Linas,

Tanks for taking time to answer.

I skimmed over the Stackexchange post and I found it interesting (not that
anyone cares :o). I tried once a while ago to learn about Category theory
from Wikipedia, but it seemed over complicated. I guess I needed examples
closer to my knowledge - not mathematical abstractions, but type theory
oriented - as noted on Stackexchange. Maybe I should give it another try,
I'll see.

Thanks again,
Ivan V.


uto, 2. tra 2019. u 20:05 Linas Vepstas <[email protected]> napisao je:

> 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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