[link] Baidu cheats in an AI contest in order to gain a 0.24% advantage

Wei Dai

Some of you may already have seen this story, since it's several days old, but MIT Technology Review seems to have the best explanation of what happened: Why and How Baidu Cheated an Artificial Intelligence Test

Such is the success of deep learning on this particular test that even a small advantage could make a difference. Baidu had reported it achieved an error rate of only 4.58 percent, beating the previous best of 4.82 percent, reported by Google in March. In fact, some experts have noted that the small margins of victory in the race to get better on this particular test make it increasingly meaningless. That Baidu and others continue to trumpet their results all the same - and may even be willing to break the rules - suggest that being the best at machine learning matters to them very much indeed.

(In case you didn't know, Baidu is the largest search engine in China, with a market cap of $72B, compared to Google's $370B.)

The problem I see here is that the mainstream AI / machine learning community measures progress mainly by this kind of contest. Researchers are incentivized to use whatever method they can find or invent to gain a few tenths of a percent in some contest, which allows them to claim progress at an AI task and publish a paper. Even as the AI safety / control / Friendliness field gets more attention and funding, it seems easy to foresee a future where mainstream AI researchers continue to ignore such work because it does not contribute to the tenths of a percent that they are seeking but instead can only hinder their efforts. What can be done to change this?

Such is the success of deep learning on this particular test that even a small advantage could make a difference. Baidu had reported it achieved an error rate of only 4.58 percent, beating the previous best of 4.82 percent, reported by Google in March. In fact, some experts have noted that the small margins of victory in the race to get better on this particular test make it increasingly meaningless. That Baidu and others continue to trumpet their results all the same - and may even be willing to break the rules - suggest that being the best at machine learning matters to them very much indeed.

(In case you didn't know, Baidu is the largest search engine in China, with a market cap of $72B, compared to Google's $370B.)

Is it important that it be lossless compression?

I can look at a picture of a face and know that it's a face. If you switched a bunch of pixels around, or blurred parts of the image a little bit, I'd still know it was a face. To me it seems relevant that it's a picture of a face, but not as relevant what all the pixels are. Does AI need to be able to do lossless compression to have understanding?

I suppose the response might be that if you have a bunch of pictures of faces, and know that they're faces, then you ought to be able to get some mileage out of that. And even if you're trying to remember all the pixels, there's less information to store if you're just diff-ing from what your face-understanding algorithm predicts is most likely. Is that it?

Well, lossless compression implies understanding. Lossy compression may or may not imply understanding.

Also, usually you can get a lossy compression algorithm from a lossless one. In image compression, the lossless method would typically be to send a scene description plus a low-entropy correction image; you can easily save bits by just skipping the correction image.

I emphasize lossless compression because it enables strong comparisons between competing methods.

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[link] Baidu cheats in an AI contest in order to gain a 0.24% advantage

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[link] Baidu cheats in an AI contest in order to gain a 0.24% advantage

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