Explore the fascinating intersection of neurobiology and artificial intelligence in this thought-provoking lecture by Professor Peter Robin Hiesinger. Delve into the complex question of how neural networks develop into intelligent brains, comparing biological and artificial systems. Examine the nature vs. nurture debate, the history of AI development, and the challenges of replicating human-level intelligence. Discover insights on topics such as random connectivity in neural networks, insect intelligence, deep learning breakthroughs, and the limitations of current AI models. Investigate the brain wiring problem, the role of genetics in neural development, and the concept of algorithmic information theory. Gain a deeper understanding of the complexities involved in creating artificial general intelligence and the ongoing quest to unravel the mysteries of biological and artificial neural networks.
Overview
Syllabus
Intro
How does a neural network become a brain?
The Neuron and the Network
The Basis of the Nature vs Nurture Debate What makes a neural network intelligent ?
The First Artificial Neural Network was Based on Random Connectivity
Human Intelligence
Bee Intelligence
How about...
Butterfly Intelligence
Artificial Intelligence knows nothing prior to learning...
Butterfly vs Artificial Intellige
Logic Theorist (and 50 years of Al history)
What happened to the PERCEPTRON? 1958
2011/12: The Deep Learning Revolution
Can training replace growth?
What level of detail do we need
The Brain Wiring Problem
A 150 year-old question Where is the information coming from?
The Genome is NOT a Blueprint
Algorithmic Information Theory
The Game of Life
Algorithmic Growth & Unfolding Information
Evolutionary Programming
How about 'Human-Level Al' or Artificial General Intelligence ?
Taught by
The Royal Institution
