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Baidu's Supercomputer Beats Google At Image Recognition

Posted by samzenpus on from the all-the-better-to-see-you-with dept.

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.”

7 of 115 comments (clear)

  1. Great power by Anonymous Coward · · Score: 4, Insightful

    I'm not sure an improvement of .5 percent on image cataloging is really that impressive to get not one but two greats...

  2. Bad summary by Anonymous Coward · · Score: 5, Insightful

    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.

  3. Your maths is off... by ZG-Rules · · Score: 5, Insightful

    As a pedant, I need to point out that the improvement is 0.24%

    "The system trained on Baidu’s new computer was wrong only 4.58 percent of the time. The previous best was 4.82 percent, reported by Google in March. One month before that, Microsoft had reported achieving 4.94 percent, becoming the first to better average human performance of 5.1 percent."

    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!

    1. Re:Your maths is off... by rudy_wayne · · Score: 4, Insightful

      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!

      Because this is Slashdot and it is required that all stories be written as poorly as possible.

      Baidu's new computer was wrong only 4.58 percent of the time. The previous best was 4.82 percent, reported by Google in March.

      If Google is only wrong 4.82% of the time then why is it whenever I search for an image I get thousands of pictures that have absolutely nothing to do with what I am searching for?

  4. Your monthly algorithm tweak brought to you by... by Idarubicin · · Score: 4, Insightful

    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
  5. Re:Note to Slashdot Editors by ShanghaiBill · · Score: 1, Insightful

    I'm curious how much difference in computational power was thrown at training these

    Training a NN requires a lot of cycles (usually GPU farms) but there is a limit to how much is useful. If you just continue to cycle over the same data, you end up over training your network, so that it basically just memorizes the training set, but fails to generalize to other data sets. Rather than just throwing more computational power at the problem, it is usually more productive to use more data, and improve your algorithms and configurations. Using more data wasn't an option in this case, since it was a standard benchmark, so the training set and the test set were fixed.

    Anyway, we live in interesting times. I went to see Ex Machina yesterday. I want my own Ava (but with the "stab" feature disabled).

  6. Re:Huh? This is not a very powerful computer. by Anonymous Coward · · Score: 2, Insightful

    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