[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.)

You have the problem reversed. AI safety/conrol/friendliness currently doesn't have any standard tests to measure progress, and thus there is little objective way to compare methods. You need a clear optimization criteria to drive forward progress.

It would be great to have such tests in AI safety/control/Friendliness, but to me they look really difficult to create. Do you have any ideas?

Yes.

I think general RL AI is now advanced to the point where testing some specific AI safety/control subproblems is becoming realistic. The key is to decompose and reduce down to something testable at small scale.

One promising route is to build on RL game playing agents, and extend the work to social games such as MMOs. In the MMO world we already have a model of social vs antisocial behavior - ie playerkillers vs achievers vs cooperators.

So take some sort of MMO that has simple enough visuals and or world complexity while retaining key features such as ... (read more)

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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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