You could use this technique to forecast stock price movements, by giving a neural network a large set of historical prices, and improving its performance in predicting how they change over time. In this, the ML system is provided with data from which it’s to learn, and the correct answers. Perhaps the simplest to understand is generally known as supervised learning. Over the last few decades, different designs of ML have developed. Yet, thanks to ML using neural networks, even a humble iPhone can now do that quite reliably, and Ventura goes on to extend the range of objects recognised, and can now identify many for you. An example of this is object recognition in images, where vision scientists and computer programmers have worked for many years and still not produced a good algorithm to recognise a human face against a background. In other fields such as human vision there has been a vast amount of research but little progress has been made on what appear to be relatively everyday problems, which human brains can solve in a fraction of a second. Given the success of numerical weather forecasting, ML would there be up against stiff competition. The neural network isn’t anything like the forecasting models developed by humans, but provides the tools for the computer to develop its own form of model. If that hadn’t worked as well as it has, an ML approach might have been to design a large neural network, load it with several years of observational data, and see what predictions it came up with. They’ve progressively built more sophisticated mathematical models into which they load measurements taken around the world, and the model will forecast changes over the coming hours and days. Over the last couple of centuries, meteorologists and atmospheric physicists have learned a lot about how our atmosphere and weather work. What’s most distinctive about machine learning is that, instead of humans laying down exactly how predictions should be made using equations and rules, ML provides the computer with the tools to develop and build its own predictive techniques.
The distinction between those and machine learning is blurred, yet no one seems worried about the impacts on society of improving weather models and their forecasts. This is strange as one of the earliest major goals in computing has been to improve predictions, something for which we’re accustomed to using statistics and conventional modelling. Machine learning (ML), and artificial intelligence as a whole, have become a vogue and, for some, an alarming trend in computing.
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