cycles around ai occurred but had very little staying power by the early sinterest in and funding for ai had hit trough ai is backbut why nowai has re-emerged with vengeance over the past two decades--first as purely academic area of interest and now as full-blown field attracting the brightest minds at both universities and corporations three critical developments are behind this resurgencebreakthroughs in machine learning algorithmsthe availability of lots of dataand superfast computers firstinstead of focusing on overly ambitious strong ai projectsresearchers turned their attention to narrowly defined subproblems of strong aialso known as weak ai or narrow ai this focus on improving solutions for narrowly defined tasks led to algorithmic breakthroughswhich paved the way for successful commercial applications many of these algorithms--often developed initially at universities or private research labs--were quickly open-sourcedspeeding up the adoption of these technologies by industry seconddata capture became focus for most organizationsand the costs of storing data fell dramatically driven by advances in digital data storage thanks to the internetlots of data also became widely and publicly available at scale never before seen thirdcomputers became increasingly powerful and available over the cloudallowing ai researchers to easily and cheaply scale their it infrastructure as required without making huge upfront investments in hardware the emergence of applied ai these three forces have pushed ai from academia to industryhelping attract increasingly higher levels of interest and funding every year ai is no longer just theoretical area of interest but rather full-blown applied field figure - shows chart from google trendsindicating the growth in interest in machine learning over the past five years xii preface