At Gravyty, we can't keep our hands off of a good read, especially if that book is a useful resource on Artificial Intelligence (AI). Great literature has always moved society forward, and our Top AI Books List sheds light on the research and work that's being done in the fields of AI, machine learning, and predictive analytics. Today, we're adding Prediction Machines by Ajay Agrawal, Joshua Gans, and Avi Goldfarb to the Top AI Books List.
Where many popular AI-focused books are written from either a technologist perspective or an operator perspective, Prediction Machines by Ajay Agrawal, Joshua Gans, and Avi Goldfarb analyzes AI through the lens of an economist. The book explores the intersection of implications surrounding applying AI with age-old economic decision-making theory to help organizations better decide how machine learning can improve operations.
The argument is made that AI in its current form is less about "intelligence" and more about a component of intelligence: prediction. However, there is a certain amount of give-and-take with predictive analytics: more data leads to less privacy, more speed results in less accuracy, more autonomy offers less control, and so forth.
One of the prevailing conclusions is that AI is going to make what we need to predict orders of magnitude cheaper; but since computers still cannot think, "thought" will not become cheaper, rather it will be more informed by better predictions. This creates a "flywheel" where better predictions lead to better information which leads to better decisions, and back again.
The authors encapsulate this thinking and their unique perspective on AI into a canvas which helps readers build a landscape around components of decision making: prediction, judgement, action, outcome, input, training, and feedback. As AI decisions move from operational to strategic, understanding tradeoffs and having clarity of thought are important to maximizing AI’s positive impact on an organization.