Explainable and Reliable AI

Hello and welcome everyone. It is June 28th, 2022, and we are here in Acton Flab guest stream number 24.1. Today we're here with Professor Steven Grossberg, and the agenda will be as follows. First, Ali will provide a short introduction. We will then play a 45-minute pre-recorded video, followed by a Q&A. So, thanks everyone for joining, and Professor Grossberg, really appreciate joining, and I'll pass to Ali for the introduction. Hello and welcome. I'm Ali. I'm an independent researcher from Iran. I'm very happy and excited to be here and be able to speak with Professor Grossberg today. So, I'd like to thank Professor Grossberg for joining us. Steven Grossberg is the one Professor of Cognitive and Neural Systems and a Professor Emeritus of Mathematics and Statistics, Psychological and Brain Sciences, and Biomedical Engineering at Boston University. For more than 50 years, he has led pioneering research in discovering and developing neural design principles for autonomous adaptive intelligence based on biological and machine learning. His neural network models have been applied to many large-scale problems in engineering and technology, including the design of increasingly autonomous adaptive algorithms and mobile agents. In fact, this is what Carl Friston says about him. Whenever you claim to be the first to do this or that in artificial intelligence, it is customary and correct to add, with the exception of Steven Grossberg. Quite simply, Steven is a living giant and foundational architect of the field. Professor Grossberg is the recipient of the 2015 Norman Anderson Lifetime Achievement Award of the Society of Experimental Psychologists, the 2017 Frank Rosenblatt Award of the IEEE Computational Intelligence Society, and the 2019 Donald O. Hebb Award of the International Neural Network Society. His latest book, Conscious Mind, Resonant Brain, as a combination of his decades-long research, written in a rather non-technical and conversational style, is published in 2021 by Oxford University Press, and is the winner of the Association of American Publishers in 2022 Prose Award for the Best Book of the Year in Neuroscience. Now, I'll pass it to Professor Grossberg, and then we'll continue with the 45-minute pre-recorded lecture. If you'd like to say hi, otherwise I'll begin the recording video. I just saw my face frozen on the screen. Well, I'm delighted to be here, and I hope you find some points of interest in the lecture, and I'll look forward to the Q&A. Ali has prepared a series of questions that I've thought about and have some prepared sketched answers, and then after that, if you're still interested, I'm happy to do live Q&A about anything related to the topics of the day. Okay. On to the main course. I will play the video. No? No. And you won't hear anything on the live stream. I'll crop it, and the audio will be coming through fine now. Hello. I'm delighted to be able to speak to you today about a topic concerning artificial intelligence, which, as you know, is very much in the news these days, and I'll be contrasting two very different approaches to artificial intelligence. But to do that, I need to pull up my PowerPoint slides and share them with you, and let me maximize them and minimize my face. So, my topic today is explainable and reliable AI, comparing deep learning with adaptive resonance. This lecture is based on the following article from this year, which is both open access and on my webpage. The article summarizes core problems of deep learning, such as its untrustworthiness because it's unexplainable, and its unreliability because it experiences catastrophic forgetting. The article explains how adaptive resonance overcomes these problems, indeed overcomes 17 problems of deep learning, and outlines a blueprint for achieving autonomous adaptive intelligence. The article is part of a Frontiers in Neurobotics special issue about explainable AI, whose editors wrote, and I quote, Though deep learning is the…