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?

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.

UPDATE: after a little reorganisation, a colleague of mine here at the Gatsby Unit, Demis Hassabis will be talking about how systems neuroscience can inform AGI design. I’ll then give a follow up talk on measuring machine intelligence.

“In 2008, the National Academy of Engineering chose as one of its grand challenges to reverse-engineer the human brain. When will this happen? Some are predicting that the first wave of results will arrive within the decade, propelled by rapid advances in both brain science and computer science. This sounds astonishing, but it’s becoming increasingly plausible. So plausible, in fact, that the great race to reverse-engineer the brain is already triggering a dispute over historic firsts. The backdrop for the debate is one of dramatic progress.”

You can find the whole article here. For those who don’t know, Sejnowski is one of the most respected neuroscientists in the world.

It’s certainly been quite a week. Last Friday I headed back to my old home town of Lugano in Switzerland for the AGI 10 conference. I had a great time. In a way it was a bit like the Singularity Summit for me in that the talks were not where the interesting stuff was going on — all the action was taking place in the coffee breaks, over lunch and over dinner. That’s when you get to see what people really think!

Overall, my (biased) summary is that there seemed to be a greater sense of focus among the community than I’ve picked up before, plenty of passion and even a tiny touch of excitement creeping in there. That said, there also seemed to be some significant disconnections in the community, with instances of people being shocked to discover than what they thought was a discredited minority view wasn’t a minority view at all. To me that’s a good sign that the conference is working. Another theme that I picked up was how central Hutter’s AIXI and my work on the universal intelligence measure has become: Marcus and I were being cited in presentations so often that by the last day many of the speakers were simply using our first names. As usual there were plenty of people who disagree with our approach, however it was clear that our work has become a major landmark in the area.

I think the most important missing ingredient of the conference was a lack of discussion about AGI safety issues. From what I recall, during the main conference presentations Mark Waser was the only person to directly take on the topic. During the final workshop session Roko Mijic appeared out of the blue and gave a talk on Yudkowsky style Friendly AI. A show of hands revealed that while half the audience had heard of SIAI, few had heard of CEV. Roko then kicked off his talk by describing the creation of an AGI as likely being worse than the Sicilian mafia combined with grey goo. It’s hard to say what the audience who’d never encountered CEV etc. were thinking at this point, but I’d hazard a guess that they’d written him off as some kind of paranoid crackpot. In any case, what did become clear is that a sizable part of the AGI community is not familiar with FAI thinking.

FHI, Oxford

On Wednesday I gave a talk at the Future of Humanity Institute at the University of Oxford, along with Robin Hanson. My talk was on what neuroscience can teach us about how to build an AGI. Going by all the praise afterwards, it seems that my talk went over very well indeed. I have to say that it was a real pleasure for me too: the topic is a passion of mine and getting to visit Oxford, meet everybody there and stay in one of the colleges was great fun. I often complain when visiting churches (to admire the building and art) that it’s a pity that researchers don’t get such nice buildings. Oxford seems to be one of the exceptions in this regard.

I’m a bit busy for blogging at the moment, but I will be at AGI 2010 this weekend. I’ll also be giving a talk at Oxford next week on how developments in theoretical neuroscience are giving us significant insights into AGI design.

Another interesting paper is Coherence Potentials: Loss-Less All-or-None Network Events in the Cortex by Thiagarajan, Lebedev, Nicolelis and Plenz. They have evidence that above a certain threshold level of activity LFP information is sometimes transmitted across regions of cortex with surprisingly high fidelity.

Another cool recent paper is Rewarded Outcomes Enhance Reactivation of Experience in the Hippocampus by Singer and Frank.  They show that, well, basically what the title says.  This is not surprising, but until now there hasn’t been good evidence to show that this was happening.  If this can be replicated, and some people I know here are considering doing this, it would fill out another part of our understanding of reinforcement driven learning in the brain.

I’m coming across so many interesting neuroscience papers these days I can hardly keep up with reading them, let alone blogging about all of them.  The thing that amazes me is how the architecture of the brain is so logical — it almost looks designed.

This short film, The Third & The Seventh, by Alex Roman, is a great example of cutting edge computer graphics.  The airy elegant style reminds me a bit of Kubrick.  I’m not sure what impressed me the most: the wonderful cinematography, the fact that it’s entirely computer generated, or that one guy did it alone in his spare time — including putting the sound track together.  Be sure to watch it full screen and in high definition.

I’ve been scratching my head about these predictions for the last few days.  By and large, I feel like I’m just predicting the obvious — which is a bit of a let down.  However, when I look at the noughties, while the specific details were not predictable, the general trends were pretty obvious already in 2000.  So perhaps predicting the seemingly obvious is not such a bad idea.  And what seems obvious to me often is anything but obvious to others, indeed many will flatly disagree with my predictions.  So, here goes.  Hopefully these precitions are specific enough that I’ll be able to perform a decent analysis come 2020 to see how well I fared.

