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The Difference Between AI, Machine Learning, and Deep Learning

blogs.nvidia.com

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Re: The Difference Between AI, Machine Learning, and Deep Learning

#11

Post by cinquemb »

oliwaw wrote: post #4

Deep learning is just a rebranding of "neural networks". When neural nets became unpopular in the 90s and early 2000s, people talked about "multilayer networks" (dropping the "neural") since it wasn't really useful to think about this approach from the neuro perspective (since it's such a cartoonish model of real neural networks anyway). Now that very deep networks have become possible, and various graphical models a…

>…since it wasn't really useful from the neuro perspective (since it's such a cartoonish model of real neural networks anyway).

Still isn't and still masses of people go on to think that these "neural" networks work the same as neurons in a body do… while neuroscientists are still trying to understand how real neuronal networks operate with a bunch of pet phenomenological theories that most pretty much ignore physics despite the tools used lol

Re: The Difference Between AI, Machine Learning, and Deep Learning

#12

Post by ktta »

Also, an interesting read related to this would 'the AI effect'[0]. A lot of the stuff Deep Learning/Machine Learning is able to do today would be looked at as something that only 'true' AI (whatever consensus on what that means is; I think of it as AGI) would be able to do.

But as soon as we are able to solve a problem that we think (feel?) only true AI (AGI) would be able to solve, as soon as we know how it was solved, it is no longer a mystery that warrants amazement and we argue that it is not real intelligence, just like the link below states.

But just a decade ago, if you saw that a computer was able to recognize pictures better than humans, you would think there was something fishy going on, or we have achieved true AI.

My opinion is true AI is which is comparable to human intelligence, in that it is sentient and/or capable of abstract thought, not necessarily being able to hold a conversation or solve concrete mathematics problems, or dump out a story by neural networks.

[0]https://en.wikipedia.org/wiki/AI_effect

Re: The Difference Between AI, Machine Learning, and Deep Learning

#13

Post by argonaut »

ktta wrote: post #12

Also, an interesting read related to this would 'the AI effect'[0]. A lot of the stuff Deep Learning/Machine Learning is able to do today would be looked at as something that only 'true' AI (whatever consensus on what that means is; I think of it as AGI) would be able to do. But as soon as we are able to solve a problem that we think (feel?) only true AI (AGI) would be able to solve, as soon as we know how it was sol…

I hear this repeated a lot. But I've never actually seen a source from, say, the 90's or the 2000s, actually saying something like "if we're able to classify images better than humans, that's true AI."

I suspect it's just hearsay that's been repeated over and over again.

Re: The Difference Between AI, Machine Learning, and Deep Learning

#14

Post by argonaut »

paulsutter wrote: post #6

I disagree. Machine learning is more general than AI and therefore should be the outer circle (unless you believe the meme "did a regression - called it AI") Deep learning is, yes, a subcase of machine learning, and AI may be a circle within machine learning and enclose deep learning. But truth be told, we will all regret the way we use the term AI now. Eventually the term AI will refer only to general intelligence (…

"AI" is a specific term used in academia / research. In that context, ML is most certainly a subset of AI.

The fact that many laymen have a different conception of the term is hardly a reason to change all our uses of the term.

Re: The Difference Between AI, Machine Learning, and Deep Learning

#16

Post by svantana »

argonaut wrote: post #13
ktta wrote: post #12

Also, an interesting read related to this would 'the AI effect'[0]. A lot of the stuff Deep Learning/Machine Learning is able to do today would be looked at as something that only 'true' AI (whatever consensus on what that means is; I think of it as AGI) would be able to do. But as soon as we are able to solve a problem that we think (feel?) only true AI (AGI) would be able to solve, as soon as we know how it was sol…

I hear this repeated a lot. But I've never actually seen a source from, say, the 90's or the 2000s, actually saying something like "if we're able to classify images better than humans, that's true AI." I suspect it's just hearsay that's been repeated over and over again.

