Future Trends for Human-AI Collaboration: A Comprehensive Taxonomy of AI/AGI Using Multiple Intelligences and Learning Styles

TL;DR

This paper explores future directions for AGI development by categorizing AI systems based on multiple human intelligences and learning styles, emphasizing social, emotional, and ethical facets for more collaborative and meta-learning capabilities.

Contribution

It introduces a comprehensive taxonomy of AGI using multiple intelligences, highlighting new cognitive capacities and collaborative potentials for future AI systems.

Findings

AGI can be categorized by diverse human-like intelligences.

Future AI will enable cooperative knowledge exchange.

Multi-agent systems can solve complex problems collaboratively.

Abstract

This article discusses some trends and concepts in developing new generation of future Artificial General Intelligence (AGI) systems which relate to complex facets and different types of human intelligence, especially social, emotional, attentional and ethical intelligence. We describe various aspects of multiple human intelligences and learning styles, which may impact on a variety of AI problem domains. Using the concept of 'multiple intelligences' rather than a single type of intelligence, we categorize and provide working definitions of various AGI depending on their cognitive skills or capacities. Future AI systems will be able not only to communicate with human users and each other, but also to efficiently exchange knowledge and wisdom with abilities of cooperation, collaboration and even co-creating something new and valuable and have meta-learning capacities. Multi-agent systems such as these can be used to solve problems that would be difficult to solve by any individual intelligent agent. Key words: Artificial General Intelligence (AGI), multiple intelligences, learning styles, physical intelligence, emotional intelligence, social intelligence, attentional intelligence, moral-ethical intelligence, responsible decision making, creative-innovative intelligence, cognitive functions, meta-learning of AI systems.