First up, things generally will become more energy efficient and we will see more solar power.  But overall not much will change in energy — we’ll keep on using oil and coal and pumping out lots of CO2.

Chinese GDP on a PPP basis will be roughly comparible to that of the US and the EU (i.e. within 25%).  India will be about half their size.  The UK and France won’t be in the top 10 countries anymore, though they will still like to think that they are.  China will become increasingly associated with luxury designer goods.

Computers will become about 50x faster, though I’m a bit nervous about this prediction.  Later in the decade we will have major trouble with silicon chip technology.  We might also see computer power overshoot general consumer demand which would spell serious trouble for the big chip manufactures.  Everything goes very multi-core, even your cell phone.  The graphics card market collapses due to them overshooting consumer demand* and possibly being subsumed by new CPUs.

All things internet and mobile will continue to grow.  Smart “phones” will become fully funcational computers.  You’ll be able to connect your smart phone to a large monitor, keyboard, mouse, projector etc., just like you’d do with a PC today.  It will even become your wallet as you’ll be able to use it to pay for things at the supermarket.  The expanding internet will swallow up most of TV and radio.  High definition video conferencing will become common, making distance collaboration significantly more natural.  High definition matters as it will allow people to have a wider field of view and to more clearly see facial expressions.

Machine learning will grow in importance due to ever increasing quantities of data, computer power, and better algorithms.  It mostly won’t be publically seen, however, much like how it’s heavily used in Google and a few financial and pharmaceutical companies at the moment.

Significant progress will be made in understanding the brain.  We will have a rough high level sketch of how the brain works, and some of its processes we will understand quite well.  We probably still won’t understand cortical function very well, that will take longer.

More groups will start AGI projects, particularly from 2015 onwards.  These groups will become increasingly mainstream, serious and well funded.  This will be driven by faster computers, better machine learning algorithms and a better understanding of the brain’s architecture.  Some of these groups will produce small AGIs that will learn to do some interesting things, but they will be nowhere near human level intelligence.  They will, however, be preparing the way for this.  Concern at the dangers of artificial intelligence will become less fringe but it won’t go mainstream.

In short, I’m predicting a bigger brighter expanded version of the last few years — nothing particularly radical.  I think the real significance of the teenies will be to lay the foundations for more important things to come.

* UPDATE 15/1/2010: I’ve thought a bit about the main criticism of my predictions above, namely that the graphics chip business will collapse.  As a result I’ve decided to soften my prediction.  I’m now thinking that 10 more years probably won’t be enough for it to collapse due to overshooting demand.  Going to 3D creates 2x the computational demand, going to higher resolution can create 5x demand, and better quality and more sophisticated graphics techniques can drive another 10x, maybe a bit more.  Overall this approximately 100x might be enough to drive demand through until the end of the teenies.  If a collapse does come, I think it will more likely be due to somebody like Intel getting aggressive and building cutting edge GPUs into their CPU chips thus making GPUs redundant.

It’s hard to believe that almost everybody was on dial-up internet in 2000, broadband existed, but it was slow and not many had it. The rise of blogging was interesting. To start with many more traditional media sources were freaking out about the idea that some 15 year old from his bedroom could get as much exposure as their latest newspaper article. Now blogging is just another part of the information ecosystem. Wikipedia: the encyclopedia’s went through the classic Ghandi stages of ignore, ridicule, attack and then lose. The iPod completely changed the music business, espeically combined with file sharing. Nobody I knew had DVD‘s before 2000, this was the decade they became big. Same for flatscreen monitors and TVs. I got a digital camera in 2000 when they were just coming out and still cost a fortune. During the Noughties they revolutionised photography. Wifi, nobody I knew had it in 2000, now it’s almost everywhere. Same for internet to the phone. Or text messages, that’s been quite a change. I remember when online banking was seen as strange and a bit risky, now it’s how many people do most of their banking. Google existed, but they really only became huge during this decade. Youtube, another big change in how many people used the internet. Same for Facebook. I still remember how people would react to my enthusiasm for open source software, basically it was seen as a hippy movement that wasn’t something that most serious business people would entertain. That certainly has changed. The iPhone revolutionised the smart phone industry.

In a nutshell, I’d say that the Noughties were all about a massive proliferation of digital communication. In a way the dot coms had roughly the right idea, but it took another decade for the vision to mature.

