The article by Kurzweil seemed to be insightful. Jim Bromer
On Thu, Jan 14, 2016 at 3:24 PM, Raymond D Roberts Jr. via AGI < [email protected]> wrote: > > http://www.kurzweilai.net/why-doesnt-my-phone-understand-me-yet?utm_source=KurzweilAI+Daily+Newsletter&utm_campaign=0481d44bf4-UA-946742-1&utm_medium=email&utm_term=0_6de721fb33-0481d44bf4-282058098 > > Raymond D. Roberts Jr. > > > -----Original Message----- > From: Jim Bromer <[email protected]> > To: AGI <[email protected]> > Sent: Thu, Jan 14, 2016 3:16 pm > Subject: Re: [agi] Re: If Deep Learning is It then Why Are Search Engines > Incapable of Thinking (Outside the Box or Otherwise)? > > I don't think that deep learning only applies to pure deep learning. It > could be used as part of a system which is attuned to discovering > relations. And it could return relationships (in language for example) > which could then be evaluated. Supervised learning is part of machine and > deep learning so a system which returns candidate samples that can be > evaluated still could be classified as machine learning (or could be said > to have something in common with machine learning). > > Older AI paradigms have a much more fixed definition than newer ones. > Comparing Watson, which was apparently able to learn new things about > language, to an old Expert System does not sound right. My argument is that > almost all contemporary AI paradigms involve some kind of network of > relations so to presuppose that an advanced nlp program cannot 'learn' > about nlp does not make sense. When I get some time I will ask someone at > IBM what the phrase "Deep NLP" denotes. Does it mean deep search nlp or > does it mean something that is closer to deep learning nlp because there is > no reason to rule the possibility that new relationships in nlp could be > detected in an applied network (of some kind) and then be used as an > abstraction to search for other cases that might have a similar *kind* of > relationship. > > I am interested in what you said Ben but I get the sense that Watson was > used to detect relationships in nlp which were then evaluated (probably in > different ways.) > > Jim Bromer > > On Thu, Jan 14, 2016 at 10:30 AM, Ben Goertzel <[email protected]> wrote: > >> *** >> My question was why haven't there been clear advances in search engine >> technology in the 2 years since Deep Learning and Watson have made very >> obvious advances in AI? >> *** >> >> The Web is very big. Internally within Google, the API calls you can >> make against the whole Web are many fewer than the ones you can make >> against, say, Wikipedia >> >> But business-wise, there is more $$ to be made in making crude searches >> against the whole Web slightly less crude, than in making more refined and >> intelligent searches against smaller text-bases... >> >> Also, >> >> -- Watson is basically an expert system, albeit a very clever one.... >> Expert system methods don't scale, not even fancy ones... >> >> -- Deep learning in its current form works best for high-dimensional >> floating point data, not discrete data like text .... Also, current deep >> learning algorithms rely essentially on bottom-up pattern recognition, with >> limited top-down feedback. But real language understanding can't get >> approximated very well without sophisticated top-down feedback.... I.e., >> image and speech understanding can get further without cognitive feedback, >> than language understanding... >> >> >> ... ben >> >> >> >> On Thu, Jan 14, 2016 at 11:22 PM, Jim Bromer <[email protected]> wrote: >> >>> >>> http://www.ibm.com/blogs/think/2016/01/14/the-next-grand-challenge-computers-that-converse-like-people/ >>> >>> Jim Bromer >>> >>> On Thu, Jan 14, 2016 at 10:20 AM, Jim Bromer <[email protected]> >>> wrote: >>> >>>> I watched the Presenti presentation on youtube a few days ago. >>>> >>>> Neural Networks can learn but they cannot use that learning efficiently >>>> in many important ways. Discrete AI can acquire more specific >>>> (discrete) 'objects' as they learn. So back in the 90's people started >>>> using hybrids that combined neural networks with discrete methods. Machine >>>> learning includes advances on hybrid methods. >>>> >>>> Most discrete methods are built around networks of relations between >>>> the data objects which represent 'concepts' or 'ideas' or 'knowledge' or >>>> 'know how' or whatever it is that you want to call the data objects that >>>> would be used to hold knowledge in a (more) discrete AI program. So a >>>> contemporary discrete AI program is also going to be an implementation of a >>>> network. The network may include numerical values but even if it doesn't >>>> it probably will represent categories of association. That definition is >>>> not meant to be complete because I am only trying to get an idea across: >>>> Modern discrete AI methods involve network methods that can potentially be >>>> seen as representatives of 'thought' that are more sophisticated than >>>> neural networks. That makes sense. >>>> >>>> Presenti was talking about an IBM researcher from the 70s who found >>>> that he could use statistical methods to *learn* about speech without a >>>> linguist. That would be a form of machine learning. Therefore it is fairly >>>> safe for me to conclude that Watson used machine learning in what Watson >>>> researcher's called, "Deep NLP". >>>> >>>> My question was why haven't there been clear advances in search engine >>>> technology in the 2 years since Deep Learning and Watson have made very >>>> obvious advances in AI? I did an image search for "cats" on google and it >>>> was very good. I only found one dog (a small dog which had been photo >>>> shopped with multiple legs somewhat like a caterpillar.) I tried some other >>>> searches on images and the results were also very good. The results were >>>> really amazing. So there have been some advances on image searches in the >>>> past 2 years. The search for "castles on the moon" did not distinguish >>>> between castles pictured as being on the moon from castles with the moon in >>>> the scene. So even though I am nit-picking to some extent the point is that >>>> it looks like you have to