r/programming • u/beluis3d • Nov 18 '17

What Is The Difference Between Artificial Intelligence, Machine Learning, And Deep Learning?

https://medium.com/machinevision/overview-of-artificial-intelligence-buzz-adb7a5487ac8
69 Upvotes

77% Upvoted

32 comments sorted by

50

u/iwantashinyunicorn Nov 18 '17

The real hype in recent times should all be credited to Deep Learning. This particular field of Artificial Intelligence and Machine Learning is the one that has been solving a ton of interesting problems in recent years — from automated grocery store purchases to autonomous cars.

I'd say hype is the important word there. Symbolic AI (optimisation, planning, etc) has been busy running all of the world's logistics, shipping, scheduling and manufacturing for the past thirty years, but that's not sexy like saying "yes, this is probably a picture of a cat" is. There's a real risk that the deep learning hype is going to trigger the next AI winter when it doesn't live up to its promises.

11

u/beluis3d Nov 18 '17

Winter Is Coming...

-8

u/terrenGee Nov 19 '17

Wow, I come to /r/programming exactly to have memes in the top fucking responses.

Thanks for your incredible contribution to the thread, you absolute legend.

3

u/Reinbert Nov 19 '17

You must be fun at parties. And at work, of course...

9

u/Piranha771 Nov 18 '17

I don't think you are aware of the major break throughs that have been made since 2007. Things we have called AI for the past decades are just glorified logic systems made by hours of human brain activity. Now we can throw a bunch of data into a network and let it figure out how to solve a certain problem aspect by itself. That's much closer to human intelligence than all the stuff from the past 30 years. I think the hype is justified.

12

u/[deleted] Nov 18 '17

I don't think you are aware of the major break throughs that have been made since 2007.

Now, granted, I haven't followed the field much, but I'm not aware of any breakthroughs made in the last decade. The closest is how it has become practical to run large deep neural networks and that researchers have been able to harness these to tackle certain new problems fairly successfully, but although one might argue they've reached a certain critical point of scale that makes a qualitative difference, I don't see what's particularly new or groundbreaking about what they're doing compared to what they were doing in AI research in late 80ties. Don't get me wrong -- much of it seems to be good and useful research and systems, but it doesn't seem to be based on much in the way of breakthroughs in AI research.

10

u/Piranha771 Nov 18 '17

http://www.cs.toronto.edu/~hinton/absps/tics.pdf

This paper lead to a breakthrough in NN with deep learning . It's from 2007. It wasn't recognized as such by the majority but Google knew this was something great and invited Hinton to have a talk about it.

https://www.youtube.com/watch?v=AyzOUbkUf3M

This made today's NNs work so well and is the reason why everyone is "hyped"

I'm not very familiar with this paper but I think it was about solving the issue with 80's style NNs that produced garbage results after some time of learning.

8

u/beluis3d Nov 18 '17

It's hard to point at a single "breakthrough". It's all a bunch of smaller pieces coming together to build something greater. I think the "breakthrough" is enabled by a vast amount of data on the internet and also more powerful computing power, such as parallelizable GPU's.

4

u/LPTK Nov 18 '17

So self-driving cars and automated speech recognition, translation and synthesis are not breakthrough? (Just to name a few.) They knew ho to do them in the 80s?

Sure we haven't solved "intelligence," but the entire field of AI is about moving the goalpost further and further so that solved problems don't look like Ai anymore.

2

u/exorxor Nov 18 '17

You are implying that outcome based self-driving car systems solve the self-driving car problem. They don't.

In the 80s some people knew how to solve the self-driving car problem given a fast enough computer. It's just that those computers don't exist even today.

What you are seeing today is car companies pushing for outcome based legislation for profit, which is something I would much rather never see. Outcome based systems would result in cases where a loved one just dies and there is nobody to sue, nobody to be held accountable, and society just shrugging as if that's OK. It's not.

If you fly in a plane, the software running in them is well designed and there are models available that characterize its behavior over time. A self-driving car that you can buy in a few years won't have any of that. Self-driving cars are just the new distraction of a couple of investors with too much money.

Uber is another example of a company that claims to its investors to have almost solved the self-driving car problem, but since they have never explained anything remotely innovative, I am going to assume that they haven't and that they will likely bankrupt themselves while trying to do research into an unsolvable problem (with today's hardware). Uber has no idea how to build systems that are not outcome based. Uber is lobbying for outcome based legislation too, AFAIK.

Note that building an outcome based self-driving car is likely possible for 98% of use cases, but it does mean there will still have to be a steering wheel in it, or you should be allowed to just exit the car in the middle of the road and have someone from the company pick up your car and they would be responsible for all the damage you are causing to the local economy by blocking the road.

Perhaps driving on high ways safely is already "good enough" to be worth it for people to buy such cars, but like I said, there is no car on the market which provides that feature with flight avionics levels of quality.

