Machine learning isn't enough

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In this video, I explore the reliability of ChatGPT. While it impresses with human-like responses, its limitations in logic, reasoning, and fact-checking raise concerns. I delve into the quest for Artificial General Intelligence (AGI) and question whether the current trajectory of deep learning and neural networks is truly aligned with our values and the transparency needed for trustworthy AGI (and AI in general).

00:00 Intro
00:40 Defining AI Advancement
02:10 Is GPT Overhyped Technology?
05:32 The Model Behind GPT
06:42 How to Combat this?
08:40 Why Deep Learning is Prevalent
09:12 Can we Trust Deep Learning?
11:36 What Should Humans Do?

Sources:
Shevlin, Henry; Vold, Karina; Crosby, Matthew; Halina, Marta (4 October 2019). "The limits of machine intelligence: Despite progress in machine intelligence, artificial general intelligence is still a major challenge". EMBO Reports. 20 (10): e49177. doi:10.15252/embr.201949177. ISSN 1469-221X. PMC 6776890. PMID 31531926.

Staudacher, Natalie (2022). “ChatGPT FAQ”. OpenAI

Vincent, James (5 December 2022). “AI-generated answers temporarily banned on coding Q&A site Stack Overflow”. The Verge

Altman, Sam (11 December 2022). Tweet from @sama. Twitter.

Chomsky, Noam (1957). “Syntactic Structures”. The Hague/Paris: Mouton. ISBN 9783110172799

Hardesty, Larry (14 April 2017). "Explained: Neural networks". MIT News Office.

Marcus, G.; Davis, E. (2019). Rebooting AI: Building Artificial Intelligence We Can Trust. Pantheon/Random House.