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Researchers: Are we on the cusp of an ‘AI winter’?

bbc.co.uk

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Re: Researchers: Are we on the cusp of an ‘AI winter’?

#101

Post by Veedrac »

cbanek wrote: post #19

> While AGI isn't going to be created any time soon, machines have learned how to master complex tasks like: > translating text into practically every language Note: they said they have mastered these tasks. Yeah... I'm not sure a lot of native speakers would agree. Here's a great example of using Google Translate to automatically translate a video game. https://www.youtube.com/watch?v=_uNkubEHfQU > Driving cars I'm…

I don't disagree that translation is far from mastered, but bear in mind that Google Translate isn't state of the art, mostly because of computational constraints, and 2016 GTranslate was even worse.

Re: Researchers: Are we on the cusp of an ‘AI winter’?

#102

Post by ForHackernews »

icandoit wrote: post #44
ForHackernews wrote: post #10

Earlier quoted context omitted.

Does GPT-2 really "understand" anything? I feel like this is pretty quickly going to devolve into a semantic argument, but having interacted with some trained GPT-2 models, it seems to produce only what Orwell would have called duckspeak[0]. There's very clearly no mind behind the words, so it's hard for me to credit it with understanding. [0] http://www.orwelltoday.com/duckspeak.shtml

If something acts as though it understands a thing then it does. What else could understand mean? Behold the damage that Searl hath wrought, lol. Maybe it makes more sense to talk about predicting the behavior of a system independent of the composition of the system.

> If something acts as though it understands a thing then it does.

I've had the misfortune of working on teams where the hiring manager subscribed to this philosophy.

Re: Researchers: Are we on the cusp of an ‘AI winter’?

#103

Post by crubier »

pauljurczak wrote: post #84
crubier wrote: post #77

Earlier quoted context omitted.

My startup is using AI in many forms in order to build an accurate digital twin of the world cheaply, and extract valuable insights from it. In a few years we will have an accurate digital twin of the world, almost indistinguishable from the real world. this would have been impossible or way too expensive without massive automation with AI

> In a few years we will have an accurate digital twin of the world No, you will not, unless you redefine what "accurate" means.

Google has 3D models of cities nowadays. 20years ago we only had 2D maps. Why do you think this trend will not continue?

Re: Researchers: Are we on the cusp of an ‘AI winter’?

#104

Post by crubier »

speedplane wrote: post #78
crubier wrote: post #77

Earlier quoted context omitted.

My startup is using AI in many forms in order to build an accurate digital twin of the world cheaply, and extract valuable insights from it. In a few years we will have an accurate digital twin of the world, almost indistinguishable from the real world. this would have been impossible or way too expensive without massive automation with AI

> In a few years we will have an accurate digital twin of the world, almost indistinguishable from the real world. this would have been impossible or way too expensive without massive automation with AI Is the rent cheaper there?

By helping city and infrastructure planning it should help get more resource efficient, so hopefully yes?

Re: Researchers: Are we on the cusp of an ‘AI winter’?

#105

Post by mantap »

KKKKkkkk1 wrote: post #63
rococode wrote: post #5

I think there's an interesting disconnect right now between research and practice. Cutting-edge research does feel like it's reaching a plateau - across most AI fields even "major" breakthroughs are only gaining a couple percentage points and we're probably starting to hit the limits of what current approaches can achieve. When the state-of-the-art is 97% on a task, there's only so much room for improvement. Yoav Gol…

Andrej Karpathy showed in the Tesla autonomy day how Tesla had to retrain their DNNs such that they don't get confused by bicycles mounted on vehicles. If 97% means your models get confused by something you see on the road every day, I wouldn't be too pleased about the state of the art.

Autonomous vehicles are clearly several steps up the difficulty ladder from bread and butter tasks such as speech recognition. The progress over the last 10 years is such that some subfields have exceeded human parity while others are only just getting started.

Depending on which subfield interests you, progress may be slowing or accelerating. That's why another "AI winter" is a bogus and alarmist concept. Winter for whom?

Re: Researchers: Are we on the cusp of an ‘AI winter’?

#106

Post by semi-extrinsic »

allovernow wrote: post #39
semi-extrinsic wrote: post #24

Earlier quoted context omitted.

