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A Big Problem With AI: Even Its Creators Can't Explain How It Works (technologyreview.com)
Last year an experimental vehicle, developed by researchers at the chip maker Nvidia was unlike anything demonstrated by Google, Tesla, or General Motors. The car didn't follow a single instruction provided by an engineer or programmer. Instead, it relied entirely on an algorithm that had taught itself to drive by watching a human do it. Getting a car to drive this way was an impressive feat. But it's also a bit unsettling, since it isn't completely clear how the car makes its decisions, argues an article on MIT Technology Review. From the article: The mysterious mind of this vehicle points to a looming issue with artificial intelligence. The car's underlying AI technology, known as deep learning, has proved very powerful at solving problems in recent years, and it has been widely deployed for tasks like image captioning, voice recognition, and language translation. There is now hope that the same techniques will be able to diagnose deadly diseases, make million-dollar trading decisions, and do countless other things to transform whole industries. But this won't happen -- or shouldn't happen -- unless we find ways of making techniques like deep learning more understandable to their creators and accountable to their users. Otherwise it will be hard to predict when failures might occur -- and it's inevitable they will. That's one reason Nvidia's car is still experimental.
http://rocknrollnerd.github.io... - I recommend.
It's really hard to predict what the deep learning is in fact learning. It may be often useful over the training, this very much does not mean that it's going to do the expected when faced with the unexpected, and not for example decide that it should go over an intersection because the person next to it is wearing a green hat that looks more like a green light than the red light looks like a red light.
Based on this statement I'm guessing you've never worked with statistically based machine learning. Take a "simple" artificial neural network trained to do classification. The person who wrote the algorithm knows how samples from the training set are presented to the network, i.e. what features hit the first layer. The author also knows how data propagates through the network (i.e. a value is propagated to the next layer along the edges connected to a previous layer's node) and even how the weighting on different edges connecting the nodes are updated based on classification failures.
Once that network is trained it may spit out correct answers time and time again, but the author who knows the algorithms inside and out doesn't know exactly how the network decides that it's looking at a lunar crater and not a volcano. Not knowing those details means that it is incredibly hard to define how the trained AI will fail when faced with an unexpected input.
There's the problem: if you have a trained AI and not some sort of expert system based on a collection of human knowledge it's nearly impossible to say how it will handle the unexpected near-garbage input.
Obama bombed seven different countries during his last year in office.
Yes, but Syria wasn't one of them.