Abstract

Foundation models, including Chat-GPT and its many variants, have come into prominence in the natural language processing (NLP) community thanks the ubiquity of text data readily available on the internet and the design of modern transformer architectures that can effectively learn from such data. However, the development of a foundation model for sequential decision-making (e.g., reinforcement learning, planning) is faced with additional challenges not present in NLP. In this talk, we discuss some of these challenges with the hope of informing future investments that funding agencies and the academic community should engage in. The problem of transfer learning in the context of sequential decision-making is also discussed and constitutes one of the challenges that foundation models must address.

Bio

Alvaro Velasquez a program manager at the Defense Advanced Research Projects Agency (DARPA), where he currently leads programs on neuro-symbolic AI. Before that, Alvaro oversaw the machine intelligence portfolio for the Information Directorate of the Air Force Research Laboratory (AFRL). Alvaro is a recipient of the distinguished paper award from AAAI and best paper and patent awards from AFRL, the National Science Foundation Graduate Research Fellowship. He has authored over 70 papers and two patents and serves as Associate Editor of the IEEE Transactions on Artificial Intelligence.