I think I follow your point here, but be careful to distinguish Conceptual Integration from neural nets. The first is more like an upper level meta-approach, while the latter is the next layer down in some development stack, an implementation layer. You could build Conceptual Integration on top of neural nets or some symbolic-neural hybrid.
Mike A On 1/11/16, Jim Bromer <[email protected]> wrote: > Thanks for that information. I will look for it when I get a chance. > However, I thought that the "statistical analysis of vast, unstructured > piles of text," that was mentioned by the writer in the MIT Press > was probably like deep learning. So I am interested in reading the comments > that Jerome Pesenti made. > > However, it is not relevant to the point I was trying to make. To repeat > one more time: There have been many achievements in AI, some of which have > surprised me. But if this is 'It' then why have search engines gone through > a period of decline during the past 2 years. There could be implementation > problems or even corporate ethical dilemmas - but I doubt that either of > those are the problem. > > But to say that Watson or Deep Learning or Deep Searches are narrow AI > because they lack generality is just not true. Their applications may have > been 'relatively narrow' but the methods have broad generality. So the > criticism of what these programs seem to lack has to be brought up a notch. > > My suggestion is that the thing that has been lacking is Conceptual > Integration. But if this is a reasonable possibility, why haven't people > been interested in discussing this? My answer now is that they either do > not understand what I am talking about or they are in denial. Let me give > an extreme example of someone in denial. Suppose someone believes that > neural networks work like the mind works. Then they already have the answer > to how AGI (or stronger AI) should be created. So even though they might > recognize that different implementation methods have to be developed for > their neural nets, they could still reject the idea that explicit > discussion of Conceptual Integration could be beneficial. Why? Because they > already have solved the fundamental problem and their neural networks do > not involve an explicit modelling of Conceptual Integration (or of > Concepts). So they simply deny that Conceptual Integration might be a key > to solving contemporary AGI problems. > > If Conceptual Integration is 'It' then why am I not able to produce > stronger AI? The reason is that I do not have all the answers to > implementing Conceptual Integration in an actual AI program. I am still > struggling just to explain what it is that I am talking about. > > Jim Bromer > > On Sun, Jan 10, 2016 at 10:56 PM, LAU <[email protected]> wrote: > >> There are few conference available with Jerome Pesenti, Vice President of >> Watson Core Technologie, who talks about techniques inside Watson. >> >> Jerome Pesenti said at a conference (at Paris Tech, a french engineering >> school, date unknown ~09/2015) that : >> - Watson did not use deep learning in the jeopardy version >> - But the system evolves continously, they are replacing many things in >> Watson by deep learning. >> He said that they are replacing codes in the jeopardy version by deep >> learning because it's much more efficient in natural language processing >> and others. With deep learning, there will soon be a version of jeopardy >> for other languages than English. >> >> LAU >> >> >> >> Le 10/01/2016 15:02, Jim Bromer a écrit : >> >> I am interested in the question of whether Watson used deep learning in >> the Jeopardy version because I am skeptical that there is a clear cut >> distinction between hybrids of computational methods that train on large >> corpora of data and Deep Learning. A few lines in a computer review does >> not convince me. Are you saying (for instance) that the statistical >> analysis that was used in Watson was not "Deep"? How could you know? What >> are the differences? >> >> There are times when editorial criticisms are useful and there are times >> when they are trivial to the issue being discussed. If I asked people to >> read something that I posted on my website or which read like I might try >> to get it published then I probably would appreciate comments about typos >> and grammatical issues. Some time ago someone pointed out that I was >> using >> the word 'discreet' when the word should have been 'discrete'. I >> appreciated knowing that I was making that mistake because it is >> important >> to the subject being discussed and I kept repeating the mistake. However, >> he also made some put down suggesting that the fact that I was not using >> the word 'discrete' when I meant 'discrete' showed that I did not know >> too >> much about computer programming. I disagree with that point of view >> because >> the word 'discreet' is, in my opinion, a very important concept in >> psychology and a major problem in contemporary AI. I think contemporary >> AI >> programs lack discretion when confronted by interpretations that might >> take multiple paths. So while my mistake was a serious one (when talking >> in >> a computer group) it was not an indication that I did not have too much >> experience thinking about the subject of this group. A lack of discretion >> can be taken as a lack of insight, but the psychology of discretion is, >> in >> my opinion, something that is very seriously lacking in contemporary >> AI. Narrow AI can show some discretion as long as the problem is within >> the >> narrow range and the response is within the range of appropriate >> responses. >> >> I would be interested in following up on the question of how Jeopardy's >> Watson, which the reviewer said uses statistical analysis on vast >> unstructured piles of text is essentially different from Deep Learning. >> >> The chess playing programs are narrow but similar methodologies can be >> used for situations where 'positions' can be evaluated so the underlying >> methods have much broader general applications. >> >> Conceptual integration is a thing that is very important to me. However, >> I do not have it all figured out. >> >> But I can look at (simple) computational analyses and see, for example, >> that not all the parts in an algorithm are alike. Operations can be >> numbered and they can even - to some extent - be used in numerical >> processes. However, that does not mean they are the same or can then be >> used in the same way as the numerical operands of the function. So here >> you >> might see that knowledge about an operand and an operation In a >> computer function can be useful as long as that knowledge is then >> integrated in a suitable way. For another simple example, people will >> sometimes try to take the enumeration of a column of the digits in an >> arithmetic problem and treat is as if it were a digit (of one of the >> operands). (A n-ary number will consist of digits in columns. For binary >> the columns are the ones column, the twos column, the fours column and so >> on.) Using the ordinal value of a column might workout in some cases but >> in others it might not because the ones in the columns will stand for 1 >> or >> 2 or 4 or 8 and so on. So you have to keep track that an >> explicit enumeration of the columns may have more than one meaning in an >> algorithm. Suppose that this was the first time someone ever considered >> this problem. In order to make sense of this he would have to be able to >> integrate a number of very simple concepts. Even if someone is capable of >> understanding the simple concepts when they are taken out of context >> (what >> do I mean by a column of a number, what do I mean by a digit, what do I >> mean by an n-ary number, what do I mean by the enumeration of the columns >> of a number can take on different meanings) they still might be totally >> baffled by what I am talking about. Not only do they have to integrate >> these different simple concepts they also have to do so in a very >> discreet >> way. They would need to try to integrate the concepts in different ways >> but >> show great discretion in limiting the number of the ways that they tried. >> Just mashing all the concepts together and trying to make them all act >> like >> they were of the same kind of thing (a countable digit in this example) >> isn't going to cut it. >> >> Jim Bromer >> >> On Sat, Jan 9, 2016 at 11:00 PM, John Smith <[email protected]> wrote: >> >>> "The idea that Deep Blue and Watson were not cases of Deep Learning is >>> irrelevant. (You are effectively criticizing my topic headline rather >>> than >>> what I was getting at.)" >>> >>> Maybe you shouldn't have a title that says one thing while intending >>> something else? >>> >>> "But, Deep Learning is being used in visual recognition and my feeling >>> is >>> that since Watson did use machine learning I believe that it must have >>> used >>> something that had some correspondence to Deep Learning." >>> >>> Your feeling is wrong, Watson didn't have deep learning when it won >>> jeopardy, it was only added recently >>> http://www.technologyreview.com/news/539226/ibm-pushes-deep-learning-with-a-watson-upgrade/ >>> There are many kinds of machine learning that are different in kind >>> from >>> deep learning. >>> >>> "The argument that they were just narrow AI is also irrelevant." >>> >>> No it isn't, because narrow AI.. like a machine specifically designed to >>> play chess, will not be able to do something like play checkers, or drive >>> a >>> car, or write poetry. It will only be able to play chess. >>> >>> "There is no question that Watson and methodologies that are on par with >>> contemporary Deep Learning have a wide variety of applications." You >>> know >>> duck tape has lots of applications too.. >>> >>> "So they are capable of some generalization." >>> >>> Again a chess playing machine can't do jack, but play chess, so too with >>> a jeopardy playing machine. >>> >>> "Human beings, which represent the model of general intelligence, are >>> not >>> capable of figuring out many kinds of problems including many that >>> computers can and will solve. " >>> >>> This is the first true thing you've said. >>> >>> "The problem is that these contemporary AI programs are not capable of >>> integrated general intelligence and they are end up working within >>> relatively narrow fields." >>> >>> Okay first of all please use proper grammar. Second of all what is this >>> "integrated" general intelligence you speak of, please define, and >>> please >>> keep in mind I'm a very simple person who has difficulty with >>> obscure terminology that is only understood in the mind of the speaker. >>> >>> "But to say that they are narrow as opposed to genera is not quite >>> right." >>> >>> So if someone creates