Generative AI in the Real World

Timothy Persons of PricewaterhouseCoopers (PwC) talks with Ben Lorica about adoption of AI in the enterprise. They discuss the challenges enterprises experience, including the need to change corporate culture. To succeed, it’s important to focus on solving well-defined problems rather than just doing something cool with AI. Good data strategies and data governance are essential. Persons also highlights the importance of training and education for everyone in the organization and the need to create safe environments where people can experiment.Points of Interest0:00: Introduction.1:00: We are seeing an uptick in adoption of AI in the enterprise. CEOs are planning to adopt AI and pursue business reinvention. Many companies are still kicking the tires. There is more adoption in the backend where risks are lower.3:36: AI budgets are on an upward trend. It is not a small spend and there’s a tendency to underestimate cost.4:54: What are some of the key challenges that enterprises face when they go to deployment?5:10: It’s all about trust and culture: getting employees and executives comfortable with the technology. That implies upskilling and internal conversations.7:09: What is a data strategy for generative AI?7:37: Companies need data governance, which must be more than a well-written policy document.Governance means operationalizing the policy. Once you focus on quality data and abide by governance, you have the foundation for a good future.9:26: How do you measure that you’re delivering ROI? How do you evaluate so that you know your LLM-backed application is ready to go?10:50: ROI—We need to separate R&D. For R, ROI doesn’t work well. But when you cross from R to D and investments scale, you need to think about ROI.12:15: Evaluation—We can measure LLMs today. But what does that mean in the context of the problem you’re solving? AI in autonomous vehicles is different from AI in medical systems.13:58: Companies need to invest in educating the workforce. Upskilling is not just for expertise; it is also for interdisciplinarity. Changing organizational culture means changing the way organizations communicate and partner.15:38: People underestimate the importance of creating a good user experience. Design thinking is needed. Focus on end-user experience and work back from that.16:59: What are some of the most common use cases for AI?17:17: In the back office, you often have a corpus of information customized to your situation. You can build fit-for-purpose chatbots for key support functions. The best lawyers can’t read everything possible in the corpus or keep up with all the regulatory changes coming in.21:11: AI will increase the value of labor investments. It will expedite the L&D curve for new employees. It will improve users’ lives. And AI is getting much better. We’ve only seen the floor, not the ceiling.24:38: Do you have a checklist or a playbook to help companies prioritize use cases?24:57: Companies need to think “What problems do I need to solve?” Think from a problem-centric approach.27:32 Are there best practices for sharing learning across different groups?28:17: We’ve seen centers of excellences rise. Sharing what didn’t work is important. GenAI is very democratizing—not everyone needs a PhD. When companies reward sharing, including what didn’t work, it really engenders collective learning and great ideas.30:15: What have leading companies done to prepare their workforces?30:31: PwC made a major investment in MyAI, which was focused on the ability to get AI into the hands of users, down to entry-level interns. It was an intentional L&D process that was focused on AI. We gave people the tools and a safe space to use them.32:43: It’s learning by doing, and it’s fun. And it can be customized to a company or a firm.33:03: If we didn’t provide a controlled environment, our people would go out into an uncontrolled environment.

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.Points of Interest0:00: Intro0:54: 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.

Andrew Ng is one of the pioneers of modern AI. He was Google Brain’s founding technical lead, Coursera’s founder, Baidu’s Chief Scientist, DeepLearning.ai’s founder, a Professor at Stanford—and much more. Andrew talks with Ben Lorica about scaling AI, agents, the future of open source AI, and openness among AI researchers. Have you experienced an “agentic moment” when you’re surprised and thrilled by AI’s ability to generate a plan and then to enact that plan? You will.Points of interest0:00: Introduction1:00: Advancing AI required scaling up. Better algorithms weren’t the issue.2:57: Just as we needed GPUs and other new hardware for training, we may need new hardware for inference.3:18: People are pushing Data-centric AI forward. Engineering the data is important—maybe even more important than engineering the model.4:41: The idea of agents has been around for a while. What’s new here?6:00: Agentic workflows let AI work iteratively, which yields a huge improvement in performance.8:01: Agent can be used for Robotic Process Automation (RPA), but it’s much bigger than that. We will experience “agentic moments” when we see AI that plans and executes a task without human intervention.10:42: Do you anticipate new Agentic applications that weren’t possible before?12:21: What are the risks of training on copyright-free datasets? Will using copyright-free datasets degrade performance?15:05: AI is a tool; I dispatch it to do things for me. I don’t see it as a different “species.”16:17: How do we know when an application is ready to release? What are best practices for enterprise use?17:18: It’s still very early. We need more work on evaluation. It’s easy to build applications—but when you build an app in a week, it’s hard to spend 10 weeks evaluating it.19:14: A lot of people build an application on one LLM, but won’t switch because evaluation is hard.20:12: Are you concerned that Meta is the only consistent supplier of open source language models?22:10: The cost of training is falling. The decrease in the cost of training means that the ability to train large models will become open to more players.26:15: The AI community seems less open than it was, and more dominated by commercial interests. Is it possible that the next big innovation won’t get published?26:50: We’re starting to see papers about alternatives to transformers. It’s very difficult to keep technical ideas secret for a long time.

