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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’?

#71

Post by YeGoblynQueenne »

allovernow wrote: post #56
YeGoblynQueenne wrote: post #55

Earlier quoted context omitted.

Were would the speedup come from? I don't understand. If I understand your comment correctly, essentially you have a hand-crafted simulator for some physical process and then you train a neural net model to approximate the simulator. Why would the approximated simulator have "an order or more of magnitude increase in simulation speed"? Unless the approximation has massive losses in accuracy, of course. Honestly askin…

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 approximator, really) to approximate a "precision heuristic" faster than a hand-coded heuristic and without a gross loss of well, precision in the order that would make the results unusable for engineering or scientific tasks. Could you elaborate?

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

#72

Post by xyhopguy »

dpflan wrote: post #68
tintor wrote: post #33

Earlier quoted context omitted.

Difference between 97% and 99.99% in perception is huge for autonomous driving purposes. 300x less likely to cause an accident.

Do you know of any good resources to learn more about this idea of the rate of improvement of perception per percentage point?

https://en.wikipedia.org/wiki/Odds_ratio

odds of crash a => 97% => 3 / 100

odds of crash for b => 99.99% => 1 / 10000

improvement (odds ratio in this case) is then 300x = odds of crash for b / odds of crash for a

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

#73

Post by richk449 »

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?

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

#74

Post by richk449 »

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…

I’m also skeptical, but after reading the explanation above, I am intrigued.

Say I have a cube with 100 x 100 x 100 mesh cells inside, and ports on opposing faces. Given enough time, I can literally run through every possible combination of PEC and air for every cell and solve the FD form of maxwells equations, then save the results. Now, a user can ask my solver for any of those cases, and I simply pull the presolved result, and give the user the answer with orders of magnitude reduction in time.

Obviously, the presolving approach doesn’t scale. More materials, more mesh cells, eventually it is impractical to presolve every case. But the beauty of neural networks is that they can be very good at generalizing from a partial sample of the problem space. In effect, they can give results close enough to the presolve solution with drastically reduced numbers of computations.

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

#75

Post by YeGoblynQueenne »

richk449 wrote: post #74
YeGoblynQueenne wrote: post #71

Earlier quoted context omitted.

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…

I’m also skeptical, but after reading the explanation above, I am intrigued. Say I have a cube with 100 x 100 x 100 mesh cells inside, and ports on opposing faces. Given enough time, I can literally run through every possible combination of PEC and air for every cell and solve the FD form of maxwells equations, then save the results. Now, a user can ask my solver for any of those cases, and I simply pull the presolve…

>> But the beauty of neural networks is that they can be very good at generalizing from a partial sample of the problem space.

That is really not the case. Neural nets generalise very poorly, hence the need for ever larger amounts of data: to overcome their lack of generalisation by attempting to cover as many "cases" as possible.

Edit: when this subject comes up I cite the following article, by François Chollet, maintainer of Keras:

The limitations of deep learning

https://blog.keras.io/the-limitations-of-deep-learning.html

I quote from the article:

This stands in sharp contrast with what deep nets do, which I would call "local generalization": the mapping from inputs to outputs performed by deep nets quickly stops making sense if new inputs differ even slightly from what they saw at training time. Consider, for instance, the problem of learning the appropriate launch parameters to get a rocket to land on the moon. If you were to use a deep net for this task, whether training using supervised learning or reinforcement learning, you would need to feed it with thousands or even millions of launch trials, i.e. you would need to expose it to a dense sampling of the input space, in order to learn a reliable mapping from input space to output space.

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

#76

Post by richk449 »

YeGoblynQueenne wrote: post #75
richk449 wrote: post #74

Earlier quoted context omitted.

