![]() ![]() Arora, A., Fosfuri, A., & Gambardella, A.Back to basics: Why do firms invest in research? NBER working paper: 23187. Strategic Management Journal, 39(1): 3–32. Arora, A., Belenzon, S., & Patacconi, A.Cui bono? The selective revealing of knowledge and its implications for innovative activity. Academic freedom, private-sector focus, and the process of innovation. Aghion, P., Dewatripont, M., & Stein, J.We conclude that the phenomenon we observe reflects an overall shift in the sources of competitive advantage in AI, from exclusivity in technology to exclusivity in data. Owning strategic data resources makes firms lead users of AI tools, gives them a novel comparative advantage over universities in doing research in AI, and constitutes a specialized complementary asset that facilitates value appropriation. We suggest that a central aspect of digitalization-the rising importance of data as a strategic resource-drives corporate participation in AI science and publication. Conventional explanations of corporate science fail to fully explain why corporations would undertake this research and disseminate their results. In AI, we show that a number of large corporations including Google, Facebook, and their Chinese counterparts hire leading researchers and publish increasing amounts of high-quality basic research. Whereas corporations in recent decades have generally shifted away from scientific research, this has not been the case in the field of AI. ![]() ![]() We show that this has implications also in the context of innovation, specifically, for basic research in the field of artificial intelligence (AI). ![]() Example solutions for a subset of the exercises are available from the book web site, while solutions for the remainder can be obtained by instructors from the publisher.Digitalization has vastly increased the availability, the use, and the value of data. Extensive support is provided for course instructors, including more than 400 exercises, graded according to difficulty. The book is suitable for courses on machine learning, statistics, computer science, signal processing, computer vision, data mining, and bioinformatics. Familiarity with multivariate calculus and basic linear algebra is required, and some experience in the use of probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory. No previous knowledge of pattern recognition or machine learning concepts is assumed. It is aimed at advanced undergraduates or first-year PhD students, as well as researchers and practitioners. This completely new textbook reflects these recent developments while providing a comprehensive introduction to the fields of pattern recognition and machine learning. ![]()
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