Opinion
Artificial Intelligence And Big Data: Good For Innovation?
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Data portability is not a panacea, but provides many benefits and will likely be part of a suite of solutions ultimately embraced by regulators.
By Robert Seamans
Artificial intelligence is firmly embedded throughout the economy. Financial services firms use it to provide investment advice to customers, automakers are using it in vehicle autopilot systems, technology companies are using it to create virtual assistants like Alexa and Siri, and retailers are using artificial intelligence (AI) together with customers’ prior sales histories, to predict potential purchases in the future, to name but a few examples. The potential of AI to boost economic growth has been discussed in numerous forums, including by Accenture, the Council on Foreign Relations, the McKinsey Global Institute, the World Economic Forum, and President Obama’s Council of Economic Advisers, among others.
The most dramatic advances in AI are coming from a data-intensive technique known as machine learning. Machine learning requires lots of data to create, test and “train” the AI. Thus, as AI is becoming more important to the economy, so too is data. The Economisthighlighted the important role of data in a recent cover story in which it stated “the world’s most valuable resource is no longer oil, but data.” In this sense, both the ability to obtain data about customers, together with the ability to program AI to analyze the data, have become important tools businesses use to compete against each other, and against potential entrants.
A potential entrant that lacks access to good data faces substantial hurdles, and this has led some regulators to question the extent to which control over data creates barriers to entry. For example, in December 2015 FTC Commissioner Terrell McSweeney asked: “Can one company controlling vast amounts of data possess a kind of market power that creates a barrier to entry?” This is a worry, because if barriers to entry are too high, entrants will not enter, established firms will not feel competitive pressures, and innovation may suffer. Thus, in March 2017 CFPB Director Richard Cordray noted: “We recognize that data access makes it possible to realize the many benefits of competition and innovation.”
Read the full article as published by Forbes.
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Robert Seamans is an Associate Professor of Management and Organizations.
The most dramatic advances in AI are coming from a data-intensive technique known as machine learning. Machine learning requires lots of data to create, test and “train” the AI. Thus, as AI is becoming more important to the economy, so too is data. The Economisthighlighted the important role of data in a recent cover story in which it stated “the world’s most valuable resource is no longer oil, but data.” In this sense, both the ability to obtain data about customers, together with the ability to program AI to analyze the data, have become important tools businesses use to compete against each other, and against potential entrants.
A potential entrant that lacks access to good data faces substantial hurdles, and this has led some regulators to question the extent to which control over data creates barriers to entry. For example, in December 2015 FTC Commissioner Terrell McSweeney asked: “Can one company controlling vast amounts of data possess a kind of market power that creates a barrier to entry?” This is a worry, because if barriers to entry are too high, entrants will not enter, established firms will not feel competitive pressures, and innovation may suffer. Thus, in March 2017 CFPB Director Richard Cordray noted: “We recognize that data access makes it possible to realize the many benefits of competition and innovation.”
Read the full article as published by Forbes.
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Robert Seamans is an Associate Professor of Management and Organizations.