The essential question is: Can it go from basic reading comprehension to advanced just by adding more rules. Is intelligence simply 10 million rules? If so, how do we go about creating new rules as language evolves? By hard-coding them, as in the example code?
The real test for general AI and NLP is how well it, well, generalizes; i.e. how well does it deal with situations we have not explicitly anticipated?
In my opinion, the fuzzy, statistical methods @davesullivan mentions have a better chance at generalizing (although they may well be augmented by rules-based AI).
If an AI doesn't have a good way of transferring what it knows to novel problems, then it is severely limited. It's treating the world like a canned problem with a finite number of possibilities, like chess or checkers, when in fact the world is much more complex.
The way DeepMind combines deep learning and reinforcement learning is one way of acknowledging that complexity.
Deep learning learns patterns in raw sensory data, which means it can ingest and handle the new. Reinforcement learning learns to perform actions over a series of unknown states, improving its choices by monitoring the rewards it receives for those actions. They both maximize within uncertainty, and I think that's our best bet going forward.
Because the world, and language, cannot be known in their entirety. The number, motion and interrelation of the atoms of air in the room where I'm typing this are all too large and complex to be computable. Their fluid dynamics can only be vaguely guessed at, not deterministically predicted in a few lines of code.
The trick will be to bridge the gap between the hard-coded, limited rules and the unlimited recombinations of language, which is inventing new rules and words all the time.
The essential question is: Can it go from basic reading comprehension to advanced just by adding more rules. Is intelligence simply 10 million rules? If so, how do we go about creating new rules as language evolves? By hard-coding them, as in the example code?
I believe that it can go from basic reading comprehension to more advanced by adding many rules but of course manually adding them is not very feasible or scaleable.
In my opinion, the fuzzy, statistical methods @davesullivan mentions have a better chance at generalizing (although they may well be augmented by rules-based AI).
I agree that a statistical model would be better since it will be able to handle more complexity. It would be much easier to train the rules from a dataset instead of hard coding all of them and it would be able to adapt to new rules as well. However, I could not find a good data set for the task I wanted.
Comments
This is a really neat, rules-based, Chomskyian NLP (as opposed to the statistical kind represented by Word2vec). It's an old division...
http://norvig.com/chomsky.html
The essential question is: Can it go from basic reading comprehension to advanced just by adding more rules. Is intelligence simply 10 million rules? If so, how do we go about creating new rules as language evolves? By hard-coding them, as in the example code?
The real test for general AI and NLP is how well it, well, generalizes; i.e. how well does it deal with situations we have not explicitly anticipated?
In my opinion, the fuzzy, statistical methods @davesullivan mentions have a better chance at generalizing (although they may well be augmented by rules-based AI).
If an AI doesn't have a good way of transferring what it knows to novel problems, then it is severely limited. It's treating the world like a canned problem with a finite number of possibilities, like chess or checkers, when in fact the world is much more complex.
The way DeepMind combines deep learning and reinforcement learning is one way of acknowledging that complexity.
Deep learning learns patterns in raw sensory data, which means it can ingest and handle the new. Reinforcement learning learns to perform actions over a series of unknown states, improving its choices by monitoring the rewards it receives for those actions. They both maximize within uncertainty, and I think that's our best bet going forward.
Because the world, and language, cannot be known in their entirety. The number, motion and interrelation of the atoms of air in the room where I'm typing this are all too large and complex to be computable. Their fluid dynamics can only be vaguely guessed at, not deterministically predicted in a few lines of code.
The trick will be to bridge the gap between the hard-coded, limited rules and the unlimited recombinations of language, which is inventing new rules and words all the time.
I believe that it can go from basic reading comprehension to more advanced by adding many rules but of course manually adding them is not very feasible or scaleable.
I agree that a statistical model would be better since it will be able to handle more complexity. It would be much easier to train the rules from a dataset instead of hard coding all of them and it would be able to adapt to new rules as well. However, I could not find a good data set for the task I wanted.