Generative AI in the Real World: Learning How to Do AI Effectively with Alfred Spector

Alfred Spector has been a leader in AI and machine learning at Google, IBM, and Two Sigma. He is now a visiting scholar at MIT, an advisor at Blackstone, and coauthor of the text book Data Science in Context. Alfred talks with Ben Lorica about what people developing with AI need to be successful. Succeeding with AI is about more than just a model. We need to think about the application and its context. We need humanities and social sciences in addition to technology. Alfred also discusses the AI skills gap, resistance to adopting AI, “hybrid intelligence,” and the calls to regulate AI.

About the Generative AI in the Real World podcast: In 2023, ChatGPT put AI on everyone’s agenda. In 2024, the challenge will be turning those agendas into reality. In Generative AI in the Real World, Ben Lorica interviews leaders who are building with AI. Learn from their experience to help put AI to work in your enterprise.

Points of Interest

• 0:00: Intro
• 0:54: What do we need to do to apply generative AI effectively?
• 2:10: Why did you end up writing the book Data Science in Context?
• 3:14: Data science is about more than the model. More than "just get some data and hope."
• 8:22: Ethics alone isn't enough.
• 11:08: Students need a good basis in economics, political science, history, and literature. We have to think more broadly than "which ad gets the most clicks."
• 14:20: There's an AI literacy and skills gap, particularly outside Silicon Valley.
• 15:43: Companies be probing opportunities.
• 16:20: Is there resistance to adopting AI? Fear of displacement or distrust?
• 18:18: Most people think there is more to do than people to do the work.
• 19:21: To what extent are companies trying to come up with an overarching vision for AI?
• 19:51: For some companies, GenAI will be formative. Others need to kick the tires and put together a road map.
• 21:35: Internal applications can be more fault tolerant. Keep employees in the loop; don't be lazy.
• 23:12: Prior to ChatGPT, barrier to entry was higher. AI is now very developer friendly.
• 24:13: What level of data science or ML knowledge should companies have?
• 25:01: There are two categories of expertise; broad perspective on products and services.
• 28:25: It may take a long time to evaluate whether an application can be deployed.
• 29:07: With agents, the stakes are higher.
• 30:07: Hybrid intelligence will be a coalition that includes AI.
• 32:38: Even task-specific agents can break. Agents are fragile. Humans aren't fast but are good at dealing with things we haven't encountered before.
• 33:43: Regulate uses of technology, not technologies.