The Economics of Artificial Intelligence

Agrawal, Ajay; Gans, Joshua S.; Goldfarb, Avi

doi:10.7208/chicago/9780226613475.001.0001

Cited by 558 publications

(671 citation statements)

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“…One way is to ask the economic question posed by Ajay Agrawal, who says, "When looking at artificial intelligence from the perspective of economics, we ask the same, single question that we ask with any technology: What does it reduce the cost of?" 15 Artificial intelligence, says Agrawal, reduces the price of prediction. Computing, Agrawal says, reduces the price of arithmetic.…”

Section: What Has Changed?mentioning

confidence: 99%

Reimagining the Academic Library

Lewis

2019

Proceedings

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This paper is a summary with some added thoughts of the closing plenary address at the American Theological Library Association meeting in Indianapolis, Indiana on June 16, 2018. The talk explores issues of productivity in the library context and how libraries need to explore, discover, and invent in order to remain vital. It also considers what has changed, most notably the unrelenting pace of increases in the capacity of computer technology as expressed in Moore’s Law. It considers the fundamental shift libraries need to make from an outside-in to an inside-out approach and how this requires investing in open content and infrastructure. Finally, a challenge was issued to the group to create the open scholarly commons for theology and religion.

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Section: What Has Changed?mentioning

confidence: 99%

Reimagining the Academic Library

Lewis

2019

Proceedings

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“…Machine intelligence (MI) lowers the cost of goods and services that rely on prediction. This is important since prediction is an input to a host of activities including transportation, agriculture, healthcare, energy manufacturing, and retail [2].…”

Section: Introductionmentioning

confidence: 99%

Machine Intelligence

Sadiku¹,

Adesina²,

Musa³

2020

International Journal of Advanced Research in Computer and Comm

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Researchers worldwide have always attempted to put human intelligence into machines by the ways of algorithms. Machine intelligent results from the development of Artificial Intelligence (AI). The goal of machine intelligence is giving machines the ability to learn from the changing environment and making predictions. The paper provides a brief introduction to machine intelligence. several books and papers. His areas of research interest include computational electromagnetics and computer networks. He is a fellow of IEEE. Damilola S. Adesina is a doctoral student at Prairie View A&M University. His research work is currently focused on optimizing communication systems using machine learning and deep learning.

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“…They are being developed to diagnose illnesses, drive cars, suggest purchases to potential customers and mine the human genome. The built environment has the opportunity to leverage the same algorithms and techniques to improve efficiency and to create new business models [1]. Building performance analysis has dozens of uses for temporal prediction of electricity, heating and cooling energy.…”

Section: Introductionmentioning

confidence: 99%

More Buildings Make More Generalizable Models—Benchmarking Prediction Methods on Open Electrical Meter Data

Miller

2019

MAKE

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Prediction is a common machine learning (ML) technique used on building energy consumption data. This process is valuable for anomaly detection, load profile-based building control and measurement and verification procedures. Hundreds of building energy prediction techniques have been developed over the last three decades, yet there is still no consensus on which techniques are the most effective for various building types. In addition, many of the techniques developed are not publicly available to the general research community. This paper outlines a library of open-source regression techniques from the Scikit-Learn Python library and describes the process of applying them to open hourly electrical meter data from 482 non-residential buildings from the Building Data Genome Project. The results illustrate that there are several techniques, notably decision tree-based models, that perform well on two-thirds of the total cohort of buildings. However, over one-third of the buildings, specifically primary schools, performed poorly. This example implementation shows that there is no one size-fits-all modeling solution and that various types of temporal behavior are difficult to capture using machine learning. An analysis of the generalizability of the models tested motivates the need for the application of future techniques to a board range of building types and behaviors. The importance of this type of scalability analysis is discussed in the context of the growth of energy meter and other Internet-of-Things (IoT) data streams in the built environment. This framework is designed to be an example baseline implementation for other building energy data prediction methods as applied to a larger population of buildings. For reproducibility, the entire code base and data sets are found on Github.

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The Economics of Artificial Intelligence

Cited by 558 publications

References 0 publications

Reimagining the Academic Library

Reimagining the Academic Library

Machine Intelligence

More Buildings Make More Generalizable Models—Benchmarking Prediction Methods on Open Electrical Meter Data

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