The Theory of Anything (Bruce Nielson and Peter Johansen)

With guest Ivan Phillips, we discuss and debate subjective vs objective morality. Does the concept of objective morality ever make sense given “Hume’s guillotine”? Can humans ever really live as though morality is subjective? Along the way, we take detours into Bayesian epistemology vs critical rationalism.

Our discussion with Bart Vanderhaegen finishes with a discussion about why businesses prefer to tell their employees what to do and why this is preferred to chaos. Yet if they want to survive in the modern era, this is no longer the best way to lead your company because it doesn't lead to knowledge creation.

We also discuss the difference between a good and a bad compromise as well as male and female stereotypes in business. And should math tests be timed? Or is that just sexist? Enquiring minds want to know. 

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Over the years Bruce collected a series of 'problems' with the Popperian concept of refutation. Or so he thought. A chance encounter with Popper scholar Danny Frederick led to him re-evaluating Popper's writings and realizing that Popper sometimes uses terms (such as 'refutation', 'falsification', and even 'theory') in idiosyncratic ways that aren't quite how most people would understand those terms. This leads to both Popper's opponent and fans alike sometimes misreading him. It turns out that the 'problems of refutation' that many philosophers cite as disproof of Popper are actually due to misunderstanding Popper due to his specialized vocabulary. 

In this episode, we cover Bruce's list of 'problems of refutation' (which he know believes are all pseudo-problems) and explains his encounter with Danny Frederick and how it led to him re-imagining Popper's epistemology in different terms that were easier for laymen (and philosophers) to understand. 

Blog Post Series on The Problems of Refutation

1. A Summary of Deutsch’s Epistemology
2. The Problems of Refutation
3. Popper Explains The Asymmetry Between Refutation and Verification
4. Do Deutsch and Popper Disagree Over Refutation?
5. There is Nothing Wrong with the Language of Support
6. Are Refutations and Verification Really Symmetrical Within A Theory Comparison?
7. Demarcation: What Does it Mean to Be Empirical?
8. But What If You Verify a Theory That Can Only Be Verified?
9. The Two (or More) Kinds of Refutation
10. How to Make Popper’s Epistemology More Clear

Sadia Naeem joins us to discuss her own research and musings into the problems and mystery presented by time.

In this video upload available on Spotify (we'll try this once and see how it's received), we revisit Reinforcement Learning (from way back in episode 28) and this time discuss how to turn it into Deep Reinforcement Learning by swapping out the Q-Table and putting a neural network in its place. The end result is a sort of 'bootstrapping intelligence' where you let the neural net train itself. 

We also discuss: 

• How this, if at all, relates to animal intelligence. 
• Is RL a general purposes learner? 
• Is it a path to AGI?

Links:

• Github Code Base
• Presentation Slide Pack
• Youtube version

Does every one of us live forever in the multiverse? Is death a solvable problem? What is “quantum suicide”? Is quantum torment a concern? Does every fantastical thing we can imagine occur somewhere in the multiverse? What are “Harry Potter universes? Are we Boltzmann brains? Bruce, Cameo, and Peter consider these questions in this week’s episode.

Image from jupiterimages on Freeimages.com

The postmoderns were wrong! Knowledge can progress, despite uncertainty!

What makes a good explanation vs a bad explanation? You probably intuitively recognize when an explanation is bad, but do you know why you prefer one explanation over another? Just what exactly is wrong with "because I said so" or "fairies did it"?

In Episode 3 Bruce and Cameo finally solved the problem of how we can make progress in knowledge despite our lack of certainty using Karl Popper's Theory of Knowledge. In this episode, we finally ask what makes a good explanation vs a bad explanation? And how can we recognize the good ones?  

This is part 4 of our 4 part series covering the Theory of Knowledge of Karl Popper as interpreted by physicist David Deutsch. Bruce explains this epistemology to Cameo and Carey, who are hearing it for the first time as we record. Will they agree with it or will they think it's a bunch of baloney? Find out by downloading this podcast. 

If you enjoy this podcast, please give us a 5-star rating.

Youtube Version with optional visuals:

https://www.youtube.com/watch?v=iSquq_JYI4c&list=PLbU2Yvjwp2jI0mTFUVLVtRw8MQJev0QYt&index=4

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Part 3 of our series on Computational Theory. Using the theory we've built up, we now prove that Artificial General Intelligence is possible due to what is called "The Turing Principle" which is the most profound philosophical implication of Computational Theory.

Plus Cameo asks Bruce about how religious people look at these theories. 

Youtube version with optional visuals

Note: Due to the nature of these Computational theory episodes, it might be helpful to see the Youtube visuals. 

In an episode that may (or may not) be his magnum opus, Bruce introduces his term for Karl Popper’s idea that you are only allowed to solve problems with your (scientific) theory by making it more empirical, not less empirical.

Bruce makes the case that this is one of Karl Popper’s least appreciated ideas, as all of us are tempted by ad hoc saves that move our ideas in the direction of vagueness.

Bruce also considers where conjectures come from and if Popper thought there existed a scientific method.

What is the “two sources hypothesis,” or the idea that there exist only two sources of knowledge in the known universe: Darwinian natural selection and human minds? Does a “genetic programming algorithm” used to make a robot walk create knowledge?

Thus begins our deep dive into Deutsch's Theory of Knowledge and particularly his "Two Source Hypothesis." Bruce hints that this is leading towards an investigation into the difference between a non-testable (or philosophical) explanation and a bad explanation as our series on knowledge continues.

Donald Campbell made the bold prediction that all expansions of knowledge will be found to require the Universal Darwinism algorithm of variation and selection. In this episode, we're going to test that prediction and see if it holds up against what we currently know about Artificial Intelligence and Machine Learning.   

For example, does (apparent) knowledge created by Gradient Descent require variation and selection? Or is it really and truly inductive? Or does it just fail to create knowledge at all despite clearly creating improvements?   

Ultimately, we'll find that Machine Learning creates an exciting set of epistemological problems that need to be solved!

Youtube version with optional visuals

Political polarization is dangerously on the rise. People feel uncomfortable speaking out so they seek comfortable echo chambers that reinforce their views, making them even less capable of interacting with alternative opinions. 

In this episode Cameo and Bruce talk about the fictional story "Shiri's Scissor" which is about a machine learning algorithm that creates polarization. When the algorithm accidentally escapes into the wild it polarizes the nation and starts to destroy it. The story strikes all too close to reality at times due to the role machine learning played in creating our polarized environment on social media and elsewhere. 

