Watershed of Artificial Intelligence: Human Intelligence, Machine Intelligence, and Biological Intelligence
It addresses the need for clearer theoretical categorization in AI research, but is incremental as it builds on existing concepts without introducing new methods or data.
This article proposes a new classification framework for AI, dividing it into Artificial Human Intelligence (AHI), Artificial Machine Intelligence (AMI), and Artificial Biological Intelligence (ABI), based on a review of historical mechanisms like 'Once learning' and modern successes such as one-shot learning and YOLO.
This article reviews the "Once learning" mechanism that was proposed 23 years ago and the subsequent successes of "One-shot learning" in image classification and "You Only Look Once - YOLO" in objective detection. Analyzing the current development of Artificial Intelligence (AI), the proposal is that AI should be clearly divided into the following categories: Artificial Human Intelligence (AHI), Artificial Machine Intelligence (AMI), and Artificial Biological Intelligence (ABI), which will also be the main directions of theory and application development for AI. As a watershed for the branches of AI, some classification standards and methods are discussed: 1) Human-oriented, machine-oriented, and biological-oriented AI R&D; 2) Information input processed by Dimensionality-up or Dimensionality-reduction; 3) The use of one/few or large samples for knowledge learning.