The Economics Of Artificial Intelligence – How Cheaper Predictions Will Change The World
Artificial Intelligence (AI) is a lot of things. It’s a game changer for business, it can enable humans to work smarter and faster than ever before, and it could potentially have a significant impact on economies and the labor market.
But at the root of it all – the function which gives AI value – is the ability to make predictions. Calculating – more quickly and accurately than has ever been possible – what the likelihood is of a particular outcome, is the fundamental advance which AI brings to the table.
To start with, it’s worth defining what we mean when we talk about AI. In recent years the leaps in technology which have been generating the biggest buzz are around machine learning and deep learning. These are specific implementations of technology which can be used to give machines the ability to learn, without human input, by merely being fed data.
This means they can become increasingly better at routine tasks – such as examining image data from cameras and working out what is shown, or reading through thousands of pages of documents and understanding the relevant pieces of information for the task at hand.
How this will affect the role of humans is a hot topic and the question is very much up in the air. Some predict that the near-future will see us becoming used to working alongside “smart” machines, hugely boosting our productivity. Others say the arrival of these machines will make us redundant when it comes to many forms of labor, leading to widespread unemployment and eventually civil unrest.
In their latest book: Prediction Machines – The Simple Economics of Artificial Intelligence, authors Ajay Agrawal, Joshua Gans and Avi Goldfarb seek to demonstrate how that prediction is fundamental to the changes that AI makes possible. In their book they explain that understanding this concept – and preparing our reaction to it – could determine which of those two possible futures is likely to come about.
Key to this, they argue, will be whether human AI “managers” can learn to differentiate between tasks involving prediction, and those where a more human touch is still essential.