You raise a good argument about understanding language as intelligence. I always wonder how I can just randomly say something without priorly thinking about what I am going to say. This is truly /A/ quality of “intelligence,” but i’m not sure if it’s the /ONLY/ quality. How do emotions come into this? Someone who is mute for example might not display this language quality, even more so if deafness was added as a variable, and yet this person would very well be conscious and could relate to the world around them and think and reason.

It leads to the whole pilosophical thing…something about “I think therefore I am.” I don’t think anyone can really know when our robots are trully AI. Look at the movie I, Robot. I wonder when that robot developed his own independent consicousness. A sense of right and wrong?! I’m not sure where I read it, but somebody made the point that if you can teach a robot to “wail” as the word was used when certain receptive sensors were combined, you would start to have activists all over the place about AI Rights. I am remembering the movie Artifical Intelligence with the boy robot that was crying. Crazy ending in that movie…hope it doesn’t happen

Anyways, take a look at this site (not spam! ) seems like they’re getting closer to AI.

http://www.readwriteweb.com/archives/numenta_artificial_intelligence.php

“The Stuff of Thought; Language as a Window into Human Nature”

I think it is important to understand language at the level presented here, before attempting to drill down…and there are many many layers below this level before thinking about software.

It is not an easy read, partly because of my lack of knowledge in linguistics, but already lights are turning on in my head.

It seems there are many words like “cute” and “love” that must have extremely complex algorithms behind them, which includes chemical reactions related to the emotions which feedback into our minds and add to our knowledge of the meanings of these emotionally-based words.

http://en.wikipedia.org/wiki/Cute

http://web.media.mit.edu/~minsky/E1/eb1.html

A harder problem, in building this for the GP world would be, at what state should the system be bootstrapped? How much knowledge should we implicitly build into the system even before the “learning” is started? Should the sentences be input as ascii characters, or as bitmap images? Should the algorithm be pre-programmed with the instructions needed to turn cells on or off, or should it even be made to learn that on its own? Should it be given an identity, or a name, so it knows the meaning of “you” when it reads a sentence, or should we allow it to evolve its “id”?
Should it know that a space character separates the words in an ascii sentence? What about punctuation? What about humor? For an entity which only knows of a 9×9 grid as its world, is it even possible for it ever learn to decode humor, even if it were left to “evolve” for millenia? Or a better question: If its sensory inputs and outputs were limited to operations in the 9×9 grid only, would it ever be able to learn it fully? Doesn’t this mean that the agent can fully manipulate its environment, as in a god-like status within its world? Maybe to evolve something that can fully control the 9×9 grid, we need to evolve it in an environment which only contains the target 9×9 grid as a constituent, besides others. But what should the others be? Is there a critical mass, or a critical ratio?

On the philosophical side: Can we ever divorce the NLU problem from the human aspect? My view is that we can’t. The only reason we want to build NLU capabilities is because it will make things easier for *us*, not the gadgets.

If we had two highly *learning* capable robots, and we programmed them with the means to hear sound and make sounds to communicate, would they evolve something like human language?

I think not. (Or actually, I am not really sure. Sounds like material for a new post…)

Those problems are indeed difficult, I’d like to add a side note. GA are “just” clever global optimizer of a fitness function, and the real difficulty is to find the “good” fitness function. GA are really far from what’s happening in real evolution. So I agree with you, I don’t think it’ll be so easy to solve those pbs withs GA (or with whatever algorithm).

->uPgRaD3 Z3R0 0n3 A : I’m not sure that scene understanding and language understanding are so different, and I guess that they share a lot, for instance the notion of compression, finding regularities, patterns in row data.

Regards

Nice argument…wrong conclusion!

Raghav Gupta, at Fractal Folds, writes about the interesting question: 'what is the most difficult artificial intelligence-related task ?' There's also a follow-up post "Why Genetic Programming cannot solve the most important AI proble…

It occurred to me that both consciousness and *understanding* are not things that either something has or it doesn’t. There are degrees of consciousness and degrees of understanding.

I tend to agree with Marvin Minsky point about words “like intuition or consciousness” are examples of “suitcase” words that: “we all use to encapsulate our jumbled ideas about our minds. We use those words as suitcases in which to contain all sorts of mysteries that we can’t yet explain.”

The word “understanding” might be another example of a suitcase word. Understanding a scene is certainly a lot different than understanding natural language, and in both instances, the understanding can be broken down still further…

Natural Language understanding is a very very hard problem even to define. The concept of “Divide and Conquer” comes to mind.

Thanks again!