... repetition rereading Ulysses Deleuze Gilles Kafka Franz machine learning commonplacing humanism Auerbach Erich authorship exile modeling theory of representation resolution vector space models close reading sense perception hypotheses Latour Bruno distributional semantics Genette Gérard...

... counterpart, thereby making three primary counter-claims to dominant scholarly narratives. First, from the perspective of machine learning, fictionality is a highly legible category at the level of linguistic content; second, not only is such legibility coherent across different kinds of narrative types...

... in combination with machine learning methods to mostly automate the process of evaluating responses to the franchise section of the 2017 EC. Our method allows us to quickly and accurately identify and recode establishments that have been mistakenly classified as not being franchise-affiliated, increasing...

... Analytics software single unit SU establishments data administrative Natural Language Processing NLP NLP Natural Language Processing textual data Mikolov T machine learning ML Word2Vec models Muchlinski D Chakraborty A Forman G Liao W Xu Y NAICS industrial classification machine learning big...

...Timely alternative data sources like credit card transactions and search queries are more readily available today, while machine learning (ML) techniques have enabled marked gains in predictive accuracy. These advances help reveal economic news earlier in the estimation cycle, reducing revisions...

... intelligence and macroeconomics international artificial intelligence and artificial intelligence machine learning trade data privacy behind-the-border agglomeration Avi Goldfarb holds the Rotman Chair in Artificial Intelligence and Healthcare and is professor of marketing at the Rotman School...

...This chapter provides an assessment of the early contributions of machine learning to economics, as well as predictions about its future contributions. It begins by briefly overviewing some themes from the literature on machine learning, and then draws some contrasts with traditional approaches...

... and connects it to modern machine learning (ML) debates. One view is that subjective judgment exhibits properties of “unregularized” ML which overfits, because human judgment does not naturally penalize overfitting. I also discuss how ML techniques, in large data sets, can help discover different “behavioral...

... music, and musical experiments with machine learning. Each chapter addresses a figure of relation between human and machine—android, hybrid, analogy, personification, and posthuman—through examples that move from French Enlightenment makings of “sounding human versus machine” to twenty-first century...

... visible. Instead of framing a debate about “digital humanities,” or a conflict between “close” and “distant" reading, the book presents statistical models as interpretive strategies akin to humanistic interpretation. The argument relies especially on the premise that machine learning can be trained...

...Through a brief history of neural network theory, this chapter raises the question of thinking about human cognition and intelligence in the context of contemporary Artificial Intelligence based on Machine Learning and its Deep Learning variants. Through an analysis of predictive processing...

... on growth, jobs, and inequality, focusing on research and tools from macro and labor economics. Third, five chapters discuss machine learning and economic regulation, with an emphasis on microeconomic consequences and industrial organization. The final set of chapters explores how AI will affect research...

... and images, and the use of fast and flexible Machine Learning (ML) algorithms in analysis. With recent improvements in Deep Neural Networks (DNNs) and related methods, application of high-performance ML algorithms has become more automatic and robust to different data scenarios. That has led to the rapid...

... Dewatripoint M Holmstrom B Jewitt I Sims C artificial intelligence machine learning decision-making complexity automation prediction judgment Ajay Agrawal is the Geoffrey Taber Chair in Entrepreneurship and Innovation at the Rotman School of Management, University of Toronto, and a research...

...This concluding chapter examines how some musicians are working with machine learning systems in ways that challenge and move beyond the conventional framings of artificial intelligence (AI) as representing either an existential threat to humans or just another tool to serve their creativity...

...Spanning five centuries in the intellectual history of mathematics, computation, and linguistic thought, Language and the Rise of the Algorithm shows that recent developments in machine learning are reopening an issue that troubled mathematicians long before the computer age: how to set...

... or the aspect level that focuses on the sentiment toward a target. Some approaches to sentiment analysis are based on categorizing the content based on machine-learning algorithms or the occurrence of particular keywords and dictionaries. The chapter also considers the importance of value orientations...

... Nardi M R Valencia O M Zhang C Cain R L Deryugina T Konar M Perry E D Sampson G S machine learning climate change agriculture irrigation crop insurance technology Farmers confronting climate change can undertake a variety of adaptation strategies to cope. Broadly speaking they can hedge...

...The coda considers how the rise of machine learning in the 2010s relates to the concept of algorithm employed in computer science. Although the idea of machine learning has existed since the early computer era, the widespread adoption of these methods, as the coda argues, represents more of a break...

...Machine learning (ML) and artificial intelligence (AI) have been around for many years. However, in the last five years, remarkable progress has been made using multilayered neural networks in diverse areas such as image recognition, speech recognition, and machine translation. AI is a general...