But what can we do about this problem? How can it be addressed? 

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Bruce and Cameo talk to Bart Vanderhaegen about how his consulting company, Pactify, implements Karl Popper's epistemology in a work environment. His process involves generating new ideas at any level of the organization and then developing those ideas via error correction until they solve the problem in an optimal way. 

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How do we square genetically influenced mental disorders with the theory of explanatory universality?

In our last episode, Tracy asked Bruce how to reconcile her experience with mental disorders, like narcissism, with the idea of Universal Explainers. In this episode, Bruce does his best to tease out an answer. (While admitting that we can't answer her entirely--yet.) 

In "The Beginning of Infinity", David Deutsch offers some solid criticisms of current experiments to determine how much of a personality trait is 'heritable.' This has led some of his fans to take his ideas to some extreme conclusions not implied by the book. For example, some people now claim that genes play no role at all in influencing Universal Explainers. In fact, Deutsch did not say this. 

According to Deutsch (in BoI), genes can influence our ideas and personality traits via something as simple as how physiology (physical traits) interact with culture (standards of beauty) and that can in turn impacts one's personality (perhaps increasing happiness.) So we now have at least one example of how genes can have an impact on our personality and ideas. (Via physiology interacting with culture.) 

With this in mind, Bruce asks the obvious question: What are other ways genes can affect personality traits and ideas that do not violate explanatory universality? 

Bruce's list (partially revealed in this episode) is a testable set of ways genes may impact our personality and ideas. This suggests how we might go about responding to critics of the theory of Explanatory Universality without violating Popper's epistemology via either ad hoc saves or ignoring basic statements (i.e. repeatable observations) from existing experiments.

O Falibilista's review of "The Ape That Understood the Universe – how the mind and culture evolve" is an excellent example of how bad evolutionary psychology can be at times. 

Dwarkesh Patel published an article called "Contra David Deutsch on AI". This article was actually a defense of IQ theory against the charge (often made by fans of David Deutsch) that the existence of Explanatory Universality destroys IQ theory entirely. But how accurately does Dwarkesh portray Deutsch's view? (For that matter, how accurately do fans of David Deutsch portray Deutsch's viewpoint?) And how good are Patel's criticisms of Deutsch's view? 

With some help from a tweet from Brett Hall on IQ theory, we compare and contrast Patel's and Hall's viewpoints and lay out the disagreements that exist. 

Brett argues that Explanatory Universality implies we are all equally intelligent (i.e. have an equal capacity to learn) and that the only difference between people is our levels of interest in the knowledge that currently society happens to value. Is he correct? Or are the experiments cited by Patel wrong? If so, how?

Or to put this another way, if we did demonstrate via an experiment that some people do gain knowledge faster than others (as Patel claims), would that refute the theory of explanatory universality? Or are Brett's claims not actually implications of explanatory universality?

How intelligent are animals?

In this episode, we introduce our series on animal intelligence rooted primarily in the research of Richard Byrne. Richard Byrne (mentioned in Beginning of Infinity) is a first-class Popperian researcher (though he doesn't realize it).

We first talk about how Bruce got interested in this subject after reading Fabric of Reality (but before reading Beginning of Infinity) and how animal intelligence is at once beyond anything we know how to program but also unbelievably unintelligent at times. We consider how the Pseudo-Deutsch Theory of Knowledge has misled the Deutsch fan community about how much of an animal's knowledge is "in its genes" as well as how many fans of Deutsch (due to the same misunderstandings) have accidentally fallen into Lamarkism because they don't understand the importance of the Baldwin effect on the evolution of animal algorithms.

Links:

• The Monkey Fairness Experiment
• Dog Playing Jenga
• Cat Playing Jenga (Another Example)
• Richard Byrne's book Evolving Insight: How it is we can think about why things happen
• Richard Byrne's book The Thinking Ape: The Evolutionary Origins of Intelligence
• A primer on Donald Campbell's Theory (including animal learning and the Baldwin effect)
• A short summary of how Popper and Campbell (apparently) disagree with David Deutsch on what counts as knowledge creation

In Episode 2 we asked the hard question "Does Science Work?" and discussed all the seemingly insurmountable problems a correct "Scientific Method" must solve.   

Bruce and Cameo now discuss the proposed solution to the problem using the Theory of Knowledge (epistemology) of Karl Popper as modified and explained by David Deutsch.   

This is part 3 of our 4 part series covering the Theory of Knowledge of Karl Popper as interpreted by physicist David Deutsch. Bruce explains this epistemology to Cameo and Carey, who are hearing it for the first time as we record. Will they agree with it or will they think it's a bunch of baloney? Find out by downloading this podcast.   

If you enjoy this podcast, please give us a 5-star rating!

Youtube Version with optional visuals:

https://www.youtube.com/watch?v=-w6KXp8QKf0&list=PLbU2Yvjwp2jI0mTFUVLVtRw8MQJev0QYt&index=3

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In this episode, we discuss the value of failure and how businesses have yet to fully embrace the Popperian notion that we learn from our failures, so we should want to fail more, not less. 

Bruce continues to consider what our best theories tell us about knowledge. Is there something special (or even physically different) about the knowledge created by nature through biological evolution and human minds (i.e. the 'two sources hypothesis')? How should we think about knowledge created in human minds that could take us to the moon and beyond or divert an asteroid? Is it physically different from the kind of adapted information created by animals or the immune system? Or does it merely a broader and deeper search for solutions?

Along the way, he delves into machine learning, animal behavior, the immune system, trade secrets, robots, and many other concepts related to David Deutsch’s ideas about knowledge but are outside the 'two sources' and thus not considered 'knowledge' by David Deutsch.

Richard Byrne has spent his whole career trying to determine when animals learned to 'think.' We discuss Richard Byrne's methodology for determining which animals have what he calls 'insight' (the ability to utilize mental models) and why his methodology is awesomely Popperian. Then we go over many examples of animal behavior that can't be explained via genetic programming or trial-and-error learning. We also compare machine learning and animal intelligence and why animal intelligence is beyond our current machine learning capabilities.

Links:

• Richard Byrne's book Evolving Insight: How it is we can think about why things happen
• Richard Byrne's book The Thinking Ape: The Evolutionary Origins of Intelligence
• A primer on Donald Campbell's Theory (including animal learning and the Baldwin effect)
• A short summary of how Popper and Campbell (apparently) disagree with David Deutsch on what counts as knowledge creation

The popular media confuses Artificial Intelligence and Artificial General Intelligence. All the progress is in the first while all the interest is in the second. But what is Artificial Intelligence? In this episode, we explain the umbrella term and its subfields. Plus we introduce how Artificial Intelligence actually ties to all four of David Deutsch's four strands. That makes it an exciting field all of its own even though it's not a path to AGI.

