Not to be discouraging, but I think research along these lines http://arxiv.org/abs/1503.08895 stands an exponentially better chance of leading towards what OP is talking about. For anyone interested in "building AI", read that paper and all it's references.
You know, high order logic memories are a completely different kind of intelligence, that, if somebody makes it work, can be much more capable than plain pattern matching techniques.
Granted that it's a fundamentally more difficult problem, so the pattern matching will probably get there earlier.
They're the current hip thing because they work really well and keep working better. They take a fundamentally different (I think better) approach than he's taking.
My recommendation comes from a more informed place than "Dur, neural networks!" Of course, this is my opinion, and you are welcome to have a different one.
What is your recommendation on the topic of most promising research areas for teaching reading comprehension to computers? Skip deep learning and read what instead? Or the OP has it figured out?
I think the strength lies in combining NNs with less fuzzy approaches (like the OP, or more explicit pattern matching). Coulda made that a bit clearer, I have to admit. Cuz who would want to spend an hour training their net on a big GPU for every new command they add? :P
To take a concrete example: we trust neural networks to do the handwriting recognition at the postal office, but once the address is digitized we use a simple database.
> I think the strength lies in combining NNs with less fuzzy approaches
Haha, I think you'll find the deep learning camp agrees. Read the paper I posted, that's what the research is about (going from fuzzy knowledge to more specific/discrete knowledge.)
Comments
Not to be discouraging, but I think research along these lines http://arxiv.org/abs/1503.08895 stands an exponentially better chance of leading towards what OP is talking about. For anyone interested in "building AI", read that paper and all it's references.
You know, high order logic memories are a completely different kind of intelligence, that, if somebody makes it work, can be much more capable than plain pattern matching techniques.
Granted that it's a fundamentally more difficult problem, so the pattern matching will probably get there earlier.
Just because it could be done with the current hip thing (neural nets) doesn't mean all other approaches should be disregarded.
They're the current hip thing because they work really well and keep working better. They take a fundamentally different (I think better) approach than he's taking.
My recommendation comes from a more informed place than "Dur, neural networks!" Of course, this is my opinion, and you are welcome to have a different one.
What is your recommendation on the topic of most promising research areas for teaching reading comprehension to computers? Skip deep learning and read what instead? Or the OP has it figured out?
I think the strength lies in combining NNs with less fuzzy approaches (like the OP, or more explicit pattern matching). Coulda made that a bit clearer, I have to admit. Cuz who would want to spend an hour training their net on a big GPU for every new command they add? :P
To take a concrete example: we trust neural networks to do the handwriting recognition at the postal office, but once the address is digitized we use a simple database.
Haha, I think you'll find the deep learning camp agrees. Read the paper I posted, that's what the research is about (going from fuzzy knowledge to more specific/discrete knowledge.)
It's interesting stuff. Don't really see why you were being so dismissive towards OP tho.