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about this episode
Scale AI CEO & Co-founder Alexandr Wang creates training data for all AI applications to improve machine learning, shares insights on the future of autonomous vehicles, China’s AI advantages over US, importance of humans focusing on higher-value work & next major trends in AI
1:04 Jason intros Alexandr
2:19 Alexandr shares his personal startup history
5:17 How & why did Scale start?
8:26 What is the best example of Scale in practice? What problem are they solving?
10:44 Video demo of Scale’s platform
15:34 Acquiring the scale.com domain name & insights on the unique spelling of Alexandr
17:31 How does Scale deal with data-sharing between customers?
21:34 LIDAR vs. non-LIDAR… or both?
32:29 When will we have capable self-driving vehicles from Palo Alto to San Francisco? Over/under 2030? How will gov’t regulations affect self-driving?
36:03 China vs. US in the race of self-driving
41:22 Explainability in ML
47:26 Does it matter that we sometimes don’t know the answer to ML systems?
51:39 Should explainability have to be proven in ML?
55:13 How should inherently biased data-sets (like US justice system) be handled via ML?
1:00:00 Importance of focusing on higher-value work
1:02:41 Are dangers of AI overblown?
1:08:50 Will “General AI” happen in our lifetime?
1:12:26 What’s the next major AI trend after self-driving?
1:23:46 Does Alexandr remember a time before the Internet?
1:26:35 Jason plays “good tweet/bad tweet” with Alexandr
2:19 Alexandr shares his personal startup history
5:17 How & why did Scale start?
8:26 What is the best example of Scale in practice? What problem are they solving?
10:44 Video demo of Scale’s platform
15:34 Acquiring the scale.com domain name & insights on the unique spelling of Alexandr
17:31 How does Scale deal with data-sharing between customers?
21:34 LIDAR vs. non-LIDAR… or both?
32:29 When will we have capable self-driving vehicles from Palo Alto to San Francisco? Over/under 2030? How will gov’t regulations affect self-driving?
36:03 China vs. US in the race of self-driving
41:22 Explainability in ML
47:26 Does it matter that we sometimes don’t know the answer to ML systems?
51:39 Should explainability have to be proven in ML?
55:13 How should inherently biased data-sets (like US justice system) be handled via ML?
1:00:00 Importance of focusing on higher-value work
1:02:41 Are dangers of AI overblown?
1:08:50 Will “General AI” happen in our lifetime?
1:12:26 What’s the next major AI trend after self-driving?
1:23:46 Does Alexandr remember a time before the Internet?
1:26:35 Jason plays “good tweet/bad tweet” with Alexandr
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