We conclude that Shank's 1980 observation, that intelligence is all about generalization and that AI is not particularly good at this, has, so far, withstood the test of time

How much intelligence is there in artificial intelligence? A 2020 update. Han L.J. van der Maas, Lukas Snoek, Claire E. Stevenson. Intelligence, Volume 87, July–August 2021, 101548. https://doi.org/10.1016/j.intell.2021.101548

Highlights

• Recent AI breakthroughs, such as deep learning and reinforcement learning, have deep roots in psychology.

• Modern AI models are much more human and brain like at the implementational level.

• There is nothing wrong with AI's crystallized intelligence, but generalization is still a weakness of AI systems.

• The psychological relevance of AI extends to areas such as the study of individual differences and cognitive development.

• We expect fruitful interactions with regard to the measurement of natural and artificial intelligence.

Abstract: Schank (1980) wrote an editorial for Intelligence on “How much intelligence is there in artificial intelligence?”. In this paper, we revisit this question. We start with a short overview of modern AI and showcase some of the AI breakthroughs in the four decades since Schank’s paper. We follow with a description of the main techniques these AI breakthroughs were based upon, such as deep learning and reinforcement learning; two techniques that have deep roots in psychology. Next, we discuss how psychologically plausible AI is and could become given the modern breakthroughs in AI’s ability to learn. We then access the main question of how intelligent AI systems actually are. For example, are there AI systems that can solve human intelligence tests? We conclude that Shank's observation, that intelligence is all about generalization and that AI is not particularly good at this, has, so far, withstood the test of time. Finally, we consider what AI insights could mean for the study of individual differences in intelligence. We close with how AI can further Intelligence research and vice versa, and look forward to fruitful interactions in the future.

Keywords: Artificial IntelligenceDeep learningIndividual differencesIntelligence testsReinforcement

5. Discussion

AI has seen multiple cycles of enthusiasm and disappointment, but the current wave seems to be of a different order. As we stated in the introduction of this paper, one of the original goals of AI was to learn more about human intelligence. This endeavor could be misguided as AI may only produce “cognitive wheels”, techniques that have no equivalent in human cognition. In this paper we argued that this might have been true for some older approaches (e.g., brute force search techniques), but is less the case for much of current AI. The progress made in recent years is certainly technologically driven, but inspired by biological and psychological knowledge about human information processing and learning.

We expect that the recent progress in AI will change the way we think about intelligence. AI forces us to rethink the definition of intelligence. Definitions that center on just information processing and problem solving are perhaps insufficient. Shank's observation that intelligence is all about generalization has, so far, withstood the test of time. Many information processing problems, from processing speech to playing chess, appear to be less difficult than perhaps expected. The really hard problem is to deal with completely novel cases. One requirement for solving this hard problem is the ability to learn invariant and thus generalizable patterns. And especially with regard to learning, the progress in AI has been spectacular. The main difference between AI systems of the past, such as expert systems, and modern AI is the fact that they learn. That deep learning and reinforcement learning, the core techniques in current AI, have deep roots in psychology is remarkable and promising for studying how artificial and human intelligence are related.

AI is relevant to intelligence research because it enhances our understanding of the core mechanisms of human cognition. How the immense neural systems in our brain are able to process extremely complicated information such as speech and produce logical thinking is an extremely difficult question. Having an artificial system that performs such tasks using the same basic principles is extremely useful. Classic questions regarding the modularity of the mind, the origin of creativity, and the organization of long-term memory spring to mind. In addition, we argued that the psychological relevance of AI extends to unexpected areas such as the understanding of individual differences and the development of cognition. It is relatively easy to create a population of AI systems with minor differences in architecture and training regime. Modern AI provides us with a new playing field for individual differences research.

On a practical level we expect fruitful interactions regarding the measurement of natural and artificial intelligence. As modern AI systems are incredibly complex, our experience in examining such systems may be relevant for AI. Vice versa, insights from AI may lead to new developments in (adaptive) intelligence testing and educational interventions.

We attempted to shed light on the future of intelligence research from the point of view of AI. Our overview is necessarily limited and probably quickly outdated, but hopefully we have given intelligence researchers some insights in the rapid developments in AI and the possible consequences for our field.