Excitement over the prospects for artificial intelligence (AI) has driven US stock market valuations to a historic high. Can AI technologies deliver on their promise? Or is this yet another case of irrational exuberance?
In the latest New Money Review podcast I am joined by Eric Siegel, a former Columbia University professor who has taught computer science courses in machine learning and AI. Siegel, now a consultant, has just published a new book called “The AI Playbook—Mastering the Rare Art of Machine Learning Deployment”.
In the podcast, we explore some of the paradoxes surrounding AI: why this tech tool with apparently unlimited greatest promise may be the hardest to use, and why computers promising us greater autonomy may in fact require more supervision.
We cover:
- What is artificial intelligence (AI) and what is machine learning (ML)?
- What is generative AI?
- What explains the current AI hype?
- How predictive analytics can improve organisations’ performance
- Examples of successful machine learning in practice: UPS and credit scoring
- Why do so many ML projects fail to reach deployment?
- Why artificial general intelligence (AGI) is “the most compelling ghost story ever”
- Why computers that seem more human-like may give us less autonomy
- What goals can ChatGPT reach and where does it fall short?
- Why AI hype may be costly
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