Agile Digital Transformation
Eric Siegel - Are we headed towards an AI winter?
Feb 15, 2024
Eric Siegel, former Columbia professor and leading ML consultant, discusses whether we're heading towards an AI winter and the implications for future innovation in AI and ML. Topics include the law of human-like autonomy, challenges in deploying ML projects, and the impact of an AI winter on AI and ML development. Guest emphasizes the need for clear goals and understanding before starting projects.
22:51
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Quick takeaways
- AI winter can be avoided by distinguishing machine learning from the overblown concept of AI.
- Collaboration between data scientists and non-data scientist stakeholders is crucial for successful deployment of machine learning models.
Deep dives
The concept of AI winter and its implications
AI winter refers to a state of disillusionment and negative sentiment towards AI caused by overhype and mismanagement of expectations. When people become tired of waiting for the promised advancements and start focusing on the negatives, AI gets stigmatized. This can lead to the undervaluation and rejection of machine learning, which is a real technology with valuable applications. Machine learning should be distinguished from the overblown concept of AI that often dominates discussions.
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