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 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
