This is basically the Chinese Room argument though?
The Limitations of Deep Learning
11–20 of 296 posts
Re: The Limitations of Deep Learning
#12As someone primarily interested in interpretation of deep models, I strongly resonate with this warning against anthropomorphization of neural networks. Deep learning isn't special; deep models tend to be more accurate than other methods, but fundamentally they aren't much closer to working like the human brain than e.g. gradient boosting models. I think a lot of the issue stems from layman explanations of neural net…
Perhaps the problem simply lies in calling them neural networks.
This terminology goes back to McCulloch and Pitts in 1943, who said they were making an analogy or model based on the behavior of biological neurons.
https://en.wikipedia.org/wiki/Artificial_neuron#History
There are many things that are inexact about this analogy or model, and many of them were known to be inexact in 1943, but that was the direct inspiration.
Apparently there are lots of different mathematical models available about biological neuron behavior:
Re: The Limitations of Deep Learning
#13There is some good information in there and I agree with the limitations he states, but his conclusion is completely made up. "To lift some of these limitations and start competing with human brains, we need to move away from straightforward input-to-output mappings, and on to reasoning and abstraction." There are tens of thousands of scientists and researchers who are studying the brain from every level and we are m…
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Re: The Limitations of Deep Learning
#14This point is very well made: 'local generalization vs. extreme generalization.' Advanced NN's today can locally generalize quite well and there's a lot of research spent to inch their generalization further out. This will probably be done by increasing NN size or increasing the NN building-blocks complexity.
Or maybe increasing NN size/complexity is the 21st century version of adding epicycles to make geocentrism work.
Re: The Limitations of Deep Learning
#15This is basically the Chinese Room argument though?
Not really. Deep learning does not give you an Artificial General Intelligence (what the Chinese Room is supposed to be). The author just explains why this is so (admittedly, in a handwavy, not necessarily convincing fashion).
Re: The Limitations of Deep Learning
#16As someone primarily interested in interpretation of deep models, I strongly resonate with this warning against anthropomorphization of neural networks. Deep learning isn't special; deep models tend to be more accurate than other methods, but fundamentally they aren't much closer to working like the human brain than e.g. gradient boosting models. I think a lot of the issue stems from layman explanations of neural net…
It isn't anthropomorphizing. There are undeniable architectural similarities between ANN's and biological neural networks. We don't understand either very well yet but the parts we do understand have led to a lot of cross pollination. I don't think computational intelligence will ever match biological networks detail by detail due to the different substrates and resource usage tradeoffs, and they don't need to match. Intelligence can develop in different ways and we are learning about the universal aspects of it.
Re: The Limitations of Deep Learning
#17There is some good information in there and I agree with the limitations he states, but his conclusion is completely made up. "To lift some of these limitations and start competing with human brains, we need to move away from straightforward input-to-output mappings, and on to reasoning and abstraction." There are tens of thousands of scientists and researchers who are studying the brain from every level and we are m…
Hardly a "made-up" conclusion -- just a teaser for the next post, which deals with how we can achieve "extreme generalization" via abstraction and reasoning, and how we can concretely implement those in machine learning models.
Re: The Limitations of Deep Learning
#18Then people are assuming Deep Learning can be applied to a Self Driving Car System end-to-end! Can you imagine the outcome?!
Re: The Limitations of Deep Learning
#19I'm excited to hear about how we bring about abstraction.
I was wondering how a NN would go about discovering F = ma and the laws of motion. As far as I can tell, it has a lot of similarities to how humans would do it. You'd roll balls down slopes like in high school and get a lot of data. And from that you'd find there's a straight line model in there if you do some simple transformations.
But how would you come to hypothesise about what factors matter, and what factors don't? And what about new models of behaviour that weren't in your original set? How would the experimental setup come about in the first place? It doesn't seem likely that people reason simply by jumbling up some models (it's a line / it's inverse distance squared / only mass matters / it matters what color it is / etc), but that may just be education getting in my way.
A machine could of course test these hypotheses, but they'd have to be generated from somewhere, and I suspect there's at least a hint of something aesthetic about it. For instance you have some friction in your ball/slope experiment. The machine finds the model that contains the friction, so it's right in some sense. But the lesson we were trying to learn was a much simpler behaviour, where deviation was something that could be ignored until further study focussed on it.
Re: The Limitations of Deep Learning
#20As someone primarily interested in interpretation of deep models, I strongly resonate with this warning against anthropomorphization of neural networks. Deep learning isn't special; deep models tend to be more accurate than other methods, but fundamentally they aren't much closer to working like the human brain than e.g. gradient boosting models. I think a lot of the issue stems from layman explanations of neural net…
Well said. It's just curve fitting.
Maybe everything is "curve fitting." -- Note: I think it's more hierarchical than that but curve fitting is certainly one of the important capabilities of biological systems.