Youtube video with optional visuals

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?

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Though our guest Mark Biros is clearly immersed in critical rationalism and the worldview of Popper and Deutsch, he also has some fairly strong criticisms of some of the ideas popular in what could be called the CritRat community. Here we try to work out our differing ideas on environmentalism, epistemology, quantum mechanics, social media, optimism, monarchies, cults, human extinction, and more.

Computational Theory is possibly the most underrated of all scientific theories. In fact, most scientists think of it as a branch of mathematics rather than what it really is, a branch of physics. Computational Theory is the science of what the laws of physics allow you to compute. As such, it is one of David Deutsch's "4 Strands" which are the 4 most important scientific theories we have.    

In this episode, Bruce and Cameo cover the bare minimum of Computational Theory that you'll need to be able to understand the profound philosophical implications of the theory. In future episodes, we'll discuss such implications such as why we know the laws of physics allow for the creation of Artificial General Intelligence (AGI).

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Youtube version with optional video:

https://www.youtube.com/watch?v=smGuNwKy8oA

Note: Due to the nature of these Computational theory episodes, it might be helpful to see the Youtube visuals.

A deep dive into David Deutsch’s “principle of optimism” featuring Sam Kuypers, Vaden Masrani, Hervé Eulacia, Micah Redding, Bill Rugolsky, and Daniel Buchfink. (Plus, of course, Peter and Bruce). Are all evils due to a lack of knowledge? Are all interesting problems soluble? ALL the problems, really?!?! And what exactly is meant by interesting? Also, should “good guys” ignore the precautionary principle, and do they always win? What is the difference between cynicism, pessimism, and skepticism? And why is pessimism so attractive to so many humans?

Bruce Caldwell (a scholar interested in Popper and Hayek) wrote a long paper in the Journal of Economic Literature (March 1991) called 'Clarifying Popper'. In this episode, Bruce Nielson summarizes and discusses Caldwell’s paper on how Popper’s ideas could be applied to economics. How well did Bruce Caldwell do in his goal of clarifying Popper's epistemology?

Out next episode is another interview with Brian Boutwell and we discuss this paper a few times. So this summary will help those that don't have access to it.

Copy of Bruce Caldwell's "Clarifying Popper"

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. ;)

We wrap up our discussion with Sadia Naeem covering possible solutions and criticisms of those solutions. 

Historian Matt Bowman discusses his new book, The Abduction of Betty and Barney Hill: Alien Encounters, Civil Rights, and the New Age in America. Betty and Barney Hill were one of the first and most famous persons who claimed to be abducted by aliens. Aside from being a story about UFOs, their life story hinges on a complicated relationship with religion, race, politics, science, and psychology in America in the 50s and 60s.

Brian Boutwell is a professor of criminal justice at the University of Mississippi who specializes in “quantitative genetics, with a focus on environmental and psychological risk factors for antisocial and violent behavior.” He has a TED talk, numerous articles in Quillette, and has been published in many journals. Here we discuss his upcoming meta-analysis on twin studies soon to be published in Nature.

We discuss the following two articles:

• Behavioural genetic methods by Willoughby, Polderman, and Boutwell in Nature.
• Meta-analysis of the heritability of human traits based on fify years of twin studies by Polderman, etc. in Nature.

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.

Karl Popper's philosophy is often heavily associated with the concept of "criticism." But most people don't react well to criticism. Why is that? Should you just give criticism whether people want to hear it or not?   

Kim Scott's book "Radical Candor" answers this question with some surprising answers. In part 1 we went over the motivations for and basic framework for Scott's approach. In this episode, we get into the details of how to set up a culture of criticism that will be actually effective.

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Unlike the universally acclaimed Marvel movies, Disney has struggled to turn their Purchase of the Star Wars franchise into the powerhouse they were hoping for. Why is there such strong split opinions on these movies? What did Disney do well and what did they get wrong?   

We argue that good art is hard-to-vary, so it's easy to get something wrong that detracts from the overall movie. The recent Disney Star Wars movies aren't bad movies, but they aren't great Star Wars. We discuss how the movies slowly departed from the myth creation stories they started out as and how this impacted the overall storytelling.

Tracy leads a discussion about Narcissistic Personality Disorder (NPD) and Borderline Personality Disorder (BPD). We discuss various other mental disorders as well. We sadly admit that some disorders are currently so serious that there is little hope of helping those that have them. (And they may not even be aware that they have a disorder!) But will this always be true? If all problems are soluble and human beings are universal explainers, then the answer should be a resounding "no!" But Tracy asks 'if we're all universal explainers, then why can't we help people today?' as well as 'does this have any relevance to AGI safety programs?'

What's it like to leave your home, your family, and your country and go to a whole new land and culture -- possibly never to return home again? What challenges would you have to overcome? How much help would you receive? How would you blend your culture with the new one you just moved to?  

What's it like to dance 2000-year-old ancient forms as a way to express your culture's ancient myths?   

Sarika Nayak and her family left India for their careers and then started a family here. They didn't originally intend to put down permanent roots, but now they are working on becoming citizens. Sarika brought with her a love of dance and love of people. She teaches dance to others emphasizing dancing from her own culture -- both Bollywood and Hindu dancing.   

In this episode, we talk with Sarika and learn what it's like to start a new life in a new culture and how she successfully integrated her love of both cultures.

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Youtube version with optional video:

https://www.youtube.com/watch?v=7wascWH6dTc&list=PLbU2Yvjwp2jI0mTFUVLVtRw8MQJev0QYt&index=6

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In this (mostly) standalone episode, we cover how Deutsch and Byrne each interpret Byrne's theory differently. Deutsch emphasizes the micro-level actions and gestures of great apes and the clear lack of understanding of what each gesture does. Byrne emphasizes the macro-level and the flexible intelligence required to come up with a program of action to accomplish a novel goal. Byrne's theory of 'animal insight' makes specific testable claims. To Byrne, great apes (especially Chimps) can 'think.' His theory says that animal insight was a necessary precursor to human insight and that humans utilize both kinds. If he's right, then animal insight has relevance to AGI studies. Deutsch has doubts about all of this and thinks of Bryne's theory more as evidence that animals cannot think.

