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Baidu's Supercomputer Beats Google At Image Recognition
catchblue22 writes: Using the ImageNet object classification benchmark, Baidu’s Minwa supercomputer scanned more than 1 million images and taught itself to sort them into about 1,000 categories and achieved an image identification error rate of just 4.58 percent, beating humans, Microsoft and Google. Google's system scored a 95.2% and Microsoft's, a 95.06%, Baidu said. “Our company is now leading the race in computer intelligence,” said Ren Wu, a Baidu scientist working on the project. “I think this is the fastest supercomputer dedicated to deep learning,” he said. “We have great power in our hands—much greater than our competitors.”
I'm not sure an improvement of .5 percent on image cataloging is really that impressive to get not one but two greats...
This is actually News for Nerds.
I'm curious how much difference in computational power was thrown at training these by Google, Microsoft, and Baidu, though it's going to be great to watch how these continue to evolve.
After all the news stories from the past couple of years, it seems like you could just guess "Yeah, that's a penis" and be correct about half the time. Seems like most people if you give them a camera, they're going to take a picture of a penis with it. And subsequently post that picture to the internet somewhere.
I'm trying to teach myself to set people on fire with my mind... Is it hot in here?
The summary is written to imply that Google/MS have error rates in the 90's, while the competition only has about 5% error. The values got inverted - Google/MS also have error rates around 5%, but are behind by fractions.
As a pedant, I need to point out that the improvement is 0.24%
Also why are the numbers reversed to quote success rates for Google and Microsoft in the summary on Slashdot - it would have been much clearer if the actual numbers in the article (which were all error rates) were quoted!
Okay, so we have a benchmark where the bog-standard human being scores 94.9%.
Then in February (that's three months ago), Microsoft reports hitting 95.06%; the first score to edge the humans.
Then in March, Google notches 95.18%.
Now it's May, and Baidu puts up a 95.42%.
Meh. Swinging dicks with big iron are twiddling with their algorithms to squeeze out incremental, marginal improvements on an arbitrary task.
“Our company is now leading the race in computer intelligence,” said Ren Wu, a Baidu scientist working on the project. ... “We have great power in our hands—much greater than our competitors.”
I presume that next month it will be IBM boasting about "leading the race" and being "much greater than their competitors". The month after that it will be Microsoft's turn again. Google will be back on top in August or so...unless, of course, some other benchmark starts getting some press.
~Idarubicin
The real question, of course, is whether Google, Microsoft, and Apple will soon have to face a serious international competitor. It's true that Baidu's incremental image recognition changes might not be a game changer. But if there's any substance to these claims about speech recognition, Baidu might be on track to produce an actual competitive advantage in ways highly relevant to consumers.
Baidu said "Your Kung Fu no good in my village"
"If any question why we died, Tell them because our fathers lied."
I only took basic AI in university but...
The power of the computers is not the important thing here if it takes 3 weeks to train the neural network or 1 day does not change the ACCURACY. The running of the NN to identify a picture is also only a fraction of the training time.
Maybe its more about the TRAINING SET here rather than CPU power. It seems extraordinary. 1 000 000 images sorted into 1000 categories must have been done by humans right? Humans sitting like dog,dog,dog,airplane,dog, house, dog OHPLEASELETMEJUSTDIE.
A bigger trainingset is usually a good thing with NN to avoid overtraining which makes it generalize worse on test examples.
The computer has 72 processors and 144 GPU's. That's tiny. Seriously tiny. Sure, GPU's are powerful, especially for image processing. But the larger computers these days are running tens to hundreds of thousands of processors in parallel.
For example, assuming each shelf has 2 processors and 4 GPU's, and they can fit 12 shelves into a single rack, that's a total of 2 racks. Compare that to this image of one of Google's datacenters, where you can see dozens of racks, each containing 14 shelves by my count. And that's just one row. These are gigantic warehouses, with row upon row of racks.
The level of processing power claimed here is closer to the level of a university processing cluster. The larger scientific clusters can be ten or a hundred times larger, and it's not clear just how big private datacenters are.
So overall I'm very, very skeptical. There's a very good chance that they fudged the data somehow to make theirs appear better. But if it is better, well, there's no reason why Google and Microsoft couldn't easily outcompete them in short order.
