One can see the next steps from the analogy of deep NNs for computer vision
First they did straightforward visual analytics, then they started worrying more about the internal representations, and now in the last 6 months or so there is finally a little progress in getting sensible internal representations within deep NNs analyzing visual scenes. Don't get me wrong tho, I don't think this is the golden path to AGI or anything.... However, the next step is clearly to try to tweak the architecture to get more transparent internal representations. As it happens this would also be useful for interfacing such deep NNs with symbolic systems or other sorts of AI algorithms... -- Ben On Sun, Feb 17, 2019 at 9:05 PM Stefan Reich via AGI <[email protected]> wrote: > > I'm not sure how one would go the next step from a random-speech-generating > network like that. > > We do want the speech to mean something. > > My new approach is to incorporate semantics into a rule engine right from the > start. > > On Sun, 17 Feb 2019 at 02:09, Ben Goertzel <[email protected]> wrote: >> >> Rob, >> >> These deep NNs certainly are not linear models, and they do capture a >> bunch of syntactic phenomena fairly subtly, see e.g. >> >> https://arxiv.org/abs/1901.05287 >> >> "I assess the extent to which the recently introduced BERT model >> captures English syntactic phenomena, using (1) naturally-occurring >> subject-verb agreement stimuli; (2) "coloreless green ideas" >> subject-verb agreement stimuli, in which content words in natural >> sentences are randomly replaced with words sharing the same >> part-of-speech and inflection; and (3) manually crafted stimuli for >> subject-verb agreement and reflexive anaphora phenomena. The BERT >> model performs remarkably well on all cases." >> >> This paper shows some dependency trees implicit in transformer networks, >> >> http://aclweb.org/anthology/W18-5431 >> >> This stuff is not AGI and does not extract deep semantics nor do >> symbol grounding etc. For sure it has many limitations. Bu it's >> also not so trivial as you're suggesting IMO... >> >> -- Ben G >> >> On Sun, Feb 17, 2019 at 8:42 AM Rob Freeman <[email protected]> >> wrote: >> > >> > On the substance, here's what I wrote elsewhere in response to someone's >> > comment that it is an "important step": >> > >> > Important step? I don't see it. Bengio's NLM? Yeah, good, we need >> > distributed representation. That was an advance. but it was always a >> > linear model without a sensible way of folding in context. Now they try to >> > fold in a bit of context by bolting on another layer to spotlight other >> > parts of the sequence ad-hoc? >> > >> > I don't see any theoretical cohesiveness, any actual theory let alone >> > novelty of theory. >> > >> > What is the underlying model for language here? In particular what is the >> > underlying model for how words combine to create meaning? How do parts of >> > a sequence combine to become a whole, incorporating the whole context? >> > Linear combination with a bolt-on spotlight? >> > >> > I think all this ad-hoc tinkering will be thrown away when we figure out a >> > principled way to combine words which incorporates context inherently. But >> > nobody is even attempting that. They are just tinkering. Limited to >> > tinkering with linear models, because nothing else can be "learned". >> > >> > On Sun, Feb 17, 2019 at 1:05 PM Ben Goertzel <[email protected]> wrote: >> >> >> >> Hmmm... >> >> >> >> About this "OpenAI keeping their language model secret" thing... >> >> >> >> I mean -- clearly, keeping their language model secret is a pure PR >> >> stunt... Their >> >> algorithm is described in an online paper... and their model was >> >> trained on Reddit text ... so anyone else with a bunch of $$ (for >> >> machine-time and data-preprocessing hacking) can download Reddit >> >> (complete Reddit archives are available as a torrent) and train a >> >> language model similar or better >> >> than OpenAI's ... >> >> >> >> That said, their language model is a moderate improvement on the BERT >> >> model released by Google last year. This is good AI work. There is >> >> no understanding of semantics and no grounding of symbols in >> >> experience/world here, but still, it's pretty f**king cool to see what >> >> an awesome job of text generation can be done by these pure >> >> surface-level-pattern-recognition methods.... >> >> >> >> Honestly a lot of folks in the deep-NN/NLP space (including our own >> >> SingularityNET St. Petersburg team) have been talking about applying >> >> BERT-ish attention networks (with more comprehensive network >> >> architectures) in similar ways... but there are always so many >> >> different things to work on, and OpenAI should be congratulated for >> >> making these particular architecture tweaks and demonstrating them >> >> first... but not for the PR stunt of keeping their model secret... >> >> >> >> Although perhaps they should be congratulated for revealing so clearly >> >> the limitations of the "open-ness" in their name "Open AI." I mean, >> >> we all know there are some cases where keeping something secret may be >> >> the most ethical choice ... but the fact that they're willing to take >> >> this step simply for a short-term one-news-cycle PR boost, indicates >> >> that open-ness may not be such an important value to them after all... >> >> >> >> -- >> >> Ben Goertzel, PhD >> >> http://goertzel.org >> >> >> >> "Listen: This world is the lunatic's sphere, / Don't always agree >> >> it's real. / Even with my feet upon it / And the postman knowing my >> >> door / My address is somewhere else." -- Hafiz >> > >> > Artificial General Intelligence List / AGI / see discussions + >> > participants + delivery options Permalink >> >> >> -- >> Ben Goertzel, PhD >> http://goertzel.org >> >> "Listen: This world is the lunatic's sphere, / Don't always agree >> it's real. / Even with my feet upon it / And the postman knowing my >> door / My address is somewhere else." -- Hafiz > > > > -- > Stefan Reich > BotCompany.de // Java-based operating systems > Artificial General Intelligence List / AGI / see discussions + participants + > delivery options Permalink -- Ben Goertzel, PhD http://goertzel.org "Listen: This world is the lunatic's sphere, / Don't always agree it's real. / Even with my feet upon it / And the postman knowing my door / My address is somewhere else." -- Hafiz ------------------------------------------ Artificial General Intelligence List: AGI Permalink: https://agi.topicbox.com/groups/agi/T581199cf280badd7-M6c9d911c92c2bdb71f513f2e Delivery options: https://agi.topicbox.com/groups/agi/subscription