We also discuss how Byrne and Deutsch both understand the mirror test differently. And finally, we dip just a bit into animal sentience and discuss why the theory that animals feel things is the prevailing theory not so much because it's a great theory but more because it has no real current competitors. It's difficult to explain much animal behavior without either tacitly referring to animal feelings or just clearly making up bad ad hoc explanations. 

While it's helpful to have listened to the 3 previous episodes, this episode mostly stands alone.

Links:

• Richard Byrne's book Evolving Insight: How it is we can think about why things happen
• Richard Byrne's book The Thinking Ape: The Evolutionary Origins of Intelligence
• Video on dolphin intelligence/communication

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?"

Back in 2012, David Deutsch wrote an article called "Creative Blocks: How Close are we to Creating Artificial Intelligence?" This article inspired Bruce to go back to school and study Artificial Intelligence and get a Master's degree in the field.

A decade later, a lot has changed in the field of AI, and the field has never seemed so exciting. But are we really any closer to the goal of true universal intelligence?

We take a look back at the article and assess it from the vantage point of what we know now, a decade later. How much did Deutsch get right and how much is on less solid ground?

This week we discuss the chapter “Why are Flowers Beautiful?” from the book Beginning of Infinity by David Deutsch. Through our discussion we consider: Does relativism make any sense? Is preferring Mozart to a child banging on a piano really just an arbitrary preference? If progress in art is real, will human minds ever stop increasing the level of beauty in the world? Are humans more objectively beautiful than other species? (And are women more beautiful than men?) Is music “cheesecake for the ears,” as Steven Pinker puts it? And is cheesecake itself even “cheesecake for the mouth”? Is progress in science also intertwined with aesthetic progress?

Reinforcement Learning is a machine learning algorithm that is a 'general purpose learner' (with certain important caveats). It generated a lot of excitement with its stunning victory of Alpha Go against Lee Sedol the world Go champion.   

In this podcast, we go over the theory of reinforcement learning and how it works to solve any Markov Decision Problem (MDP).   

This episode will be particularly useful for Georgia Tech OMSCS students taking classes that deal with Reinforcement Learning (ML4T, ML, RL) as we briefly explain the mathematics of how it works and show some simple examples.  

This episode is best when watched on the Youtube channel, though we'll release an audio version as well. But the visuals are helpful here. The audio version is abbreviated and removes the mathematical theory and proof.

Bruce and Cameo are joined by Dennis Hackethal, Ella Hoeppner, and Thatchaphol Saranurak as we discuss Artificial General Intelligence (AGI).

In part 3, each of the guests talks about their own theories about Artificial General Intelligence and where they are taking their research. 

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Link to "Evolution is exponentially more powerful with frequency-dependent selection" paper.

In this episode, we interview Chiara Marletto about her recent book The Science of Can and Can't: A Physicist's Journey Through the Land of Counterfactuals as well as discussing Constructor Theory in general and how it might help us form a new mode of explanation in physics. We ask her some tough questions about constructor theory and she fields the questions very well. 

For those interested in q-numbers vs real numbers, see Sam Kupyer's lecture on our Youtube channel.

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In the last episode, we showed that Artificial General Intelligence was possible according to the laws of physics. This episode is part 1 of a 3-part panel discussion among computer scientists interested in AGI. Bruce and Cameo are joined by Dennis Hackethal, Ella Hoeppner, and Thatchaphol Saranurak -- all interested in both AGI and Karl Popper's epistemology and believe Popper's theories can shed light on how to discover AGI.

In part 1, we discuss how AI (Artificial Intelligence) differs from AGI (Artificial General Intelligence). 

Our Christmas gift to you this year is episode 100: an interview with The Man (TM) himself!

Bruce stumbles over himself fan-boying as he asks all his burning (but geeky) questions about cosmology, the omega point, and probability. How do Deutsch and Tipler differ on optimistic end-time cosmology? Is the Omega point refuted by observation (Deutsch) or not (Tipler)? Does heat death contradict the principle of optimism? Is it a bummer? Does stochasticity really not exist? And is it rational to wear a mask during COVID? How do you apply epistemology to a question like that when you lack enough data to severely test your theories but still need to make a decision?

Peter asks: Are free will and downward causation related? Do our genes attempt to coerce us? Why are explanatory and computational universality so confusing? And what if studies show that authoritative parenting is best for children?

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Karl Popper has a radical theory of 'dualistic evolution' where behavior had to evolve first before physical evolutionary changes could be taken advantage of. As part of his theory, Popper pointed out that an animal's ability to learn would be paramount to making evolution work at all -- similar to the Baldwin effect discussed in the last episode, but now for physical adaptions. This means evolution would have had intense pressure to evolve learning algorithms early in the evolutionary tree. 

As it turns out, Richard Byrne's work largely corroborates Popper's theory of dualistic evolution. Nearly all animals show an ability to do trial-and-error learning and this is the main source of 'animal intelligence' in the animal world. Byrne even argues that this ability to do trial-and-error learning is a form of evolution where animals let their behaviors 'die in their place' rather than having to wait for the slow biological evolutionary learning processes of the genes.

We also discuss what split-brain patients might teach us about human explanations and go over examples of animal-like gene channeled learning in humans. 

Links:

• Richard Byrne's book Evolving Insight: How it is we can think about why things happen
• Richard Byrne's book The Thinking Ape: The Evolutionary Origins of Intelligence
• Kenneth Stanley's work on the problem of open-endedness
• The Monkey Fairness Experiment
• Frans Waal's Paper: Monkeys Reject Unequal Pay
• A primer on Donald Campbell's Theory (including animal learning and the Baldwin effect)
• A short summary of how Popper and Campbell (apparently) disagree with David Deutsch on what counts as knowledge creation

There seems to be broad agreement, even among Karl Popper's own students, that he was a deeply dogmatic individual. In this episode we ask the question 'Was Karl Popper Dogmatic?' by reviewing a humorous article in Scientific American by John Horgan on August 22, 2018. Along the way, we discuss by what means we judge dogmatism. How do we even tell if someone is dogmatic or not? Is there a litmus test for dogmatism? If so, what is it? 

Link to John Horgan's article.

Bruce and Cameo are joined by Dennis Hackethal, Ella Hoeppner, and Thatchaphol Saranurak as we discuss Artificial General Intelligence (AGI). 

In part 2, among other things, we discuss various objections people make to AGI and why they can't be correct due to the existence of universality. We also discuss what a "Universal Explainer" is and if it is possible to create a quantum "oracle machine" which is a computer that can compute things that the Turning machine can't. 

