Abstract
Strong AI—artificial intelligence that is in all respects at least as intelligent as humans—is still out of reach. Current AI lacks common sense, that is, it is not able to infer, understand, or explain the hidden processes, forces, and causes behind data. Main stream machine learning research on deep artificial neural networks (ANNs) may even be characterized as being behavioristic. In contrast, various sources of evidence from cognitive science suggest that human brains engage in the active development of compositional generative predictive models (CGPMs) from their self-generated sensorimotor experiences. Guided by evolutionarily-shaped inductive learning and information processing biases, they exhibit the tendency to organize the gathered experiences into event-predictive encodings. Meanwhile, they infer and optimize behavior and attention by means of both epistemic- and homeostasis-oriented drives. I argue that AI research should set a stronger focus on learning CGPMs of the hidden causes that lead to the registered observations. Endowed with suitable information-processing biases, AI may develop that will be able to explain the reality it is confronted with, reason about it, and find adaptive solutions, making it Strong AI. Seeing that such Strong AI can be equipped with a mental capacity and computational resources that exceed those of humans, the resulting system may have the potential to guide our knowledge, technology, and policies into sustainable directions. Clearly, though, Strong AI may also be used to manipulate us even more. Thus, it will be on us to put good, far-reaching and long-term, homeostasis-oriented purpose into these machines.
Highlights
The development of truly intelligent, Strong AI seems to be only possible if we employ the right inductive processing and learning biases to enable the learning of compositional generative predictive models (CGPMs) [6, 14, 15, 62]
We have recently shown that a suitably-structured recurrent artificial neural networks (ANNs) architecture can yield similar compositional structures, that is, a sensorimotor-grounded CGPM [28]
I have argued that the current AI hype may be termed a Behavioristic Machine Learning (BML) wave
Summary
An event we have seen before in so many disciplines. Starting conditions are marked by surprising and partially ground-breaking successes, which are pushed by skilled protagonists. The wave is fueled by investments and hope for further revenues. Protagonists and influential companies, having built up their infrastructure, team size, and social networks, focus on both optimizing the available techniques and selling the currently best system approaches. We have seen and experienced the ceasing power of such wave-like events. In the AI community, the subsequent time period has been termed ‘AI Winter’, namely the event that is characterized by low investments, general skepticism, and a focus on other potent computational approaches. Are we heading in this direction again, seeing that the limits of the currently favored end-to-end deep learning approaches become acknowledged? Are we heading in this direction again, seeing that the limits of the currently favored end-to-end deep learning approaches become acknowledged? Or is there potential for a sustainable, AI-supported future?
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