[Dzugaru]

> Even though I’m one of the beneficiary of this AI craze, I can’t help but thinking this will burst.

I don't think it will. Level off - maybe.

I've started my work in Computer Vision with classical algorithms (SIFT features, geometry, correlation filters and things alike people were researching for decades). These really worked like garbage, it was a nightmare.

Then we jumped on DL bandwagon - and CV just clicked for me. Now I see it working, not perfectly, not at human level yet, but it works, it's better than everything else and it certainly brings value - not just in CV! Maybe there will be some expectations delayed or even ruined (AGI, fully self-driving cars, dunno), but the tech isn't going anywhere.

At it requires at least some experience and a specific mindset, slightly unusual for a generic programmer. So I don't see a problem with experts, courses, degrees and the like.

[jcoffland]

Pattern matching is the one thing DL is good for. Which is why it's a good match for CV. Calling DL AI in the first place was a mistake or at least over zealous marketing.

Playing go or chess or matching patterns are all things intelligent begins can do but that does not imply that doing those thing means you are intelligent.

[PeterisP]

One can argue that the parts of our brain that make us intelligent, the prefrontal cortex that is so much bigger than in "lesser" animals, is essentially an overgrown, glorified pattern matching engine. Pattern matching is the one thing our brains are good for - there's good reason to suppose that quite many intelligence-related tasks can be reduced to a form of pattern matching.

[jcoffland]

One could argue a lot of things. Humans once argued quite seriously that the human brain was composed of microscopic gears because that was the technology of the time.

[p1esk]

Ok, so what ability is worthy of "AI" name?

[jcoffland]

Getting the nations of the world to stop bickering and balance our resource usage fairly and with in the capacity of our planet.

[p1esk]

"Fairly" according to whom? One of the nations? Some individual? Or do you want the AI to come up with the definition of "fair"? What if some people disagree? What if the majority of people disagree? In any case, this could be achieved with extreme violence. Do you really want "AI" imposing its (or someone's) will on the world?

[pron]

> not at human level yet

Not even at insect level yet. There's no doubt things will improve, and there's already great value, but I hate calling ML "AI". It's been over 70 years of ML research (specifically neural networks) and I don't know how long it's going to take to reach insect-level behavior (which is still far from basic intelligence) let alone so-called AGI (which, BTW, people in the '50s were certain is just around the corner), even though I think we'll get there eventually. We'd better stop using the term "AI" to mean anything other than a field of research or an aspiration, and definitely stop using it to describe existing software.

[alexcnwy]

Actually Andrej Karpathy showed human level error on imagenet is around 5% while the winning model from 2017 had an error rate of just 2.25%.

[pron]

Computers were able to perform some tasks better than humans since they were first built in the 40s. Computers are only used for things they are better at than humans. There is nothing to suggest, however, that there is anything closer to "intelligence" in recognizing images as in compiling census statistics, and no software is as adept in "general problem solving" as insects.

[alexcnwy]

Honestly I think you're playing a language game.

I'm sure you're familiar with the line of reasoning that if you asked someone 50 years ago to describe tasks that require intelligence, they'd for sure say recognizing objects in images is one of them. Now that computers can do that, it's no longer 'intelligence' and the goalposts get moved.

In what sense is no software 'as adept in "general problem solving" as insects'?

[pron]

People also characterized doing arithmetic as intelligent, and there's little doubt that the entire idea of calulating machines -- from the time of Leibniz -- was motivated by the desire to emulate the human mind, so there's no point in describing intelligence as a binary quality. Therefore, I don't need to define what intelligent means in order to demonstrate my point. There are cognitive tasks that insects do better than computers, ergo, we are not yet at insect-level cognition. Only once computers can do everything better than an insect can we claim that they are more intelligent than insects.

[alexcnwy]

Humans can't do _everything_ better than insects, does that mean insects are more intelligent than humans?

Chimpanzees have better short term memory on certain tasks than humans [1] - humans not being better at _everything_ than chimpanzees doesn't make chimpanzees more intelligent.

[1] https://www.livescience.com/27199-chimps-smarter-memory-huma...

[IanCal]

What do you mean by insect level here?

[sdenton4]

We can't make things as smart as bees.

[bitL]

This. I don't think people even get a hint of what is possible nowadays with DL in CV, NLP etc. I am actually depressed when I talk to some friends and they are so pitifully outdated, and then even after showing them how to do some magic in 100 lines, observing they are still not getting it and continuing in their old ways :(

[CalRobert]

Just out of curiosity, where would you recommend starting for those outdated people? Machine Learning, Deep Learning, AI (for lack of a more specific acronym), NLP - these things are kind of daunting for newcomers, if only due to the acronym du jour changing constantly.

