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 <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
