Well it seems the machines have won, select your pod early, you’ll want to get a good view of the energy harvesting machines.

We were talking about Watson around the ol’ AI research lab today and someone pointed out that Watson is yet another highly tailored solution to a particular problem just like DeepBlue (click it, its Arcade Fire, just CLICK IT) was for chess. It’s using a lot of brute force and some reasoning but it’s still not solving the same problem humans are and the domain is somewhat restricted.

Now the interesting difference is that whereas chess is a deterministic game where you can search for an optimal strategy, jeopardy has layers of uncertainty hidden behind human language and the behaviour of other players. So while it’s not a very realistic setting for general AI, and doesn’t claim to be, it has stepped over an important threshold from deterministic, logic based problems to ones that require reasoning under uncertainty and statistics.

This is very fitting as the field of AI research itself has gone through the same change in focus in the past 20 years as outlined very well by Peter Norvig recently. When I took my undergraduate AI classes in the 90s I fell in love with prolog and logical planning. That’s why I went into AI research later.

When I got to grad school I found out that during my undergrad AI courses I had been missing a renaissance that had been occurring which led to modern machine learning and probabilistic AI. Watson’s achievement is only possible with these new methods and the raw computing power increases we have had over the same period.

But  apparently it did also have one other advantage. As many people have speculated, the machine did seem to have a buzzer advantage. According to op-ed by Ken Jennings himself, Watson’s speed with the buzzer was decisive in making up for questions it got wrong.  Is this just sour grapes? Maybe just a little, you need some ego to be an intense competitor like Jennings, but I think he has a point. As I pointed out yesterday the quick reaction time between making the decision to buzz and registering a button press is something a machine can clearly be faster at. Is this what winning at Jeopardy means?

It shouldn’t be.

Winning should mean the ability to answer complex questions, with ambiguous meanings, under time pressure while making the best strategic betting choices.  That is the task Watson performed admirably at. It could have had a buzzer delay and read the screens with computer vision rather than receiving a text file to parse and perhaps it still would have won.

But we’ll never know now.

So you won this round Watson. And you’re impressive (well, the engineering team that built ‘you’ is impressive actually). Hopefully everyone has learned a bit about AI and hopefully some young girls or boys will be inspired to consider computer science or engineering that otherwise wouldn’t have.

But next year…next year you should come back and put it all the table.  Play it our way, the human way, you have the capability to at least try. And may the best machine, be they biological or electronic, win.