an AI for playing chess and only chess.. it isn't >>> narrow because you believe there are other applications for it? This is >>> just wrong. The only way it would have other applications is if you >>> spent >>> the time to some how map your other application onto a chess board. But >>> that isn't the AI doing the generalizing, rather it is the user doing >>> the >>> generalizing. >>> >>> Narrow AI != General AI >>> QED >>> >>> On Sat, Jan 9, 2016 at 10:19 PM, Jim Bromer <[email protected]> wrote: >>> >>>> The idea that Deep Blue and Watson were not cases of Deep Learning is >>>> irrelevant. (You are effectively criticizing my topic headline rather >>>> than >>>> what I was getting at.) But, Deep Learning is being used in visual >>>> recognition and my feeling is that since Watson did use machine learning >>>> I >>>> believe that it must have used something that had some correspondence >>>> to >>>> Deep Learning. >>>> >>>> The argument that they were just narrow AI is also irrelevant. There is >>>> no question that Watson and methodologies that are on par with >>>> contemporary >>>> Deep Learning have a wide variety of applications. So they are capable >>>> of >>>> some generalization. Human beings, which represent the model of general >>>> intelligence, are not capable of figuring out many kinds of problems >>>> including many that computers can and will solve. The problem is that >>>> these >>>> contemporary AI programs are not capable of integrated general >>>> intelligence >>>> and they are end up working within relatively narrow fields. But to say >>>> that they are narrow as opposed to genera is not quite right. >>>> >>>> Jim Bromer >>>> >>>> On Sat, Jan 9, 2016 at 8:31 PM, John Smith <[email protected]> wrote: >>>> >>>>> "winning at chess (IBM Deep Blue [doesn't use deep >>>>> learning]), recognizing objects in pictures (Many Companies and >>>>> different algorithms [some just use mechanical turk]) and winning at >>>>> jeopardy (IBM Watson [didn't use deep learning when it won at >>>>> jeopardy])." >>>>> >>>>> So none of those achievements used deep learning. Google's deep mind >>>>> hasn't "solved intelligence" yet, so it would be a mistake to expect >>>>> the >>>>> kinds of advanced search capabilities you are thinking of. >>>>> >>>>> IBM did the Jeopardy grand challenge specifically because they saw >>>>> Ken Jennings winning streak and the amount of attention it was >>>>> attracting, >>>>> and they thought if we create a software system that could do that we >>>>> would >>>>> get a great deal of attention, which I'm sure they thought would >>>>> subsequently lead to big contracts. So yes it was in a way a >>>>> publicity >>>>> stunt from its inception. And since the algorithms were hand crafted >>>>> for a >>>>> single end (win at Jeopardy) of course it wasn't going to have a large >>>>> impact on the field of AGI in general! Watson wasn't AGI, it was the >>>>> waste >>>>> of time/money narrow AI that the short sighted people in industry find >>>>> easy >>>>> to sell. >>>>> >>>>> On Sat, Jan 9, 2016 at 3:34 PM, Jim Bromer <[email protected]> >>>>> wrote: >>>>> >>>>>> The hype and the implied conquest of AI that winning at chess, >>>>>> recognizing objects in pictures and winning at jeopardy seems to >>>>>> imply >>>>>> just does not jive with the fact that search engine technology lacks >>>>>> any noticeable intellect even though the computing power that Google, >>>>>> Bing or IBM and thousands of other corporations possess is extremely >>>>>> impressive. >>>>>> Jim Bromer >>>>>> >>>>>> >>>>>> On Sat, Jan 9, 2016 at 3:29 PM, Jim Bromer <[email protected]> >>>>>> wrote: >>>>>> > If industry has AI pretty well figured out then why are search >>>>>> engines >>>>>> > so incapable of thinking outside the box? The conclusion looks >>>>>> > inescapable to me. Yes there will be a day when someone makes a >>>>>> > significant achievement while the rest of us might miss it >>>>>> > completely >>>>>> > but the idea that contemporary deep search (or some other AI >>>>>> > method) >>>>>> > has achieved the hype or the implied conquest that winning at chess >>>>>> > and jeopardy seems to imply just does not jive with the computing >>>>>> > power Google, Bing or IBM have. There is a substantial disconnect >>>>>> > between low level -almost- human reasoning and deep learning. >>>>>> > Jim Bromer >>>>>> >>>>>> >>>>>> ------------------------------------------- >>>>>> AGI >>>>>> Archives: https://www.listbox.com/member/archive/303/=now >>>>>> RSS Feed: >>>>>> https://www.listbox.com/member/archive/rss/303/26973278-698fd9ee >>>>>> Modify Your Subscription: https://www.listbox.com/member/?&; >>>>>> Powered by Listbox: http://www.listbox.com >>>>>> >>>>> >>>>> *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> >>>> <https://www.listbox.com/member/archive/rss/303/26973278-698fd9ee> | >>>> Modify <https://www.listbox.com/member/?&;> Your Subscription >>>> <http://www.listbox.com> >>>> >>> >>> *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> >> <https://www.listbox.com/member/archive/rss/303/27172223-36de8e6c> | >> Modify <https://www.listbox.com/member/?&;> Your Subscription >> <http://www.listbox.com> >> >> >> *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/303/11943661-d9279dae > Modify Your Subscription: > https://www.listbox.com/member/?&; > Powered by Listbox: 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
