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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