Hi Jose,
On Sun, Jul 19, 2020 at 1:21 PM Jose Ignacio Rodriguez-Labra <
[email protected]> wrote:
> This is a very interesting conversation. Thank you all for sharing your
> insights. Perhaps I could add that another thing to consider is that maybe
> humans don't have what I would call general pattern recognizers. Perhaps
> our ability to recognize patterns come from pattern recognizing modules
> that are specialized to work in various domains (ie. visual,
> time-frequency, audio, conceptual, etc) created through evolution. This
> would be similar to a pattern recognizer 'trained' by a neural network,
> lacking generality. Perhaps there are patterns in the world we don't have
> the mental faculty to detect. In that case, the pattern recognition would
> be developed through the evolutionary domain, rather than the learning
> domain.
>
Yes. Here is a paper that talks about this in a very illuminating kind of
way:
"Forced moves or good tricks in design space? Landmarks in the
evolution of neural mechanisms for action selection", Tony J. Prescott
(2007)
https://www.academia.edu/30717257/Forced_Moves_or_Good_Tricks_in_Design_Space_Landmarks_in_the_Evolution_of_Neural_Mechanisms_for_Action_Selection
--linas
> Now, if this is the case for biological brains, it doesn't mean it is
> impossible to develop a general pattern recognizer, able to be fed
> multi-dimensional data (images, audio, internal neural processes, etc) and
> have it be able to recognize similarities/patterns within the data, all
> developed during learning. Generally, when we recognize a pattern, we've
> built a model of the pattern and we're able to make predictions on it. The
> thing I can't wrap my head around is what kind of structure/architecture or
> mathematical model would be able to do the recognition?
>
> Regards,
> Jose Ignacio Rodriguez-Labra
>
> On Saturday, July 18, 2020 at 8:26:38 PM UTC-4, linas wrote:
>>
>> The word "training" is problematic. If you mean "memorize an association
>> list of pairs" (e.g. faces+text-string) well, technically that is
>> "training" in the AI jargon file, but it's of little utility for AGI.
>>
>> The word "pattern" is problematic. Exactly what a "pattern" is, is ...
>> tricky. Much (most? almost all?) of my effort is about trying to define
>> "what is a pattern, anyway". I'm not sure what you had in mind, when you
>> used that word. (Its a tricky word. Everyone obviously knows what it
>> means, but how to turn it into an algorithmically graspable "thing"?)
>>
>> --linas
>>
>> On Sat, Jul 18, 2020 at 6:44 PM Dave Xanatos <[email protected]> wrote:
>>
>>> "If you can't spot the pattern, you've not accomplished anything."
>>>
>>>
>>>
>>> Every significant – and truly useful - advance I've made on my own
>>> language apprehension code has been based on recognizing a pattern, and
>>> coding for it. I fully agree.
>>>
>>>
>>>
>>> Can a neural network be trained on patterns instead of things?
>>>
>>>
>>>
>>> Can code designed to recognize – for example, faces (like eigenfaces) –
>>> be trained to instead recognize blocks of data that look the same, despite
>>> perhaps being in vastly dissimilar fields?
>>>
>>>
>>>
>>> Apologies if I'm intruding, or seem to be "out of my lane"… a popular
>>> buzzword these days.
>>>
>>>
>>>
>>> Dave – LONG time lurker…
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>> *From:* [email protected] <[email protected]> *On Behalf Of
>>> *Linas Vepstas
>>> *Sent:* Saturday, July 18, 2020 6:54 PM
>>> *To:* link-grammar <[email protected]>; opencog <
>>> [email protected]>
>>> *Subject:* [opencog-dev] Re: [Link Grammar] Sutton's bitter lesson
>>>
>>>
>>>
>>> Well yes. What's truly remarkable is how frequently that lesson has to
>>> be re-learned. There are vast swaths of the AI industry that still have
>>> not learned it, and are deluding themselves into thinking that they've made
>>> bold progress, when they've gotten nowhere at all, and seem blithely
>>> unaware that they are repeating the same mistake... again.
>>>
>>>
>>>
>>> I refer, of course, to the deep-learning true-believers. They have made
>>> the fundamental mistake of thinking that their various network designs
>>> provide an adequate representation of reality. How little do they seem to
>>> realize that all that code, running hand-tuned on some GPU is just, and I
>>> quote Sutton, here: "leveraged human understanding of the special
>>> structure of chess". Except, cross out "chess" and replace with
>>> "dimensional reduction" or "weight vector" or whatever buzzword-bingo is
>>> popular in the deep-learning field these days.
>>>
>>>
>>>
>>> I'm back again to insisting that "patterns matter". If you can't spot
>>> the pattern, you've not accomplished anything. Neural nets can't spot
>>> patterns. They're certainly interesting for various reasons, but, as an AGI
>>> technology, they are every bit a dead-end as the hand-crafted English
>>> link-grammar dictionary.
>>>
>>>
>>>
>>> This is one reason I'm sort of plinking away, working on unfashionable
>>> things. I'm thinking simply that they are more generic. and more powerful.
>>> But perhaps the problem is recursive: perhaps I'm just "leveraging my human
>>> understanding of the special structure of patterns", and will hit a wall
>>> someday. For now, it seems that my wall is more distant. If only I could
>>> convince others ...
>>>
>>>
>>>
>>> --linas
>>>
>>>
>>>
>>>
>>>
>>> On Sat, Jul 18, 2020 at 5:14 PM Paul McQuesten <[email protected]>
>>> wrote:
>>>
>>> Linas,
>>>
>>>
>>>
>>> I think this reinforces your view of learning from data, instead of
>>> adding more human-curated rules:
>>>
>>> http://incompleteideas.net/IncIdeas/BitterLesson.html
>>>
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>>> .
>>>
>>>
>>>
>>> --
>>>
>>> Verbogeny is one of the pleasurettes of a creatific thinkerizer.
>>> --Peter da Silva
>>>
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>>>
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>>> .
>>>
>>
>>
>> --
>> Verbogeny is one of the pleasurettes of a creatific thinkerizer.
>> --Peter da Silva
>>
>> --
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> .
>
--
Verbogeny is one of the pleasurettes of a creatific thinkerizer.
--Peter da Silva
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