Thomas Dietterich, "What’s Wrong with Large Language Models, and What We Should Be Building Instead" 

ABSTRACT:
Large Language Models provide a pre-trained foundation for training many interesting AI systems. However, they have many shortcomings. They are expensive to train and to update, their non-linguistic knowledge is poor, they make false and self-contradictory statements, and these statements can be socially and ethically inappropriate. This talk will review these shortcomings and current efforts to address them within the existing LLM framework. It will then argue for a different, more modular architecture that decomposes the functions of existing LLMs and adds several additional components. We believe this alternative can address many of the shortcomings of LLMs.
About the Johns Hopkins Institute for Assured Autonomy: Led by APL and the Whiting School of Engineering, the IAA is becoming a nationally recognized center of excellence in autonomous systems, showcasing the robust portfolio of research and work from two premier divisions of JHU and creating strategic external partnerships. The IAA seeks to ensure the safe, secure, and reliable integration of autonomous systems and artificial intelligence (AI) in society. As autonomous systems proliferate, both physically and virtually, the institute seeks to ensure the systems will be trusted and safe in their operations, will withstand corruption by adversaries, and will integrate seamlessly into ecosystems and communities. In this burgeoning field, JHU strives to advance a clear vision for an autonomous future.

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