AI: right or wrong?
ANN (artificial neural networks) + deep learning = artificial intelligence that could grow through training and the consumption of massive quantities of data. But can a lot of data really be equated with intelligence?
ANN can’t tell the difference between a photo of a cat and a photo of a dog, unless it has been trained to do so by analyzing an enormous cache of photos of the two animals. That’s worth repeating: ANN needs a great deal of data and training to recognize anything, and even after all that, it won’t be able to explain why it made a particular choice. This is the “black box” effect of the all-powerful ANN + deep learning combination: although the data, like the conclusion, is known, the AI decision-making process is entirely impenetrable.
And it’s often accompanied by a mantra that is being challenged more and more: “It’s the correct answer on average”. So, what would you do if your AI, after it has been trained, still can’t distinguish a puppy from a cat? The temptation would be to force-feed it new data. But would that make it any smarter? Thomas Solignac, co-founder of Golem.ai, draws a neat parallel: “It’s as though, if you wanted to teach a child how to read, you just left them alone with hundreds of books.
And, when they failed, you decided to add a few hundred more!” Plainly speaking, we shouldn’t confuse huge volumes of data with intelligence: the most efficient algorithms know how to select the right information to make a decision. When will AI be more like a human brain, rather than a super statistical analyst? There are those who predict it will take 20 to 30 years to reach that point, while others say we’ll only have to wait until 2020.
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