train a deep learning neural network with a >>>> narrow training sample in order to teach it to recognize something that >>>> would require a little thinking outside the box. That was also a problem >>>> with Watson. Its Deep NLP could be trained with all the questions from past >>>> Jeopardy shows (and Jeopardy-style questions that researchers could create) >>>> but can it be trained to handle juxtapositions of linguistic 'concepts' >>>> that might require some thinking outside of the box? (Incidentally I tried >>>> "cat in a box" and Google did very well. But when I tried "full stadium" it >>>> did include pictures of stadiums that were not empty. I could spot them as >>>> I was paging quickly through the images.) But I guess I there have been >>>> some significant advances in the past 2 years. They just do not include >>>> using language to refine your searches. >>>> >>>> My idea of Concept Integration is that different concepts cannot always >>>> be merged, as in a neural network to take an example, because as more >>>> concepts are integrated the requirements of a part of the conceptual >>>> integration may change. To restate that in another way,the integration of a >>>> number of concepts will typically change if additional concepts are >>>> integrated with them. This is what would happen if you tried to refine your >>>> search using conversation. >>>> >>>> Jim Bromer >>>> >>>> >>>> On Tue, Jan 12, 2016 at 10:21 PM, LAU <> wrote: >>>> >>>>> OK, as you wish ... It's just a word. We do not agree on the >>>>> signification. But, it's OK. If you call it "deep learning", or >>>>> "conceptual >>>>> learning" ... or "Hakuna Matata's learning", it's not important. Stop >>>>> playing with words. >>>>> >>>>> Try to back to the topic of this thread. If I understand what you want >>>>> to promote : >>>>> 1) You note that deep learning implemented in the industry is not so >>>>> intelligent than espected taking in account the computation power >>>>> available. >>>>> 2) Watson seems to less "narrow" than other implementations. >>>>> 3) What it miss there is "conceptual integration". >>>>> Correct me if I'm wrong. >>>>> >>>>> >>>>> In my humble opinion, there's no intelligent machines just because >>>>> people don't try to, or most likely don't figure out how to, make it more >>>>> intelligent. >>>>> Implementing"conceptual integration" is certainly a way that some >>>>> researchers tried, but lead to no significant results until now. If I look >>>>> at wikipedia, the theory is dated from 1990s. Twenty years later, still >>>>> nothing. >>>>> >>>>> There's no magic behind deep learning, I mean neural networks, used by >>>>> Google or Facebook. Very roughly, it's just a kind of "universal >>>>> approximator". And it's not the computation power that with make it >>>>> spontaneously more intelligent. >>>>> Deep learning becomes very popular these last years because it's >>>>> easier to make a neural network to accomplish picture or voice recognition >>>>> task (*I've made a small one myself from scratch in few days*) than >>>>> handcrafted codes, and for a better result. >>>>> But basically, a neural network is just another kind of programming. >>>>> Instead of coding a multitude of operation to achieve a complex task, a >>>>> neural network can do it itself by learning from examples. >>>>> And the question will be how to teach a neural network what is >>>>> "conceptual integration" ? >>>>> >>>>> In the paris tech conference video (*on youtube, but it's in french >>>>> ...*), Jerome Pesenti said something else interesting. He cite a >>>>> IBM's 70s researcher, Fred Jelinek, who said "*Every time I fire a >>>>> linguist the performance of the speech recognizer goes up*". The >>>>> Jelinek speech recognizer team was composed by part of linguist and >>>>> engineers. By replacing a linguist who treats language as a human do by an >>>>> engineer who does mathematics and statistics on words, the result is >>>>> better. And it seems to be the philosophy at IBM to work differently that >>>>> a >>>>> human do, and it seems to give better result. Instead of playing jeopardy >>>>> in human way, watson applies statistics on the database (*which was >>>>> wikipedia*). >>>>> >>>>> What I want to say is that may be the "conceptual integration" is a >>>>> track to explore for building AGI. Or, may be the solution will come from >>>>> elsewhere. >>>>> >>>>> >>>>> LAU >>>>> >>>>> >>>>> >>>>> Le 12/01/2016 10:46, Jim Bromer a écrit : >>>>> >>>>> Deep Learning is Deep Machine Learning and Machine Learning is in no >>>>> way limited to Neural Networks. So there is no way that Deep Learning >>>>> is going to be forever defined to refer Machine Learning that uses >>>>> Neural Networks (in certain ways). From that point of view I can say >>>>> that Watson-Jeopardy probably did use a kind of deep learning. >>>>> >>>>> >>>>> ------------------------------------------- >>>>> AGI >>>>> Archives: https://www.listbox.com/member/archive/303/=now >>>>> RSS Feed: https://www.listbox.com/member/archive/rss/303/27172223-36de8e6c >>>>> Modify Your Subscription: https://www.listbox.com/member/?&; >>>>> Powered by Listbox: http://www.listbox.com >>>>> >>>>> >>>>> *AGI* | Archives | Modify Your Subscription >>>>> >>>> >>>> >>> *AGI* | Archives | Modify Your Subscription >>> >> >> >> >> -- >> Ben Goertzel, PhD >> http://goertzel.org >> >> "The reasonable man adapts himself to the world: the unreasonable one >> persists in trying to adapt the world to himself. Therefore all progress >> depends on the unreasonable man." -- George Bernard Shaw >> *AGI* | Archives | Modify Your Subscription >> > > *AGI* | Archives | Modify Your Subscription > *AGI* | Archives <https://www.listbox.com/member/archive/303/=now> > <https://www.listbox.com/member/archive/rss/303/24379807-653794b5> | > Modify > <https://www.listbox.com/member/?&;> > Your Subscription <http://www.listbox.com> > ------------------------------------------- AGI Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/21088071-f452e424 Modify Your Subscription: https://www.listbox.com/member/?member_id=21088071&id_secret=21088071-58d57657 Powered by Listbox: http://www.listbox.com
