Let’s start with what was going on in 1999: I had my first cell phone. Black and white LCD screen. No text messaging. I started working for Intelligenesis (later called Webmind, founded by Ben Goertzel). The machines we had were 500 MHz and had 256 MB of RAM. I discovered Google. Internet at home at 56k, but something like 256k at work. I was using Linux and was well aware of open source software. Quake was popular. Computers had CD drives, but DVD drives were starting to come out. Nobody had LCD monitors except on laptops. Dot.com boom was going crazy. The Matrix was a big hit.

Ok, so what would be the biggest surprise for 1999 me? I think the single biggest surprise would be that a black man had been elected president of the United States.  I thought it would be at least another generation or two before this would be possible.  The next most surprising thing would have been Wikipedia. Though given that Linux development was working well at the time, I guess with the right control structures in place it shouldn’t have been all that surprising.  Still, it continues to amaze me at just how good it in fact is.

Many other things seem to have been fairly predictable: internet got faster, bigger, computer specs all went up, people started watching video on the internet, voice and video chatting over the internet, more mobile internet… Would any of these things have surprised me in 1999?  I don’t think so.  Even the recent rise of social networking: I couldn’t have predicted what that would have looked like, but it’s not all that surprising.  Same for internet banking.  A lot of what seems to have been going on over the last 10 years is just the maturation of the internet and mobile devices.

What are the most surprising things for you over the last 10 years?

EDIT: Add to my list: free email service with almost 10 GB of storage (gmail), and Google street view.

One example of this has been trying to predict the medium term direction of the stock market over the last 15 years.  The evidence so far shows that I’m consistently good at predicting what will happen, but that I predict that it will happen much sooner than it actually does; I roughly need to double my time estimates.  I’m now trying to mentally correct for this bias in the trades I make, but it will take some years to see if this is working.

In technological matters, I’ve generally done quite well.  My picks for Sun, java, digital music, linux, MySQL and open source in general were pretty much on the mark.  I thought machine learning use in industry would be bigger than it is today, but I wasn’t too far off.  My biggest mistake was to badly underestimate how much Microsoft’s revenues would grow over the last 10 years — I thought they’d already almost saturated the market and its ability to pay.  Like stock markets, my most consistent error has been to be to predict that things will happen faster than they actually do.  I typically need to add about 50% to the time required.

As I don’t have a lot of my own technological predictions to look at, and some remain in the future, I’ve recently been looking at predictions made by others.  I found a few of my old computer and science magazines from the early 80′s through to the late 90′s which contained predictions, and I also dug up Kurzweil’s “The age of spiritual machines” written in 1999 in which he has a whole chapter about 2009.  There were a lot of hits and misses, but if I stand back and try to see the big picture, a pattern becomes clear: Predictions about basic hardware performance, even one I saw in a magazine from 25 years ago, are amazingly accurate.  But you probably knew that already.  Predictions about what would be technologically possible to do at a given point in time were not as accurate, but were still pretty good.  Where things really started to go wrong was when they tried to predict not what would be possible, but what the majority of people would actually be doing.

Perhaps some examples would best explain this.  State of the art speech recognition systems, such as some of the systems that were being developed at IDSIA when I was there, work impressively well.  However, once you’ve learnt to touch type it is typically easier, quieter, more convenient (especially when editing or coding) and far more private to use a keyboard.  I don’t care how good speech recognition is, I don’t want to sit in a room full of people talking out loud to their computers all day.  I only know one person who routinely uses speech recognition to input text.  The fact that speech recognition is technologically doable, doesn’t translate into it being practically useful for many everyday situations.

There are plenty of predictions that fail in this way: the prediction that everybody now would be making video calls on their cellphones.  It’s certainly technologically possible, I saw a guy with a phone that could do it two years ago, but almost nobody does it.  Or that most long distance air travel would be in supersonic jets.  Again, technologically possible, has been for a long time, but not done in practice.  Or that all mice would be wireless by now.  Technologically possible, has been for years, but as far as I can tell most new mice still have cords.  Or that most people driving long distance on freeways would get their car to automatically drive itself.  I’m sure that’s technologically possible, but I don’t see anybody doing it.  Or that your computer would log you in based on recognising your face or your voice.  Technologically possible today, but not done in practice.  And so on.

In short: Predicting raw performance is surprising accurate.  Predicting what will be possible using the knowledge and technology of some future date can also be done with moderate success.  Predicting what the population will routinely do, however, is much harder.  The latter is largely decided by habit, cost and convenience.  Simply being possible isn’t enough.  Note that predicting the development of the first powerful AGI is of the second type.

A few months ago I started reading the Overcoming Bias blog, on which Yudkowsky was discussing physics (I’m still not sure why he’s so involved with physics now, but he’s slowly getting around to explaining this).  Anyway, I have found his writings here to be much more to my liking.  His ideas seem clearer, more focused and organised and I find the style and tone to be much improved.  If you like some interesting philosophical discussions and you haven’t seen the blog already, you might want to check it out.

Encouraged by this I decided to have another look at Yudkowsky’s writings on the ethics and safety of artificial general intelligence.  This time I went for one of his most recent pieces: Artificial Intelligence as a Positive and Negative Factor in Global Risk, a book chapter he did for Global Catastrophic Risks.  If, like me, you were put off by some of his earlier writings, you might want to have a look at this newer document.  While I naturally don’t agree with everything in the chapter, in my opinion the points are clearly argued and fit together well.  Indeed, I found myself agreeing with most of his points.  In short, if you are new to the safety of powerful AI technologies, I’d suggest that you put this document at the top of your reading list.

In part 3 I’ll get back to my own thoughts on the matter…