• What is the difference between supervised and unsupervised ML?

  • How is supervised ML conducted?

  • Types of supervised ML

  • How are startups developing supervised ML?

  • Is there anything that supervised ML can’t do?


Copyright: venturebeat.com – “What is supervised machine learning?”


The training process for artificial intelligence (AI) algorithms is designed to be largely automated innately. There are often thousands, millions or even billions of data points and the algorithms must process all of them to search for patterns. In some cases, though, AI scientists are finding that the algorithms can be made more accurate and efficient if humans are consulted, at least occasionally, during the training.

The result creates hybrid intelligence that marries the relentless, indefatigable power of machine learning (ML) with the insightful, context-sensitive abilities of human intelligence. The computer algorithm can plow through endless files of training data, and humans correct the course or guide the processing.

The ML supervision can take place at different times:

  • Before: In a sense, the human helps create the training dataset, sometimes by adding extra suggestions to the problem embedding and sometimes by flagging unusual cases.
  • During: The algorithm may pause, either regularly or only in the case of anomalies, and ask whether some cases are being correctly understood and learned by the algorithm.
  • After: The human may guide how the model is applied to tasks after the fact. Sometimes there are several versions of the model and the human can choose which model will behave better.

To a large extent, supervised ML is for domains where automated machine learning does not perform well enough. Scientists add supervision to bring the performance up to an acceptable level.

It is also an essential part of solving problems where there is no readily available training data that contains all the details that must be learned. Many supervised ML problems begin with gathering a team of people who will label or score the data elements with the desired answer. For example, some scientists built a collection of images of human faces and then asked other humans to classify each face with a word like “happy” or “sad”. These training labels made it possible for an ML algorithm to start to understand the emotions conveyed by human facial expressions.

What is the difference between supervised and unsupervised ML?

In most cases, the same machine learning algorithms can work with both supervised and unsupervised datasets. The main difference is that unsupervised learning algorithms start with raw data, while supervised learning algorithms have additional columns or fields that are created by humans. These are often called labels although they could have numerical values too. The same algorithms are used in both cases. […]

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