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Carlos E. Perez Profile picture
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8 Oct, 5 tweets, 2 min read
Damn, this book is thick!! @coecke Image
I've observed that most books have very few diagrams. I really don't understand why authors think that it's easier to explain something without a diagram.
Perhaps there exists a lack of ability to express something in a diagram. This book has an unimaginable number of diagrams. I randomly opened the book and there were 7 diagrams between two pages.
If this 'clown' @coecke can write an 800+ page book with this many diagrams, then I should be able to do so too! I'm looking forward to groking diagrammatic reasoning as described in the book.
Now for some more shockers. Imagine studying computer science and your only tools were machine (or assembly) language. I did not know that quantum theory was taught only at that level!

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More from @IntuitMachine

Carlos E. Perez Profile picture
10 Oct
Wonderful discussion with Paul Cisek at the Learning Salon. Paul proposes a refactoring of our taxonomy for understanding cognition. He argues that the structure should be driven by studying the history of evolution. crowdcast.io/e/learningsalo…
What I love about the Learning Salon that is hosted by @criticalneuro @neuro_data John Krakauer is that the hosts are all ready to tear apart the arguments of the speaker. Krakauer has an uncanny ability in conjuring up strong cases against the speaker.
I subscribe to Cisek's thesis in that to understand cognition, we should be informed by evolution. Cognition is a consequence of history (or the baggage) that lead us to our present state. Studying this information can lead to explanations of the peculiarities of human thinking.
Read 13 tweets
Carlos E. Perez Profile picture
6 Oct
Brian Cantwell Smith lecture on philosophy and the meaning of computation explains why the language of philosophy just uses a different vocabulary from that of computer science.
In this lecture, he argues that 4 common definitions of computation are inadequate: (1) Symbol processing (2) Turing equivalence (3) Information Processing and (4) Digital.
His more abstract definition is that computation is the interplay of meaning and mechanism. It is the mechanization of an agent's intentionality.
Read 13 tweets
Carlos E. Perez Profile picture
6 Oct
Found this by Steve Bynes that I think is worth reading lesswrong.com/posts/diruo47z…
7 guiding principles of the brain: (1) 2 subsystems (2) cortical uniformity (3) blank-slate neocortex (4) A neocortical algo (5) subcortex steers neocortex (6) neocortex is blacbox wrt subcortex (7) unknown subcortex algos.
To summarize, Steve Byrnes argues that the subcortex is underexplored, more complex than the neocortex and is critical to AI safety. I don't disagree.
Read 5 tweets
Carlos E. Perez Profile picture
6 Oct
Is Philosophy just Psychology?
Here is George Lakoff explaining how they examined the work of philosophers and realized that each one took a subset of metaphors and took them literally.
But let us take this even further, metaphor is a tool for human brains. But what are brains other than computational systems. Here Brian-Cantwell-Smith explains the meaning of computation:
Read 11 tweets
Carlos E. Perez Profile picture
5 Oct
Civilizations and governments exist to improve the welfare of everyone. Yet we have a civilization and a government that focuses on the few. This is obvious when we see spending for all the wrong reasons. ebaumsworld.com/videos/carl-sa…
Civilizations and bureaucracies have always been gamed by the cleverness of humans to gain individual advantages. The biggest deception is that this self-dealing is inevitable and those more cunning deserve to be at the top.
So rather than physical violence, we have instead social and political violence. We seem to separate them and are manipulated to think that the latter kind of violence is acceptable. Coercion over consensus is simply unacceptable.
Read 7 tweets
Carlos E. Perez Profile picture
29 Sep
The classic explanation of Deep Learning networks is that each layer creates a different layer that is translated by a layer above it. A discrete translation from one continuous representation to another one.
The learning mechanism begins from the top layers and propagates errors downward, in the process modifying the parts of the translation that has the greatest effect on the error.
Unlike an system that is engineered, the modularity of each layer is not defined but rather learned in a way that one would otherwise label as haphazard. If bees could design translation systems, then they would do it like deep learning networks
Read 26 tweets

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