Is the Image Net a Good Benchmark for Deep Learning?

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I think image net has been extremely valuable. It was basically a benchmark that allowed the deep learning community to demonstrate that deep neural works actually work. So I think image net was useful, but basically it's become a bit of an MNIST at this point. That's kind of a joke dataset that everyone like just crashes. Those people are more likely to cheer you. There's a because they know how difficult it is. Well, they actually sometimes don't know how difficultIt is to create a product that scale,. They actually deployed in the real world.

Andrej Karpathy is a legendary AI researcher, engineer, and educator. He’s the former director of AI at Tesla, a founding member of OpenAI, and an educator at Stanford. Please support this podcast by checking out our sponsors:
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OUTLINE:
Here’s the timestamps for the episode. On some podcast players you should be able to click the timestamp to jump to that time.
(00:00) – Introduction
(05:41) – Neural networks
(10:45) – Biology
(16:15) – Aliens
(26:27) – Universe
(38:18) – Transformers
(46:34) – Language models
(56:45) – Bots
(1:03:05) – Google’s LaMDA
(1:10:28) – Software 2.0
(1:21:28) – Human annotation
(1:23:25) – Camera vision
(1:28:30) – Tesla’s Data Engine
(1:32:39) – Tesla Vision
(1:39:09) – Elon Musk
(1:44:17) – Autonomous driving
(1:49:11) – Leaving Tesla
(1:54:39) – Tesla’s Optimus
(2:03:45) – ImageNet
(2:06:23) – Data
(2:16:15) – Day in the life
(2:29:31) – Best IDE
(2:36:37) – arXiv
(2:41:06) – Advice for beginners
(2:50:24) – Artificial general intelligence
(3:03:44) – Movies
(3:09:37) – Future of human civilization
(3:13:56) – Book recommendations
(3:20:05) – Advice for young people
(3:21:56) – Future of machine learning
(3:28:44) – Meaning of life