Support us! https://www.patreon.com/mlst Dr. Walid Saba recently reviewed the book Machines Will Never Rule The World, which argues that strong AI is impossible. He acknowledges the complexity of modeling mental processes and language, as well as interactive dialogues, and questions the authors' use of "never." Despite his skepticism, he is impressed with recent developments in large language models, though he questions the extent of their success. We then discussed the successes of cognitive science. Walid believes that something has been achieved which many cognitive scientists would never accept, namely the ability to learn from data empirically. Keith agrees that this is a huge step, but notes that there is still much work to be done to get to the "other 5%" of accuracy. They both agree that the current models are too brittle and require much more data and parameters to get to the desired level of accuracy. Walid then expresses admiration for deep learning systems' ability to learn non-trivial aspects of language from ingesting text only. He argues that this is an "existential proof" of language competency and that it would be impossible for a group of luminaries such as Montague, Marvin Minsky, John McCarthy, and a thousand other bright engineers to replicate the same level of competency as we have now with LLMs. He then discusses the problem of semantics and pragmatics, as well as symbol grounding, and expresses skepticism about grounded meaning and embodiment. He believes that artificial intelligence should be used to solve real-world problems which require human intelligence but not believe that robots should be built to understand love or other subjective feelings. We discussed the unique properties of natural human language. Walid believes that the core unique property is the ability to do abductive reasoning, which is the process of reasoning to the best explanation or understanding. Keith adds that there are two types of abduction - one for generating hypotheses and one for justifying them. In both cases, abductive reasoning involves choosing from a set of plausible possibilities. Finally, we discussed the book "Machines Will Never Rule The World" and its argument that the current mathematics and technology is not enough to model complex systems. Walid agrees with the book's argument but is still optimistic that a new mathematics can be discovered. Keith suggests the possibility of an AGI discovering the mathematics to create itself. They also discussed how the book could serve as a reminder to temper the hype surrounding AI and to focus on exploration, creativity, and daring ideas. Walid ended by stressing the importance of science, noting that engineers should play within the Venn diagrams drawn by scientists, rather than trying to hack their way through it. Transcript: https://share.descript.com/view/BFQb5iaegJC Discord: https://discord.gg/aNPkGUQtc5 Pod: https://anchor.fm/machinelearningstreettalk/episodes/88-Dr--WALID-SABA---Why-machines-will-never-rule-the-world-UNPLUGGED-e1sb1di TOC: [00:00:00] Intro [00:06:52] Walid's change of heart on DL/LLMs and on the skeptics like Gary Marcus [00:22:52] Symbol Grounding [00:32:26] On Montague [00:40:41] On Abduction [00:50:54] Language of thought [00:56:08] Why machines will never rule the world book review [01:20:06] Engineers should play in the scientists Venn Diagram! Panel; Dr. Tim Scarfe Dr. Keith Duggar Mark Mcguill References; Why Machines Will Never Rule the World: Artificial Intelligence without Fear by Jobst Landgrebe & Barry Smith (Book review) [Book Review/Saba] https://philarchive.org/rec/SABMWN Why Machines Will Never Rule the World: Artificial Intelligence without Fear [Jobst Landgrebe, Barry Smith] https://www.amazon.co.uk/Machines-Will-Never-Rule-World/dp/1032315164 Connectionism and Cognitive Architecture: A Critical Analysis [Fodor, Pylyshyn] https://ruccs.rutgers.edu/images/personal-zenon-pylyshyn/proseminars/Proseminar13/ConnectionistArchitecture.pdf Neural Networks and the Chomsky Hierarchy [Grégoire Delétang] https://arxiv.org/abs/2207.02098 On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? [Bender] https://dl.acm.org/doi/10.1145/3442188.3445922