Outside of technology, 9/11, Bush and Iraq feature strongly in my mind. I think the rise of robotic weapons is something that is currently under appreciated. The rise of China and the way in which global warming went from fringe to mainstream were also significant. For me seeing a black man elected president of the US was one of the most surprising, and thrilling, things to happen in the last ten years. If you’d asked me in 2000 about the probability of that happening, I’d have put it at something like 1%. Was I grossly mis-calibrated, or was Obama really a rare event? I’m still not sure. Then finally we have the financial crisis and the continuing repercussions from that now. I can only presume that the next decade is likely to bring a similar amount of change. It should be an interesting time to be alive…

First question, what will we call the next decade? The “teens”? That seems kind of lame to me! Second question, what do you think are likely to be the changes of the coming decade? Are we in for some big surprises, or just a continuation of current trends?

I’ve been thinking about what I might write about his work. Rather than quoting something pertaining to complexity, prior probability or induction I’ve decided to quote a relatively unknown paper that shows something of his futurist interests. The paper is called “The time scale of artificial intelligence: Reflections on social effects” and was published in 1985.

The last 100 years have seen the introduction of special and general relatively, automobiles, airplanes, quantum mechanics, large rockets and space travel, fission power, fusion bombs, lasers, and large digital computers. Any one of these might take a person years to appreciate and understand. Suppose that they had all been presented to man kind in a single year! This is the magnitude of “future shock” that we can expect from our AI expanded scientific community. In the past, introduction of a new technology into the culture has usually been rather slow, so we had time to develop some understanding of its effect on us, to adjust the technology and culture for an optimum “coming together”. Even with a slow introduction, our use of a new technology has sometimes been very poor.

…We should be able to get our intelligent machines to explain each new technology in a way that is intelligible to man. If this can’t be done, and the new technology is essentially un-understandable to man, then man would be foolish indeed to use it in any way!

However, understanding does not always assure success in dealing with very complex problems. Mankind will continue to have to make decisions under conditions of uncertainty. In the past he has usually chosen his courses of action relatively blindly — controlled more by his own perceived wants and needs than by considerations of the likelihoods of alternative possible futures and their effects upon him.

It’s been an interesting year in which I’ve been exposed to far more neuroscience than ever before. What I’ve learnt, plus other news I’ve absorbed during the year, has helped to clarify my thinking on the future of AI. First, let’s begin with computer power. I recently gave a talk at the Gatsby Unit on the singularity in which I used the following graph showing the estimated LINPACK scores of the fastest computers over the last 50 years.

The first two points beyond 2010 are for some supercomputers that are already partly constructed. In the past performance estimates for these kinds of machines near to their delivery have been reasonably accurate so I’ve put these on the graph. Rather more speculative is the 2019 data point for the first ExaFLOPS machine. IBM is in discussions about how to put this machine together based on the technology used in the 20 PetaFLOPS machine due in a year and a bit. Based on articles on supercomputer sites like top 500, it appears to be a fairly mainstream opinion that this target should be achievable. Nevertheless, 9 years is a while away so I’ve marked it in grey.

First observation: just like the people who told me in 1990 that exponential growth in supercomputer power couldn’t continue for another decade, the people who told me this in 2000 were again completely wrong. Ha ha, told you so! So let me make another prediction: for the next decade this pattern will once again roughly hold, taking us to about 10^18 FLOPS by 2020.

Second observation: I’ve always been a bit sceptical of Kurzweil’s claim that computer power growth was double exponential, but I’m now thinking that there is some evidence for this having spent some time putting together data for this graph and attempting to compensate for changes in measurement etc. in the data. That said, I think it’s unlikely to remain double exponential much longer.

Third observation: it looks like we’re heading towards 10^20 FLOPS before 2030, even if things slow down a bit from 2020 onwards. That’s just plain nuts. Let me try to explain just how nuts: 10^20 is about the number of neurons in all human brains combined. It is also about the estimated number of grains of sand on all the beaches in the world. That’s a truly insane number of calculations in 1 second.

Desktop performance is also continuing this trend. I recently saw that a PC with just two high end graphics cards is around 10^13 FLOPS of SGEMM performance. I also read a paper recently showing that less powerful versions of these cards lead to around 100x performance increases over CPU computation when learning large deep belief networks.

By the way, in case you think the brain is doing weird quantum voodoo: I had a chat to a quantum physicist here at UCL about the recent claims that there is some evidence for this. He’d gone through the papers making these claims with some interest as they touch on topics close to his area of research. His conclusion was that it’s a lot of bull as they make assumptions (not backed up with new evidence) in their analysis that essentially everybody in the field believes to be false, among other problems.

Conclusion: computer power is unlikely to be the issue anymore in terms of AGI being possible. The main question is whether we can find the right algorithms. Of course, with more computer power we have a more powerful tool with which to hunt for the right algorithms and it also allows any algorithms we find to be less efficient. Thus growth in computer power will continue to be an important factor.

