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Artificial intelligence can go wrong – but how will we know?

Mary Branscombe | Oct. 26, 2015
You needn't worry about our robot overlords just yet, but AI can get you into a world of trouble unless we observe some best practices.

When he was running Bing Ad Center, he abandoned a complex but powerful algorithm for something far simpler. "It took a week to train 500 million parameters using 20 machines and every time something went wrong people pointed to the algorithm and we had to prove it was computing the right thing - and then a week later, the same thing would happen again. I replaced it with a very simple algorithm that was similar in performance but could train in a matter of minutes or hours." It was easier to understand, easier to develop and there were no more time-wasting arguments about whether the algorithm was wrong.

Being able to retrain quickly is key to keeping machine learning systems current, because the data feeding into machine learning systems is going to change over time, which will affect the accuracy of the predictions they make. With too complex a system, Simard warns "You'll be stuck with an algorithm you don't understand. You won't know if you can keep the system if no-one has the expertise to tell you whether it still works. Or you might have one system that depends on another and one of those systems gets retrained. Can you still rely on it?"

And if AI is really effective, it's going to change our world enough that it will have to evolve to keep up, Horvitz points out. A system to identify patients at risk of hospital readmission that keeps them out of the emergency room, will change the mix of patients it has to assess.

On the one hand, AI systems need to know their limitations. "When you take a system and put out in the real open world, there are typically many unforeseen circumstances that come up. How do you design systems that one explicitly understand they're in an open world and explicitly know that the world is bigger than their information?"

But, on the other hand, they also need to know their own impact. "The AI systems themselves as we build them have to understand the influences they make in the world over time, and somehow track them. They have to perform well, even though they're changing the world they're acting in."

Source: CIO 


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