How AI can improve the deployment crisis in machine learning projects – Dr. Eric Siegel

On today’s episode of The Small Business Show, we’re exploring the realm of machine learning technology and its practical applications. Dr. Eric Siegel, author, founder of Machine Learning Week, and former Columbia University professor, share insights from his latest book, ‘The AI Playbook,’ which offers readers a comprehensive understanding of how machine learning operates and strategies for leveraging it effectively.

Key Takeaways 

1. Dr. Siegel explains that machine learning is a technology that predicts outcomes by learning from data or past experiences. This includes predicting various events or behaviors, like fraudulent activities or equipment failures, which is essential for businesses looking to leverage artificial intelligence (AI) for operational efficiency.

2. According to Dr. Siegel, he notes the distinction between predictive and generative AI. Predictive AI focuses on forecasting specific outcomes and is where most current financial returns are seen. Generative AI, which is increasingly popular in the media, creates new content like text, images, and music, showcasing the versatile applications of machine learning.

3. Moreover, Dr. Siegel discusses how businesses can use machine learning to enhance large-scale operations, including targeted marketing, fraud detection, supply chain management, and operational decision-making, thus improving efficiency and reducing costs.

4. The conversation sheds light on the challenges businesses face in deploying machine learning projects, with many failing to reach full implementation. Dr. Siegel’s book, “The AI Playbook,” is mentioned as a guide to navigating these challenges, emphasizing the need for a semi-technical understanding among business stakeholders to ensure successful deployment.

5. The interview conveys the importance of integrating machine learning into business operations to drive efficiency and innovation. Dr. Siegel encourages business leaders to develop an understanding of machine learning to effectively harness its predictive capabilities, illustrating this with success stories from companies like UPS, which significantly improved operational efficiency through predictive modeling.

"The majority of new machine learning projects actually fail to get to deployment." – Dr. Eric Siegel

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