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. 

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: 

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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

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

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.

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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.

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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. 

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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.

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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. 

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.