In this episode, we discuss how the work of Michael Levine intersects with the work of Raymond and Denise Noble's as well as Donald Campbell's. Levine recently did a TED talk on how the bioelectrical system between the cells is itself an evolutionary error correction process that determines the phenotype. This is a strong example of both Campbell's 'hierarchy of evolution' and the Noble's "purposiveness" in evolution where one level of the evolutionary hierarchy can cause levels below to teleologically evolve towards a purpose. The Noble's claim this refutes the classical formulation of Neo-Darwinism which they say is gene-centric. This also means we possible breakthroughs in anti-aging and medical treatments that don't require gene therapy or CRISPR. 

Levine's TED talk

Was the Watchmaker Blind? Or Was She One-Eyed?

An Introduction to Campbell's Evolutionary Epistemology

Here we discuss a 1992 interview with David Deutsch where he makes the case that video games are inherently educational, not addictive, and that children should not be stopped from playing as much as they want. We contrast the view of humans, science, and knowledge promoted there by David Deutsch with the more pessimistic view of thinkers such as Jonathan Haidt today. Bruce and Peter reflect on their own mixed feelings on this issue both as critical rationalists and parents.

David Deutsch on video games: https://takingchildrenseriously.com/video-games-a-unique-educational-environment/

Peter briefly quotes from this recent article by Jonathan Haidt: https://www.thefp.com/p/jonathan-haidt-worried-about-the-boys-too

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Here we move three arguments from social media to the podcast. 1. Given Deutsch’s universal explainer hypothesis, does it make sense to say that men commit more crimes due to testosterone? Are humans only 'approximately' Universal Explainers? 2. Can anything in reality be simulated? What exactly does it mean to be simulated? 3. Is “heat death” a bummer? What would Conan the Cimmerian say?

Sadia Naeem continues the discussion about unsolved problems in physics. This time we talk about (among many other things) symmetry and novelty. 

In this episode Cameo, Tracy, and Bruce geek out over how good the Marvel TV shows are and how much they really get right. Spoilers abound, so be warned. 

Many Worlds Quantum Mechanics is the only current explanation we have of quantum physics. Yet most scientists today still prefer to not have an explanation at all rather than accept it. 

Sam Kuypers joins us to discuss his paper "Everettian relative states in the Heisenberg picture" that he co-authored with David Deutsch. He explains why the Heisenberg picture of quantum physics lends itself naturally to a local many worlds view of quantum physics. 

Also, we discuss if King Arthur could possibly be both real and fictional at the same time. Whaaaattt!?

This audio podcast requires no mathematical knowledge. However, for those interested in reading the actual paper, Sam prepared a math primer available on our youtube channel. 

This week we discuss neo-Darwinism vs post-Darwinism. Neo-Darwinism meaning a gene centric view of evolution, which is also called the great synthesis since it unifies natural selection with genetics and paleontology and perhaps even human psychology.

Post-Darwinism is a view that emphasizes factors outside random mutation, like epigenetics or the assertion that organisms and cells can alter their own genome in a beneficial way.

Here Bruce specifically concentrates on the work of biologist James Shapiro’s critical look at Richard Dawkins’ neo-Darwinism.We consider, does it really make sense to see our bodies and minds as tools governed by our masters DNA? Does post-Darwinism, also called “third way evolution,” offer a meaningful alternative to both neo-Darwinism and the theism of intelligent design? Does this way of looking at biology say something about the very nature of reality and the laws of physics?

This is part 1 of a loose series. Part 2 will cover criticisms of Noble and Shapiro. Part 3 will cover the work of Michael Levin. However, you don't really need to listen to them in order and we provide context each time.

James Shapiro's Evolution: A View from the 21 Century

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Bruce wraps up his epic 6 part series on knowledge and the 'two sources hypothesis' (i.e. Deutsch's theory that all 'knowledge' comes from only two sources: Biological evolution and human minds).

What happens if we take all the non-two sources examples of 'adapted information that cause itself to remain so' (e.g. the walking robot, the immune system, trade secrets, animal learning, animal memes, etc.) and give them their own theory distinct from the theory of 'knowledge'? Sort of like a theory of "a simulacrum of knowledge" (to uses Deutsch's own term) or "Simul-Knowledge" for short.

This turns out to be remarkably easy: you just take the constructor theory of knowledge without any implicit additional criteria. Doing this has immediate profound implications that impact how we see and understand Deutsch's theory of knowledge.

Like to a version of the drawing Bruce refers to throughout the episode.

Special guest, Lulie Tanett, asked me if she could come on my podcast and interview me about religion. Lulie and Peter ask me numerous religion-related questions such as:

• How is the theology of the Church of Jesus Christ of Latter-day Saints (i.e. Mormon church) similar and different from Deutsch's Four Strands worldview?
• What might the Deutsch Four Strands worldview learn from religion?
• In a modern world, what (if anything) can religion still teach us?
• Is religion an ally or a foe of a rational worldview?
• For what matter, what is the most widely accepted rational worldview?
• What about supernatural truth claims of religion? Can they be reconciled with a rational worldview?
• How was the Omega Point theory (from the final chapter of Fabric of Reality) informed by religion?
• What is the Omega Point theory? Why did Deutsch abandon it (in Beginning of Infinity)? What did he replace it with?
• Is Frank Tipler (creator of the Omega Point theory) a nutter or a mad genius?

Is the government hiding a secret UFO recovery program? What should the critical rationalist attitude be towards these kinds of claims? Why exactly would aliens want to hide from us? We discuss these questions and much more.

If you missed it, be sure to check out the congressional hearings on UFOs (UAPs). It was actually quite interesting.

Mick West's video criticizing the theory that aliens are behind all this.

We continue our discussion of ⁠Dwarkesh Patel's article "Contra David Deutsch on AI"⁠ compared to ⁠Brett Hall's tweet on IQ theory⁠. This time we concentrate on criticisms of Brett Hall's theory.  Along the way, we ask the ultimate question:

Why did Karl Popper make his epistemology specifically about refuting empirical scientific theories instead of just generalizing it (like Deutsch does) to criticizing all theories and ideas?

And why is this important?

And then, we talk about how much we really like Brett's theory.

How does ChatGPT really work? Is there a relationship between a program like ChatGPT and artificial general intelligence (AGI)?

This time we review the famous paper "Sparks of Artificial General Intelligence: Early Experiments with GPT-4" from Microsoft Research as well as Melanie Mitchell's criticisms of it.

