On Tue, Apr 2, 2019 at 4:36 PM Ivan V. <[email protected]> wrote:

> 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,
>

Wikipedia is not a good way to learn math, in general.  You kind-of already
have to know it.

but it seemed over complicated.
>

Turns out addition is really complicated, if you are a number theorist.
But if you are not, then it doesn't have to be.

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.
>

I pointed you at the PDF.  Go for the PDF.

--linas


>
> 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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