[bitL]

It's difficult to say to be honest; for me the "enthusiasm" works best, I simply picked an area I wanted to know (e.g. self-driving cars using DL) and then learned some mindblowing approaches, like NVidia/Tesla using a few layers of simple convolutional neural network and static images to predict steering angles, and then some people stacked RNN on top of this CNN and made it estimate steering angle from 10 previous frames and a current frame. See e.g. selfdrivingcars.mit.edu

If you are into CV, first start with very simple static image recognition with AlexNet/VGG/Inception etc. in Keras, try to understand CNNs a bit (it's inspired by biological neurons, they can do simple things like direction detection, edge detection etc. and overlap each other's field of vision; if you look at computational photography, convolutions do something similar, so the idea is why not use a layer of multiple convolutions, then make a hierarchy of those convolutional layers, and let the optimization/learning part of Deep Learning during training figure out what exact convolutions does it need instead of force-feeding them by hand). Play with the ways to improve training (batch normalization, image augmentation etc.) Once you understand this, your mind would probably explode and then it's time to understand RNNs/LSTMs/GANs and have fun applying it on voice, natural language, generating art etc.

You'll have a blast for sure when you realize what you can now easily do! Have fun! ;-)

[JSONwebtoken]

The acronyms aren't changing, machine learning, deep learning, and neural networks have been around for 50 years. It's only recently that code libraries like TensorFlow have abstracted away a lot of the math to the point that it's relatively accessible to normal people that can write code.

Deep learning is a subset of machine learning that utilizes more than one layer of neural networks. So these terminologies just refer to different parts of the same process. The 'process' is just tweaking a program to progressively make more accurate yes or no assumptions about a set of statistics that you give it. That's my best shot at it, hope it makes sense.

[31reasons]

Great thing about Deep Learning is that decades of "old school" machine learning research that was way too math intensive is far inferior now. DL is actually pretty approachable and intuitive.

[rocqua]

To someone mathy like me, that kinda sucks. It's part of why I don't like deep learning.

[bitL]

Treat it as non-linear optimization, then you are right in the class of most complex problems. Make a better optimizer enabling more useful applications and you can be both scientifically famous and make billions with it! ;-)

[mncharity]

> where would you recommend starting [emphasis mine]

Play with http://playground.tensorflow.org/ . Read today's https://news.ycombinator.com/item?id=14992865 about https://pair-code.github.io/deeplearnjs/ .

[CalRobert]

Thank you! much appreciated.

[nabla9]

These two things are not exclusive:

1. Something works past all expectations.

2. There is craze, overhype, bubble and bust.

The markets have the ability to overvalue things that are great successes. In fact overinvestment and bubbles are often the result of real success.

If something generates 100X return, its completely feasible that markets value it in level that would require 200X return to be profitable investment.

[tnecniv]

> I don't think it will. Level off - maybe.

I mean, the dotcom bubble popped but websites are still here and more profitable than ever. The bubble popping doesn't mean that DL is going to go away. People will just have more reasonable expectations about what it can do.

[gaius]

I don't think it will. Level off - maybe.

https://en.m.wikipedia.org/wiki/AI_winter

[ansible]

Eh, we've reached a crossover point already.

Various products and services have shown that DL and other ML techniques are useful and profitable to implement. And corporations can see the benefits of incremental improvements. That alone will continue the momentum, even without amazing breakthroughs.

[gaius]

Various products and services have shown that DL and other ML techniques are useful and profitable to implement.

Very few organisations are seeing ROI on these projects. I've seen figures showing average for every million spent the return is less than half, for the quarter of them that actually get into production...

[dmreedy]

This is precisely the pattern of past winters. The technical achievements don't go anywhere. But the hype dwindles, and funding and public interest accordingly, as disappointment and skepticism grows. It doesn't permanently stop progress, just as a burst economic bubble doesn't necessarily kill an economy. Just dramatically slows it, at great cost.

[visarga]

Computer vision is the part of DL that is most suited to produce economic value. CV will be worth trillions of dollars in a couple of decades. All those cars, drones, agricultural equipment, medical scanners, robots and security cameras will be able to understand what they see and act intelligently. It's like the most universally useful thing since the invention of the wheel.

[rubidium]

"It's like the most universally useful thing since the invention of the wheel."

I'll choose refrigeration, combustion engine, concrete, and probably hundreds of other things before computer vision.

[visarga]

I said universally useful - a refrigerator is just that. A CV system can be used in hundreds of totally different applications. Like the wheel and yes, the engine. The engine is universal as well, you could see it as the upgrade of the wheel.