In this intriguing episode, we have a conversation with Dr. Ari Benjamin, focusing on the parallels between human sensory systems and artificial neural networks in terms of their sensitivity to common features in the environment.

Human sensory systems are notably more sensitive to common environmental features, and Dr. Benjamin's research reveals that artificial neural networks trained in object recognition demonstrate similar sensitivity patterns aligned with the statistics of image features.

Dr. Benjamin explains a mathematical interpretation of these findings, showing that learning with gradient descent in neural networks preferentially forms representations that are more sensitive to common features, a characteristic of efficient coding. This effect appears in systems with otherwise unconstrained coding resources and occurs when learning towards both supervised and unsupervised objectives.

Through our discussion, we delve into the notion that efficient codes can naturally emerge from gradient-like learning, highlighting the connections between human perception and AI learning mechanisms.

Key Words: Artificial Intelligence, Sensory Perception, Gradient Descent, Neural Networks, Efficient Coding, Object Recognition, Supervised Learning, Unsupervised Learning.

Benjamin, A.S., Zhang, LQ., Qiu, C. et al. Efficient neural codes naturally emerge through gradient descent learning. Nat Commun 13, 7972 (2022). https://doi.org/10.1038/s41467-022-35659-7