The High Cost of AI Hype

WebinarJuly 2024

Machine learning has an “AI” problem. With new breathtaking capabilities from generative AI released every several months—and AI hype escalating at an even higher rate—it’s high time we differentiate most of today’s practical ML projects from those research advances. Including all ML initiatives under the “AI” umbrella oversells and misleads, contributing to a high failure rate for ML business deployments. In this keynote address, consultant and bestselling author Eric Siegel shows that, for most ML projects, the term “AI” goes entirely too far—it alludes to human-level capabilities. By unpacking the meaning of “AI,” he'll reveal just how overblown a buzzword it is.

Summary

The session investigated the important topic of AI expectations, particularly the heightened anticipations surrounding machine learning and generative AI. Eric Siegel, a respected consultant and former Columbia University professor, brought attention to the ongoing challenge of implementing AI models into practical business operations. He highlighted the importance of predictive AI, which focuses on enhancing existing large-scale operations through value-driven implementation. Siegel presented a six-step strategy called BizML, which assists in the implementation of predictive AI projects. He discussed the importance of understanding the business metrics and potential value of AI models, noting that many projects fail to reach implementation due to this oversight. Moreover, he explored the potential of combining predictive and generative AI to achieve semi-autonomous systems. Siegel stressed the need for collaboration between technical and business stakeholders to ensure successful AI implementations and highlighted ethical considerations in AI applications.

Key Takeaways:

AI expectations often leads to unrealistic anticipations, causing implementation challenges.
Predictive AI focuses on improving large-scale operations through data-driven predictions.
The BizML strategy offers a structured approach for implementing AI projects effectively.
Understanding business metrics is important for assessing the value of AI models.
Combining predictive and generative AI can lead to semi-autonomous systems.
Collaboration between technical and business stakeholders is essential for successful AI implementation.
Ethical considerations are important when implementing AI technologies.

Deep Dives

The Challenge of AI Expectations

AI expectations has ...
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escalated rapidly, often overshadowing the reality of practical machine learning applications. Despite impressive advancements in generative AI, a significant gap remains between research innovations and their implementation in real-world business processes. Eric Siegel highlighted that many AI models fail to reach production due to this disconnect. He pointed out that while generative AI presents exciting possibilities, it is often oversold, leading to unmet expectations. Siegel emphasized the need to focus on concrete value-driven implementations rather than getting carried away by the allure of AI's potential. He argued that predictive AI, which leverages data to enhance existing operations, offers more immediate and tangible benefits for businesses.

Value-Driven Implementation and the BizML Strategy

Siegel introduced the BizML strategy as a structured approach to guide AI projects from inception to implementation. The six-step framework emphasizes the importance of defining the implementation goal, specifying prediction objectives, and establishing both technical and business metrics. He noted that many AI projects fail in the "last mile" due to a lack of planning for implementation and inadequate collaboration between technical and business stakeholders. Siegel advocated for a deeper understanding of business metrics, arguing that evaluating AI models purely on technical performance is insufficient. By focusing on business value and promoting cross-functional collaboration, organizations can overcome the common pitfalls of AI implementation.

Combining Predictive and Generative AI

The potential of combining predictive and generative AI was a key theme in Siegel's presentation. He proposed a model where predictive AI is used to determine when human intervention is necessary in generative AI processes, thereby achieving a degree of autonomy. This combined approach aims to balance efficiency and reliability, particularly in applications where fully autonomous generative AI is not viable. By integrating predictive models to assess the need for human review, businesses can tap into the strengths of both AI types, optimizing operations while maintaining control over the quality and accuracy of AI-generated outputs. Siegel suggested that this combination could be a significant development in realizing the practical benefits of AI.

Ethical Considerations in AI Implementation

Ethical considerations were highlighted as an important aspect of AI implementation. Siegel emphasized that AI models, particularly in predictive use cases, make consequential decisions that impact individuals' access to resources, credit, and opportunities. He stressed the importance of addressing issues such as machine bias and discrimination, urging organizations to consider the ethical implications of their AI applications. Siegel's commitment to ethics was evident in his reference to his extensive work on the subject, including op-eds and publications. He advocated for transparency and accountability in AI systems, emphasizing that ethical governance is essential to ensure fair and responsible use of AI technologies.