Meet the algorithm that can learn “everything about anything”
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Re: Meet the algorithm that can learn “everything about anything”
#2Here is the actual paper for those interested
http://levan.cs.washington.edu/ngrams/objectNgrams_cvpr14.pd...
Re: Meet the algorithm that can learn “everything about anything”
#3The problem with all approaches to machine learning I see today is that they only focus on grouping and separating concepts based on certain characteristics. They seem to be all fundamentally statistical.
None of them seem to work towards a fundamental understanding of what the concepts mean. I'm not sure how that could be accomplished though.
Is there even a meaningful distinction to be made between beeing able to identify a concept and understanding what a concept actually means?
Re: Meet the algorithm that can learn “everything about anything”
#4The problem with all approaches to machine learning I see today is that they only focus on grouping and separating concepts based on certain characteristics. They seem to be all fundamentally statistical. None of them seem to work towards a fundamental understanding of what the concepts mean. I'm not sure how that could be accomplished though. Is there even a meaningful distinction to be made between beeing able to i…
This is in fact a classic criticism of modern AI. Classic approaches tried to replicate human reasoning mechanisms. The work was very cool, but far harder to understand and scale, compared to modern statistical algorithms. To some extent, you are faced with a choice: a small palette of mediocre techniques whose workings are beautiful theories of the human mind; or, a broadly-applicable, incredibly practical set of tools with little motivation or spirit.
Re: Meet the algorithm that can learn “everything about anything”
#5The problem with all approaches to machine learning I see today is that they only focus on grouping and separating concepts based on certain characteristics. They seem to be all fundamentally statistical. None of them seem to work towards a fundamental understanding of what the concepts mean. I'm not sure how that could be accomplished though. Is there even a meaningful distinction to be made between beeing able to i…
I don't think there is a meaningful distinction. Do chess computers have a "fundamental understanding" of chess, which humans traditionally considered a benchmark of human intelligence/strategy?
Analogous to the philosophical zombie thought experiment, I think that "real intelligence/understanding" is indistinguishable from simply being able to perform actions to accomplish the same tasks that humans traditionally consider to require intelligence.
Of course, that's one of the PR problems that AI has always had: once a computer can outperform a human at some task, that task is no longer considered to be something that requires "true intelligence." Most people would consider someone who can multiply numbers together to be intelligent, but when computers do that (incomprehensibly faster and more reliably), few people consider even for a moment that it's AI. Same with more advanced mathematics, like computer algebra and automated theorem proving. Same with facial and voice recognition. And I'm sure it will be the same with self-driving cars.
Re: Meet the algorithm that can learn “everything about anything”
#6The problem with all approaches to machine learning I see today is that they only focus on grouping and separating concepts based on certain characteristics. They seem to be all fundamentally statistical. None of them seem to work towards a fundamental understanding of what the concepts mean. I'm not sure how that could be accomplished though. Is there even a meaningful distinction to be made between beeing able to i…
I don't think that anyone can answer that categorically. It's a question for philosophy or metaphysics. Almost merely a matter of opinion. It's hard to criticize software for not working towards a "fundamental understanding of what the concepts mean" if we admit that we don't really even agree on whether that is a valid sentence or what it might mean.
Re: Meet the algorithm that can learn “everything about anything”
#7The problem with all approaches to machine learning I see today is that they only focus on grouping and separating concepts based on certain characteristics. They seem to be all fundamentally statistical. None of them seem to work towards a fundamental understanding of what the concepts mean. I'm not sure how that could be accomplished though. Is there even a meaningful distinction to be made between beeing able to i…
This is in fact a classic criticism of modern AI. Classic approaches tried to replicate human reasoning mechanisms. The work was very cool, but far harder to understand and scale, compared to modern statistical algorithms. To some extent, you are faced with a choice: a small palette of mediocre techniques whose workings are beautiful theories of the human mind; or, a broadly-applicable, incredibly practical set of to…
I understand that other being hard to scale in a machine efficiency sense, the old techniques were also rather limited in applicable domain. They were often based on unjustified models of whatever the author decided was a good model of thought/reasoning, whether "frames", predicate calculus, constraint propagation or whatever. It seems to me that although you say statistical methods have little motivation, the motivation of the alternative - heuristics - is very questionable. Can anyone correct me on this?
Re: Meet the algorithm that can learn “everything about anything”
#8The problem with all approaches to machine learning I see today is that they only focus on grouping and separating concepts based on certain characteristics. They seem to be all fundamentally statistical. None of them seem to work towards a fundamental understanding of what the concepts mean. I'm not sure how that could be accomplished though. Is there even a meaningful distinction to be made between beeing able to i…
Machine Learning is more of an engineering discipline than a philosophical one. The aim (mostly, with exceptions) is to make machines that learn, not to research the nature of "understanding" or "concepts".
There are people who are looking at broader ideas of "what is intelligence", sometimes even the same people doing the more statistical research, but it's an open ended problem.
The main reason science/math/statistics doesn't spend time worrying about philosophy is because you can't really specify the problem cleanly - and having a clear idea of the problem is 90% of the way to finding the solution.
[edit]: If you want brain-inspired ML stuff, it's worth looking at what neuroscientists and cognitive scientists are doing. From what I've seen of some of their work they're developing ML algos as minimal tests for understanding how parts of the brain or consciousness works.
Re: Meet the algorithm that can learn “everything about anything”
#9The problem with all approaches to machine learning I see today is that they only focus on grouping and separating concepts based on certain characteristics. They seem to be all fundamentally statistical. None of them seem to work towards a fundamental understanding of what the concepts mean. I'm not sure how that could be accomplished though. Is there even a meaningful distinction to be made between beeing able to i…
This reminds me of one of my favorite Dijkstra quotes. "The question of whether machines can think... is about as relevant as the question of whether submarines can swim"
Re: Meet the algorithm that can learn “everything about anything”
#10The problem with all approaches to machine learning I see today is that they only focus on grouping and separating concepts based on certain characteristics. They seem to be all fundamentally statistical. None of them seem to work towards a fundamental understanding of what the concepts mean. I'm not sure how that could be accomplished though. Is there even a meaningful distinction to be made between beeing able to i…
I'm of the impression that concepts only have fundamental meaning to us because we can relate them to sensory input. For example, initially a child is taught what a boat is by seeing pictures, it eventually learns that boats, cars and bicycles share common characteristics and can be grouped together as vehicles. These common characteristics are all understood and identified by seeing, hearing, touching or smelling.
It would be impossible completely describe a concept such as "horse" to a person with no knowledge of animal physiology, without recourse to pointing at a a horse or impersonating a horse noise. Resorting to analogies with other concepts - "a horse is like a cow" - doesn't work because the person has no knowledge of the other concepts. This person could be a young child or an untrained computer, and they can be taught fundamental meanings of concepts by feeding them information in the form of sights, sounds, smells etc.
I haven't thought about this very deeply but these are the thoughts that always come into my mind when this topic comes up. Concepts have no meaning without relating them to sensory input. Humans learn by connecting the two. Why can the same not be applied to computers?