This class takes an engineering approach to exploring possible research paths toward building human-level intelligence. The lectures will introduce our current understanding of computational intelligence and ways in which strong AI could possibly be achieved, with insights from deep learning, reinforcement learning, computational neuroscience, robotics, cognitive modeling, psychology, and more. Additional topics will include AI safety and ethics. Projects will seek to build intuition about the limitations of state-of-the-art machine learning approaches and how those limitations may be overcome. The course will include several guest talks. Listeners are welcome.
Interested in the class? Here are some things you could do:
- Register an account on the site to stay up-to-date. The material for the course is free and open to the public. If you're an MIT student and would like to get credit for the course, pre-register for it here.
- Join our Slack channel (deep-mit.slack.com). There are two ways:
(a) if you have an mit.edu email, join here
(b) get an invite by clicking here.
- If you have questions, check out the FAQ Google Doc.
- Interact with Lex on Twitter, LinkedIn, Instagram, Facebook, or YouTube.
- Check out MIT 6.S094: Deep Learning for Self-Driving Cars.
- Time/Dates: Every day, 7pm, Jan 22 - Feb 2
- Duration: 60-90 minutes
- Location: MIT, 54-100 (location details) with some exceptions.
- Instructor: Lex Fridman
- Contact: firstname.lastname@example.org
2018 Schedule of Lectures and TalksMost (but not all) lectures and talks will be at 7pm in Room 54-100. See below for exact time and location.
Previously: OpenAI, Stanford University
Previously: Google Brain, Stanford, U of Toronto
None of this course would be possible without the great community of bright young minds at MIT and beyond. Thank you.