Ajay Agrawal > Quotes

“We are narrow thinkers, we are noisy thinkers, and it is very easy to improve upon us.”
― ajay agrawal, Prediction Machines: The Simple Economics of Artificial Intelligence

“Having better prediction raises the value of judgment. After all, it doesn’t help to know the likelihood of rain if you don’t know how much you like staying dry or how much you hate carrying an umbrella. Prediction machines don’t provide judgment. Only humans do, because only humans can express the relative rewards from taking different actions. As AI takes over prediction, humans will do less of the combined prediction-judgment routine of decision making and focus more on the judgment role alone.”
― Ajay Agrawal, Prediction Machines: The Simple Economics of Artificial Intelligence

“when your predictions are accurate enough—something happens. You cross a threshold where you should actually rethink your whole business model and product based on machine learning.…”
― Ajay Agrawal, Power and Prediction: The Disruptive Economics of Artificial Intelligence

“Prediction Machines is not a recipe for success in the AI economy. Instead, we emphasize trade-offs. More data means less privacy. More speed means less accuracy. More autonomy means less control.”
― Ajay Agrawal, Prediction Machines: The Simple Economics of Artificial Intelligence

“From religion to fairy tales, knowledge of the future is consequential. Predictions affect behavior. They influence decisions.”
― Ajay Agrawal, The Economics of Artificial Intelligence: An Agenda

“Before machine learning, multivariate regression provided an efficient way to condition on multiple things, without the need to calculate dozens, hundreds, or thousands of conditional averages. Regression takes the data and tries to find the result that minimizes prediction mistakes, maximizing what is called “goodness of fit.”
― Ajay Agrawal, Prediction Machines: The Simple Economics of Artificial Intelligence

“the new wave of artificial intelligence does not actually bring us intelligence but instead a critical component of intelligence—prediction.”
― ajay agrawal, Prediction Machines: The Simple Economics of Artificial Intelligence

“More often than not, that was a tough sell. If you go to a business and tell it you can save it $50,000 per year in labor costs if it eliminates this one job, then your AI product better eliminate that entire job. Instead, what entrepreneurs found was that their product was perhaps eliminating one task in a person’s job, and that wasn’t going to be enough to save their would-be customer any meaningful labor costs. The better pitches were ones that were not focused on replacement but on value. These pitches demonstrated how an AI product could allow businesses to generate more profits by, say, supplying higher quality products to their own customers. This had the benefit of not having to demonstrate that their AI could perform a particular task at a lower cost than a person. And if that also reduced internal resistance to adopting AI, then that only made their sales task easier. The point here is that a value-enhancing approach to AI, rather than a cost-savings approach, is more likely to find real traction for AI adoption.”
― Ajay Agrawal, Power and Prediction: The Disruptive Economics of Artificial Intelligence

“During the shopping process, Amazon’s AI offers suggestions of items that it predicts you will want to buy. The AI does a reasonable job. However, it is far from perfect. In our case, the AI accurately predicts what we want to buy about 5 percent of the time. We actually purchase about one of every twenty items it recommends. Considering the millions of items on offer, that’s not bad!”
― Ajay Agrawal, Prediction Machines: The Simple Economics of Artificial Intelligence

“People should stop training radiologists now. It’s just completely obvious that within five years, deep learning is going to do better than radiologists.”
― Ajay Agrawal, Power and Prediction: The Disruptive Economics of Artificial Intelligence

“What will new AI technologies make so cheap? Prediction. Therefore, as economics tells us, not only are we going to start using a lot more prediction, but we are going to see it emerge in surprising new places.”
― Ajay Agrawal, Prediction Machines: The Simple Economics of Artificial Intelligence

“But we need to do more. We are now in The Between Times for AI—between the demonstration of the technology’s capability and the realization of its promise reflected in widespread adoption.”
― Ajay Agrawal, Power and Prediction: The Disruptive Economics of Artificial Intelligence

“As AI takes over prediction, humans will do less of the combined prediction-judgment routine of decision making and focus more on the judgment role alone.”
― Ajay Agrawal, Prediction Machines: The Simple Economics of Artificial Intelligence

“What does regression do? It finds a prediction based on the average of what has occurred in the past. For instance, if all you have to go on to determine whether it is going to rain tomorrow is what happened each day last week, your best guess might be an average. If it rained on two of the last seven days, you might predict that the probability of rain tomorrow is around two in seven, or 29 percent. Much of what we know about prediction has been making our calculations of the average better by building models that can take in more data about the context.”
― Ajay Agrawal, Prediction Machines: The Simple Economics of Artificial Intelligence

“Value versus Cost Economists tend to focus on cost, and, as economists, we are as guilty of that as anyone. The entire premise of our first book, Prediction Machines, was that AI advances were going to dramatically reduce the cost of prediction, leading to a scale-up of its use. However, while that book suggested that the initial uses of AI would be where prediction was already occurring, either explicitly in, say, forecasting sales or the weather, or implicitly in classifying photos and language, we were mindful that the real opportunity would be the new applications and uses that were enabled when prediction costs fell low enough.”
― Ajay Agrawal, Power and Prediction: The Disruptive Economics of Artificial Intelligence

“Just as electricity enabled decoupling the machine from the power source and thus facilitated shifting the value proposition from “lower fuel costs” to “vastly more productive factory design,” AI enables decoupling prediction from the other aspects of a decision and thus facilitates shifting the value proposition from “lower cost of prediction” to “vastly more productive systems.”
― Ajay Agrawal, Power and Prediction: The Disruptive Economics of Artificial Intelligence

“people form habits or keep to rules, they are acknowledging that the costs of trying to optimize are too high. So they, in effect, decide not to decide.”
― Ajay Agrawal, Power and Prediction: The Disruptive Economics of Artificial Intelligence

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― Ajay Agrawal, Good Old Soul

“automation has to be scaled back. What is being automated, he argues, are more routine situations, so you require human interventions for more extreme situations. If the way you learn to deal with the extreme is by having a great feel for the ordinary, therein lies a problem.”
― Ajay Agrawal, Prediction Machines: The Simple Economics of Artificial Intelligence

“human judgment becomes more important when machine predictions proliferate, such judgment necessarily involves subjective means of performance evaluation. If objective means are available, chances are that a machine could make such judgment without the need for any HR management. Thus, humans are critical to decision making where the goals are subjective. For that reason, the management of such people will likely be more relational.”
― Ajay Agrawal, Prediction Machines: The Simple Economics of Artificial Intelligence

“People who have never missed a flight have spent too long in airports.”
― Ajay Agrawal, Prediction Machines: The Simple Economics of Artificial Intelligence

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― Ajay Agrawal