Francois Chollet: What is the future of artificial intelligence?

This is my take on Francois Chollet (the creator of Keras) in collaboration with Yannic Kilcher and Connor Shorten. We cover his views on the limitations and future of deep learning and his recent “On the measure of intelligence paper”. Francois has been a huge inspiration for me, and this video was special to make. Been a long time coming I can tell you that!
If you want to see the entire conversation with Yannic and Connor, wait a day or two for the ML Street Talk podcast to drop!

00:00:00 What is intelligence / Limits of DL
00:03:12 Chollets Twitter
00:04:13 Deep Learning with Python Book
00:04:52 Limits of deep learning
00:10:26 Uncrunching a paper ball
00:11:41 Doing Maths with DL
00:13:50 Collective Intelligence
00:15:33 On the measure of intelligence paper
00:18:00 What's harder, playing chess or picking up the phone?
00:18:45 Lineage of expert systems in the 1980s
00:20:30 Chess evolution (Y)anacedote
00:22:20 Two conceptions of intelligence (learning and blank slate)
00:23:13 Evaluation types
00:24:30 McCorduck effect and levels of generalisation
00:25:45 Yannic on types of generalisation
00:28:15 Pyramid of generalisation local - broad - extreme
00:29:15 Leg and Hutter (universal intelligence)
00:30:30 Anthropocentric conception of intelligence
00:35:45 ARC Challenge
00:43:39 Yannic's take on ARC challenge
00:50:20 ARC - potential meta learning algorithm
00:55:26 Intelligence explosion (sorry I meant to make this part of introduction)
01:00:00 Intelligence is specialised
01:00:15 Chollet's formalism of intelligence (Yannic)
01:13:00 What makes intelligence? (Yannic)
01:14:15 Yannic/Connor/Tim concluding remarks

Machine learning street talk:

Note that I use many clips of Francois from Lex Fridman's incredible AI Podcast (see If you enjoyed this, I really recommend you check out the original interview too. Clearly I feel this is fair use and I am adding a lot of fresh content on the top, pretty sure Lex wouldn't mind!

Music credit:



#timscarfe #deeplearning #machinelearning

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