Abstract
This article discusses some trends and concepts in developing a 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 affect 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 AGIs 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 cocreating something new and valuable and have metalearning capacities. Multiagent systems such as these can be used to solve problems that would be difficult to solve by any individual intelligent agent.
Highlights
Both human intelligence, as defined by innate, biological intelligence, and artificial intelligence (AI), commonly defined as machine intelligence, have been hot topics in a wide spectrum of scientific literature
Research in Artificial General Intelligence (AGI) may go far beyond a single mental-intellectual or logical-mathematical intelligence, toward the concept of multiple intelligences. We refer to this phenomenon as “AGI with multiple intelligences” as until now most AI methods and approaches are based on one single type of intelligence and perform only one specific task or a set of a few closely related tasks, without fully exploring and implementing sophisticated cognitive skills, emotional-social intelligence (ESI), and responsible group decision-making
Just to mention a few others, the AIVA (Artificial Intelligence Virtual Artist) system has a musical intelligence with the ability to compose original music for films, the Intelligent Atlas robot developed by Boston Dynamics possesses impressive bodilykinesthetic intelligence, and DeepMind’s AlphaGo, which employs a Monte Carlo tree search combined with a reinforcement learning algorithm, possesses sophisticated logical-mathematical intelligence to play, almost perfectly, a complex game Go
Summary
As defined by innate, biological intelligence, and artificial intelligence (AI), commonly defined as machine intelligence, have been hot topics in a wide spectrum of scientific literature (see, e.g., [1,2,3,4,5,6,7,8,9,10,11,12,13,14]). Many AI systems of the future will interact with other AI subsystems (like smart robots or multiagents) alongside human users to solve dynamically changing and complex problems For this type of interaction to take place, multiagent systems must have the ability to continuously learn, review, and evolve their interaction strategies during an ongoing communication. Research in AGI may go far beyond a single mental-intellectual or logical-mathematical intelligence, toward the concept of multiple intelligences We refer to this phenomenon as “AGI with multiple intelligences” as until now most AI methods and approaches are based on one single type of intelligence and perform only one specific task or a set of a few closely related tasks, without fully exploring and implementing sophisticated cognitive skills, emotional-social intelligence (ESI), and responsible group decision-making. Artificial Intelligence (DAI), which is an important subfield of AI research, dedicated to the development of distributed solutions for specific problems (see, e.g., [15, 16, 21, 22])
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