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Artificial Intelligence: Supervised Machine Learning
Supervised Machine Learning is the training of an AI algorithm by using many examples. The algorithm looks at training input data and tries to find patterns in it and then matches those patterns with the expected correct output. After training with huge numbers of examples, the algorithm is generally able to classify new input data with great accuracy. The problem with supervised learning is that vast amounts of training data are required and the trained algorithm is only valid for a very specific type of data.
Yann LeCun, the chief AI scientist at Facebook/Meta, said that “there is a limit to what you can apply supervised learning to today due to the fact that you need a lot of labeled data.” Yoshua Bengio, who founded Mila, the Quebec AI Institute, said that supervised AI learning “is not going to be enough for human-level AI. Humans don’t need that much supervision.”
Unlike supervised learning, unsupervised learning doesn’t provide any guided information about what is expected as ‘correct’ and the algorithm needs to identify and group prominent patterns that are found in the input data to determine what is ‘interesting’ about it. As you might expect, the unsupervised learning algorithm needs to attack a more complex problem.
Bharath Thota, chief solutions officer at Kearney, said that “we choose supervised learning for applications when labeled data is available and the goal is to predict or classify future observations. We use unsupervised learning when labeled data is not available and the goal is to build strategies by identifying patterns or segments from the data.”
Paolo Perrotta, software developer and author, wrote on Medium that “modern supervised learning systems are very good at approximating. They can approximate complicated relations, like the one between an X-ray scan and a diagnosis. A function that approximates that relation would look maddeningly complicated to us humans, but it’s par for the course for those systems.”
