#208 The three paths AI could take from here - Shawn Wang SWYX interview

About this episode

Quincy Larson interviews Shawn Wang about the future paths of AI and essential skills for developers.

Today Quincy Larson interviews Shawn Wang. He's a software engineer, founder of the AI Engineer conference, and host of the Latent Space podcast focused on applying the latest models toward getting work done. We talk about: - How even if LLMs plateau, there will be still paths to better output through surrounding harness code - And three big areas researchers are exploring to further improve model performance: World Models, Multi-modality, and Embodied AI - Which skills Shawn thinks are most important for developers going forward - And why Shawn thinks you should switch your own self teaching from "just-in-time learning" to "just-in-case learning" Support for this podcast comes from the 10,113 kind folks who donate to our charity each month. Join them and support our mission at https://donate.freecodecamp.org Get a freeCodeCamp tshirt for $20 with free shipping anywhere in the US: https://shop.freecodecamp.org Links from our discussion: - Shawn's Tiny Teams Playbook: https://www.latent.space/p/tiny - Shawn's interview with FeiFei Li: https://www.latent.space/p/after-llms-spatial-intelligence-and?utm_source=publication-search - Boots Theory…

People in this episode

Host: Quincy Larson

Guest: Shawn Wang

Topics covered

• AI development
• software engineering
• self-teaching
• model performance
• technology trends

Keywords

• AI paths
• LLMs
• self-teaching
• model performance
• software engineering

Mentioned in this episode

Organizations: AI Engineer conference, Latent Space podcast, freeCodeCamp, Kali Linux

Books & works: Shawn's Tiny Teams Playbook, Boots Theory, Wirth's Law, Adversarial Reasoning

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