 Название: Artificial Intelligence for Everyone
Автор: Steven Finlay
Издательство: Relativistic
Год: 2020
Страниц: 215
Язык: английский
Формат: epub, pdf (conv)
Размер: 10.1 MB
Artificial Intelligence (AI) is everywhere these days. Barely a day goes by without the media reporting some wonderful new application of this marvellous technology and how it’s changing our lives forever. But how are things changing, where and in what ways? Artificial Intelligence for Everyone provides a jargon free guide to this fascinating subject without any mathematics or complex formulas. It’s the ideal book for anyone with an inquisitive mind who wants to learn more about artificial intelligence and its impact on society.
“Self-Learning Machines,” “Algorithms,” “Deep Neural Networks,” “Robotics,” “Automation.” These are just a few of the terms that are being bandied about to describe the seemingly endless torrent of new “Intelligent” tools, apps and gadgets that are sweeping across the world at an ever-increasing rate.
By all accounts, the big driver of these new technologies, “Artificial Intelligence” or “AI,” as it is commonly abbreviated to, is already influencing or changing almost every aspect of our lives. This spans everything from how we work, travel and shop, the way we obtain news and information, to the gadgets in our homes.
The vast majority of AI/machine learning applications that you will come across in the everyday world (whether you know it or not), such as target marketing, voice recognition, fraud detection, content recommendation and employee vetting will be examples of supervised learning. If you have a collection of labeled data available and you want to use this to predict some type of outcome or event, then a supervised learning approach is usually the right one to follow and will yield good results. There are however, certain types of activity where there isn’t a database of labelled data for the training algorithm to use. When labelled data is not available, a different set of techniques, referred to as unsupervised learning, can be applied.
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