Artificial Intelligence (AI) is driving considerable parts of the US economy, influencing almost every industrial and scientific sector. Yet, the "intelligence" of these systems still cannot match the flexibility and breadth of even simple biological systems. The objective of this project is to develop a revolutionary new class of AI by focusing on four insights from the biological intelligence (BI) of animals that, unlike current AI agents, (1) do not start as blank slates (2) do not forget when they learn new things (3) have curiosity, and (4) interpret the world in terms of cause and effect. This project brings together outstanding scientists from a wide variety of disciplines and diverse backgrounds to tackle this problem. Through planning meetings and collaborative exercises, the team will generate preliminary data, and preparatory work for a future center focused on reframing the fundamental question of "intelligence." Outreach efforts will engage many diverse participants through large-scale online teaching.

At the founding of AI, Alan Turing proposed a test to determine when an AI behaves like a BI, and specifically human intelligence (HI). This test set the stage for the following 70 years of AI development. It has largely been replaced by narrow competitions aimed at imitating humans at specific tasks, such as playing certain games, identifying objects, or translating languages. But neither the Turing test nor today’s AI competitions utilize modern conceptualizations of what it means to be intelligent. It has become clear that intelligence evolved to incorporate several complex capabilities, which are critically missing from today’s AI: (1) preprogramming biases and baseline behaviors, (2) continually leveraging of many previous experiences to improve decision making in newly encountered tasks, (3) actively seeking out information that is useful for future decisions even if these differ from past decisions, and, finally, (4) constructing causal models relevant to decisions and communicating these models. This project brings together a unique group that truly understands both AI and BI/HI to address this gap. This group aims to define what is missing in AI relative to BI/HI, and to determine which research paths can enhance future approaches. In year one, the project will develop a test to measure a specific aspect of intelligence found in animals, but not current AI. This test will be sufficiently simple that it can be performed by several different biological taxa, humans, as well as AI. In year two, the participants will conduct pilot experiments to quantify current levels of performance on these tests and distill insights from BI/HI for AI. The resulting benchmarks will provide explicit and quantitative milestones for the eventual institute, whose goal will be to develop AI that matches HI on the new tests of intelligence.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

National Science Foundation (NSF)
Division of Information and Intelligent Systems (IIS)
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Sridhar Raghavachari
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University of Pennsylvania
United States
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