4 ms·

It does this for 20,000 different objects categories - this is getting close to matching human visual ability (and there are huge societal implications if compu

It does this for 20,000 different objects categories - this is getting close to matching human visual ability (and there are huge societal implications if computer reach that standard). This is the most powerful AI experiment yet conducted (publicly known).

frisco 14y ago
> This is the most powerful AI experiment yet conducted (publicly known). That's an ill-defined statement. AI is a vast and diverse field: what makes one demonstration more "powerful" than another? There are definitely other projects that could be viewed as being in the same class of "powerful" as this cluster. This is certainly an interesting paper, but it has to be viewed in the context of a large and active field.
- marshallp 14y ago
  That's true. Let's say in machine learning then.
  - JohnHaugeland 14y ago
    Let's not, because it's still wrong on the order of thirty counterexamples. Let's say we'll stop making broad proclamations about the global best in a field we know very little about.
    - robrenaud 14y ago
      Andrew Ng has set many state of the art results on various data sets using similar approaches as the one described in the paper. Here is a reasonably approachable talk he gave about it. http://www.youtube.com/watch?v=ZmNOAtZIgIk http://www.youtube.com/watch?v=ZmNOAtZIgIk
      - JohnHaugeland 14y ago
        This is a very interesting talk. Thank you for sharing with me. :) In return, I will offer you two interesting non-sequiturs, because I don't have anything topical and a non-sequitur seems like it's worth half what something germane would be. . Bret Victor, "Inventing on Principle." First 5 minutes are terribly boring. Give him a chance; it's 100% worth it. http://vimeo.com/36579366 http://vimeo.com/36579366 . Damian Conway, "Temporally Quaquaversal Virtual Nanomachine." It's as funny as it sounds. http://yow.eventer.com/events/1004/talks/1028 http://yow.eventer.com/events/1004/talks/1028
tansey 14y ago
> It does this for 20,000 different objects categories With 15.8% accuracy. > This is the most powerful AI experiment yet conducted (publicly known). It's only powerful because they threw more cores at it than anyone else has previously attempted. From a quick skimming of the paper, there does not appear to be a lot of novel algorithmic contribution here. It's the same basic autoencoder that Hinton proposed years ago. They just added in some speed ups for many cores. It's a great experiment though. You shouldn't detract from its legitimate contributions by making outlandish claims.
- robryan 14y ago
  That in itself is fairly interesting, it says we can make dramatic improvements just throwing more processing power at the problem. Whatever happens on the algorithms research side of the problem in coming years, you can count on us having access to more processing power.
- marcusrobbins 14y ago
  I think this is the most important aspect of this paper. Throwing more computing power at the problem increases performance significantly. It is possible that our algorithms are adequate but our hardware is not.
- redmoskito 14y ago
  To a computer vision researcher, 15.8% on 20k categories is phenomenal.
  - deleted 14y ago
    [deleted]
JohnHaugeland 14y ago
"It does this for 20,000 different objects categories - this is getting close to matching human visual ability" No, it isn't. This classifier cannot identify theme variations, unknown rotations, will confuse new objects for objects it already knows, is unable to cope with camera distortion, needs fixed lighting, has no capacity for weather, does not work in the time you need to run away from a tiger, requires hundreds of times more data than a human eye presents, and does a far lower quality job, all while completely losing the ability to give a non-boolean response. To say this is approaching human abilities is to have no idea what human abilities actually are. "This is the most powerful AI experiment yet conducted" No, it isn't. Please stop presenting your guesses as facts. Cyc runs circles around this, as do quite a few things from the Netflix challenge, as well as dozens of other things. I personally have run far larger unsupervised neural networks than this, and I am not a cutting edge researcher.
- nkoren 14y ago
  I'm not a Machine Learning / AI expert, so I have to ask: if running a neural network on 16,000 cores with a training set of 10 million objects isn't cutting edge research -- and if running "far larger" networks than this, as you say you have, also isn't cutting edge research -- then please tell me: what is cutting edge research? I ask this question in all seriousness; I'd really like to know. (And yes, I see that your username is that of a noted AI researcher. Who died in 2010. So if you're actually his beta simulation, then I'll indeed be rather impressed...)
  - coldarchon 14y ago
    There are projects which connect computers during the downtimes of corporations. They don't need all their power at night and offer this to open projects of distributed neural nets. The smallest one has 25.000 cores ..
  - JohnHaugeland 14y ago
    I'm his son. Let's take the example of The Netflix Prize, a $1 million bounty that the movie shipping organization ran several years ago. Their purpose was to improve their ratings prediction algorithm, under the pretext that people frequently ran out of ideas of what to rent, and that a successful suggestion algorithm would keep people as customers longer after that point. So, they carefully defined the success rate of their algorithm - that is, make it predict some set of actually-rated movies X on a 1-5 half-integer scale, take the arithmetic mean of (the sum of (the square of each error from the real rating)) - which we'll call root mean square error, or RMSE - and you have your "score," where towards zero is perfect. Their predictor had a score of I think 0.973 something (it's been years, don't quote me on that.) Their challenge was simple. Beat their score by ten percent, and you trigger a one month end-of-game. At the end of that month, whoever's best wins le prize. One million dollars, obligatory Doctor Evil finger and all. Netflix provided (anonymized) a little over 100 million actual ratings, where all you had was a userID, a movieID, a real rating, and separately, a mapping "this movieID is this title." You were only allowed to use datasets in your solution that were freely available to everybody, and you had to reveal them and write a paper about your strategy within one month after you accept le prize, honh honh honh. Seriously, it was awesome. They were going to do a second one, but lawyers, and the world sadded. So, there, you've got a ten times larger dataset. So surely sixteen thousand cores is the drastic thing, right? Well, not really. I was running my solution on 32 Teslas, which in the day were $340 in bulk and had 480 cores each. So I actually "only" had 15,360 cores, which falls a whopping four percent short of Google's approach, which several years ago cost me about the price of a recently used car, and which I was able to resell afterwards as used, but without the bulk discount, for almost exactly what I paid for them in the first place. Swish. And I mean, I've got to imagine that someone else chasing that million dollar prize who thought they were going to get it invested more than I did. There were groups of up to a dozen people, data mining companies, etc. So if one dude sitting in his then-Boise apartment can spend like $11k on a ten times this dataset dataset over a commercial prize? Yeah. Cyc still pantses all of us.

