There’s a lot of current hype around AI, it’s abilities but also the potential threat that it poses to humanity. The standard narrative sounds like a sci-fi movie, one of these systems is on the verge of waking up and taking over the whole world and has been for at least the last five years. In this episode we take a skeptical look at AI, the current state of the art, the history and development of these systems, examples of AI outside of LLMs. And take a close look at the argument that AI is about to pull a skynet and kill us all, then discuss who benefits from that narrative the most. Last we talk about the idea of Dumb AI and how that can be a problem for people even if it’s not superintelligent, with some real world examples that already exist in the world and the very real damage that has already happened from proliferating cultural biases to the literal deaths of innocent children.

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Created by: Ryan Pevey at evolio.org and Zach Jobe @zhjobe

GitHub page: github.com/rpevey

Concept: A PhD neuroscientist and a comedian discuss the subject of artificial intelligence and the real risks that it poses as it currently stands.

Tools: Python, C++, OpenGL, DaVinci Resolve, GIMP, Inkscape

Outro music by Maksym Malko from Pixabay (pixabay.com/music/upbeat-podcast-interview-music-254186/)

Podcast streams:

redcircle.com/shows/04ecc7a3-3383-4ca0-bff3-c55d04100ed5

podcasts.apple.com/us/podcast/hidden-science-stories/id1874450380

open.spotify.com/show/50xaIb6jjmSJv7georpfVt?si=16e6c0e030414593

music.amazon.com/podcasts/b0415a99-f209-4c66-80c5-d8eb2b490811/hidden-science-stories

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Citations:

1. Turing, A.M. (1950). "Computing Machinery and Intelligence." Mind, 59(236), 433–460.

2. Searle, J.R. (1980). "Minds, Brains, and Programs." Behavioral and Brain Sciences, 3(3), 417–424.

3. Stanford Encyclopedia of Philosophy: "The Chinese Room Argument" (plato.stanford.edu/entries/chinese-room/)

4. McCarthy, J., Minsky, M., Rochester, N., & Shannon, C. (1955). "A Proposal for the Dartmouth Summer Research Project on Artificial Intelligence."

5. Wiener, N. (1950). The Human Use of Human Beings: Cybernetics and Society. Houghton Mifflin.

6. Lighthill, J. (1973). "Artificial Intelligence: A General Survey." British Science Research Council.

7. Bostrom, N. (2014). Superintelligence: Paths, Dangers, Strategies. Oxford University Press.

8. Legg, S. & Hutter, M. (2007). "Universal Intelligence: A Definition of Machine Intelligence." Minds and Machines, 17(4), 391–444.

9. Angwin, J. et al. (2016). "Machine Bias." ProPublica.

10. Thomas, R. & Uminsky, D. (2022). "Reliance on Metrics is a Fundamental Challenge for AI." Patterns (Cell Press).

11. Dressel, J. & Farid, H. (2018). "The Accuracy, Fairness, and Limits of Predicting Recidivism." Science Advances, 4(1).

12. Omohundro, S. (2008). "The Basic AI Drives." Proceedings of the 2008 Conference on Artificial General Intelligence.

13. Vaswani, A. et al. (2017). "Attention Is All You Need." Advances in Neural Information Processing Systems.

0:00 AI Intro

2:36 The history of AI

8:08 Alan Turing enters the fray

11:22 The imitation game

16:11 GOFAI

18:39 AI winter is coming

22:06 The modern era of AI

25:15 Definition of AI

28:44 Examples of modern AI

30:41 Attention is all you need

33:52 LLMs can kinda do everything, right?

40:32 The (Mandarin) room

43:44 Weak vs Strong AI

46:19 AGI

52:51 SI brought to you by DJT

55:16 Value alignment problem

58:21 The dangers of Dumb AI

01:09:52 AI bubble bursting

01:17:27 Point of Optimism

01:23:37 Wrap up