The Problem With LLMs

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The idea of having LLMs be purely the linguistic interface and the common sense necessary to sport the linguistic interface, that will probably shrink their size greatly. But that's a science problem. It's not clear how difficult that is. I mean, it's a funny thing that some things in the history of sort of modern technology got centralized some didn't,. The web didn't really get centralized. You know, search engines got centralized, social media got centralized. Is it necessary that those got centralized? Well, not really.

In this thought-provoking podcast episode, we discuss the mysteries surrounding Large Language Models (LLMs) and their implications for the future of artificial intelligence (AI) with Stephen Wolfram, creator of Mathematica, Wolfram | Alpha & Wolfram Language.

We dive into the fascinating world of Large Language Models (LLMs) and their surprising capabilities. He also discusses the underlying scientific principles behind LLMs, including the concept of computational irreducibility, and how they function as probabilistic sentence finishers. Wolfram reflects on the potential applications of LLMs in computational contracts and the challenges of aligning AI systems with human aspirations.

As the conversation delves into the future of AI governance, Wolfram explores the complexities of regulating AI and the importance of finding a balance between human intervention and autonomous decision-making. This episode offers a captivating exploration of the evolving landscape of AI and its impact on various industries and job roles.

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Disclosures: This conversation is for informational purposes only and does not constitute legal or investment advice. Actual results may vary materially from any forward looking statements made and are subject to risks and uncertainties. This podcast is not investment advice. Do not buy or sell tokens based off this episode.