Learning in Artificial Neural Networks

This lecture explores the evolution of artificial neural networks, focusing on the shift from handcrafted architectures to data-driven learning processes. It delves into the challenges of supervised learning tools, the bias-variance trade-off, and the comparison between learning in artificial neural networks and neuroscience. The discussion extends to the concept of innate behaviors encoded in the genome, the role of evolution in shaping behavior over generations, and the implications for artificial neural network research. The lecture concludes by emphasizing the importance of mimicking the principles of supervised evolution to achieve human brain-like capabilities.