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Carlos E. Perez

6 Oct, 13 tweets, 3 min read

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.

He further argues that there is nothing special about computation and therefore anything we need to say about in philosophy has the same equivalent meaning in computer science.

However, because this is true, computer scientists overlook meaning but see only mechanisms. Shannon's information theory overlooks the notion of 'aboutness' of information and other subtleties identified by Pierce in understanding signs.

The definition of 'understanding' is obvious when we frame it from a subjective perspective of intentionality that involves the mechanisms of semiotics. Intentionality with semiotics is computation, but computation that involves meaning-making.

Meaning is the difference in form that makes a difference in action.

The entire developmental history of computer science can in fact be framed from the perspective of translating intention into mechanism. medium.com/intuitionmachi…

But what about biological evolution? It can be argued that computation is a means for abstractions (models) to predict physical evolution: ncbi.nlm.nih.gov/pmc/articles/P…

This definition is a subset of intention+mechanism where intention is the goal of predicting another system. But what about evolution itself, what is its intention?

C.S. Peirce has in fact a lot to say about this. "Three modes of evolution have thus been brought before us: evolution by fortuitous variation, evolution by mechanical necessity, and evolution by creative love."

The third one is the most surprising. But if you think about it, it makes sense. After all, what drives technological innovation?

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Carlos E. Perez

@IntuitMachine

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.

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Carlos E. Perez

@IntuitMachine

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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:

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Carlos E. Perez

@IntuitMachine

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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…

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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.

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Carlos E. Perez

@IntuitMachine

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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

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Carlos E. Perez

@IntuitMachine

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I confess I don't understand philosophy. I don't understand the language nor do I understand the train of thinking. I suspect that my comfort in understanding how the mind works relate to my inability to understand philosophy!

I have an intuition for Wittgenstein, but I can't follow most philosopher arguments. It seems that they are following mental scripts that I have not studied. Different philosophers have different mental scripts and it seems the task is to stitch together these scripts.

The validity of a script is based on the stature of the philosopher. So it's kind of like a franchise of comic books with different narratives and the task is to come up with a universe story where everything fits.

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