Hi Ivan:

Google "category theory for computer scientists" I get this:

> [PDF]Category Theory for Computing Science - Mathematics and Statistics
> http://www.math.mcgill.ca/triples/Barr-Wells-ctcs.pdf
> by M Barr - ‎Cited by 1642 - ‎Related articles
> Aug 4, 2012 - This book is a textbook in basic category theory, written
specifically .... been a major source of interest to computer scientists
because they are.

I have not read it. I think that "cited by 1642" means its a good,
high-quality book. There are several of these kinds of books -- one that I
did skim, maybe a decade ago, gave all of its examples and homework
problems in CaML -- it was an older book, predating Haskell.

See also this:
https://cs.stackexchange.com/questions/3028/is-category-theory-useful-for-learning-functional-programming

Notice that the answer with largest upvotes says "category theory is type
theory".  For any complete newbies reading this, "int", "float", "char*",
"class FooBar" and "int FooBar::myMethod(int x)"  are all (C/C++/Java)
examples of types.

I'm making three additional claims, in addition to this highly-upvoted
answer:

1) Link-grammar connectors/link-types are types, in the sense of type
theory.  (This is not really a new claim; the original link-grammar authors
made more-or-less this same claim, in 1993, in one of their original papers)

2) Deep-learning neural-nets perform classification by classifying into
types. (type-theoretical types) (this claim is kind-of
shallow/stupid/"obvious", and needs to be articulated to become interesting
and non-trivial.)

3) There is an almost-direct, one-to-one correspondence of  deep-learning
neural-nets types to link-grammar connectors/link-types, if you know where
to look for them.  This is the controversial claim that everyone rejects.
And perhaps I am hallucinating and completely making this up. Like 2+2=3.
I'm struggling with this myself, as the details remain unclear and
confusing.  So it's OK if you don't believe this one.  But this is the
claim I'm interested in.

-- Linas


On Wed, Mar 27, 2019 at 4:40 PM Ivan V. <[email protected]> wrote:

> Linas, you lost me at Category theory... Nevertheless, I also find the
> idea of integrating symbolic system into neural networks amusing. The proof
> I really do is a recent speculation from a paper I'm writing from time to
> time, just to gather my thoughts (maybe I got this from you at some point,
> I really don't remember now):
>
> Nevertheless, symbolic approach may support structure forms on top of
>> which artificial neural networks could operate, thus forming a synergy
>> between the two seemingly opposite philosophies in designing AI.
>>
>
> But then I develop things in symbolic direction because with this, I'm
> currently interested only in improvement of OpenCog URE engine, as far as I
> plan to offer contribution around here if the language I'm building passes
> the stages a, b, c, d, and also e, just in case.
>
> Realizing ideas take time, and life is too short to do it all, while I'm
> not a fan of bossing around... I also like to see creativity in other
> people too...
>
> sri, 27. ožu 2019. u 20:04 Linas Vepstas <[email protected]> napisao
> je:
>
>> Hi Sergei,
>>
>> On Wed, Mar 27, 2019 at 12:14 PM Sergei Kaunov <[email protected]> wrote:
>>
>>> Amused by your work, Linas, and you describe it very interesting. Where
>>> should we watch further progress on the topic?
>>>
>>
>> Thanks!  Progress is hard, because several things have to happen in
>> parallel.
>>
>> -- I (or you, or anyone else) has to think hard across multiple different
>> difficult mathematical abstractions.
>>
>> -- Once I (or you, or anyone else) has some "great idea", it has to be
>> transmitted to others, either by email or by writing papers.
>>
>> -- Others can understand those "great ideas" only if they have the
>> appropriate mathematical background:  in Ivan's case: hilbert-systems and
>> natural deduction and proof theory and type theory and category theory and
>> neural networks and deep learning and statistical mechanics (for example,
>> the "objective function" that neural net guys love to minimize/maximize is
>> exactly the same thing as a boltzmann distribution from statistical
>> echanics)   So my personal progress on the topic is blocked by the
>> inability to communicate it to others. If you don't understand what I'm
>> talking about, its deeply frustrating for me. (And this is symmetric:
>> sometimes, I am told about great ideas, which I don't understand, because I
>> lack the background knowledge)
>>
>> -- A "great idea" is great only if it *actually works*. And, here, that
>> means (a) writing software (b) running experiments (c) analyzing data. (d)
>> describing experimental results to others.  So, not only are steps a,b,c,
>> extremely time consuming, but step (d) is often mis-understood/neglected,
>> because the intended audience didn't understand why the experiment is
>> important.
>>
>> -- After you've conquered steps a,b,c,d then and only then can you do
>> step e) build an insanely great demo that will wow everyone who sees it,
>> even if they are a complete moron. For example, "deep fakes". You don't
>> need math to know that something unusual is happening there.
>>
>> The pressure I'm under, that I feel, is that I've got a collection of
>> "great ideas", I'm trying to articulate them, having trouble finding an
>> audience, struggling with steps a,b,c,d and meanwhile everyone is shouting
>> out loud "you guys are a bunch of losers because you don't have step e) you
>> suck!" and dealing with the psychological and financial fallout from that.
>>
>> I'm not unique, here -- most researchers/scientists deal with these same
>> issue. The commonly accepted solution for this is to create collaborations
>> and teams -- "division of labor" -- and tehre's chicken-and-egg problems to
>> solving that, also.
>>
>> --linas
>>
>>
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>>
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