I did not get confused, I was trying to make the point that someone like Searle seems to refer to these strong dichotomies in his theories, but Kurzweil did not as far as I could tell. I happened to agree with Kurzweil so perhaps my opinion is based on the common ground I seem to have with Kurzweil. On the other hand, I do agree with a lot of the things that Searle was saying in the video I started to watch.
I do think that there are a lot of interesting things happening and developing hybrid systems might be a good way to go about it. On the other hand, I do think that there are other kinds of networks - like a conceptual network - which should be able to do some of the things that Deep Learning is doing now. A Neural Network is a simple kind of network (although there can be different variations) so I guess that it may be the quickest way to get some fast results using network-related methods. But although a Conceptual Network would be inherently more problematic I do think that it holds the greatest potential. Jim Bromer On Tue, Jan 19, 2016 at 6:11 PM, Mike Archbold <[email protected]> wrote: > Jim, > > Sorry, we got tangled up in different references.... I was commenting > on a link to "kurzweilaI" about "why my phone doesn't understand me" > posted by Dr Roberts Jr, but I think you were commenting on a talk by > Kurzweil which I have not seen. > > Anyway my only point in all this is that while deep learning > considered in isolation may not be tantamount to AGI it can still be > used in an overall architecture. Everybody realized this, I think, > but there is nothing lost in emphasizing it and not get caught in the > trap of "either this technique solves AGI *OR* we are not making > progress toward AGI." > > Mike > > On 1/18/16, Jim Bromer <[email protected]> wrote: >> Mike Said: >> The statement: >> >> “Apple’s Siri focuses on statistical regularities, but communication >> is not about statistical regularities,” he said. “Statistical >> regularities may get you far, but it is not how the brain does it. In >> order for computers to communicate with us, they would need a >> cognitive architecture that continuously captures and updates the >> conceptual space shared with their communication partner during a >> conversation.” >> >> Well, to me that sounds a bit like a false dichotomy. We could use >> both (what he calls) statistical regularities working within a >> cognitive architecture framework. >> >> Mike >> ------------------------------------------------- >> Kurzweil said that computers would need x to communicate with someone >> during conversation. He did not say that an assessment of statistical >> regularities would need to be excluded to achieve this, so I did not >> see his statement as a dichotomy other than to say that statistical >> regularities were not enough. >> >> When I first started listening to the Searle Google Talk I thought >> that he was not going to be so iconoclastic about his one main issue. >> I agree with him about a lot of the things he said. Computers are >> syntactic, we do not understand how consciousness works. His comments >> did help me to rethink my plans a little in a way that might lead to >> more practical results. However, I realized that we have a fundamental >> difference because he thinks that the human brain is not a syntactic >> device. Furthermore using Searle's dichotomy we can say that human >> consciousness is (subjective) observer relative. In spite of the fact >> that we do not know how the mind produces consciousness and regardless >> of the mysteriousness of conscious experience, our experience of >> consciousness is still relative to our observation (just as our >> feelings that a computer is doing thinking when it does some >> computation is relative.) >> >> I don't want to spend the time to get more precise quotes from the >> Searle talk, but, using my recall Searle started by talking about the >> dichotomy of epistemological knowledge and ontological knowledge. >> Ontological knowledge is knowledge that comes from existence and >> epistemological knowledge is more like knowledge that has been written >> down or derived from higher abstractions. And he says, even though he >> realizes that computers can do amazing things, that the computer is >> syntactic but it does not have any semantic knowledge about anything. >> I do not agree that Searle's dichotomies are absolute. Syntactic >> knowledge does contain some semantic information and we can represent >> semantic knowledge using syntax. Epistemological knowledge can be used >> to encode ontological knowledge. And the brain is a syntactic device. >> >> So I think that Searle's dichotomies are excessive even though I agree >> with some of his view points and I feel that they can be used to help >> produce more effective results. In contrast, I don't see a dichotomy >> in what Kurzweil was saying unless you are saying that the observation >> and utilization of statistical regularities is enough to produce a >> cognitive architecture capable of true conversation between computers >> and people. >> Jim Bromer >> >> >> On Mon, Jan 18, 2016 at 7:00 PM, Mike Archbold <[email protected]> wrote: >>> On 1/17/16, Jim Bromer <[email protected]> wrote: >>>> The article by Kurzweil seemed to be insightful. >>>> >>> >>> >>> To me it sounded like another take on the combinatorial explosion >>> issue, which is well known, coming from the angle of the observed >>> neural structure of context. >>> >>> The statement: >>> >>> “Apple’s Siri focuses on statistical regularities, but communication >>> is not about statistical regularities,” he said. “Statistical >>> regularities may get you far, but it is not how the brain does it. In >>> order for computers to communicate with us, they would need a >>> cognitive architecture that continuously captures and updates the >>> conceptual space shared with their communication partner during a >>> conversation.” >>> >>> Well, to me that sounds a bit like a false dichotomy. We could use >>> both (what he calls) statistical regularities working within a >>> cognitive architecture framework. >>> >>> Mike >>> >>> >>> >>> >>>> 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. 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