> one example is massively accelerated modeling as an alternative to slow finite difference/finite element simulation I work in the field of numerical modelling (fluid simulation), and I haven't seen any convincing demos of anything but utterly trivial problems solved this way. Care to share some examples? (There are some works that focus on fluid simulation for CGI. That's neat, but a) only interpolates between real…

>Care to share some examples? Unfortunately, the only example I know of is confidential, with a provisional patent filed. But what I can tell you is that we are already solving non-trivial problems some 3-6 orders of magnitude faster than FDM/FEM. Not quite navier-stokes tier, but still in the realm of PDEs. Sorry I can't be more specific, such is the nature of cutting edge industry, as I'm sure you understand!

"Nullius in verba" - I'll believe it when I see it.

And not sure I see the point in patenting such a thing. If it's as good as you say, in less than six months a research group in some country where US patents aren't held in very high regard will have duplicated the work and either sell it as a service or publish it in the scientific literature. They might not even mention knowing about the patent at all! Things have been discovered by multiple people almost simultaneously so many times in the history of science that it's almost a natural law; it'd be impossible to prove foul play.

Re: Researchers: Are we on the cusp of an ‘AI winter’?

#107

Post by richk449 »

soVeryTired wrote: post #97
richk449 wrote: post #73

Why would there be an AI winter? Was there a car winter after cars became a growing product? Was there a processor winter after microprocessors became a growing product? ERP software? Didn’t the previous AI winter happen because the hardware wasn’t advanced enough to make the technology useful to most people? Since that is no longer the case, why this consistent belief that there will be another winter?

2001 wasn't exactly great for the semiconductor industry...

Sure, there will be ups and downs like any industry. But a “winter” implies a decade at least without substantial industry or research, not just a bad year.

Re: Researchers: Are we on the cusp of an ‘AI winter’?

#108

Post by Piskvorrr »

Veedrac wrote: post #100
Piskvorrr wrote: post #92

Earlier quoted context omitted.

Do we have any other indicators that it's actually progressing somewhere, besides screaming? Even such a triviality as "how do we recognize that we got there"? The research is still in very early phases, IMNSHO: impressive practical applications appear, but they're side effects of what appears as random flailing: "build it bigger, see if it helps. Build it sideways, see of it helps. Build it at full moon, see if it h…

MuZero is in some sense the proto-holy grail, in that it implements learning and planning into unstructured tasks over purely internal models. While there is an obvious chasm between it and the end point, this is still something that has only recently become more than an abstract goal, at least to any effect. Being able to perform planning over ‘simple’ domains like Atari games and Go (and not even in the same traine…

Sure, that looks promising. I'm not holding my breath for The Holy Grail: in an environment where it's been just-around-the-corner for as long as the field exists, there's always another unexpected corner;)

Re: Researchers: Are we on the cusp of an ‘AI winter’?

#109

Post by Piskvorrr »

Veedrac wrote: post #101
cbanek wrote: post #19

> While AGI isn't going to be created any time soon, machines have learned how to master complex tasks like: > translating text into practically every language Note: they said they have mastered these tasks. Yeah... I'm not sure a lot of native speakers would agree. Here's a great example of using Google Translate to automatically translate a video game. https://www.youtube.com/watch?v=_uNkubEHfQU > Driving cars I'm…

I don't disagree that translation is far from mastered, but bear in mind that Google Translate isn't state of the art, mostly because of computational constraints, and 2016 GTranslate was even worse.

No true Scotsman? What is, then, the state of the art in 2020 machine translation?

Re: Researchers: Are we on the cusp of an ‘AI winter’?

#110

Post by allovernow »

YeGoblynQueenne wrote: post #71
allovernow wrote: post #56

Earlier quoted context omitted.

It's all about precision heuristics, derived from joint probabilities of inputs and outputs. That, by and large, is how I am increasingly coming to understand the power of neural networks. Imagine you are given a picture of a candle, overlaid with a grid, and asked to fill in, with colored pencils, colors for the air surrounding the candle representing relative temperature. Of course a human utilizes intuition to rap…

I'm skeptical of the claim that it's easier to train a neural net than to hand-code a set of heuristics _when the heuristics are already known_. For the time being, optimal results with neural nets need more data and more computing power ("more" because it's never enough) and are primarily useful when a hand-coded solution is not possible. I also don't understand how it is possible for a neural net (or any approximat…

Well, there are pretty convincing examples in other domains: try hardcoding rules to classify animals or objects in photos, especially an algorithm which can handle thousands of different categories. Totally impractical - but if we appropriately design the net and structure the training data, you can train a pretty accurate net on a mid-range GPU in a matter of hours to do what would take far, far longer to hardcode!

Perhaps not quite appropriate to call them heuristics in this context, but the principle is the same - you are leveraging joint probabilities of pixels to generate some conditional output. Similar principle in ML accelerated modeling.

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