Some people say that all that’s necessary to improve the capabilities of AI is to scale up existing systems. That is, to use more training data, to have larger models with more parameters in them, and more computer chips to crunch through the training data. However, in this episode, we’ll be hearing from a computer scientist who thinks there are many other options for improving AI. He is Alexander Ororbia, a professor at the Rochester Institute of Technology in New York State, where he directs the Neural Adaptive Computing Laboratory.

David had the pleasure of watching Alex give a talk at the AGI 2024 conference in Seattle earlier this year, and found it fascinating. After you hear this episode, we hope you reach a similar conclusion.

Selected follow-ups:

Alexander Ororbia - Rochester Institute of Technology
Alexander G. Ororbia II - Personal website
AGI-24: The 17th Annual AGI Conference - AGI Society
Joseph Tranquillo - Bucknell University
Hopfield network - Wikipedia
Karl Friston - UCL
Predictive coding - Wikipedia
Mortal Computation: A Foundation for Biomimetic Intelligence - Quantitative Biology
The free-energy principle: a unified brain theory? - Nature Reviews Neuroscience
I Am a Strange Loop (book by Douglas Hofstadter) - Wikipedia
Mark Solms - Wikipedia
Conscium: Pioneering Safe, Efficient AI
The Hidden Spring: A Journey to the Source of Consciousness (book by Mark Solms)
Carver Mead - Wikipedia
Event camera (includes Dynamic Vision Sensors) - Wikipedia
ICRA (International Conference on Robotics and Automation)
Brain-Inspired Machine Intelligence: A Survey of Neurobiologically-Plausible Credit Assignment
A Review of Neuroscience-Inspired Machine Learning
ngc-learn

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