vetta project

I’ve decided to once again leave my prediction for when human level AGI will arrive unchanged.  That is, I give it a log-normal distribution with a mean of 2028 and a mode of 2025, under the assumption that nothing crazy happens like a nuclear war.  I’d also like to add to this prediction that I expect to see an impressive proto-AGI within the next 8 years.  By this I mean a system with basic vision, basic sound processing, basic movement control, and basic language abilities, with all of these things being essentially learnt rather than preprogrammed.  It will also be able to solve a range of simple problems, including novel ones.

Some of you might remember the talk I gave at the 2010 Singularity Summit about Algorithmic IQ, or AIQ for short.  It was an attempt to convert the theoretical Universal Intelligence Measure into a working practical test of machine intelligence.  The results were preliminary, but it seemed to work…

It’s now over a year later so I guess some of you are wondering what happened to AIQ! I’ve been very busy working on other cool stuff, however Joel Veness and I have been tinkering with AIQ in our spare time.  We’re pleased to report that it has continued to perform well, surprising well in fact.  There was some trickiness to do with getting it to work efficiently, but that aside, it worked perfectly straight out of the box.

We recently wrote a paper on AIQ that was accepted to the Solomonoff Memorial Conference.  You can get the paper here, the talk slides here, and we have also released all the Python AIQ source code here.  It’s designed to be easy to plug in your own agents, or other reference machines, if you fancy having a go at that too.

If you’re not sure you want read any of that, here’s the summary:

We implemented the simple BF reference machine and extended it in the obvious ways to compute RL environments.  We then sampled random BF programs to compute the environments, and tested against each of these.  This can be a bit slow, so we used variance reduction techniques to speed things up.  We then implemented a number agents.  Firstly, MC-AIXI, a model based RL agent that can learn to play simple games such as TicTacToe, Kuhn poker and PacMan, but is rather slow to learn.  Then HLQ(lambda), a tabular RL agent similar to Q learning but with an automatic learning rate.  Then Q(lambda), a standard RL agent, and Q(0), a weaker special case. Finally, Freq, a simple agent that just does the more rewarding action most of the time, occasionally trying a random action.  There was also a random agent, but that always got an AIQ of zero, as expected.  The results appear below, across various episode lengths:

The error bars are 95% confidence intervals for the estimates of the mean.  As you can see, AIQ orders the agents exactly we would expect, including picking up the fact the MC-AIXI, while quite powerful compared to the other agents, is also rather slow to learn and thus needs longer episode lengths.  We ran additional tests where we scaled the size of the context used by MC-AIXI, and the amount of search effort used, and in both cases the AIQ score scaled sensibly.  See the talk slides for more details, or the paper itself.

Seven months ago China claimed the number one position in supercomputing with a 2.6 Peta FLOPS machine.  I thought that might stand for a year, but I was wrong: last week Japan unveiled their new “K Computer” at a massive 8.2 Peta FLOPS.

Seems that we’re now running a couple of years ahead of even Kurzweil’s predictions.

Samuel Rathmanner and Marcus Hutter recently wrote a treatise on universal induction.  While there are no proofs, it does get into some fairly deep aspects of the problem of inductive inference.  Download it here:

http://dx.doi.org/10.3390/e13061076

Prof. Rich Sutton, probably the most famous person in the field of reinforcement learning, gave a talk today at the Gatsby Unit.  I was expecting a standard introduction to reinforcement learning to begin with, but it wasn’t to be.  Instead he kicked off with 20 minutes about the singularity.

Audience: So when do you expect human level AI?

Rich: Roughly 2030.

Whether or not you agree, views like this seem to be becoming more common in academia.

Looking over my predictions for the teenies from a year ago, they already look pretty lame. Take 1/3 off USA’s PPP GDP and you already get China, the latest Sony portable device has a 4 core processor, Intel’s latest set of CPUs are once again pretty awesome, schemes to let you pay for stuff with your phone are already getting under way (via both screen bar codes and near field communication), and a graphics card review I read the other day noted that the most graphically demanding games on high resolution monitors with all the graphical bells and whistles switched on now run very well on the latest “mid range” graphics cards.

At the time that I made my teenies predictions I thought they seemed a bit like predicting the obvious. But I’m now starting to wonder whether many of my predictions could have been more tightly assigned to the following 3-4 years, rather than the next decade.

One thing I’ve noted in the past is that it’s usually easier to predict fundamental things like FLOPS per dollar than is it to predict how these technological fundamentals will translate into applications. That might be true, but knowing that your computer of five years hence will have X bytes of storage and perform Y computations per second is a bit abstract for most purposes. What will be the new toys, the new applications, the new businesses? These are the things that impact people.

If predicting specific applications is a bit much to ask for (and if I could I might not want to tell you!), perhaps the next best is to predict the general nature of applications during a period of time. What you might call the “technological theme” of a period.

1980 to about 1995 was the period of the PC. Starting with hobbyists and niche applications and spreading to take over a large chunk of the office. The IBM PC marked the point at which this went mainstream. The defining characteristic was that the communication was typically local, if the machines were networked at all.

