Exploring the Impact of Artificial Intelligence: Prediction vs. Judgment
Recent progress in machine learning has significantly advanced the field of AI. Please describe the current environment and where you see it heading.
In the past decade, artificial intelligence has advanced markedly. With advances in machine learning — particularly ‘deep learning’ and ‘reinforcement learning’ — AI has conquered image recognition, language translation and games such as Go. Of course, this raises the usual questions with regard to the impact of such technologies on human productivity. People want to know, will AI mostly substitute or complement humans in the workforce?
In a recent paper, my colleagues and I present a simple model to address precisely what new advances in AI have generated in a technological sense, and we apply it to task production. In so doing, we are able to provide some insight on the ‘substitute vs. complement’ question, as well as where the dividing line between human and machine performance for cognitive tasks might lie.
At the core of your work is a belief that recent developments in AI constitute advances in prediction. Please explain.
Prediction occurs when you use information that you have to produce information that you do not have. For instance, using past weather data to predict the weather tomorrow, or using past classification of images with labels to predict the labels that apply to the image you are currently looking at. Importantly, this is all machine learning does. It does not establish causal relationships and it must be used with care in the face of uncertainty and limited data.
In an economic sense, if we were to model the impact of AI, the starting point would be a dramatic fall in the cost of providing quality predictions. As might be expected, having better predictions leads to better and more nuanced decisions. In terms of organizations embracing AI, there has been a lot of activity and discussion — along, , — have been implementing AI in their products for a few years now, and they continue to roll it out. For the rest of us, not much has happened yet — but there is huge simmering potential and opportunity. Over the next decade, I believe we will see a lot of activity, but we are still at the very earliest stages of this.
