From ADP to the Brain: Foundations, Roadmap, Challenges and Research Priorities
This foundational work aims to guide research and funding priorities for developing universal learning systems, with potential broad impact across AI and neuroscience.
The paper defines Mouse Level Computational Intelligence (MLCI) as a grand challenge for the next century and outlines a roadmap to achieve it, focusing on vector intelligence as a first step toward understanding mammalian brain learning.
This paper defines and discusses Mouse Level Computational Intelligence (MLCI) as a grand challenge for the coming century. It provides a specific roadmap to reach that target, citing relevant work and review papers and discussing the relation to funding priorities in two NSF funding activities: the ongoing Energy, Power and Adaptive Systems program (EPAS) and the recent initiative in Cognitive Optimization and Prediction (COPN). It elaborates on the first step, vector intelligence, a challenge in the development of universal learning systems, which itself will require considerable new research to attain. This in turn is a crucial prerequisite to true functional understanding of how mammal brains achieve such general learning capabilities.