In order to develop better AI technologies, we first need to understand what “intelligence” is, say Dagmar Monett and Colin W.P Lewis of the AGI Sentinel Initiative
Understanding intelligence is one of the major scientific challenges of our time; however, the science of intelligence is very much in its infancy. By working closely with scientists and leading thinkers from multiple disciplines, we can start to help humanity to better understand intelligence.
If human beings have a better understanding of intelligence, it will not only help to continue to develop artificial intelligent machines, but it will also help to improve individuals’ situational awareness, decision making, and values, and ultimately greatly improve people’s knowledge of each other and our world, and thereby improve the quality of life for society overall.
An important distinction should be made, however – defining is not the same as understanding.
Despite the fact that many definitions of intelligence have been proposed so far, our understanding of what intelligence is has been limited by the lack of breakthrough advances in intelligence research, especially in neuroscience and cognitive science.
The more we understand the functioning of the human brain, where intelligence is “located,” how it relates to exogenous or endogenous factors, such as social and genetics, the better we can envision methods and policies that enhance intelligence and, with it, improve human well-being. More intelligent individuals will be more successful citizens, capable of living more meaningful lives, conscious lives.
Our understanding of intelligence could be strengthened and reinforced if we had well-defined definitions of intelligence, the advantages of which would be: a better educated general public, more synergy between politics and society (since the understanding of what (machine) intelligence is will diminish the fears of technological innovation), as well as enhanced understanding and knowledge of other capabilities that could contribute to the development of advanced intelligent systems and human-computer interactions.
Complexities of consensus
Over the last 100 plus years the concept of intelligence has been defined on numerous different occasions, and by different fields both formally and informally. There is a myriad of informal definitions of both human and machine or artificial intelligence (AI). AGISI research analysed more than one hundred informal definitions from the literature and Shane Legg and Marcus Hutter (2007a) collected 71 definitions divided into three broad categories: collective definitions, psychologist definitions, and AI researcher definitions. Despite many attempts and suggestions, there is still no generally accepted definition of intelligence.
Defining intelligence has been a rather controversial topic in the AI community and it remains one of its fundamental problems since the creation of the field. This is a perceived stumbling block in the pursuit of understanding intelligence and building machines which replicate and exceed human intelligence, as addressed by Rodney A. Brooks in 1991.
More than 25 years later, Michael Wollowski, Peter Norvig, and others (Wollowski et al., 2016) outlined in their study a stark difference of opinion with respect to the definition of AI. Very little has changed since. This stark difference is further reflected in our study “Defining (machine) Intelligence” (Monett & Lewis, 2018).
Two mainstream perspectives divide the conceptions into definitions of human intelligence and of artificial (or machine) intelligence. But there are also definitions of animal intelligence (non-human animals) and even intelligence in plants. And then there are socio-cultural meanings of intelligence (Ema et al., 2016), too.
In the AI field, there is a distinction between narrow (or weak) and general (or full) artificial intelligence, the former being focussed on solving specific problems without the capacity to generalize to other contexts and situations, which the latter does. The main debate over the last years has not been around a definition per se, however, but it has been centred on whether machines or systems can be developed that replicate or even surpass the most intelligent humans, which has been called superintelligence.