When the algorithms now in the lab make it to the front lines, Bill Gate’s remark that a breakthrough in machine learning would be worth ten Microsofts will seem conservative…
— From The Master Algorithm – How the Quest for the ultimate learning machine will remake our world by Pedro Domingos P. 22
Where Are we Headed?
Technology trends come and go all the time? What’s unusual about machine learning is that, through all these changes, through boom and bust, it just keeps growing. Its first big hit was in finance, predicting stock ups and downs, starting in the late 1980s. The next wave was mining corporate databases, which by the mid-1990s were starting to grow quite large, and in areas like direct marketing, customer relationship management, credit scoring, and fraud detection. Then came the web and e-commerce, where automated personalization quickly became de rigueur. When the dot-com bust temporarily curtailed that, the use of learning for web search and ad placement took off. For better or worse, the 9/11 attacks put machine learning in the front line of the war on terror. Web 2.0 brought a swatch of new applications, from mining social networks to figuring out what bloggers are saying about products. In parallel, scientists of all stripes, were increasingly turning to large-scale modeling, with molecular biologists and astronomers leading the charge. The housing bust barely registered; its main effect was a welcome transfer of talent from Wall Street to Silicon Valley. In 2011, the ‘big data’ meme hit, putting machine learning squarely in the center of the global economy’s future. Today, there seems to be hardly an area of human endeavor untouched by machine learning, including seemingly unlikely candidates like music, sports, and wine tasting.
As remarkable as the growth is, it’s only a foretaste of what’s to come. Despite its usefulness, the generation of learning algorithms currently at work in industry is, in fact, quite limited. When the algorithms now in the lab make it to the front lines, Bill Gate’s remark that a breakthrough in machine learning would be worth ten Microsofts will seem conservative. And if the ideas that really put a glimmer in researchers’ eyes bear fruit, machine learning will bring about not just a new era of civilization, but a new stage in the evolution of life on Earth.
What makes this possible? How do learning algorithms work? What can’t they currently do, and what will the next generation look like? How will the machine-learning revolution unfold? And what opportunities and dangers should you look out for? That’s what this book is about — read on.”