Episode 89: Tradition as a Source of Knowledge: Popper vs. Chesterton

Here we interview AI researcher Kenneth Stanley, who makes the case that in complex systems, pursing specific objectives can actually be counterproductive. Instead, whether in machine learning, business, science, education, or art, we should pursue what is interesting. It is in this search for novelty—fueled by curiosity—where innovation and open-ended knowledge creation occurs. Get Ken's book! Why Greatness Cannot Be Planned: The Myth of the Objective Also: Can Bruce find a counter example to Ken's thesis? How does one 'detect novelty' using an algorithm? Is creativity really a search algorithm? --- Support this podcast: https://podcasters.spotify.com/pod/show/four-strands/support

This time we invited some of the coolest and smartest people we know to have a freewheeling discussion on morality loosely centered on Jonathan Haidt's “rider and the elephant” metaphor. We take a deep dive into this idea that moral reasoning is a slave to our passions. Guests: • Lulie Tanett (https://open.spotify.com/show/6OPFnEt6uTOTGeSpnZ1YDp?si=4exIQOUfQzOg4TIU2hZ5hA) • Vaden Masrani (https://open.spotify.com/show/1gKKSP5HKT4Nk3i0y4UseB?si=Iu1WkwJMR1GHlm3OLrUwNA) • Ivan Phillips (https://www.amazon.com/dp/B08VGCFVJB?ref=cm_sw_r_mwn_dp_33ZJEY7V0RP00CG7566Z&ref_=cm_sw_r_mwn_dp_33ZJEY7V0RP00CG7566Z&social_share=cm_sw_r_mwn_dp_33ZJEY7V0RP00CG7566Z&language=en_US) • Ray Scott Percival (https://www.amazon.com/Myth-Closed-Mind-Understanding-Rational-ebook/dp/B007ED2YOG/ref=mp_s_a_1_1?crid=18OW1OJ7SHU0F&dib=eyJ2IjoiMSJ9.JSCCp7cMzHYl926ph94huzUH8e6nS5VFbeyXnBuWHk_8xfeA3aYMNGdbPKf51RTbatD5MJ6psFT9Md-wcXMohLMIVZMTtZYFZPkdvMPLieZem163A_H5xch8hiTt28hByPAtMm3xFqIUtQ9GLpkOI_5Pr7TzJ8Fw7bfiYqt36gnx4yeJSb8a4eOSff3p5QJ04oLY9PUNBdGPtxcILt_ung.cTeFXFI-PZaMPhyBZtFcJ7mIY2k4Kkq1fTEIafAEsxs&dib_tag=se&keywords=ray+scott+percival&qid=1728763752&sprefix=ray+scott+percival+%2Caps%2C156&sr=8-1⁠; https://open.spotify.com/artist/3B1Bh10uUljUX9iNmPOYZo?si=NWnRyuv1T7aHRGWZIXZYzA) --- Support this podcast: https://podcasters.spotify.com/pod/show/four-strands/support

This episode we interview Professor of Philosophy Stephen Hicks. In his excellent books Explaining Postmodernism and Nietzsche and the Nazis it becomes clear that the history of bad and good ideas—which he sees through the lens of Enlightenment and counter-Enlightenment philosophers—is more than an academic issue but something with monumental importance for human life and prosperity. Rather than focus on this aspect of his work, which is widely known, we thought we’d ask him questions on epistemology, focusing on contrasting critical rationalism and objectivism. --- Support this podcast: https://podcasters.spotify.com/pod/show/four-strands/support

Can philosophical theories be refuted? What is a bad explanation? Can all theories be made more empirical? In search of an answer to these questions, Bruce takes a deep dive into what he believes is the correct way to apply “Popper’s ratchet” to metaphysical or philosophical theories. Along the way, Bruce puts forward a generalization of testability he calls “checkability” and explains why “vague-maning” our theories is “worse than dogmatism.” --- Support this podcast: https://podcasters.spotify.com/pod/show/four-strands/support

Continuing from episode 91, we continue our deep dive into Popper's Conjectures and Refutations Chapter 8 where Popper explains how to use his epistemology on philosophical theories that (by definition) can't be 'refuted'. Despite agreeing with most of Popper's specific arguments, we offer some considerable criticisms to Popper's approach to criticizing philosophical theories -- particularly to Popper's criticisms of the theory of Determinism which is a 'best theory' by any fair standard but Popper (incorrectly) thought was false. Bruce argues that Popper's approach in C&R Ch. 8 is problematic because it opens the 'Crit Rat Loophole', which is a common way CritRats interpret Popper that allows any preferred theory to be declare a 'best theory' based on the scantest of criticisms. Bruce argues that Chapter 8 of C&R fails in this important regard because it doesn't give a good answer to the question "How does one tell the difference between a good philosophical explanation and a bad explanation?" --- Support this podcast: https://podcasters.spotify.com/pod/show/four-strands/support

