I recently attended a very interesting talk , - Exploring the Impact of Artificial Intelligence: Prediction versus Judgment, - by University of Toronto professor Avi Goldfarb. The talk was based on recent research conducted with his UoT colleagues Ajay Agrawal and Joshua Gans. In addition to an in-depth paper aimed at a research audience, they’ve explained their work in two more general interest articles, one in the Harvard Business Review and the second in the MIT Sloan Management Review.
In their opinion, “the best way to assess the impact of radical technological change is to ask a fundamental question: How does the technology reduce costs? Only then can we really figure out how things might change.” For example, the semiconductor revolution can be viewed as being all about the dramatic reductions in the cost of arithmetic calculations. Before the advent of computers, arithmetic was done by humans with the aid of various kinds of devices, from the abacus to mechanical and electronic calculators.
Then came digital computers, which are essentially powerful calculators whose cost of arithmetic operations has precipitously decreased over the past several decades thanks to Moore’s Law. Over the years, we’ve learned to define all kinds of tasks in terms of such digital operations, e.g., inventory management, financial transactions, word processing, photography. Similarly, the economic value of the Internet revolution can be described as reducing the cost of communications and of search, thus enabling us to easily find and access all kinds of information, - including documents, pictures, music and videos.
How does this framing now apply to our emerging AI revolution? After decades of promise and hype, AI seems to have finally arrived, - driven by the explosive growth of big data, inexpensive computing power and storage, and advanced algorithms like machine learning that enable us to analyze and extract insights from all that data. Agrawal, Fans and Goldfarb provide an elegant answer to this question in their HBR article. “Machine intelligence is, in its essence, a prediction technology, so the economic shift will center around a drop in the cost of prediction.”