Artificial Communication by Elena Esposito

Author:Elena Esposito [Esposito, Elena]
Language: eng
Format: epub
Publisher: MIT Press

Averages versus Individual Prediction

The key to the smartness of algorithms and all they can do, including make predictions, is the techniques that make it possible for machine-learning systems to autonomously develop the ability to process data and produce their own information. To do this, algorithms need examples of tasks to fulfill, and the web offers a lot of them. If a software program is able to learn, those examples can be used to train algorithms in a more and more accurate and differentiated way. The diversity of contexts on the web becomes the resource for learning and increasing the performance of algorithms.

How do machines learn from examples? To develop this ability, the programmers in machine learning use the tools of statistics.38 In fact, statistics and probability calculus addressed for centuries the problem of learning from data and produced a number of computational tools to extract information: regression, classification, correlation, and so on. Now machine learning inherits and adopts them, yet uses data in a different way. The goal of statistics is to manage present uncertainty. It addresses the knowledge (or lack of knowledge) of the present, maintaining and confirming the insuperable barrier between the present moment and the open future. Machine learning, instead, addresses the future and has the goal of predicting it. The difference between the two approaches produces a curious relationship of closeness and opposition between machine learning and the tradition of statistics, two formally almost identical cultures that are progressively diverging.39 Even if they use the same tools, the attitude of machine-learning programmers is very different from that of statisticians, as their problems are different from the ones raised by the “avalanche of numbers” in the nineteenth century.40

Statistics wants to contribute to knowing the world by activating a procedure that matches the classical Galilean method: inserting past data into the model and then using it to predict future data, thus verifying the accuracy of the model and eventually correcting it. The goal is explanation: when you do statistics, you want to infer the process by which the data were generated. For machine learning, on the contrary, the purpose is not to explain the phenomena elaborating a model. In many cases, you do not even know if there can be an intelligible model, and the machine can operate without one. The goal of algorithmic processing is not truth but predictive accuracy.41 In machine learning you start from the assumption that you are dealing with “complex, mysterious and, at least, partly unknowable” models.42 You do not want to understand them but to know how the future will look like with regard to some variables. Machine learning faces the future and tries to predict it as accurately as possible, independently of our knowledge of the world. As we can read in a web debate, “statistics emphasizes inference, whereas machine learning emphasizes prediction.”43

As a consequence of their different attitudes, statistics and machine learning produce fundamentally different forms of prediction. Statistics uses samples based on a limited amount of specifically prepared and selected experimental data in order to deal with the statistical universe.

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