To Root Artificial Intelligence Deeply in Basic Science for a New Generation of AI

TL;DR

This paper advocates for deeply integrating artificial intelligence with basic sciences like neuroscience and cognitive science to foster revolutionary advances in understanding and developing brain-inspired AI systems over the next 20 years.

Contribution

It outlines grand challenges and strategic research directions for AI, emphasizing foundational science, brain-inspired models, and knowledge-driven reasoning for future breakthroughs.

Findings

Identifies key scientific challenges in understanding brain mechanisms.

Proposes integrating brain-computer interfaces with scientific research.

Highlights the importance of knowledge-driven reasoning and multi-modal perception.

Abstract

One of the ambitions of artificial intelligence is to root artificial intelligence deeply in basic science while developing brain-inspired artificial intelligence platforms that will promote new scientific discoveries. The challenges are essential to push artificial intelligence theory and applied technologies research forward. This paper presents the grand challenges of artificial intelligence research for the next 20 years which include:~(i) to explore the working mechanism of the human brain on the basis of understanding brain science, neuroscience, cognitive science, psychology and data science; (ii) how is the electrical signal transmitted by the human brain? What is the coordination mechanism between brain neural electrical signals and human activities? (iii)~to root brain-computer interface~(BCI) and brain-muscle interface~(BMI) technologies deeply in science on human behaviour; (iv)~making research on knowledge-driven visual commonsense reasoning~(VCR), develop a new inference engine for cognitive network recognition~(CNR); (v)~to develop high-precision, multi-modal intelligent perceptrons; (vi)~investigating intelligent reasoning and fast decision-making systems based on knowledge graph~(KG). We believe that the frontier theory innovation of AI, knowledge-driven modeling methodologies for commonsense reasoning, revolutionary innovation and breakthroughs of the novel algorithms and new technologies in AI, and developing responsible AI should be the main research strategies of AI scientists in the future.