I’m a terrible Go player. Perhaps that’s why I hadn’t quite understood AlphaGo until recently reading more about it in Erik Brynjolfsson and Andrew McAfee’s book Machine, Platform, Crowd.
Machines became better than humans at chess a while back as increasing computing power enabled Deep Blue and the like to calculate possible moves and rank whether they were likely to help the computer win. But there are more possible positions in Go than there are atoms in the universe. In fact there are enough possible positions for there to be a universe of atoms for every atom in the universe (that’s 10 to the power 82 in case you’re wondering).
Even today cracking that by brute force would require more computing power than we have available. But what I hadn’t understood was that the best human Go players don’t know why they’re so good.
“How do the top human Go players navigate this absurd complexity and make smart moves? Nobody knows — not even the players themselves. Go players learn a group of heuristics and tend to follow them. Beyond these rules of thumb, however top players are often at a loss to explain their own strategies. As Michael Redmond, one of the few Westerners to reach the game’s highest rank, explains, “I’ll see and move and be sure it’s the right one, bit won’t be able to tell you exactly how I know. I just see it.”
So programming a computer to play Go is tricky because we don’t know what to teach it. What AlphaGo did was teach itself. Back in the 1950s when the theoretical basis for artificial intelligence was being laid out there were two branches — one was rule-based (a bit like the way adults learn a new language) while the other was essentially statistical (like a child learning to talk by trying things over and over again). What AlphaGo shows is just how powerful this second version of AI has now become.
I signed up to the waiting list about a year ago. I was visiting Craig and Kanyi at the Collaborative Fund in New York and Craig had simply copied ‘Amy’ into our emails to sort out the details. I noticed the strange email address straight away and did some digging. Amy it turned out was an artificial assistant created by New York based company x.ai. The process worked without a hitch and I remember thinking how unremarkable the whole experience was.
Even though I knew Amy was an AI, I remember feeling that I should be polite to her. I generally make a point of thanking all the PAs who arrange meetings with other people for me and treat them in the way I would treat the person I’m meeting. It was hard to shake that habit.
Fast forward nine months and I got the email saying that I could now use Amy too as the beta trial grew. I’d actually forgotten I’d signed up (nobody else I’d met had been using x.ai in the period between), but I quickly clicked on the link and went through the very simple setup process of sharing calendars and answering some basic questions about common types of meeting (how long they should be, favourite locations etc). I also got to choose whether I wanted Amy or Andrew and decided on Andrew.
Then I got a bit stuck. It was not so much that I’d never had an AI personal assistant before — I’d never had a personal assistant at all.
Before trying it out on others I felt I needed to give it a quick test so sent my colleague Vicky at BGV an email suggesting we go for a coffee and that Andrew (cc’d) could arrange a time. Now Vicky in a past life used to be an executive assistant so I thought she might be intrigued. In fact she was just rude and deliberately awkward making Andrew’s task all the more difficult by changing her mind and suggesting venues that she knew wouldn’t work. Andrew gave up and politely ‘reverted this one back to me’.
Next I tried letting him organise a few phone calls that weren’t time sensitive and Andrew did fine. Then a few coffees, which also went fine. I didn’t ever use it for my social life — that would seem a bit weird to me — and if it was a really important meeting I still did it myself.
I did a bit of maths and realised scheduling takes about 5-10% of my working day so anything that can reduce that is very valuable. It’s also a real drag — even the PAs I know would rather do more valuable things if they could so my feeling at the moment is that x.ai is creating job displacement rather than job replacement.
Most people I meet who have interacted with Andrew want to talk about it and they usually only have positive things to say. A couple of people didn’t notice that he’s an AI at all and a lot of people have asked if I can get them bumped up the waiting list (I can’t).
I’m not sure whether I’ll go all in and let Andrew organise all my meetings. It’s going to take a bit of getting used to but the barriers are more human and social (what other people think) than technological. It’s not quite ‘Her’ or ‘Ex Machina’ but it does feel like the future, albeit in a very mundane way.