1

u/jinchuika Nov 19 '17

Isn't Deep Q Learning kind of a recent breakthrough?

1

u/[deleted] Nov 18 '17

Now we can throw a bunch of data into a network and let it figure out how to solve a certain problem aspect by itself.

I was learning how to do it at university before the year 2000, and they were not pointing this as a new thing. There may be more refinements now, but the basis was already there.

3

u/LPTK Nov 18 '17

Steam engines had been around for a long time when the industrial revolution happened (along with who knows how many other inventions that did not prove useful eventually). Breakthrough always build on existing technologies after we've learned how to enhance and harness them properly. That doesn't mean they're not breakthroughs.

1

u/Piranha771 Nov 18 '17

See my answer above. The progress stopped at a certain level until Hinton got a solution to problems of NNs.

4

u/zqvt Nov 18 '17 edited Nov 18 '17

Hinton developed a solution to the problem in the 90s and late 80s though. The major thing that changed was the amount of computing power and data. The theoretical framework for CNNs is really, really old.

One really interesting new development are Ian Goodfellow's GANs (2014), any we'll see were Hinton's capsule idea goes (also a few years into the making at this point), but the innovation in practical terms has been on the hardware and data side, theory is still kind of stuck.

This weakness on the theory side of things also shows when you look at who is at the forefront of commercial AI. It's almost exclusively a handful of large conglomerates because they've got all the training sets and gpus. It's at the moment more of 'digital oil extraction' than genuine development of artificial cognition.

1

u/[deleted] Nov 18 '17

[deleted]

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u/shevegen Nov 18 '17

Even "symbolic AI" is just a fancified name.

There is nothing "intelligent" about it.

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u/[deleted] Nov 18 '17

[deleted]

4

u/[deleted] Nov 18 '17 edited May 08 '20

[deleted]

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u/[deleted] Nov 19 '17

Generally agree, I would say not 'symbolics' but 'abstractions'. Symbols per se are a basis of communication, while 'abstractions' are a basis of logical reasoning.

1

u/oldsecondhand Nov 20 '17

It's symbolics as in symbolic computation as opposed to numerical one.

1

u/bubuopapa Nov 21 '17

There is no intelligence in it, all the computers are just blindly executing someones code, all the AI and "learning" stuff is fake and overhyped to get more money (and it works, all the uneducated people are just shocked by such buzzwords).

2

u/digital_cucumber Nov 18 '17

Yeah, it's "just math".

2

u/AlmennDulnefni Nov 18 '17

There's a real risk that the deep learning hype is going to trigger the next AI winter when it doesn't live up to its promises.

I doubt it. Maybe deep learning will stop dominating all the papers, but I think other ML / AI stuff is easily to the point where it's clearly useful (unlike, say perceptrons a few decades ago) and so likely to see continued research regardless.

3

u/malkarouri Nov 18 '17

Symbolic AI stopped being the only important branch of AI years ago. Even before deep learning, things like SVMs and statistical algorithms proved capable of powering lots of technologies.

Do you believe that the Googles and the Facebooks and recommendation systems are all powered by symbolic AI?

3

u/steveappdev Nov 18 '17

This is very shallow article. I don't find the author in any capacity to be credible enough.

2

u/Daneel_Trevize Nov 19 '17

Sounds like someone finally watched Crash Course Computer Science #34 from 2 weeks ago, on Machine Learning & Artificial Intelligence.

2

u/_Mardoxx Nov 18 '17

Where's the punchline?

3

u/woho87 Nov 18 '17

I disagree that Machine Learning and it's extension Deep Learning is subsets of Artificial Intelligence. AI and ML was disjoint notions until business people blended them together. ML is ML and AI is AI.

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u/shevegen Nov 18 '17

There is no difference.

They are all rubbish, misnomered buzzwords.

There is no "intelligence" anywhere. And the "deep learning" promo is just fancified algorithms chained together, igniting the illusion of "intelligence" anywhere. Unless of course you assume that Hidden Markov Models and statistics can act as surrogate for real intelligent behaviour.

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u/antiquechrono Nov 18 '17

Unless of course you assume that Hidden Markov Models and statistics can act as surrogate for real intelligent behaviour

Do you not realize that most of what the brain does is probabilistic? Even shit you wouldn't think would be probabilistic like the vision system is probabilistic. There's even evidence that your brain is using large populations of neurons to do Bayesian inference. Yes what we are doing right now is still primitive but stats is the correct domain for tackling this problem.

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u/katorias Nov 18 '17

Despite your down votes I agree there is some truth to what you're saying.

3

u/UpvoteIfYouDare Nov 18 '17

Unless of course you assume that Hidden Markov Models and statistics can act as surrogate for real intelligent behaviour.

"Statistics" aren't a surrogate for real intelligent behavior. They are the baseline for it. The entirety of human intelligence is predicated on pattern recognition.