M$ has done great stuff applying deep learning to speech-to-text, probably
that's the NLP stuff they're referring to...


On Tue, Jul 15, 2014 at 6:10 PM, Chris Nolan via AGI <[email protected]>
wrote:

> It's interesting that they say it's been applied to NLP but provide no
> description on results, which makes one think it didn't do as well as a NL
> classifier as it did for the image classifying application. I wonder if
> that's a result of training size, or there's something more inherent in
> undering NL that just makes it much harder, and that DNNs won't do as well
> on in comparison to Image classification? That later point is interesting
> for AGI, because it implies that there's still a larger ML paradigm that's
> needed for NL, before we'll see real breakthroughs.
>
> They also talk about these big improvements in accuracy, but give no
> figures. It'd be nice to know what it's relative to. Guess we have to wait
> for the paper...
>
> -Chris
>
>
>   On Tuesday, July 15, 2014 6:07 AM, Jim Bromer via AGI <[email protected]>
> wrote:
>
>
> It is scalable they say. OK, but in how many cognitive categories? Are the
> neural networks able to use that information for combination and selection
> at a higher cognitive order? I am asking if they are able to use their
> capabilities in a creative and imaginative application of knowledge. If you
> could get a neural network to detect imagination then you might be able to
> train it to use directed imagination in problem solving. But you can't
> because imagination is too broad a concept.
>
> Jim Bromer
>
>
> On Tue, Jul 15, 2014 at 5:51 AM, Jim Bromer <[email protected]> wrote:
>
> It is scalable they say. OK, but in how many cognitive dimensions?
> (Throwing a term like "cognitive dimensions" out in front of you guys may
> be a mistake.) Perhaps I should have asked, in how many cognitive
> categories? This has been a persistent problem with neural networks. The
> point of view that Chilimbi seems to be taking is that the [parallel]
> system is able to detect different features and combine them somehow.
> However, that does not mean, for example, that the neural networks are able
> to use that information for combination and selection at a higher cognitive
> order. Just because I cannot word my criticism in just the right way does
> not mean that the criticism is completely invalid. A counter argument is
> that since the algorithms are effectively making some kinds of combinations
> and some kinds of selections then that proves that they are theoretically
> capable of making the kinds of subsequent combinations and selections that
> I am thinking of.  However, this (kind of) argument still hasn't been
> established experimentally. It's the same old same old.
>
> Jim Bromer
>
>
> On Tue, Jul 15, 2014 at 12:22 AM, Ben Goertzel via AGI <[email protected]>
> wrote:
>
>
> ;)
>
> Hmm..  the quote about quantum physics is
>
> ***
> “It’s like in quantum physics at the beginning of the 20th century,”
> Chilimbi says. “The experimentalists and practitioners were ahead of the
> theoreticians. They couldn’t explain the results. We appear to be at a
> similar stage with DNNs. We’re realizing the power and the capabilities,
> but we still don’t understand the fundamentals of exactly how they work. "
> ***
>
> It is true that with deep learning, practice is way ahead of theory these
> days.  OTOH, quantum mechanics has some mysterious confusing-ness too it,
> which deep learning doesn't really have....  What modern DL algorithms are
> doing has made sense conceptually to a lot of people for a long time, and
> the recent breakthrough results are not based so much on new conceptual
> breakthroughs, as on the new availability of massive amounts of hardware
> and associated operational software....
>
> Still, "solving object recognition via DL" is a fair bit more AGI-ish than
> "solving chess via alpha-beta pruning", for example   These DL
> architectures have a strong conceptual resemblance to one aspect of the
> human mind-brain (parts of our visual and auditory cortices), whereas
> alpha-beta pruning has a significantly lesser resemblance to stuff that the
> human mind does (the human mind does some search like that, but on a far
> lesser scale and only in tight interaction with a lot of other
> processes)....
>
>  I think these modern DL architectures bear moderately close resemblance
> to something that could serve as a significant component of an AGI system.
> In their details, though, they are not architected for integration with
> other AGI components, unlike how the human visual and auditory cortices,
> with their pronounced hierarchical sensory pattern recognition
> functionality, are architected (well, evolved) for interaction with the
> rest of the brain...
>
> -- Ben
>
>
> On Mon, Jul 14, 2014 at 9:04 PM, David Hart <[email protected]> wrote:
>
> I skimmed it. Is it a joke? DNN like quantum physics? Really? Do they
> really think the two are in the same league?
>
> -dave
>
>
> On Tue, Jul 15, 2014 at 11:52 AM, Ben Goertzel <[email protected]> wrote:
> >
> > This time from Microsoft...
> >
> > http://research.microsoft.com/en-us/news/features/dnnvision-071414.aspx
> >
> > --
> > Ben Goertzel, PhD
> > http://goertzel.org
> >
> > "In an insane world, the sane man must appear to be insane". -- Capt.
> James
> > T. Kirk
> >
> > "Emancipate yourself from mental slavery / None but ourselves can free
> our
> > minds" -- Robert Nesta Marley
>
>
>
> --
> David Hart |  +852 9559 9580
> OpenCog Foundation | http://opencog.org
>
>
>
>
> --
> Ben Goertzel, PhD
> http://goertzel.org
>
> "In an insane world, the sane man must appear to be insane". -- Capt.
> James T. Kirk
>
> "Emancipate yourself from mental slavery / None but ourselves can free our
> minds" -- Robert Nesta Marley
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-- 
Ben Goertzel, PhD
http://goertzel.org

"In an insane world, the sane man must appear to be insane". -- Capt. James
T. Kirk

"Emancipate yourself from mental slavery / None but ourselves can free our
minds" -- Robert Nesta Marley



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