frisco 14y ago

> This is the most powerful AI experiment yet conducted (publicly known). That's an ill-defined statement. AI is a vast and diverse field: what makes one demonstration more "powerful" than another? There are definitely other projects that could be viewed as being in the same class of "powerful" as this cluster. This is certainly an interesting paper, but it has to be viewed in the context of a large and active field.

marshallp 14y ago
That's true. Let's say in machine learning then.
- JohnHaugeland 14y ago
  Let's not, because it's still wrong on the order of thirty counterexamples. Let's say we'll stop making broad proclamations about the global best in a field we know very little about.
  - robrenaud 14y ago
    Andrew Ng has set many state of the art results on various data sets using similar approaches as the one described in the paper. Here is a reasonably approachable talk he gave about it. http://www.youtube.com/watch?v=ZmNOAtZIgIk http://www.youtube.com/watch?v=ZmNOAtZIgIk
    - JohnHaugeland 14y ago
      This is a very interesting talk. Thank you for sharing with me. :) In return, I will offer you two interesting non-sequiturs, because I don't have anything topical and a non-sequitur seems like it's worth half what something germane would be. . Bret Victor, "Inventing on Principle." First 5 minutes are terribly boring. Give him a chance; it's 100% worth it. http://vimeo.com/36579366 http://vimeo.com/36579366 . Damian Conway, "Temporally Quaquaversal Virtual Nanomachine." It's as funny as it sounds. http://yow.eventer.com/events/1004/talks/1028 http://yow.eventer.com/events/1004/talks/1028

tansey 14y ago

> It does this for 20,000 different objects categories With 15.8% accuracy. > This is the most powerful AI experiment yet conducted (publicly known). It's only powerful because they threw more cores at it than anyone else has previously attempted. From a quick skimming of the paper, there does not appear to be a lot of novel algorithmic contribution here. It's the same basic autoencoder that Hinton proposed years ago. They just added in some speed ups for many cores. It's a great experiment though. You shouldn't detract from its legitimate contributions by making outlandish claims.