Having dealt with computation, now we get to the algorithm side of things. One of the big things influencing me this year has been learning about how much we understand about how the brain works, in particular, how much we know that should be of interest to AGI designers. I won’t get into it all here, but suffice to say that just a brief outline of all this information would be a 20 page journal paper (there is currently a suggestion that I write such a paper next year with some Gatsby Unit neuroscientists, but for the time being I’ve got too many other things to attend to). At a high level what we are seeing in the brain is a fairly sensible looking AGI design. You’ve got hierarchical temporal abstraction formed for perception and action combined with more precise timing motor control, with an underlying system for reinforcement learning. The reinforcement learning system is essentially a type of temporal difference learning though unfortunately at the moment there is evidence in favour of actor-critic, Q-learning and also Sarsa type mechanisms — this picture should clear up in the next year or so. The system contains a long list of features that you might expect to see in a sophisticated reinforcement learner such as pseudo rewards for informative queues, inverse reward computations, uncertainty and environmental change modelling, dual model based and model free modes of operation, things to monitor context, it even seems to have mechanisms that reward the development of conceptual knowledge. When I ask leading experts in the field whether we will understand reinforcement learning in the human brain within ten years, the answer I get back is “yes, in fact we already have a pretty good idea how it works and our knowledge is developing rapidly.”

The really tough nut to crack will be how the cortical system works. There is a lot of effort going into this, but based on what I’ve seen, it’s hard to say just how much real progress is being made. From the experimental neuroscience side of things we will soon have much more detailed wiring information, though this information by itself is not all that enlightening. What would be more useful is to be able to observe the cortex in action and at the moment our ability to do this is limited. Moreover, even if we could, we would still most likely have a major challenge ahead of us to try to come up with a useful conceptual understanding of what is going on. Thus I suspect that for the next 5 years, and probably longer, neuroscientists working on understanding cortex aren’t going to be of much use to AGI efforts. My guess is that sometime in the next 10 years developments in deep belief networks, temporal graphical models, liquid computation models, slow feature analysis etc. will produce sufficiently powerful hierarchical temporal generative models to essentially fill the role of cortex within an AGI. I hope to spend most of next year looking at this so in my next yearly update I should have a clearer picture of how things are progressing in this area.

Right, so my prediction for the last 10 years has been for roughly human level AGI in the year 2025 (though I also predict that sceptics will deny that it’s happened when it does!) This year I’ve tried to come up with something a bit more precise. In doing so what I’ve found is that while my mode is about 2025, my expected value is actually a bit higher at 2028. This is not because I’ve become more pessimistic during the year, rather it’s because this time I’ve tried to quantify my beliefs more systematically and found that the probability I assign between 2030 and 2040 drags the expectation up. Perhaps more useful is my 90% credibility region, which from my current belief distribution comes out at 2018 to 2036. If you’d like to see this graphically, David McFadzean put together a graph of my prediction.

Given that AGI is still a young and relatively unknown part of the wider AI community, it’s great to see such well known researchers putting their names behind this conference. As a member of the program committee I’ve been able to check out some of the submissions so far and I’ve been pleasantly surprised by their quality — indeed, this is what gave me the impetus to write this post! If you’d like to submit something there’s still time: the deadline is the 1st of December.

Now, 36 years later, it’s interesting to think about how the speakers’ various views and predictions have played out. Overall, the analysis by Lighthill felt the most coherent to me, and I’d say that what has since happened largely backs him up, though it can be argued that he helped to cause this outcome. I agree that he slowed AI down a lot, but 36 years is a rather long time and in the types of problems that he was focusing on there hasn’t been much progress. In response the other debaters mostly just pointed to small advances that had occurred and indicated that they felt that more advances were on the way. Lighthill then denied that these advances showed any real progress towards intelligence.

This feels a lot like today: sceptics say that AI has made no progress, optimists point to lots of advances, and sceptics then say that these advances are not what they consider to be real intelligence. I think this points to perhaps the most fundamental problem in the field: if you can’t define intelligence, how do you judge whether progress is being made? It’s as true today as it was then, and it’s why I think that trying to define intelligence is so important. I like the fact that they keep on saying that an intelligent machine should be able to perform well in a “wide range of situations”, because, of course, this is very much the view of intelligence that I have taken.

My Halloween lecture has been uploaded to youtube. The basic outline is:

* what is intelligence?
* Solomonoff induction
* Hutter’s AIXI
* Monte Carlo AIXI (here’s the missing video of it playing pac-man)
* universal intelligence measure
* what neuroscience can teach us about AGI design
* early 2020′s: the Halloween scenario

You can get the slides here. I talked for 2 hours, so it’s broken up into many parts on youtube: Part 1 Part 2 Part 3 Part 4 Part 5 Part 6 Part 7 Part 8 Part 9 Part 10 Part 11 Part 12

Thanks to David Wood at ExtroBritannian for organising this, and all the people who attended — especially those who travelled from other cities and countries, the intelligent questions during my talk, and all the positive feedback I’ve received since. Thanks also to Anders Sandberg for the picture of me speaking that I stole from his flicker stream.