The machine learning task of learning a function that maps an input to an output based on example input-output pairs. It infers a function from labeled training data consisting of a set of training examples.
Published in Chapter:
The Future of Advertising: Influencing and Predicting Response Through Artificial Intelligence, Machine Learning, and Neuroscience
Elissa Moses (Columbia University, USA & BrainGroup Global, USA), Kimberly Rose Clark (Dartmouth College, USA & Bellwether Citizen Response, USA), and Norman J. Jacknis (Columbia University, USA & Intelligent Community Forum, USA)
Copyright: © 2021
|Pages: 16
DOI: 10.4018/978-1-7998-6985-6.ch007
Abstract
This chapter summarizes the role that artificial intelligence and machine learning (AI/ML) are expected to play at every stage of advertising development, assessment, and execution. Together with advances in neuroscience for measuring attention, cognitive processing, emotional response, and memory, AI/ML have advanced to a point where analytics can be used to identify variables that drive more effective advertising and predict enhanced performance. In addition, the cost of computation has declined, making platforms to apply these tools much less expensive and within reach. The authors then offer recommendations for 1) understanding the clients/customers and users of the products and services that will be advertised, 2) aiding creativity in the process of designing advertisements, 3) testing the impact of advertisements, and 4) identifying the optimum placement of advertisements.