Forgive the clickbait title. The episode should probably actually be called "The (Lack of) Problem of Induction" because we primarily cover Popper's refutation of induction in C&R Chapter 8. This episode starts our deep dive into answering the question "What is the difference between a good philosophical explanation and a bad explanation?" To answer that question we go over Karl Popper's "On the Status of Science and of Metaphysics" from his book Conjectures and Refutations Chapter 8. In this chapter Popper first explains why he believes 'there is no such thing as induction' (from page 18 of Logic of Scientific Discovery) by offering his historical and logical refutation of induction. In this episode we go over Popper's refutation of induction in chapter 8 of C&R in detail and then compare it to Tom Mitchell's (of Machine Learning fame) argument of the 'futility of bias free learning.' We show that Mitchell's and Popper's arguments are actually the same argument even though Mitchell argues for the existence of a kind of induction as used in machine learning. Bruce argues that the difference is not a conceptual or theoretical difference but just a difference in use of language and that the two men are actually conceptually fully in agreement. This makes machine learning both a kind of 'induction' (though not the kind Popper refuted) and also gives machine learning an interesting and often missed relationship with critical rationalism. Then Bruce asks the most difficult question of all: "Is there anyone out there in the world other than me that is interested in exploring how to apply Karl Popper's epistemology to machine learning like this?" You can find a copy of Mitchell's text here if you want to check out his argument for the futility of bias free learning for yourself. As I mention in the podcast, I'm shocked Critical Rationalists aren't referencing Mitchell's argument constantly because it is so strongly critical rationalist in nature. But the whole textbook is just like this. --- Support this podcast: https://podcasters.spotify.com/pod/show/four-strands/support

Today our guest Ivan Phillips methodically explains what Bayesianism is and is not. Along the way we discuss the validity of critiques made by critical rationalists of the worldview that is derived from Thomas Bayes’s 1763 theorem. Ivan is a Bayesian that is very familiar with Karl Popper's writings and even admires Popper's epistemology. Ivan makes his case that Bayesian epistemology is the correct way to reason and that Karl Popper misunderstood some aspects of how to properly apply probability theory to reasoning and inference. (Due in part to those theories being less well developed back in Popper's time.) This is a video podcast if you watch it on Spotify. But it should be consumable as just audio. But I found Ivan's slides quite useful. This is by far the best explanations for Bayesianism that I've ever seen and it does a great job of situating it in a way that makes sense to a critical rationalist like myself. But it still didn't convince me to be a Bayesian. ;) --- Support this podcast: https://podcasters.spotify.com/pod/show/four-strands/support

This week we discuss the book Orthodoxy by G.K. Chesterton (1908), perhaps the most famous defense of the Christian tradition. We contrast this with Karl Popper’s talk, “Towards a Rational Theory of Tradition” (1948), from his collection of essays, Conjectures and Refutations. We consider: What is the role of tradition in science and knowledge? Is there a relationship between liberalism and Christianity? Is Chesterton actually a rationalist? What are the paradoxes of Christianity? Is there a link between madness and rationality? Follow us on Twitter: ⁠https://x.com/bnielson01⁠ --- Support this podcast: https://podcasters.spotify.com/pod/show/four-strands/support

Here Bruce reflects on AI researcher Kenneth Stanley’s assertion that setting specific, measurable goals may actually hinder discovery and innovation, which he writes about in his book, Why Greatness Cannot Be Planned: The Myth of the Objective. How does Stanley’s insight relate to critical rationalism, education, and life in general? We cover topics including: Why are objective sometimes misleading? When are objectives appropriate and when are they misleading? How did Stanley and his team discover the problems with objectives? How does this relate to the problem of open-endedness? How did he implement a program to explore alternatives? What was the result? What are implications for AI/AGI, scientific research, and education? How does these theories relate to Darwinian evolution and Popperian epistemology? Are natural selection and biological evolution the same thing? How important is 'selection' to knowledge creation? Follow us on Twitter: https://x.com/bnielson01 --- Support this podcast: https://podcasters.spotify.com/pod/show/four-strands/support

Is the universal explainer hypothesis falsifiable? How does the concept of universality relate to human minds? Is anything truly beyond human comprehension? And how would you frame universality as an interesting topic at a party? This week we also feature a guest, Dan Gish, a fellow traveler Bruce has connected with on Twitter. Dan (on Twitter) had questions about if the incomprehensibility of LLMs refuted the universal explainer hypothesis. This was Bruce's attempt to give him an honest answer to Dan's questions. Follow us on Twitter. --- Support this podcast: https://podcasters.spotify.com/pod/show/four-strands/support