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Ignite AI: Machine Learning Week Founder Eric Siegel on AI’s Limitations and Potential | Ep98
In this episode of the Ignite podcast, Brian Bell hosts Eric Siegel, a renowned expert in artificial intelligence (AI) and machine learning. Siegel, who has been involved in the field for over 30 years, shares insights from his extensive career, which spans from academia to consulting. He discusses the evolution of AI, highlighting his experiences as the founder of Machine Learning Week and CEO of Gooder AI. Siegel emphasizes that despite the hype surrounding AI, it remains a tool to be used with clear value propositions, cautioning against the exaggerated expectations often associated with AI autonomy.
The conversation dives into the over-promising narratives surrounding generative AI and AGI (Artificial General Intelligence). Siegel tempers the enthusiasm, underscoring that while advancements in AI are impressive, humans still run the world, and technology should be seen as a tool for improving operations, not a replacement for human control. He uses generative AI as an example of current technology's potential and limitations, contrasting it with predictive AI, which he believes is more impactful in business settings for improving large-scale operations.
Brian and Eric explore real-world applications, such as UPS’s use of predictive models to optimize deliveries, saving the company millions of dollars and cutting emissions. Siegel stresses the importance of deploying machine learning models effectively and getting business stakeholders involved early in the process to ensure models are integrated into operations successfully. He also introduces his concept of "BizML" (Business Practice for Machine Learning), which bridges the gap between technical AI expertise and business value.
Toward the end of the episode, Siegel reflects on his journey from academia to entrepreneurship, sharing his passion for making technical concepts accessible to business audiences. His new book, focused on BizML, offers a practical framework for businesses to successfully implement AI projects. The discussion is a thought-provoking exploration of AI’s future, grounded in real-world application and tempered by a pragmatic view of its limitations.
Chapters:
00:01 - 00:27 Introduction and Eric Siegel’s Background
00:28 - 01:06 Eric’s Journey into Machine Learning
01:07 - 02:28 AI Hype: Fact vs Fiction
02:29 - 03:50 Predictive vs Generative AI
03:51 - 06:07 Debunking AI Autonomy
06:08 - 08:07 Task-Based AI Workers
08:08 - 10:18 Skepticism Around Full Autonomy
10:19 - 12:05 AI’s Limitations in Complex Tasks
12:06 - 14:36 Predictive AI in Business
14:37 - 16:20 UPS Case Study: AI-Driven Optimization
16:21 - 18:44 Predictive AI’s Potential in Operations
18:45 - 20:58 Why Predictive AI Projects Fail
20:59 - 23:45 The BizML Framework
23:46 - 26:15 Why AI Models Don’t Get Deployed
26:16 - 29:00 Visualizing AI Value for Businesses
29:01 - 31:00 AI and Data Quality
31:01 - 33:37 Deep Learning Revolution
33:38 - 35:40 Predictive AI and Its Limitations
35:41 - 38:08 Neural Networks and Deep Learning
38:09 - 40:23 Founding Machine Learning Week
40:24 - 44:27 UPS Case Study Continued: AI in Real-World Deployment
44:28 - 46:22 Book Recommendations for AI Enthusiasts
46:23 - 47:35 Advice for Companies Starting with AI
47:36 - 49:38 Favorite AI Algorithms
49:39 - 52:04 Keeping Up with AI Advancements
52:05 - 53:26 Teaching and Making AI Accessible
53:27 - 54:49 Closing Remarks and How to Reach Eric Siegel
View all episodes
By Brian BellIn this episode of the Ignite podcast, Brian Bell hosts Eric Siegel, a renowned expert in artificial intelligence (AI) and machine learning. Siegel, who has been involved in the field for over 30 years, shares insights from his extensive career, which spans from academia to consulting. He discusses the evolution of AI, highlighting his experiences as the founder of Machine Learning Week and CEO of Gooder AI. Siegel emphasizes that despite the hype surrounding AI, it remains a tool to be used with clear value propositions, cautioning against the exaggerated expectations often associated with AI autonomy.
The conversation dives into the over-promising narratives surrounding generative AI and AGI (Artificial General Intelligence). Siegel tempers the enthusiasm, underscoring that while advancements in AI are impressive, humans still run the world, and technology should be seen as a tool for improving operations, not a replacement for human control. He uses generative AI as an example of current technology's potential and limitations, contrasting it with predictive AI, which he believes is more impactful in business settings for improving large-scale operations.
Brian and Eric explore real-world applications, such as UPS’s use of predictive models to optimize deliveries, saving the company millions of dollars and cutting emissions. Siegel stresses the importance of deploying machine learning models effectively and getting business stakeholders involved early in the process to ensure models are integrated into operations successfully. He also introduces his concept of "BizML" (Business Practice for Machine Learning), which bridges the gap between technical AI expertise and business value.
Toward the end of the episode, Siegel reflects on his journey from academia to entrepreneurship, sharing his passion for making technical concepts accessible to business audiences. His new book, focused on BizML, offers a practical framework for businesses to successfully implement AI projects. The discussion is a thought-provoking exploration of AI’s future, grounded in real-world application and tempered by a pragmatic view of its limitations.
Chapters:
00:01 - 00:27 Introduction and Eric Siegel’s Background
00:28 - 01:06 Eric’s Journey into Machine Learning
01:07 - 02:28 AI Hype: Fact vs Fiction
02:29 - 03:50 Predictive vs Generative AI
03:51 - 06:07 Debunking AI Autonomy
06:08 - 08:07 Task-Based AI Workers
08:08 - 10:18 Skepticism Around Full Autonomy
10:19 - 12:05 AI’s Limitations in Complex Tasks
12:06 - 14:36 Predictive AI in Business
14:37 - 16:20 UPS Case Study: AI-Driven Optimization
16:21 - 18:44 Predictive AI’s Potential in Operations
18:45 - 20:58 Why Predictive AI Projects Fail
20:59 - 23:45 The BizML Framework
23:46 - 26:15 Why AI Models Don’t Get Deployed
26:16 - 29:00 Visualizing AI Value for Businesses
29:01 - 31:00 AI and Data Quality
31:01 - 33:37 Deep Learning Revolution
33:38 - 35:40 Predictive AI and Its Limitations
35:41 - 38:08 Neural Networks and Deep Learning
38:09 - 40:23 Founding Machine Learning Week
40:24 - 44:27 UPS Case Study Continued: AI in Real-World Deployment
44:28 - 46:22 Book Recommendations for AI Enthusiasts
46:23 - 47:35 Advice for Companies Starting with AI
47:36 - 49:38 Favorite AI Algorithms
49:39 - 52:04 Keeping Up with AI Advancements
52:05 - 53:26 Teaching and Making AI Accessible
53:27 - 54:49 Closing Remarks and How to Reach Eric Siegel