- Feb 13, 2017
- 1,486
We’ve been losing the war on cybercrime for some time. Research firm Forrester reports over a billion accounts stolen in 2016 alone, and these data breaches are going up, not down. We are having to wade through more incident data, and people cannot keep up. Could machine learning help solve the problem?
For years, researchers hoped that artificial intelligence would produce human-like machines. Now, they focus on a subset of AI that can solve more realistic and useful challenges. Machine learning cannot do everything a human can, but it doesn’t have to. Instead, we can train it to be good at narrowly-defined tasks — even better at them than humans, in some cases.
These algorithms are good at recognizing things using a technique called supervised learning. For example, say you want a computer to recognize pictures of cars. Show a machine learning algorithm a collection of car pictures. These could be sports cars, jeeps or anything else that fits the description. Then show it a set of non-car pictures, such as flowers, dogs, or something tricky like bikes. You have already classified and tagged the pictures, so the algorithm knows when it’s seeing a car and when it isn’t.
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For years, researchers hoped that artificial intelligence would produce human-like machines. Now, they focus on a subset of AI that can solve more realistic and useful challenges. Machine learning cannot do everything a human can, but it doesn’t have to. Instead, we can train it to be good at narrowly-defined tasks — even better at them than humans, in some cases.
These algorithms are good at recognizing things using a technique called supervised learning. For example, say you want a computer to recognize pictures of cars. Show a machine learning algorithm a collection of car pictures. These could be sports cars, jeeps or anything else that fits the description. Then show it a set of non-car pictures, such as flowers, dogs, or something tricky like bikes. You have already classified and tagged the pictures, so the algorithm knows when it’s seeing a car and when it isn’t.
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