Gwendolyn Stripling, author of Low-Code AI, talks about the democratization of AI, the primacy of data, the future of data science, and the coming of agents. It’s easy to think that AI is all about algorithms and models but it’s not; it’s really about understanding the business use case and the data that can be applied to that use case. We’re only beginning to have tools for the rest of the job: collecting, preparing, and exploring the data to find out what’s relevant to your business. Looking ahead, Gwendolyn sees generative AI automating even more of the workload. But focusing on the data, and collecting, understanding, and interpreting it, will always be the human part of the job.Points of interest0:57: What’s the boundary between no-code and low-code?3:10: Using the minimum amount of code necessary to achieve your goal.4:09: Low-code reduces the heavy lifting. But what if you want to learn about AI and ML?6:35: Learning ML isn’t about the tools; it’s about the business case and the data.7:55: What made you think about exposing more people to low-code AI?11:21: The key to all of this is the use case and then the data.14:32: What if I primarily use SQL?15:30: Is there an equivalent of AutoML for data collection and preparation?16:50: Generative AI looks like it will be able to help prepare data.19:22: How did the release of ChatGPT and other LLMs affect your book?24:00: Is there a low-code or no-code approach to RAG?26:30: The GenAI pipeline is becoming completely automated.26:49: The word of 2024 is agents. A lot of what can be automated will be automated.28:00: A lot of people are sharing lessons and best practices. That makes this an exciting time.29:17: Looking ahead five years, what will data scientists and ML Engineers do?

Justin Norman, author of Product Management for AI and co-founder of Vera, a startup focused on security for generative AI, talks with Ben Lorica about how product management has changed since Generative AI came on the scene. He discusses the issues retrieval-augmented generation (RAG) raises for product management; how reliability has become part of a product’s value; how companies that have lagged in their adoption of AI can use generative AI as a way to catch up; and the ability of open source AI in helping smaller companies compete with more established companies.Points of Interest0:00: You wrote Product Management for AI back in 2020 and 2021. How have things changed for product managers since then?3:04: Do companies that lead with operations and infrastructure for traditional AI maintain an advantage with Generative AI? Or does Generative AI allow companies that are just starting to catch up?5:09: Can new companies use open source to compete with established companies? Can open source help capture value as well as larger proprietary models?6:08: What do product managers struggle with when implementing RAG? What's the relationship between fine-tuning and RAG?10:58: RAG gives you value out of the box, but the key to success is how the data is organized.13:57: Are VCs underinvesting in certain parts of the pipeline? There is lots of investment in AI, but not as much investment in startups working on necessary technologies like ETL and data engineering.16:31: Why is reliability important for generative AI? How is generative AI different from other applications that we’re familiar with, and what implications does this have for product management?21:03: Are enterprises realizing that efficiency is important for succeeding with generative AI?23:44: We’re familiar with dashboards for monitoring and managing traditional software products. What would you imagine a dashboard for generative AI models to be? What do you need to be monitoring?28:49: Very few developers working in machine learning have also done frontend development or worked on user experience (UX). However, understanding user interaction can help you to improve your model.30:44: You're working with the father of digital forensics, Hany Farid. Should we be worried about DeepFakes?

Pete Warden, founder of Useful Sensors and co-author of TinyML, discusses use cases for artificial intelligence that we rarely think about: how can you run AI on very small systems? How can you put AI on consumer devices in ways that are actually useful and not just buzzword-compliant? AI doesn’t have to rely on massive GPU farms. Pete talks about what happens when you exchange one set of requirements (extreme power, heat, and expense) for another (minimal size, cost, and heat).Points of Interest00:00: Introductions, including Pete’s introduction to his company.2:22: What are some of the challenges and use cases for sensor-driven AI?4:11: Is sensor-driven AI relevant to industries other than hardware?6:22: Now we’re in the age of foundation models and large language models. Is “large” incompatible with “tiny”? Can you run language models on smaller devices?8:00: Will there be developments in tinyML that will benefit the broader LLM community?9:30: What’s deployable today in computer vision, speech, and language? What can be done with hardware that’s constrained by cost, size, and power consumption?11:15: How will product designers work with sensor-driven AI? Will they simply select from a palette of optional modules?12:37: Pete walks us through the development of AI-in-a-Box, from its conception to its reception.15:31: Your devices don’t have network connections. Without a network connection, how do you update models? Is it necessary?19:00: Do you do Retrieval Augmented Generation (RAG) on your devices?20:35: Our devices have user interfaces that combine voice and presence. A voice interface is central, but visual (and other channels) help to create an awareness of the speaker.21:35: What are some of your specific challenges, like power consumption and latency? How do you make tradeoffs?22:45: What is the future of large language models for sensor-driven AI?26:50: What are some of the security concerns for sensor-driven AI and what are you doing about them?28:22: What is Dark Compute and why is it important?30:48: What are the biggest opportunities for pushing AI into consumer devices? We need to start with problems that users actually care about.32:30: How can listeners connect to the broader movement around TinyML?

O’Reilly’s Generative AI in the Enterprise survey reported that people have trouble coming up with appropriate enterprise use cases for AI. Why is it hard to come up with appropriate use cases?Chip Huyen, cofounder of Claypot AI and author of Designing Machine Learning Systems, talks about why many companies have trouble coming up with appropriate use cases for AI, how to evaluate possible use cases, and the skills your company will need to put them into practice.Points of Interest0:00: Introduction0:49: O’Reilly’s Generative AI in the Enterprise survey report results.3:02: Now that generative AI is more accessible, will it be easier to come up with use cases?4:29: AI is easy to demo but hard to productize. Consistence, risk, and compliance.6:44: Is there a framework or checklist for thinking about applications?8:15: What are some of your favorite use cases?13:30: RAG is the “hello, world” of AI applications.17:24: How do you navigate between the desires and requirements of different stakeholders?19:00: When talking to stakeholders, you have to answer questions at the right level.21:10: How to think about staffing teams for generative AI.22:45: There’s less model development with generative AI, more application development.23:12: Frontend engineers and full-stack developers are very successful.26:27: What are companies’ concerns about risk?27:27: Understanding data gives a lot of clues about what it is good at and should be used for.29:00: The importance of documentation.30:25: Are there specific things you can do to ease the integration of AI into an organization?32:49: What companies that have deployed AI into products stand out?