Biological Blueprints for Next Generation AI Systems

TL;DR

This paper reviews how neuroscience insights are being integrated into AI development, highlighting recent collaborations, translating neurobiological understanding into algorithms, and addressing challenges with neuro-inspired solutions.

Contribution

It provides a comprehensive survey of neuroscience contributions to AI, illustrating how biological principles inform new algorithms and architectures.

Findings

Neuroscience insights aid in designing novel AI architectures.

Collaborations enhance understanding of brain-inspired AI models.

Neurobiological research suggests solutions to current AI challenges.

Abstract

Diverse subfields of neuroscience have enriched artificial intelligence for many decades. With recent advances in machine learning and artificial neural networks, many neuroscientists are partnering with AI researchers and machine learning experts to analyze data and construct models. This paper attempts to demonstrate the value of such collaborations by providing examples of how insights derived from neuroscience research are helping to develop new machine learning algorithms and artificial neural network architectures. We survey the relevant neuroscience necessary to appreciate these insights and then describe how we can translate our current understanding of the relevant neurobiology into algorithmic techniques and architectural designs. Finally, we characterize some of the major challenges facing current AI technology and suggest avenues for overcoming these challenges that draw upon research in developmental and comparative cognitive neuroscience.