论文标题
为新一代AI深入基础科学的人工智能扎根
To Root Artificial Intelligence Deeply in Basic Science for a New Generation of AI
论文作者
论文摘要
人工智能的野心之一是在基础科学上深入扎根人工智能的同时，开发了脑启发的人工智能平台，这些平台将促进新的科学发现。挑战对于推动人工智能理论和应用技术研究的前进至关重要。本文介绍了未来20年的人工智能研究的巨大挑战，其中包括：〜（i）根据理解脑科学，神经科学，认知科学，心理学和数据科学探索人脑的工作机制； （ii）人脑的电信号如何传播？大脑神经电信号和人类活动之间的协调机制是什么？ （iii）〜要对脑部计算机界面〜（BCI）和脑肌肉界面〜（BMI）技术深入涉及人类行为的科学； （iv）〜对知识驱动的视觉常识推理进行研究〜（VCR），为认知网络识别〜（CNR）开发了新的推理引擎； （v）〜要发展高精度，多模式智能感知； （vi）〜根据知识图（kg）研究智能推理和快速决策系统。我们认为，针对常识性推理，革命性的创新和新型算法和新技术中的新技术以及发展负责的AI的前沿理论创新，知识驱动的建模方法以及未来的主要研究策略应该是AI科学家的主要研究策略。
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.
