Written for and first published in the Financial Times on 4 May 2018
If you type “technology indis…” into Google, you are instantly directed to a webpage discussing Arthur C Clarke’s third law: “any sufficiently advanced technology is indistinguishable from magic”. The science fiction writer’s aphorism was published in 1962, at a time when a demonstration of Google’s autocompleting search engine would indeed have seemed like sorcery.
There are plenty of other examples: electricity, the aeroplane and the telephone would all have seemed miraculous and inexplicable to earlier generations. Each of them exemplified what a technological breakthrough is supposed to look like, deservedly winning attention as they appeared.
We need to be careful, however, not to overlook much simpler technological advances. The lightbulb is a safer and more controllable source of artificial light than the candle or the oil lamp, but what really makes it transformative is its price — the cost of illumination has fallen approximately 400-fold in the past two centuries.
Supercomputers and space travel get all the press. Merely being cheap doesn’t. But being cheap can change the world. Consider barbed wire (cheap fencing), the shipping container (cheap logistics), or the digital spreadsheet (cheap arithmetic). Ikea gave us cheap furniture, and the same principles of simple modular assembly are giving us cheaper solar panels, too.
My favourite example is paper: the Gutenberg press radically reduced the cost of producing writing, but it was of little use without an accompanying fall in the cost of a writing surface. Compared to papyrus, parchment or silk, one of paper’s most important properties was that it cost very little.
With all this in mind, what are today’s technological advances that we may be overlooking or misunderstanding because they are cheap rather than magical? The obvious answer: sensors. We are surrounded by inexpensive sensors — in our phones, increasingly in our cars — continually taking in information about the world.
A new book suggests a different, albeit related, answer. Prediction Machines (UK) (US) by Ajay Agrawal, Joshua Gans and Avi Goldfarb argues that we’re starting to enjoy the benefits of a new, low-cost service: predictions. Much of what we call “artificial intelligence”, say the authors, is best understood as a dirt-cheap prediction.
Predictions are everywhere. Google predicts that when I type “technology indis…” I am looking for information about Clarke’s third law; Amazon makes a prediction about what I might buy next, given what I have bought already, or searched for, or placed on my wishlist. A prediction may literally be a forecast about the future, or more generally it may be an attempt to fill in some blanks on the basis of limited information.
Not all such predictions are very good, but not all of them need to be. The tiny keyboards on our smartphones turn out to be quite serviceable when combined with modestly accurate predictions — from suggesting an entire one-phrase email reply (“I agree with you”) to subtly expanding the “H” and shrinking the surrounding keys on a touchscreen if the phone thinks that “H” is the more likely target for a fat-thumbed typist.
Errors in predictive text tend to be trivial and easy to correct, so a high error rate does not matter much. Clumsy text predictors can be released into the world so that they may learn. A high error rate in a self-driving car is not so easy to forgive.
As Mr Agrawal and colleagues point out, sufficiently accurate predictions allow radically different business models. If a supermarket becomes good enough at predicting what I want to buy — perhaps conspiring with my fridge — then it can start shipping things to me without my asking, taking the bet that I will be pleased to see most of them when they arrive.
Since good predictions reduce uncertainty, we may also see less demand for things that help us deal with uncertainty. If that conspiratorial fridge can arrange just-in-time delivery of meal ingredients by predicting my requirements, it can be much smaller as a result.
Another example is the airport lounge, a place designed to help busy people deal with the fact that in an uncertain world it is sensible to set off early for the airport. Route-planners, flight-trackers and other cheap prediction algorithms may allow many more people to trim their margin for error, arriving at the last moment and skipping the lounge.
Then there is health insurance; if a computer becomes able to predict with high accuracy whether you will or will not get cancer, then it is not clear that there is enough uncertainty left to insure.
All this seems a useful way to look at the fast-changing world of machine learning — more useful than pondering Clarke’s most famous creation, the murderous computer HAL 9000. Some automated predictions are already marvellously good, but many are changing the world not because they are omniscient, but because they’re good enough — and cheap.