Prediction Machines: The Simple Economics of Artificial Intelligence
by Ajay Agrawal
In "Prediction Machines: The Simple Economics of Artificial Intelligence," Ajay Agrawal explores the transformative power of AI in enhancing predictive capabilities while emphasizing the enduring significance of human judgment. The book posits that as AI increasingly takes over the prediction aspect of decision-making, humans will shift their focus to the judgment component, which incorporates subjective evaluations and personal preferences that machines cannot replicate. Agrawal highlights the trade-offs inherent in AI adoption, such as the potential erosion of privacy with increased data usage and the balance between speed and accuracy. The authors illustrate how AI's predictive prowess, while impressive, remains imperfect, as evidenced by Amazon's suggestion algorithms. This imperfection underscores the necessity for human oversight, especially in subjective scenarios where nuanced decision-making is paramount. The book argues that the proliferation of predictive technologies will lead to their application in unexpected areas, reshaping economic landscapes. However, it warns against over-automation, advocating for a relational approach to management that recognizes the importance of human intuition and experience in navigating complex situations. Ultimately, "Prediction Machines" offers a nuanced view of AI's role in the economy, emphasizing that the future will require a blend of machine efficiency and human insight to navigate the intricacies of decision-making.
9 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.
