Prediction Machines: The Simple Economics of Artificial Intelligence
by Ajay Agrawal
12 popular highlights from this book
Key Insights & Memorable Quotes
Below are the most popular and impactful highlights and quotes from Prediction Machines: The Simple Economics of Artificial Intelligence:
We are narrow thinkers, we are noisy thinkers, and it is very easy to improve upon us.
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.
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.
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.
the new wave of artificial intelligence does not actually bring us intelligence but instead a critical component of intelligence—prediction.
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!
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.
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.
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.
People who have never missed a flight have spent too long in airports.
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.
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.
