University of Wisconsin, Madison
Project Title
Distinguishing intrinsic intelligence from automatic behavior
Project Summary
Agents are open systems that dynamically and informationally interact with their environment. In biological, evolved systems, more intelligent behavior is typically associated with greater autonomy from the environment. Simple systems are thought to act in an automated, reflexive manner, while intelligent organisms perform complex tasks in an autonomous, context-dependent way, and increasingly rely on internal states, such as memory, or learned adjustable preferences. These preconceived biological notions are being challenged by recent advances in artificial intelligence (AI). Artificial neural networks (ANNs) already achieve super-human levels of performance in tasks that supposedly require intelligence, creativity, and intuition. Yet, the classically feed-forward architecture of standard ANNs suggests that they are just "machines running through the motions" and do not fit with the notion of autonomous systems as unified wholes distinct from the environment. Here, we will train artificial agents controlled by feedforward ANNs across various task domains and compare them to evolved "Markov-Brains", which resemble biological neural networks. To address the question of how these agents do what they do, we will compare the structural, informational, dynamical, and causal properties of the evolved/trained networks. In this way, we aim to elucidate what distinguishes automatic from autonomous actions in mechanistic terms.
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