Damn, had to slow down from 2x to 1.5x to even understand @coecke
Haha, I agree, I also don't understand the difference between strong or weak emergence! And I'm writing a book with emergence in it's title! gum.co/empathy
There's a big part where they discuss the decline of the university academic environment.
This is a great discussion, you got 3 ML PhDs that are grilling @coecke to understand his unique approach.
The video is over 2 hours long. I stayed till the end and discovered a nice recap. You can do it in 1 hour if you playback at 2x.
It's all a parlour trick, both Deep Learning and human intelligence! uAlso, there was talk about quantum computing and cryptocurrencies. A ton of stuff packed in a single episode.
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The "Reward is Enough" paper offers a piss-poor explanation as to how AGI is achieved. I'm actually more surprised that the @DeepMind wrote such a poorly constructed philosophical paper. sciencedirect.com/science/articl…
The major flaw of the 'Reward is Enough' paper is that the authors don't know how to disentangle the self-referential nature of the problem. So it's written in a way that sounds like a bunch of poorly hidden tautological statements.
I'm a bit perplexed about the achievement of Cyc. Have symbolic systems ever achieved a level of robustness to be of real utility? Not sure why this panel is all praise about it.
Yanic asked a good question that you can't just throw around buzzwords like 'abstractions' and 'semantics' without proposing an approach on how to achieve it. It's not clear how you get from symbolic manipulation into common sense.
Just as quantum mechanics is unintuitive to humans, it is likely that parallel distributed computation is also unintuitive. NAND gates are not intuitive. SK combinators are not intuitive. The building blocks of cognition are likely unintuitive as well.
Human minds are simply incapable of explaining how human minds work. At best we can explain the emergent properties, but not the underlying mechanisms.
Of course, we must have a good metaphor to partially explain human cognition. We need them so that we can formulate explanations for methods of teaching, decision-making, and idea generation. We cannot be blind to human cognitive nature.
It must be difficult being a neuroscientist. It's like being an alchemist before the periodic table was discovered.
Just like alchemists at the time of Newton, the tools and models to explore their domain are completely absent. You cannot make progress if you have no capability of observing and interpreting what's going on.
To be fair, neuroscience isn't about understanding cognition. It's about understanding the physical nature of the brain. Cognition is a virtual thing. The difference between hardware and software.
Existing models of neurons or even single cells are woefully inadequate to simulate what's going on in the brain. Standard models are based on toy models that are conveniently easy to simulate. Scientific research has a bias toward the tools it has at its disposal.
However, we also should not underestimate the complexity that simple components can generate. Conversely, we can't ignore the consistency of behavior that a collection of complex parts generate.
The truth about general intelligence like the brain lies somewhere in between. Humans are complex beings, yet there exists a consistency of how collections of humans behave. Civilization would not be possible if not for common behavior that leads to emergent behavior.