AI at Planetary Scale: What’s Next for Machine Learning?

UC Berkeley's Michael Jordan on the future of machine learning as it extends to a planetary scale in "The Next Evolution of AI: Markets, Uncertainty, and Engineering Intelligence at Scale." In this episode, Mike speaks with Hugo about the evolution of AI, the importance of integrating machine learning, computer science, and economics, and how AI can scale to address planetary-level challenges.

1. AI Terminology and Augmenting Human Intelligence****- The term "artificial intelligence" originally contrasted with "cybernetics" and often misrepresents the goal of AI today, which is more about augmenting human intelligence than replacing it.

• The focus should shift towards "intelligence augmentation."

2. Machine Learning’s Real-World Success****

• Machine learning’s breakthroughs in fields like supply chains, fraud detection, and recommendation systems have been driven by large-scale statistical systems rather than traditional logic-based AI.

3. Planetary Scale AI Systems****

• AI systems now operate at a planetary scale, making real-time decisions with massive data (e.g., global logistics, healthcare).
• While powerful, these systems face challenges, particularly in managing uncertainty and adapting to dynamic environments.

4. The Triangle of Machine Learning, Computer Science, and Economics****

• Future AI development requires the integration of machine learning, computer science, and economics.
• Intelligent systems must also incorporate incentives and economic principles to work effectively in complex environments.

5. Managing Uncertainty****

• AI systems often fail to manage uncertainty, leading to overconfidence.
• Handling uncertainty is crucial, especially in high-stakes environments like healthcare.
• Mike’s concept of "prediction-powered inference" bridges predictive models with reliable statistical tools to improve decision-making.

6. Failure Modes in Long-Term Systems****

• AI systems can fail over long timescales when they don’t account for changes in the environment, data drift, or shifts in measurement devices, leading to poor decisions in critical areas like healthcare and logistics.

7. Prediction-Powered Inference****

• This method combines generative AI models with statistical inference to improve hypothesis testing and scientific research, ensuring more reliable outcomes by managing uncertainty and bias in AI predictions.

8. Three-Way Markets for Sustainable Business Models****

• Platforms like United Masters, which create three-way markets (e.g., between musicians, brands, and listeners), show how AI can support sustainable business models by managing interactions and aligning incentives

9. Avoiding AI Hype and Science Fiction Narratives****

• The obsession with AGI (artificial general intelligence) distorts the conversation around AI.
• Instead of chasing sci-fi visions of AI, focus should be on building practical tools that augment human capabilities and solve real-world problems.

10. Intelligence Engineering as a New Discipline****

• Mike calls for a new discipline—"intelligence engineering"—that focuses on building adaptive, scalable systems integrating economic principles, machine learning, and human decision-making, much like the rise of civil or chemical engineering in the pas

11. Designing Systems That Integrate Economics****

• AI systems must incorporate market dynamics, incentives, and scarcity to function effectively at scale.
• Building intelligent systems requires understanding economic systems, not just technical ones.

12. Ethical and Societal Impact****

• While not explicitly discussed, the underlying theme emphasizes the need for ethical engineering in AI, ensuring that systems are built to enhance human welfare, manage uncertainty, and avoid harmful impacts in critical sectors like healthcare and global logistics.

AI’s future lies not in creating "superintelligent" machines, but in building resilient, adaptive, and ethically responsible systems that operate at a planetary scale, augmenting human intelligence and addressing complex societal and economic challenges.

You can read the transcript here.

Timestamps:

00:00 Introduction to High Signal

00:17 The Evolution of AI with Michael Jordan

00:37 The Future of AI: Combining Fields

00:55 Challenges in AI: Uncertainty and Real-World Impact

01:22 Personal Stories and Broader Impacts

01:34 Clip: The Evolution of AI

02:09 The Intersection of Fields: Machine Learning, Economics, and Computer Science

04:45 Introducing the Delphina Team

08:05 Interview with Michael Jordan Begins

08:45 Historical Perspectives on AI

10:48 Intelligence Augmentation vs. Artificial Intelligence

12:18 Supply Chains and Machine Learning

13:39 The Role of Data in AI

21:28 The Importance of Provenance in AI Predictions

24:42 The Wisdom of Crowds and AI

33:49 Generative AI and Its Applications

38:02 Monte Carlo Experiment and Error Bars

38:36 AlphaFold's Limitations

39:32 Improving Prediction Accuracy

41:07 Self-Driving Cars and Error Bars

43:54 United Masters: A Three-Sided Market

50:18 Statistical Contract Theory

53:38 Uncertainty in Decision Making

01:09:25 The Future of Intelligent Systems