There used to be a concept of "AI completeness" which meant that some problems could only be solved by True AI, which would also be able to solve all other human-level problems. Doug Hofstadter writes in Gödel-Escher-Bach that he believes grandmaster-level chess is AI complete. That was obviously false in retrospect, and it's becoming increasingly obvious that there is no AI completeness at all.

Re: The Difference Between AI, Machine Learning, and Deep Learning

#17

Post by ragebol »

svantana wrote: post #16
argonaut wrote: post #13

Earlier quoted context omitted.

I hear this repeated a lot. But I've never actually seen a source from, say, the 90's or the 2000s, actually saying something like "if we're able to classify images better than humans, that's true AI." I suspect it's just hearsay that's been repeated over and over again.

There used to be a concept of "AI completeness" which meant that some problems could only be solved by True AI, which would also be able to solve all other human-level problems. Doug Hofstadter writes in Gödel-Escher-Bach that he believes grandmaster-level chess is AI complete. That was obviously false in retrospect, and it's becoming increasingly obvious that there is no AI completeness at all.

For now, I take this blogpost [1] of Andrej Karpathy as a nice measure for AI hardness.

[1] http://karpathy.github.io/2012/10/22/state-of-computer-visio...

Re: The Difference Between AI, Machine Learning, and Deep Learning

#18

Post by seanwilson »

ktta wrote: post #12

Also, an interesting read related to this would 'the AI effect'[0]. A lot of the stuff Deep Learning/Machine Learning is able to do today would be looked at as something that only 'true' AI (whatever consensus on what that means is; I think of it as AGI) would be able to do. But as soon as we are able to solve a problem that we think (feel?) only true AI (AGI) would be able to solve, as soon as we know how it was sol…

> But as soon as we are able to solve a problem that we think (feel?) only true AI (AGI) would be able to solve, as soon as we know how it was solved, it is no longer a mystery that warrants amazement and we argue that it is not real intelligence, just like the link below states.

I think the issue is that current examples of AI can mostly only solve problems in a single domain instead of in a variety of domains. Developing a program that can generate solutions for a single specific niche of problems (while impressive) is not a convincing demonstration of intelligence. For example, while AlphaGo is incredibly impressive, it can only play Go and nothing else.

I think once we have a comprehensive AI program that can play many kinds of games, can have conversations, can complete school exam questions, can write stories etc. the question of what intelligence is will become more interesting.

Re: The Difference Between AI, Machine Learning, and Deep Learning

#19

Post by maaaats »

oliwaw wrote: post #4

Deep learning is just a rebranding of "neural networks". When neural nets became unpopular in the 90s and early 2000s, people talked about "multilayer networks" (dropping the "neural") since it wasn't really useful to think about this approach from the neuro perspective (since it's such a cartoonish model of real neural networks anyway). Now that very deep networks have become possible, and various graphical models a…

Yeah, I've always heard that expert systems are good, because there you can reason about the solution. For instance diagnosing people based on rules contributed by doctors. You can trace the steps the algorithm takes easily.

But for a neural net, you cannot say why this particular net should be trusted, as you don't know how it arrives at a solution. Therefore it's "scary" to use.

While I don't agree, it explains why it has been unpopular.

Re: The Difference Between AI, Machine Learning, and Deep Learning

#20

Post by reacweb »

argonaut wrote: post #13
ktta wrote: post #12

Also, an interesting read related to this would 'the AI effect'[0]. A lot of the stuff Deep Learning/Machine Learning is able to do today would be looked at as something that only 'true' AI (whatever consensus on what that means is; I think of it as AGI) would be able to do. But as soon as we are able to solve a problem that we think (feel?) only true AI (AGI) would be able to solve, as soon as we know how it was sol…

I hear this repeated a lot. But I've never actually seen a source from, say, the 90's or the 2000s, actually saying something like "if we're able to classify images better than humans, that's true AI." I suspect it's just hearsay that's been repeated over and over again.

In the 90's, John McCarthy was calling Go "the new Drosophila of AI". Now, we have AlphaGo that is "just" a bunch of "generic" deep learning with MC. Maybe John McCarthy was right and the ideas behind AlphaGo are the first steps toward true AI. I am very impressed.
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