I’m also skeptical, but after reading the explanation above, I am intrigued. Say I have a cube with 100 x 100 x 100 mesh cells inside, and ports on opposing faces. Given enough time, I can literally run through every possible combination of PEC and air for every cell and solve the FD form of maxwells equations, then save the results. Now, a user can ask my solver for any of those cases, and I simply pull the presolve…

>> But the beauty of neural networks is that they can be very good at generalizing from a partial sample of the problem space. That is really not the case. Neural nets generalise very poorly, hence the need for ever larger amounts of data: to overcome their lack of generalisation by attempting to cover as many "cases" as possible. Edit: when this subject comes up I cite the following article, by François Chollet, mai…

It doesn’t need to generalize, just do sophisticated interpolation.

Basing the results on a dense sampling of the input space is exactly what I was suggesting.

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

#77

Post by crubier »

mattnewport wrote: post #31
allovernow wrote: post #4

I'm not sure about the pace of progress in research, but as an ML engineer at a startup who has been following developments, even if AI research stalls out completely, we've been given a huge set of amazing tools to apply to all kinds of technical problems for years to come. I also think that even in the absence of massive breakthroughs, there's still plenty of work to be done during a "winter" in filling in the gaps…

Can you give some examples of applications that you think will have big impacts? I see places where current AI techniques can make incremental improvements but I just don't see any applications that really seem game changing. The ones that come closest tend to be dystopian unfortunately, like most applications of facial recognition.

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

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

#78

Post by speedplane »

crubier wrote: post #77
mattnewport wrote: post #31

Earlier quoted context omitted.

Can you give some examples of applications that you think will have big impacts? I see places where current AI techniques can make incremental improvements but I just don't see any applications that really seem game changing. The ones that come closest tend to be dystopian unfortunately, like most applications of facial recognition.

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?

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

#79

Post by speedplane »

mattnewport wrote: post #31
allovernow wrote: post #4

I'm not sure about the pace of progress in research, but as an ML engineer at a startup who has been following developments, even if AI research stalls out completely, we've been given a huge set of amazing tools to apply to all kinds of technical problems for years to come. I also think that even in the absence of massive breakthroughs, there's still plenty of work to be done during a "winter" in filling in the gaps…

Can you give some examples of applications that you think will have big impacts? I see places where current AI techniques can make incremental improvements but I just don't see any applications that really seem game changing. The ones that come closest tend to be dystopian unfortunately, like most applications of facial recognition.

I run a startup that analyzed litigation and judicial opinions. We can figure out what arguments are made, which were persuasive to the judge, which judges conform to the mean and which are outliers.

The long term potential is to make justice less expensive by being able to evaluate cases more cost effectively. It can also potentially identify judges that are outside of the norm.

I don't see a problem with this as long as the system is just identifying what is successful and normal. However, if judges themselves start using it and adjust their decisions, there is potential for it to create feedback loops that can move the norm into towards something that is not necessarily just. So this system can be used for good as long as there remains an independent check on it (i.e., independent judges).

This concern is not hypothetical. Today, judges are already using tools that calculate average prison times for offenders based on previous rulings. That should not be allowed as it creates feedback loops and bakes in earlier biases. However, the public should be able to use those tools to evaluate their legal position.

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

#80

Post by sgt101 »

mattnewport wrote: post #58
ganeshkrishnan wrote: post #45

Earlier quoted context omitted.

My startup analyzes historical sales via LSTM and makes future predictions based on geo location, description of products, price of products, weather and holidays. I did the similar work for IBM 10 years ago and we could hardly accomplish fraction of what my startup can achieve today. Right inventory at right location in right quantity is essential to e-commerce and our ai is helping out a lot in this regard

That sounds like incremental efficiency improvements, not something that will radically change people's lives. Not that that's not valuable and may help us keep economic growth going at reasonable clip but it doesn't seem like the type of thing that will change the character of most people's lives in the way that the Internet and smartphones have.

Matt; it's easy to say "it's incremental" - for example, an application that lets you order books online or communicate with groups of friends would be seen as incremental in 1990. The CEO's of Blackberry thought that the iPhone was good and feature rich, but they saw it as incremental as well!
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