1995 to about 2010 was the period of the internet. First emails and basic web pages, search, then ordering online, online banking, music, video, etc. Netscape marked the point at which this went mainstream. The defining characteristic was that the communication was now global but the interface with the world was usually pretty traditional: keyboard, mouse, monitor.

So what’s the next theme? Mobile internet might be an answer, but I think that it’s more general than that. As great as the internet is, most of the important stuff still occurs in that other place called reality. Maybe it’s a new house with a swimming pool, throwing a party with friends or coming down with a serious illness. I think the next theme will be for technology to interface more effectively with the world, being mobile is only one aspect of that. If I had to pin the start of this going mainstream on one thing, it’d say it was the iPhone as that’s when the internet started to show up in the day to day moments of people’s lives as they’re out and about doing things.

Once the location, state and function of many everyday objects starts to spread onto the internet, all sorts of creative efficiencies become possible. Need to pay for the coffee? Just press a button on your phone. Not sure where your car is? Ask your phone to show you the way. Need a cab or a pizza or… just select what you want on some menus on your phone. Prices, special deals, time you’ll need to wait — it will all be there. Need to keep a close eye on your health? Get a small sensor implanted that monitors your blood insulin, oxygenation, pressure, cholesterol, heart rate and so on and wirelessly updates this information to your phone. Should a problem arise, your phone can let medics know where you are and what the problem seems to be.

I don’t expect this to be a sudden change, but rather a gradual absorption of goods, services and various everyday objects into an all pervasive information network. I think this will be a hot area until about 2025. Yeah, it’s going to take a while, not so much for many of these things to become possible, but rather for them to become cheap enough to be economic.

What’s my pick for the theme that comes after that? Well, once you have so much of the economy automated and hooked up together with vast amount of information about anything and everything swirling around, the key leverage point becomes how well you can intelligently process all this in order to control and coordinate things. Thus my pick for the theme from 2025 to 2040 is machine intelligence.

Well, well, another year is drawing to a close. That means it’s once again time to review what has happened and where things are going.

It’s been a very eventful year for me, both personally and on the work front. I keep my personal life off this blog, and as for work… um, significant things are happening but I’m not ready to talk about them yet Thus, I’ll just stick to my general predictions this time around.

First of all, my set of predictions for the teenies. We’re only 1 year in so it’s not surprising that I’m still pretty comfortable with the predictions I’ve made. The only tweak I’ll make is that over the last year I’ve become slightly more confident that we’ll have a decent understanding of how cortex works before the end of the decade. That’s my only update.

My longest running prediction, since 1999, has been the time until roughly human level AGI. It’s been consistent since then, though last year I decided to clarify things a bit and put down an actual distribution and some parameters. Basically, I gave it a log-normal distribution with a mean of 2028, and a mode of 2025. Over the last year computer power has increased as expected, and so it looks like we’re still on target to have supercomputers with 10^18 FLOPS around 2018. In terms of neuroscience and machine learning, I think things are progressing well, maybe a little faster than I’d expected. I was toying with the idea of moving the prediction very slightly closer, but decided to play it safe and keep the prediction unmoved at 2028. With many people thinking I’m too optimistic, showing restraint is perhaps wise I can always move my prediction nearer in a year or two.

One thing I screwed up last year was the 90% credibility region. Going by a log-normal CDF for my predicted mean and mode that David McFadzean did (see bottom of this page) the upper end should be a bit higher at 2045, i.e. at a CDF of 0.95. It seems that I got the lower end right, however, as the CDF is about 0.05 at 2018. With 5% at each end, that gives the 90% interval.

Another great Singularity Summit. I liked the focus on neuroscience this time. I think it will be a major driving force behind AGI over the next 20 years. The talk by Demis Hassabis is the one to look for in this area, once they become available online. My own talk was well received — I had applause during the talk as I put up results, something that I’ve certainly never experienced before. Due to a manic schedule of meetings, deadlines and last minute results, I unfortunately didn’t get to spend much time socialising this year. Hopefully things will be a bit more sane next time around and I’ll be able to catch up with everybody properly. Looking forward to it already.

I don’t know if anybody has thought of a theme for next year’s conference yet, but I’d like to make a suggestion: ethics and AGI safety. The conference has been around for a few years now and had attracted some fairly big names and serious academics. How about a return to the core mission of SIAI? As I think AGI is approaching, we seriously need much deeper and broader thinking on these topics. One other suggestion: while big names draw the crowds, in my opinion they often give the least interesting talks. How about a couple of the most popular and accessible LessWrong posts get selected and their authors present them as Summit talks?

I was notified that copies of my PhD thesis Machine Super Intelligence have been shipping from lulu.com with blank covers. I’ve been in contact with lulu about this and it should now be fixed. Moreover, if you have a received a version with a blank cover you can contact lulu directly and they have told me that they will sort something out. I’m not sure if that means a refund or a new copy with a proper cover.

Sorry about this slip up, and thanks again to all 75 of you who have purchased copies. If there are any further problems please let me know.