Other papers mentioned:

• The Unreasonable Effectiveness of Recurrent Neural Networks (2015)
• GPT-4 Technical Report (2023)
• Language Models are Few-Shot Learners (2020)

"Intuitive Eating (A Revolutionary Anti-Diet Approach)" by RDN Evelyn Tribole and RDN Elyse Resch is a book about how to use the natural signals in your body instead of a self coercive diet. It's a strong example of what David Deutsch calls "The Fun Criteria" where you align the implicit information in your mind and body rather than coerce yourself because 'you know it's what's best.' Julene Nielson joins us to compare her experience with dieting vs the Intuitive Eating program. 

Also, we discuss the fact that recipes are hard-to-vary yet also parochial.

How well do the collection of assertions called “3rd way evolution” stand up to criticism? Here, in our second of at least 3 episodes on this topic, Bruce considers the criticisms of Denis Noble and James Shapiro by YouTuber and evolutionary biologist Zach Hancock in his epic video on the subject.  Perhaps the role of epigenetics is overstated, Lamarckism is not back,  and neo-Darwinism is not dead after all.

Bruce sympathetically critiques David Deutsch’s concept of “easy to varyness” as a way to judge our explanations.

Are our best theories about reality truly hard to vary? Bruce makes the case that Popper’s concept of “ad hocness” may be a strangely interwoven concept.

Along the way we get deeper into whether Popperian epistemology is best seen as an attitude or a methodology.

Here we use Nassim Nicholas Taleb’s essay “The Most Intolerant Wins: The Dictatorship of the Small Minority” as a springboard to discuss majority rule, moral progress, knowledge growth, wokism, Karl Popper’s paradox of tolerance, and “big agriculture.”

What is the “problem of open-endedness”? Bruce explores how what might sound like an esoteric machine-learning issue may actually be interwoven with our deepest theories on evolution, human consciousness, and knowledge creation.

Also included: Bruce's guide to how NOT to argue with a Creationist.

References:

• Kenneth Stanley's article: "Open-endedness: The last grand challenge you’ve never heard of"
• The Beginning of Infinity by David Deutsch
• Probably Approximately Correct: Nature's Algorithms for Learning and Prospering in a Complex World by Leslie Valiant

Blake Lemoine, the ex-Google engineer, claims LaMDA -- Google's language model -- is sentient. Is he right?

Alan Turing is perhaps most famous for his "Turing Test" which is a test of intelligence. David Deutsch has some interesting things to say about the Turing Test in "The Beginning of Infinity." Unfortunately, Deutsch's critique of the Turing Test is often misunderstood and it has led to some of his fans disparaging the Turing Test in ways that don't make sense. 

The key question is why can humans so easily -- with a high degree of accuracy -- tell if they are talking to an intelligent being or not by merely having a conversation with the person? What is special about conversation that allows it to be used as a highly accurate test of general intelligence? 

We also present a Turing Test 2.0 that improves upon the original Turing Test by removing the element of deception and formalizes the test better. 

Along the way we answer the following questions:

1. Is Blake Lemoine right that LaMDA is sentient? How can we know?
2. Under what circumstances can a chatbot pass the original Turing Test 1.0?
3. Will we ever have a chatbot that can pass the Turing Test 2.0?
4. What can we learn from the Turing Test about intelligence?

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)

Bruce summarizes his (unique?) understanding of Karl Popper’s epistemology that (possibly?) straddles the line between orthodox and unorthodox and is Influenced both by Deutsch, more old school Popperians, and his own unique interpretation of critical rationalism. Bruce claims that the key difference between regular "folk epistemology" (i.e. how humans reason without a correct understanding of epistemology) and "Popper's epistemology" (aka "Critical Rationalism" or the correct epistemology) is due to Popper's epistemology having a 'second axis' that regular folk epistemology entirely lacks. This 'second axis' is rooted in a choice to make your theories bold and risky by maximizing empirical content.

This makes Popper's epistemology 2-dimensional instead of 1-dimensional.

If this fact is missed, Bruce claims your epistemology collapses back to be regular old folk epistemology and you are no longer doing critical rationalism.

Refutation, corroboration, explanation, induction, falsification, verisimilitude, “the Popperian war on words,” and “Popper’s ratchet” -- from past podcasts! -- are all touched upon.

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Bruce had a chance to talk to David Deutsch and ask him questions about his views of knowledge to clarify if he disagreed with Popper and Campbell about what is considered knowledge. Bruce took notes and in this episode reports back on what he learned. 

This week we have criminologist Brian Boutwell on again for part 2 of our discussion on critical rationalism and social science. Does all science share the same structure? How do you apply Popper's epistemology to social sciences? Are there laws of human nature? If humans are universal explainers, what does it mean to study our behavior?

See episode 68 for a summary of Caldwell's "Clarifying Popper" that we discuss.

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?

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What did David Deutsch get right and wrong in chapter 11, “Time: The First Quantum Concept,” from his first book, Fabric of Reality? Is the flow of time real or an illusion? What does it mean to have free will in a deterministic world? And what are the implications of Bruce’s “Turing world within a Turing world” thought experiment?

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.

This is the first of our two part series (that may or may not be released back-to-back) where Bruce delves into the work Douglas Hofstadter, specifically the book Surfaces and Essences. We consider what is the relationship—if there is any—between critical rationalism and Hofstadter's idea that analogy is a core mechanism of human cognition. Is it fair to criticize Hofstadter's ideas as being inductivism in disguise? Could something like what Hofstadter suggests (i.e. analogy) be central to human consciousness and creation of AGI?

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Sadia Naeem joins us again, this time to explain clocks, block universes, and eternalism. 

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.”

Do animals create knowledge? Deutsch claims they don't because all their knowledge is in their genes. Yet he admits that animals do have memes! But aren't memes, by definition, knowledge outside the genome? How does Deutsch attempt to deal with these problems with his theory of knowledge? And how well do his arguments hold up?

We interview Bruce’s nephew, Brendon Nielson, who is a well-known electronic music artist under the name Dvddy. We discuss how he uses AI as a tool to create music and how this technology is changing how we work and learn. Could AI liberate us from menial labor and education? Along the way, Cameo makes an AI-generated comic book about David Deutsch.

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.

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What did Karl Popper really mean by refutation? How are empirical theories special? How do objective criticisms differ from subjective criticisms? What is the difference between a theory and an explanation? We consider these questions with a tangent into the theory that animals don’t have feelings.

