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1. Introduction to Collective Intelligence > Limits of Machine Learning

Limits of Machine Learning

Machine learning is not without its weaknesses. The algorithms vary in their ability to generalize over large sets of patterns, and a pattern that is unlike any seen by the algorithm before is quite likely to be misinterpreted. While humans have a vast amount of cultural knowledge and experience to draw upon, as well as a remarkable ability to recognize similar situations when making decisions about new information, machine-learning methods can only generalize based on the data that has already been seen, and even then in a very limited manner.

The spam-filtering method you’ll see in this book is based on the appearance of words or phrases without any regard to what they mean or to sentence structures. Although it’s theoretically possible to build an algorithm that would take grammar into account, this is rarely done in practice because the effort required would be disproportionately large compared to the improvement in the algorithm. Understanding the meaning of words or their relevance to a person’s life would require far more information than spam filters, in their current incarnation, can access.


  

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