On the Knowledge Problem, Tacit Knowledge, and Artificial Intelligence is an article we need to study. It begins:
In May 2023 the MIT economist Daron Acemoglu, who went on to share the 2024 Nobel prize in economics, asked a question on Twitter. Friedrich Hayek argued in 1945 that central planning fails because no planner can collect and compute all the information an economy runs on. “What if computational power of central planners improved tremendously?” Acemoglu wrote. “Would Hayek then be happy with central planning?”
About thirty economists sent him the same reply, according to Shruti Rajagopalan, who hosts the Mercatus Center’s Ideas of India podcast: Hayek never made a computational argument. He made an argument about the nature of knowledge itself. Acemoglu reached the same conclusion in print that June. In an essay titled “Hayek vs. AI Socialism”, he quoted Hayek’s warning that the relevant knowledge “by its nature cannot enter into statistics” and concluded that “not even an all-powerful large language model” could deal with it. AI socialism, he wrote, “offers only a superficial critique of Hayek.” That distinction is the whole story, and it explains why a model trained on most of what has ever been written still cannot do what a well-trained human mind does.
Thanks for linking the article. It is a good segue into many related works from various disciplines that are all relevant to AI triggered issues we discuss here. Philosophy, economics, political science, governance and regulations — impressive for a mere sales pitch piece (the author makes it at the end). His mentioning of important points of Ryle (know what/how distinction), Polyanyi (tacit dimension) and Hayek (wrongheadedness of central planning/command) are worth it on their own. IMHO important combinatorial considerations are missing — the sheer amount of AI outputs generates a difficult validation problem which is really tough in any domain where the stakes are high (high blast radius of errors as we say in software engineering) like medicine, nuclear and biological research etc. Today one AI is used to validate another A’s solution, but it is very risky.
Dmitri,
I have yet to study the article with care, but it did send me back to Polanyi who I first discovered way back in ’72. His PERSONAL KNOWLEDGE: Towards a Post-Critical Philosophy is rich, fascinating, and surprisingly relevant to out current concerns about AI.
https://polanyisociety.org/michael-polanyi/
Hi Bill
I completely agree with you on Polyanyi. I read his Personal Knowledge more than 30 years ago and have it on my shelves. He is one of those rare 20th century thinkers who are going to stay relevant for a long time.
I am familiar with some of Polanyi’s work, but have read and long admired Hayek. As such, I am pleased to see Hayek’s work showing up again among Bill’s interests. For those few among this blog’s quite astute readers who may not be familiar with him, here is a quick overview. Before he narrowed his focus, Hayek gained wide-ranging exposure to the philosophy of his day amid the intellectual ferment of Vienna (of which the Vienna Circle was a prominent part), and was an early reader of his distant cousin Wittgenstein’s Tractatus (which he later wrote made a “great impression” on him). Over the decades that followed he produced groundbreaking work in economics, the philosophy of science, and the social sciences. He remains widely cited today across such disparate fields as economics, law and jurisprudence, complexity theory, sociology, psychology, and neuroscience, and is easily one of the foremost cross-disciplinary thinkers of the twentieth century. So it is not surprising that something he wrote in 1945 remains relevant to AI issues in the twenty-first.