Artificial Intelligence: Teaching Machines to Think Like People

Adam Marblestone: What the Brain Tells Us About Building Unsupervised Learning Systems, and How AI Can Guide Neuroscience Research

Exploring the permeable border between neuroscience, cognitive science, and AI with self-styled “neurotechnologist” Adam Marblestone.

Adam Marblestone is the director of scientific architecting within the Synthetic Neurobiology Group at MIT Media Lab. Prior to that, he explored the design of scalable biological interfaces and the principles behind cognition in the cortex at Harvard.

Key Takeaways

• The worlds of AI and neuroscience are converging due to the increasing sophistication of AI models and the “shedding of assumptions” within the neuroscience community about what the brain can do and how it does it.

• Creating computers that can perform feats of unsupervised learning might require us to first create a learning system that contains a number of self-supervising learning functions, dictated by biases baked into the system. This is similar to the way that children appear to have a bias in their brains for spotting hands, which lets them ultimately learn more complex visual elements.

• The brain uses a fundamentally different model of memory than that implemented in neural network approaches like the Neural Turing Machine.

Jack: Why are the two fields, AI and neuroscience, coming closer together?

Adam: In addition to the progress in neural network-based AI, there has been a shedding of assumptions within neuroscience itself that makes room ...