Summary In this conversation, Eric Meyer discusses his extensive journey in AI and programming, emphasizing the evolution of AI technologies, the current state of developer tools, and the future of user experiences with AI. He highlights the importance of safety in AI systems, the role of natural language as a user interface, and the commoditization of AI models. Eric also shares his vision for empowering non-technical users and the integration of traditional software with AI-driven solutions.

takeaways

Eric Meyer has a rich background in AI and programming languages.

The evolution of AI has seen a shift from rule-based systems to data-driven models.

AI tools are making coding more accessible to non-developers.

Natural language interfaces are evolving but may not be the most efficient.

AI agents are conceptually similar to traditional software objects.

Safety and correctness in AI systems are critical concerns.

The commoditization of AI models is reshaping the software landscape.

Integration between traditional software and AI is essential for future development.

User experience in AI tools is becoming more interactive and iterative.

Eric is focused on building a programming language that ensures safety in AI applications.

titles

Democratizing Programming with AI

The Evolution of AI: Lessons from the Past

Sound Bites

"The gap between idea and reality has shrunk."

"Traditional software will not go away."

"Beware of the shiny object syndrome."

Chapters

00:00 Introduction to Eric Meyer and AI Journey

05:12 The Evolution of AI: Past vs Present

11:05 AI in Developer Tools: Current State and Future

16:20 User Experience and Interfaces in AI Products

21:17 Empowering Non-Technical Users with AI

23:06 Natural Language as the New UI

26:51 Understanding AI Agents and Their Impact

31:35 Probabilistic Nature of AI and Trust Issues

36:02 The Future of APIs in AI Development

42:02 The AI Stack of the Future: Integration and Evolution

David Adkins is an experienced senior technology executive who leads engineering teams at Meta AI. David holds an MS in Computer Science from the University at Buffalo focused on machine learning. In this conversation we discuss

* How to think about AI bias and fairness

* Why AI Transparency, Explainability and Control are important

* How Meta de-risked LLaMA

* How to take AI Research to production and more.

Listen now on Apple, Spotify and Amazon Music and please subscribe to my YouTube channel.

⏰ 2:06 David’s Favorite Restaurant Sammy’s Fishbox

⏰ 2:40 Career Stories: David’s Journey

⏰ 4:37 What David is working on at Meta AI

⏰ 4:50 How to organize AI Teams

⏰ 6:56 What is AI Bias and Fairness

⏰ 8:52 How to build a Product with AI Fairness in mind

⏰ 11:47 Examples of Fairness Mitigations in Meta Products

⏰ 13:33 What’s Surprising about working on AI Fairness

⏰ 16:05 What causes Bias in AI Products

⏰ 19:19 How to Mitigate AI Problems when you’ve already launched

⏰ 21:38 What is AI Transparency and Control

⏰ 24:12 What is AI Explainability?

⏰ 26:10 Why YOU should care about AI Transparency

⏰ 31:00 What is surprising about working on AI Transparency

⏰ 33:00 AI System Cards

⏰ 37:30 Developing LLaMA Responsibly

⏰ 39:30 How to de-risk large language models

⏰ 43:06 How to do AI Research to production

⏰ 46:51 What’s next for David