Robert Jasiek <jas...@snafu.de> writes:

> So there is a superstrong neural net.
>
> 1) Where is the semantic translation of the neural net to human theory
> knowledge?
>
> 2) Where is the analysis of the neural net's errors in decision-making?
>
> 3) Where is the world-wide discussion preventing a combination of AI
> and (nano-)robots, which self-replicate or permanently ensure energy
> access, from causing extinction of mankind?


As far as (1), if we could do it, it would mean we could relate the
structures embedded in the net's weight patterns to some other domain --
if nothing else, the domain of the meanings of words in some natural
language.  We cannot, and most certainly the net cannot.  So far neural
nets have been trained and applied within single domains, and any
"generalization" means within that domain.  A net may learn to recognize
and act similarly with respect to a certain eye pattern on different
parts of the board.  No one, as far as I know, has presented a net that
would be able to use a guideline like, "two eyes alive, one eye dead" to
help it speed learning of how to act on the board.  But a human can
apply "one"/"two", and "alive"/"dead" once it has been made clear that
"eye" in this context is standing for a recognizable structure of
same-color-surrounding-space, and thereby learn in one step what the net
learns in thousands of incremental iterations.

And (2) presupposes (1), since to understand why a situation was
mis-perceived or mis-acted upon requires some understanding of what
exactly the perception and judgment process was in the first place.

It may be that the recent successes in brute-force learning powered by
improved hardware together with improved crafting of the architecture
eventually play some role in understanding and recreating
"intelligence".  But so far, using the term "AI" in connection with 99%
of this kind of work is just hype.  Useful in accomplishing engineering
goals, yes.  But not so much to do with intelligence.

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