It may not be processing power alone, but perhaps a better learning algorithm.
When it comes to solving problems, elegance can sometimes beat out brute force. :D
http://image-net.org/challenge...
Has the 2014 competition, including test images and validation images.
Browsing the images, and the 200 or so categories, "artichoke", "strainer", "bowl", "person", "wine bottle"... the challenge is a bit strange: A drawing of a person isn't a "person" category, but a bottle of boyle's cream soda is a "wine bottle".
And why is "artichoke" something we need to identify in photographs?
Sure, but that's why Microsoft and Google will rapidly catch up if the numbers are real. Both employ lots of extremely talented and creative people exactly for solving problems like this, and the methods they use have been published.
Anyway, if they did really manage to produce some better algorithms, that's impressive and important work. But bragging about such a tiny computer seems seriously out of place.
a) Each CPU in these clusters typically has anywhere from 4-8 cores, and may support two or more times as many threads.
b) It's far, far more difficult to make full use of GPU hardware than CPU hardware. The best application for stressing GPU hardware is 3D graphics rendering, and even there if you run through the numbers, you find that it's rare that they really push half of their theoretical processing limit. General processing is significantly less efficient on GPU hardware, in particular because it's difficult to come up with computing problems that work well with the GPU's extremely limited I/O compared to their processing power. You need to do a lot of processing on each bit of data read or written to not be limited by either PCI Express bandwidth or video RAM bandwidth. Typical best-case real world scenarios for GPGPU programming put GPU's at closer to 10x or so the performance of CPU's, not 1000x as just looking at the number of shader cores vs. CPU cores might suggest. So they're quite powerful, but not overwhelmingly so. Whether or not they're worth it is highly dependent upon the application.
c) You can bet that companies like Microsoft and Google have a significant number of GPU's in use for specialized tasks.
Going from 99.5 to 100.0 percent is extremely impressive, while going from 50.0 to 50.5 is probably just noise.
the fact that the head guy at Baidu now, came from Google. Basically, he took Google's technology and then was funded by China's gov ( who is behind Baidu's funding on this ).
Hopefully, someday soon, the west will realize that hiring Chinese means simply giving your technology over to the CHinese gov.
I prefer the "u" in honour as it seems to be missing these days.
Okay, so we have a benchmark where the bog-standard human being scores 94.9%.
Yes, and now the algorithms are better. More importantly, the 'standard human' only does that when it is paying attention, which it can't do for more than 15 minutes or so. The computer does it day in, day out, forever. And it will get better over time.
Then in February (that's three months ago), Microsoft reports hitting 95.06%; the first score to edge the humans. Then in March, Google notches 95.18%. Now it's May, and Baidu puts up a 95.42%. Meh. Swinging dicks with big iron are twiddling with their algorithms to squeeze out incremental, marginal improvements on an arbitrary task.
You denigrate their work, but that's the way science works: incrementally almost all the time. In any field, you will see tweaking, slight improvements, variations, and a couple of new ideas. And then one of the researchers will hit on the next big idea. So what? What the hell have you done? You're just being a dick.
“Our company is now leading the race in computer intelligence,” said Ren Wu, a Baidu scientist working on the project. ... “We have great power in our hands—much greater than our competitors.”
I presume that next month it will be IBM boasting about "leading the race" and being "much greater than their competitors". The month after that it will be Microsoft's turn again. Google will be back on top in August or so...unless, of course, some other benchmark starts getting some press.
First, what they are doing is very hard. So, yeah, doing 0.25% better than someone else is a big deal. Let's see you do better.
Second, look at the performance over time. There was the NIST handwriting sets, and then the Stanford data sets, then the 'standard' was the PASCAL Visual Object Challenge and people were slowly improving to the point that someone else needed to step up and provide a better standard (more categories and more examples of each). And that was the ILSVC, and now we're down to the last couple percent on those. The next set will be bigger and harder. And performance will improve on that one too. That's expected and a good thing. Image recognition is stunningly hard; thanks to the hard work by these researchers it's gotten a lot better.
here's your obligatory XKCD
The more people I meet, the better I like my dog.