenge of bridging disciplinary gaps for advancing AI, but is incremental as it focuses on framing questions rather than new solutions.
The paper argues that artificial intelligence requires integrating insights from machine learning and neuroscience, proposing guiding questions to identify beneficial computational principles from biology amidst its complexity.
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.