A:I think predicting anything over longer than a 10 year horizon is very difficult. But there are a few thoughts I have on this question.
A lot of debates seem centered on the idea that something dramatic has happened in terms of technology. But it's not clear to me that our rate of change of innovation is increasing. To me it feels sometimes painfully slow!
If we go back just over 50 years ago, there were people whose job it was to *be* a computer. When electronic computers took over, those people became the first computer operators. Eventually the low level tasks they performed became replaced by programming languages. So those jobs evolved too. They also became higher paid and more interesting. Depressingly, as they did so, the work moved from being predominantly women's to mainly men's.
I'm not saying that all jobs will get more interesting and higher paid (or that men will displace women!! I hope we're evolving beyond that!), but there was some kind of bottle neck effect with programmers, as there is today with data scientists.
Early computers were invented to automate repetitive tasks (arithmetic, sorting) and yet most of the population's experience is that computers today make us do more repetitive tasks: have you ever watched a biologist cutting and pasting data in Excel because they don't know how to script? Or have you ever had to rename a bunch of files or reorganise things into folders?
I don't believe there's going to be a dramatic shift where people's existing roles become redundant overnight. In terms of the way in which things will change it's going to be 'more of the same' (if you see what I mean). The nature of our society will continue to change, and so will the nature of the jobs within that society.
With machine intelligence, as we do start to better emulate various characteristics that *today* are considered distinctly human, I think our the way in which we connect with other humans will evolve. You hear people talking in rather simplistic terms about 'productive' jobs being done by machines and humans becoming focused on 'entertainment'. But those terms are rarely defined satisfactorily: what is a 'productive' job and what classifies as 'entertaining'? My own job often feels like both, but sometimes feels like neither. Maybe I'm too optimistic, but I hope we'll evolve to better understand, respect and enjoy each other and that the work we find satisfying will evolve to build on that understanding.
I was talking with the owner of my local coffee shop (Upshot Espresso) on Monday about single origin coffees and the importance of a *story* behind the farm where that coffee is grown. People's expectations when they are drinking 'craft coffee' are rather different to the assumptions encoded in macroeconomics. There are many similar movements: e.g. craft beer. So maybe the real answer is that we're all going to become a bit more hipster.
A: So while most members of the public have heard of AI, very few have heard of machine learning. So when I introduce machine learning in articles targeted at a wide audience I describe it as "the principal technology underpinning the recent advances in artificial intelligence".
Artificial intelligence as a field actually includes a lot more than machine learning, it's just that recently a lot challenges that were considered very hard have been solved using ideas from machine learning. Machine learning is actually used for many applications that might not be thought of as artificial intelligence.
Machine learning is also one of the principal technologies underpinning *data science*.
And to the extent that AI underpins data science I think it does so *through* machine learning.
So there are two broad components to what machine learning does: AI and data science.
Data science itself also involves a lot more than just machine learning, as does AI.
Machine learning is a data driven approach to decision making, and it therefore overlaps a great deal with statistics. In the past, when asked to distinguish between statistics and machine learning I put it roughly as follows:
"Statistics is trying to turn humans into computers, while machine learning is trying to turn computers into humans. Neither task is currently possible so we meet somewhere in the middle."
What I mean by that is the following: the main aim of the field of statistics (at its inception) was to ensure that we weren't misled by statistics. Statistics are just summary numbers and the field was called mathematical statistics. People were interested in proving things about particular statistics you could compute that would allow you to be confident in your conclusions about the world given the data (are people in London richer or poorer than people in Manchester?). Humans have natural inductive biases which cause us to see patterns where there are none. A major preoccupation of statistics is ensuring that a particular statistic is not just exploiting this tendency. In my quote the computer represents an idealised decision maker that wouldn't have such a bias, and statisticians work towards trying to ensure that important decisions are made without such biases.
Machine learning researchers, on the other hand, are fascinated by all the things that humans can do that computers can't. Many of these things are actually the positive side effects of our inductive biases (our tendency to see patterns). So we would actually like to have methods that encode these biases.
So the philosophy of statistics and machine learning (certainly at outset) is quite different and that leads to different emphasis.
Note in all these discussions there's no one field that's right and one field that's wrong. We're just interested in slightly different things. It's very important to bear that in mind when involved in interdisciplinary discussion, otherwise frustration and argument results!
From a personal perspective I love the fact that data driven research is a pass to 'access all areas', so the interface between statistics and machine learning really excites me. And it's fantastic that machine learning has been so influential that we are asked to contribute to debates in both data science and artificial intelligence. I hope we do so constructively!