Introduction
- Gabriel Kreiman & Tomaso Poggio: Introduction
Neural Circuits of Visual Intelligence
- James DiCarlo: Reverse engineering human visual object recognition
- Nancy Kanwisher: Functional specificity in the human brain: What, whether, and why?
- James Haxby: Neural representations of faces, bodies, and objects in ventral temporal cortex
- Winrich Freiwald: The neural circuits for face recognition
- Gabriel Kreiman: Recurrent computations to the rescue
- Jacqueline Gottlieb: The frontal and parietal cortex: Eye movements and attention
- Robert Desimone: Attention
- Jeremy Wolfe: What are you searching for? … and how do you do it?
- Aleksander Madry: Adversarial examples and human-ML alignment
Neural Circuits at a Cellular Level
- Christof Koch: The diversity of cell types in the human brain
- Christof Koch: Building brain observatories for large-scale cellular surveys
- Michael Buice: Computational models of mouse visual cortex and tutorial on Allen Institute data observatory
- Christof Koch: Relating the structure of the brain to its function by tracking a net-wave of spikes up the visual hierarchy
- Jeff Lichtman: Connectomics at the nano and petascale
Modeling Human Cognition and Learning
- Josh Tenenbaum: Computational models of cognition, Part 1
- Josh Tenenbaum: Computational models of cognition, Part 2
- Josh Tenenbaum: Computational models of cognition, Part 3
- Pietro Perona: How did you learn the natural numbers?
- Leslie Kaelbling: Doing for robots what nature did for us
Language and Vision
- Boris Katz & Andrei Barbu: Grounding language acquisition
- Andrei Barbu: Language & vision
- Stefanie Tellex: Towards complex language in partially observed environments
- Richard Socher: Question answering for language and vision
Audition, Memory, and Consciousness
- Josh McDermott: Understanding auditory cortical computation
- Matt Wilson: Hippocampal mechanisms of memory and cognition
- Christof Koch: The sciences of consciousness: Progress & problems
Deep Learning
- Terrence Sejnowski: The deep learning revolution
- Tomaso Poggio: Dynamics and generalization in deep neural networks
- Max Tegmark: Connections between physics and deep learning
- Haim Sompolinsky: Neural representations of categories
- Lorenzo Rosasco: The quest for provably efficient ML algorithms
- Constantinos Daskalakis: Learning from biased data
Applications of Machine Intelligence
- Pietro Perona: Visipedia: Combining data, machines and experts to distill knowledge
- Vijay Chandrasekhar: Image instance retrieval: Overview of state-of-the-art
- Charles Cadieu: Baylabs: Impacting billions of people by increasing quality, value, and access to medical imaging
- Lisa Amini: Automation of AI
- Phil Nelson: Machine learning accelerating scientific discovery
Panels: The Big Questions
- Nancy Kanwisher, Josh Tenenbaum, Thomas Serre: What is the relationship between biological brains and AI algorithms?
- Laura Schulz, Matthew Wilson, Nicholas Roy, Venkatesh Murty: Is there anything special about human intelligence? (vs. non-human animals, vs. machines)
- Gabriel Kreiman, Tomaso Poggio, Stefanie Tellex: Hilbert questions in AI
On the Learning Hub Tutorials page, there are many tutorials on computational and empirical methods used in the research described in the above videos