# Watershed of Artificial Intelligence: Human Intelligence, Machine Intelligence, and Biological Intelligence

**Authors:** Li Weigang, Liriam Enamoto, Denise Leyi Li, Geraldo Pereira Rocha Filho  
**arXiv:** [2104.13155](https://arxiv.org/abs/2104.13155)  
**Date:** 2021-04-27  
**Summary page:** https://www.summarizepaper.com/en/paper/2104.13155v2/

## Key points

- Multi-modal learning in Artificial Intelligence (AI) is a growing trend
- Categorization of AI into Artificial Human Intelligence (AHI), Artificial Machine Intelligence (AMI), and Artificial Biological Intelligence (ABI)
- Different approaches to information input processing: Dimensionality-up or Dimensionality-reduction, one/few or large samples for knowledge learning
- Models like SOM, CNN, and BERT used for classification experiments on English datasets
- Current challenges in AI research with Machine Learning/Deep Learning (ML/DL), emphasizing excessive research waste and neglecting the initial purpose of AI
- Call for a more structured approach towards AI development by categorizing it into distinct branches such as human-like learning, machine learning, and bio-inspired learning

## Summary

This article delves into the trend of multi-modal learning in Artificial Intelligence (AI) and provides a detailed analysis of the current development in the field. It begins by reviewing the "Once learning" mechanism proposed 23 years ago and highlights the successes of "One-shot learning" in image classification and "You Only Look Once - YOLO" in object detection. The article suggests that AI should be categorized into Artificial Human Intelligence (AHI), Artificial Machine Intelligence (AMI), and Artificial Biological Intelligence (ABI) to guide theory and application development. The discussion further extends to a comprehensive analysis of the watershed of AI, outlining specific categories such as human-oriented, machine-oriented, and biological-oriented AI research and development. Different approaches to information input processing, including Dimensionality-up or Dimensionality-reduction, as well as the use of one/few or large samples for knowledge learning are explored. Additionally, the article presents Table 3 summarizing the different aspects of AI categorization, including input dimension, sample size, and knowledge learning methods for AHI, AMI, and ABI. It also touches upon various models like SOM, CNN, and BERT used for classification experiments on English datasets to demonstrate that simple neural network models can achieve desired outcomes with limited resources. Furthermore, the results presented in Table 1 shed light on current challenges in AI research with Machine Learning/Deep Learning (ML/DL), emphasizing excessive research waste and neglecting the initial purpose of AI due to an emphasis on dimensionality reduction. The need for a theoretical system and scientific classification to guide AI development is highlighted along with proposals for strengthening theoretical frameworks through rigorous reflections. In conclusion , there is a call for a more structured approach towards AI development by categorizing it into distinct branches such as human-like learning, machine learning, and bio-inspired learning. This refined approach aims to streamline research efforts and guide future advancements in Artificial Intelligence.