Bubbling questions about the limits of the AI revolution
Summary
Once hailed as a game-changer, the AI revolution is now facing scrutiny as recent developments, including ChatGPT's latest version and a high failure rate for corporate AI pilots, cast doubt on its projected exponential growth. Computer science professor Cal Newport explains the technical limitations behind this slowdown, detailing the shift from 'pre-training' to more incremental 'post-training' improvements. The discussion also critically examines the significant economic and environmental costs of AI development, prompting a re-evaluation of its true value and future direction if it's not leading to a truly transformative artificial general intelligence.Episode description
OpenAI founder Sam Altman floated the idea of an AI bubble, an MIT report found that 95% of generative AI pilots at companies are failing and tech stocks took a dip.
With the AI sector is expected to become a trillion dollar industry within the next decade, what impact might slowing progress have on the economy? NPR’s Scott Detrow speaks with Cal Newport, a contributing writer for the New Yorker, and a computer science professor at Georgetown, about the limitations of the AI revolution.
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This episode was produced by Elena Burnett. It was edited by John Ketchum and Eric McDaniel. Our executive producer is Sami Yenigun.
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