In the previous episode, Bruce pointed out an apparent contradiction between Deutsch's criteria for knowledge as 'adapted information that causes itself to remain so' and his example of the 'walking robot algorithm' which is a case of adapted information causing itself to remain so but that Deutsch doesn't consider to be knowledge.

This time we consider if we can eliminate the 'walking robot algorithm' from being considered 'knowledge' using Deutsch's and Marletto's Constructor Theory of Knowledge.

Does the Constructor Theory of Knowledge save the 'two sources hypothesis'? (i.e. the hypothesis that there are only two sources of knowledge: biological evolution and human ideas)

"Quantum fluctuation messes with the Planck scale, which then triggers the Deutsch Proposition. Can we agree on that?" asks Tony Stark.  

What the heck is he talking about? And who is Deutsch?  

Tony Stark is referring to David Deutsch, of course; a world-famous quantum physicist that is the father of Quantum Computational Theory and the inspiration for many of the ideas in this podcast. Among Deutsch's theories is one on time travel, believe it or not. See https://rb.gy/w5yqvj  

In this podcast, we discuss Deutsch's actual theories on time travel and what the Avengers borrowed from his theories (and what they completely ignored.)  

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Youtube version:

https://www.youtube.com/watch?v=LXKYwt-aRXM&list=PLbU2Yvjwp2jI0mTFUVLVtRw8MQJev0QYt&index=5

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The "Scientific Method" (as taught in schools anyhow) is actually wrong!  

Bruce, Cameo, and Carey continue their discussion about the Theory of Knowledge (i.e. Epistemology) and discuss the challenges any such theory must address. To many (Postmoderns) the problems even seem insurmountable and seem to undermine all of science.    

This is part 2 of our 4 part series covering the Theory of Knowledge of Karl Popper as interpreted by physicist David Deutsch. Bruce explains this epistemology to Cameo and Carey, who are hearing it for the first time as we record. Will they agree with it or will they think it's a bunch of baloney? Find out by downloading this podcast.   

If you enjoy this podcast, please give us a 5-star rating. 

Youtube version with optional visuals:

https://www.youtube.com/watch?v=JYJV3tHe5k8&list=PLbU2Yvjwp2jI0mTFUVLVtRw8MQJev0QYt&index=2

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Is human creativity algorithmic? What is the difference between an Inspiration and a perspiration algorithm? Can mechanical processes ever create knowledge? What is the relationship between creativity and explanation? If we had the 'inspiration' algorithm today, would it use perspiration? Here Bruce continues his exploration of these issues and more.

Is Elon Musk right that Artificial General Intelligence (AGI) research is like 'summoning the demon' and should be regulated?

In episodes 48 and 49, we discussed how our genes 'align' our interests with their own utilizing carrots and sticks (pleasure/pain) or attention and perception. If our genes can create a General Intelligence (i.e. Universal Explainer) alignment and safety 'program' for us, what's to stop us from doing that to future Artificial General Intelligences (AGIs) that we create? 

But even if we can, should we?

"I think we should be very careful about artificial intelligence. If I were to guess like what our biggest existential threat is, it’s probably that. So we need to be very careful with the artificial intelligence. Increasingly scientists think there should be some regulatory oversight maybe at the national and international level, just to make sure that we don’t do something very foolish. With artificial intelligence we are summoning the demon." --Elon Musk

In our previous episode, we asked if Karl Popper was Dogmatic. We also introduced the idea that Karl Popper wasn't convinced that dogmatism was always bad. In this episode, we further explore Karl Popper's idea that dogmatism is sometimes a good thing. We also ask difficult questions like 'How can you tell when you are being dogmatic?' and 'Is it possible to overcome your own dogmatism?'

In the last episode, we gave you the basic theory. Now we're going to show you how Computational Theory is actually used in real life. We'll discuss the various computational classes that exist and one special class in particular: NP-Complete. Using reducibility (as discussed in the previous episode) we can prove that this is a universal class of problems. This provides us evidence (but not a proof!) that many algorithms are too slow to be tractable (i.e. return a result in a useful amount of time.) Finally, we'll discuss the startling fact that some problems can't be computed at all because the laws of physics don't allow it.

Youtube version with optional visuals:

https://www.youtube.com/watch?v=rVpM8XOwmz4

Note: Due to the nature of these Computational theory episodes, it might be helpful to see the Youtube visuals.

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?

How do humans form 'fuzzy categories'? How does this all relate to essentialism? Is essentialism false? Or is it partially true? And how does this all relate to Critical Rationalism?

Picking up where we left off last week, Bruce gets deeper into Douglas Hofstadter’s ideas on language and the mind and his assertion that “analogy-making lies at the heart of intelligence.” Bruce considers how Hofstadter’s theories may be interwoven with ideas on language and cognition promoted by Steven Pinker in "How the Mind Works" along with, as usual, the epistemology of Karl Popper and David Deutsch. We again consider if this is an inductive theory? And how should critical rationalists view theories like this?

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Over the years Bruce collected a series of 'problems' with the Popperian concept of refutation. Or so he thought. A chance encounter with Popper scholar Danny Frederick led to him re-evaluating Popper's writings and realizing that Popper sometimes uses terms (such as 'refutation', 'falsification', and even 'theory') in idiosyncratic ways that aren't quite how most people would understand those terms. This leads to both Popper's opponent and fans alike sometimes misreading him. It turns out that the 'problems of refutation' that many philosophers cite as disproof of Popper are actually due to misunderstanding Popper due to his specialized vocabulary.

In this episode, we cover what Popper himself said about the asymmetry of refutation vs verification, how it relates to the demarcation between empirical and non-empirical theories, and even how it relates to induction. Then we use that knowledge to resolve the 'problems of refutation' we discussed in the last episode. 

Blog Post Series on The Problems of Refutation

1. A Summary of Deutsch’s Epistemology
2. The Problems of Refutation
3. Popper Explains The Asymmetry Between Refutation and Verification
4. Do Deutsch and Popper Disagree Over Refutation?
5. There is Nothing Wrong with the Language of Support
6. Are Refutations and Verification Really Symmetrical Within A Theory Comparison?
7. Demarcation: What Does it Mean to Be Empirical?
8. But What If You Verify a Theory That Can Only Be Verified?
9. The Two (or More) Kinds of Refutation
10. How to Make Popper’s Epistemology More Clear

AKA "David Deutsch DESTORYS the Simulation Hypothesis"

Bruce take a deep dive into solipsism in the form of the brain in a vat thought experiment, Nick Bostrom’s simulation hypothesis, and related ideas. Does the Church-Turing-Deutsch thesis suggest we could live in a simulation? What does critical rationalism say about these theories?

