I think you have some misconceptions about CLA. It's a sequence learning algorithm. HTM, on the other hand is a broader concept. What your looking for is sensorimotor behavior. I suggest you watch Jeff's speech on that subject. It's on YouTube. There is a link in CLA/HTM Theory wiki.
Sent from my iPhone > On Nov 27, 2014, at 10:13 PM, Leonardo M. Rocha <[email protected]> wrote: > > > Hi, > I'll try changing the question: > > Can I train the CLA to maintain an intelligent conversation? > Defining intelligence as being able to maintain the context and semantic in > the dialogue. > > I'm mainly interested in CLA, I will use it anyway for other toy projects. > > >> The problems depend entirely on how you define "intelligent". >> >> If "intelligent" means a machine that can ask "When was the war of 1812?" >> and then answer "no, please try again" until the student answers "1812" then >> you should be able to build such a machine that works as you expect about >> 90% of the time. > > That is why I named AIML, those kind of rule based answer (if-else basically) > are not only not intelligent, but a big burden to create and maintain. > > >> But if you want a tutor who asks "what was the relationship between the >> various Native American indian tribes during war?" and then if you get >> something wrong the machine will figure out what you likely don't know and >> tell you some things that will clear up misunderstandings. Then we might >> be 50 years away from that. If you want the machine to use words it knows >> you know and to make analogies that you can actually understand because it >> understands your life experiences (because it knows the student is >> Chinese.) then we might be 100 years away. >> >> Today we have machines that can access huge databases but true intelligent >> teaching requires the machine to contain a good, accurate model of the >> student's mind. This part, understanding the student is far past what >> anyone can do. >> >> But if you will settle for "flash cards in natural spoken language" then you >> can build it with current open source technology. >> >> What you should to as a next step is write down about a few dozen >> interactions. Scripts of what you would like to have between the student >> and tutor. Next rank those scripts based on the level of intelligence >> required. > > > The intelligence required is much more than a rule based program, that is why > the idea of the CLA being able to learn and relate different concepts > containing semantic meaning is interesting. > > I need to be able to feed books or chat logs to the tutor and the tutor be > able to answer to questions asked, even if those questions are not explicitly > told in the training set. > > >> >> As with all tutors, you evaluate their performance by looking at changes in >> student performance. You ask "did the student actually learn?" > > Actually that is the question to evaluate, how does one allow the CLA to > automagically evaluate this? > That is why I asked about how a CLA can be trained with positive or negative > feedback. > > >> >> The tutor would really be a planning machine. It first has to figure out >> where the student is. Then look where we want the student to be and then >> find a route from here to there that moves in "right sized" steps. Then the >> machine executes the plan while it continuously evaluates progress and >> re-plans as required. >> >> The problem is going to be that to do this the tutor needs an internal model >> of the student, that is a hard problem > > Ok, so if we try to simplify this with the idea of a "chatbot that acts > intelligent enough" being intelligent something that is not if-else (or > similar) based and can learn from interactions and books.... Can the CLA > handle it? > > Best > > -- > Ing. Leonardo Manuel Rocha > www.annotatit.com > www.musicpaste.com
