The Theory of Anything

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. 

The podcast discusses deep reinforcement learning, its relationship to regular reinforcement learning, and its potential application to animal intelligence and AGI. They give examples using code, explore Elon Musk's dedication to solving humanity's problems, and discuss OpenAI's approach to AI research and AGI safety. The podcast also covers Markov decision processes and the challenges of using a Q table in stochastic environments.

In this podcast, the hosts discuss the challenges and misunderstandings surrounding Karl Popper's concept of refutation. They explore how Popper sometimes uses terms in idiosyncratic ways, leading to misreadings of his work. They delve into the asymmetry of refutation versus verification and its relationship to empirical and non-empirical theories. They also touch on topics such as the problem of string theory being non-empirical and the interconnectedness of Popper's epistemology. Additionally, they discuss the importance of starting a conversation, discovering different magnitudes of infinity, sharing ideas in philosophy, and the difficulty of understanding a theory.

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

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

Researcher Richard Byrne discusses his methodology for determining animal insight, including examples of behavioral patterns that can't be explained by genetics or trial-and-error learning. The podcast explores the comparison between animal intelligence and machine learning, highlighting the current limitations of technology. Topics also include ad hoc explanations, specific modules in the brain, differences between apes and monkeys in terms of insight, examples of deceptive behavior in chimps, and the role of anthropomorphism in understanding animal intelligence.

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

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 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. 

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.