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In The Beginning of Infinity, David Deutsch claimed that no existing evolutionary algorithm has yet created knowledge. But Karl Popper and Donald Campbell beg to differ and have argued that knowledge-creating evolutionary algorithms are ubiquitous, common, and easy to implement. Who is right? 

In this episode, we look at both arguments and assess them using Popper's epistemology. And along the way, we'll define the minimum requirements for an evolutionary algorithm (aka Universal Darwinism), explore what knowledge-creation is and then, finally, we'll attempt to answer the question of whether or not existing Artificial Intelligence algorithms create knowledge.

Visuals are available on Youtube. This episode may benefit from seeing the visuals. 

For more information on Donald Campbell's theory, this blog post covers his arguments. 

A summary of the contradiction between David Deutsch's argument and Campbell/Popper's argument is in this blog post. 

This was recorded during the 24 hr. transcontinental Popperian ZOOM Meet 'n Greet of January 9th-10th, 2021 organized by OurKarlPopper.net. Bruce was asked to host a session with the subject of David Deutsch and how he brought a whole new generation to Karl Popper's philosophy. But at the last minute, we found out David Deutsch himself was attending. So we redid our plans to allow people to ask him questions.  

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Sadia, in her four episodes on unsolved problems in physics (first episode here), was clearly heavily inspired by the work of Julian Barbour. So we invited Julian to join us for an episode and got a chance to ask him questions about his theories. Julian is a world-renowned physicist and author of several books on physics including The Janus Point, The End of Time, and The Discovery of Dynamics.  His theories include a challenge to the prevailing theory of entropy (i.e. heat death) and even hint at possible apparent teleology in cosmology (in this case a tendency towards novelty and variety.) We are very excited to have him on the show and to answer our questions about his theories. 

When Alpha Go beat Lee Sedol, the world Go champion, it came up with creative new moves never previously seen before and even invented a whole new style of play unknown to humans. IBM's Deep Blue, the champion chess algorithm, failed to do either of these. What was the difference?

In this podcast, we review Alpha Go the Movie. Warning: Spoilers abound! Please go watch the movie first! This is an excellent movie. 

Bruce (using his admittedly thin knowledge of reinforcement learning) explains how Alpha Go works (using the materials previously discussed in our Reinforcement Learning episode) and how Alpha Go came up with a creative new approach to Go that went beyond the knowledge of the programmers. 

While Alpha Go definitely does not have "creativity" in the universal explainer sense of the word (it has no explanatory knowledge nor understanding), it did come up with a creative new playstyle never before seen in the history of the world that changed how humans play Go. Even the programmers were caught off guard by what it came up with. We talk about how Alpha Go challenges the Pseudo-Deutsch Theory of Knowledge but meshes well with Campbell's evolutionary epistemology. 

Have you heard the old joke about how there are liar, damn liars, and then there are statisticians? In this episode, Cameo and Bruce discuss how statistics are widely used -- and misused -- in society.   

Most people miss that statistics aren't really primarily used for true probabilities but instead are used as a way to mimic our lack of knowledge. Statistics, as a field, is often the study of ignorance, not straight probabilities. What are the ramifications of that? Find out in this episode.  

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Youtube version with optional visuals: 

https://www.youtube.com/watch?v=bZGAwUxO3CM&list=PLbU2Yvjwp2jI0mTFUVLVtRw8MQJev0QYt&index=7

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We take a deep dive into Karl Popper’s philosophical ideas about music that he outlines in four chapters in this intellectual autobiography Unended Quest:

• “Music,”
• Speculations about the Rise of Polyphonic Music,”
• “Two Kinds of Music,” and
• “Progressivism in Art, Especially in Music.” 

We are joined by Peter’s brother, Chris Johansen, who is a straight-ahead jazz tenor saxophonist living in NYC. 

We discuss how Popper’s ideas on classical music intersect with Chris’s ideas on jazz, as well as the role of conservatism in music. We examine how Popper’s thinking on music influenced his concept of the 3 worlds and his ideas on such concepts as dogmatism, essentialism, and historicism. 

Plus, you get Bruce's rant about the importance of constraints in music, science, criticism, and Popper's epistemology. Bruce argues that absent at least the attempt to outline epistemological conventions (i.e. constraints) you can't error correct Popper's epistemology and you lose what makes it special.

You can listen to more of Chris’s music here.

Bruce takes a deep dive into Stephen Wolfram’s ideas regarding computational universality, which may go further than the Church-Turing-Deutsch thesis in that Wolfram’s theories imply that all of nature could be simulated even by relatively simple systems, so even nature itself may be computational rather than something that can just be simulated on a turning machine or quantum computer. Stephen Wolfram is a renowned physicist, computer scientists, and entrepreneur.

Bruce also talks about the related ideas on philosophy of computation promoted by Rudy Rucker, who is a mathematician, computer scientist, and science fiction author associated with cyberpunk genre. Both thinkers believe, rightly or wrongly, that the complexity of life and the universe can be explained by relatively simple computational rules.

Bruce, Cameo, and Carey start their new podcast with a bang by offending experts everywhere and making sure that no one with a political opinion about Climate Change will want to listen to their podcast. 

This is part 1 of our 4 part series covering the Theory of Knowledge of Karl Popper as interpreted by physicist David Deutsch. Bruce explains this epistemology to Cameo and Carey, who are hearing it for the first time as we record. Will they agree with it or will they think it's a bunch of baloney? Find out by downloading this podcast and making sure all your friends do too!

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In this episode, we continue our discussion of Dwarkesh Patel's article "Contra David Deutsch on AI" compared to Brett Hall's tweet on IQ theory. This time we concentrate on criticisms of Patel's Hardware+Scaling hypothesis. To Patel's credit, he admits that his hypothesis is problematic. 

Then Peter asks Bruce about why Brett Hall believes explanatory universality implies 'equal intellectual capacity'. Bruce gives a steelmanned version of Brett's theory that takes us through an explanation of what explanatory universality is and how it relates to computational universality and the Turing Principle. 

What responsibility do the creators of a TV series or movie have to be faithful to the original source material? What risks are involved with either adapting the material too closely or not close enough? The much-anticipated Wheel of Time tv show is finally here and we discuss our feelings about the show compared to the books. Warning: this podcast contains extensive spoilers for both the books and the series.