by Andrew McAfee on May 8, 2012
In my last post, which recapped a fascinating lunch I had with a bunch of economists and AI researchers at MIT, I wrote
Computers are getting bigger and faster, but not ‘smarter’ in any human sense of the word. Artificial intelligence bears very little relationship to the human variety, and the two are not going to merge. One of the AI researchers referred to the idea of the Singularity as a ‘category mistake,’ which is a great academic insult.
A couple people asked for more details about this ‘category mistake,’ so I went back and reviewed my notes from the meeting. Here’s an edited, imperfect transcript of what one of the attendees said (I’ll follow the Chatham House rule)
There’s a type error there, which is conflating FLOPS with intelligence. [Some Singularity advocates] are just plotting FLOPS against brain size, saying they cross in 2035 or whenever and therefore… and that’s just obviously a mistake. There’s something missing there. [Intelligence is] not just counting cycles
The AI professionals were pretty adamant that faster machines were not automatically smarter machines, and that all the work they were doing to accomplish amazing feats like speech recognition, automatic translation, robot mobility and manipulation, driverless driving, and so on was not causing computers to become any more human.
To which I can only say, whew!
by Andrew McAfee on May 7, 2012
A little while back Frank Levy, an MIT economist whose work I’ve drawn on a lot, and Seth Teller and John Leonard of the Computer Science and Artificial Intelligence Lab (CSAIL) came to an important realization: MIT is home to both a set of people exploring the economic implications of cutting-edge technologies like AI, and many of the top AI researchers themselves. So shouldn’t these two groups come together, get acquainted, and start swapping ideas?
We did so over lunch last Thursday at CSAIL. Because we didn’t discuss blogging groundrules I’ll not disclose the attendees here, except to note that my Race Against the Machine coauthor Erik Brynjolfsson was there. The conversation flowed freely for an hour and a half, and none of us felt like we’d come anywhere close to exhausting the topic, so we’ll do it again.
I left with a full brain, the lingering sense that I was the dumbest guy in the room, and a few clear learnings and impressions. The latter include:
- The discipline of AI was Big Data before Big Data was cool. As the CSAIL researchers explained, the field of AI pivoted substantially starting in the late 80s and early 90s. Prior to that time, we were trying to teach computers rules: how language worked, how knowledge was organized, how the universe was structured. After then, we just filled them up with data and put statistical algorithms on top of them: If you see this waveform, it’s this word or this sentence; if you see this string of words in one language, it corresponds to this string in this other language; etc. This approach has only gained currency as the amount of digital data in the world continues to mushroom. (here and elsewhere I’m going to oversimplify the field of AI badly, and get important details wrong because of ignorance and/or a need for brevity. Apologies.)
- AI algorithms get better as data gets bigger. Super large datasets let researchers test their hypotheses and ideas, finding out which ones actually work. This lets the self-improving and auto-correcting features of science kick in in a way they never could when the AI debates had to remain in the realm of pure theory.
- We don’t need to worry about the Terminator or prepare for the Singularity. Computers are getting bigger and faster, but not ‘smarter’ in any human sense of the word. Artificial intelligence bears very little relationship to the human variety, and the two are not going to merge. One of the AI researchers referred to the idea of the Singularity as a ‘category mistake,’ which is a great academic insult.
- Faith in human intuition remains strong. Too strong. One of my biggest surprises came as I listened to the AI researchers defend human decision-making and pattern-matching capabilities. As I’ve said repeatedly (here, here, and here, for starters), I think our intuition, while amazing and real, is also highly overrated — clearly glitchy and biased — and should be replaced by algorithmic approaches as soon as it’s clear that the algorithms to a better job. Costs will drop and outcomes will improve as we get humans out of the loop in more and more circumstances. I found it very strange to be working to convince a bunch of world-class algorithmicists of the comparative advantages of algorithms, but I guess I should be used to it by now. Most people, including professional technologists, are enamored of intuition.
- Nobody knows where we’re headed. When one of the AI guys walked into the room with a copy of our book, my first thought was not “Wow, that’s flattering.” It was “Uh-oh — does he expect us to tell him how the future’s going to unfold? ‘Cause that’s what I was hoping to learn from him…” I’m very sure that the economic and societal consequences of recent astonishing technical progress are going to be big, and I’m not at all sure what they are. Technology-fueled capitalist creative destruction is a messy and uneven process, and its twists and turns in coming years will leave us amazed.
I’m a huge optimist about this process overall (although I’m deeply concerned about some of the labor force implications; hence Race Against…) and I very much look forward to future conversations with leading technologists to help me understand it better. Erik and I are headed to San Francisco and Silicon Valley at the end of the month to continue these conversations. I’ll be sure to report back from there…