robryan 14y ago
That in itself is fairly interesting, it says we can make dramatic improvements just throwing more processing power at the problem. Whatever happens on the algorithms research side of the problem in coming years, you can count on us having access to more processing power.
marcusrobbins 14y ago
I think this is the most important aspect of this paper. Throwing more computing power at the problem increases performance significantly. It is possible that our algorithms are adequate but our hardware is not.
redmoskito 14y ago
To a computer vision researcher, 15.8% on 20k categories is phenomenal.
- deleted 14y ago
  [deleted]

JohnHaugeland 14y ago

"It does this for 20,000 different objects categories - this is getting close to matching human visual ability" No, it isn't. This classifier cannot identify theme variations, unknown rotations, will confuse new objects for objects it already knows, is unable to cope with camera distortion, needs fixed lighting, has no capacity for weather, does not work in the time you need to run away from a tiger, requires hundreds of times more data than a human eye presents, and does a far lower quality job, all while completely losing the ability to give a non-boolean response. To say this is approaching human abilities is to have no idea what human abilities actually are. "This is the most powerful AI experiment yet conducted" No, it isn't. Please stop presenting your guesses as facts. Cyc runs circles around this, as do quite a few things from the Netflix challenge, as well as dozens of other things. I personally have run far larger unsupervised neural networks than this, and I am not a cutting edge researcher.

nkoren 14y ago
I'm not a Machine Learning / AI expert, so I have to ask: if running a neural network on 16,000 cores with a training set of 10 million objects isn't cutting edge research -- and if running "far larger" networks than this, as you say you have, also isn't cutting edge research -- then please tell me: what is cutting edge research? I ask this question in all seriousness; I'd really like to know. (And yes, I see that your username is that of a noted AI researcher. Who died in 2010. So if you're actually his beta simulation, then I'll indeed be rather impressed...)
- coldarchon 14y ago
  There are projects which connect computers during the downtimes of corporations. They don't need all their power at night and offer this to open projects of distributed neural nets. The smallest one has 25.000 cores ..
- JohnHaugeland 14y ago
  I'm his son. Let's take the example of The Netflix Prize, a $1 million bounty that the movie shipping organization ran several years ago. Their purpose was to improve their ratings prediction algorithm, under the pretext that people frequently ran out of ideas of what to rent, and that a successful suggestion algorithm would keep people as customers longer after that point. So, they carefully defined the success rate of their algorithm - that is, make it predict some set of actually-rated movies X on a 1-5 half-integer scale, take the arithmetic mean of (the sum of (the square of each error from the real rating)) - which we'll call root mean square error, or RMSE - and you have your "score," where towards zero is perfect. Their predictor had a score of I think 0.973 something (it's been years, don't quote me on that.) Their challenge was simple. Beat their score by ten percent, and you trigger a one month end-of-game. At the end of that month, whoever's best wins le prize. One million dollars, obligatory Doctor Evil finger and all. Netflix provided (anonymized) a little over 100 million actual ratings, where all you had was a userID, a movieID, a real rating, and separately, a mapping "this movieID is this title." You were only allowed to use datasets in your solution that were freely available to everybody, and you had to reveal them and write a paper about your strategy within one month after you accept le prize, honh honh honh. Seriously, it was awesome. They were going to do a second one, but lawyers, and the world sadded. So, there, you've got a ten times larger dataset. So surely sixteen thousand cores is the drastic thing, right? Well, not really. I was running my solution on 32 Teslas, which in the day were $340 in bulk and had 480 cores each. So I actually "only" had 15,360 cores, which falls a whopping four percent short of Google's approach, which several years ago cost me about the price of a recently used car, and which I was able to resell afterwards as used, but without the bulk discount, for almost exactly what I paid for them in the first place. Swish. And I mean, I've got to imagine that someone else chasing that million dollar prize who thought they were going to get it invested more than I did. There were groups of up to a dozen people, data mining companies, etc. So if one dude sitting in his then-Boise apartment can spend like $11k on a ten times this dataset dataset over a commercial prize? Yeah. Cyc still pantses all of us.