vetta project

It’s long been thought that brain oscillations play a key role in short term memory, though there hasn’t been much empirical evidence to support this.  That now seems to have changed with the publication of Phase-dependent neuronal coding of objects in short-term memory by Siegal, Warden and Miller.  There is the paper as well as a high level commentary.  This is quite a step forward for understanding some of the more sophisticated design features of the brain and cognition.

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 decided to christen the next decade the teenies.  Firstly, I’ve still heard no other suggestions; secondly, it’s phonetically consistent with the noughties and the twenties; and thirdly, the name is so downright awfully bad it’s almost quite good.  So the teenies it is.

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.

The start of the Noughties for me was Y2K. It was a non-event, thanks, I might add, to people like me making ourselves mentally unwell fixing endless date issues in crappy database code. Next was the massive dot com crash — our wonderful future of super internet everything was an illusion… except, well, the biggest technological development of the decade was in fact the growth of the internet and all its related technologies. The problem existed in the mind of the market, not in the soundness or long term significance of the underlying technology.

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?

A little over a week ago I felt rather honoured to be reviewing a new submission by a living legend of artificial intelligence, Ray Solomonoff. Sadly the great man passed away just two days later, at the age of 83. That he was still writing papers until the end of his life is a great testament to the passion he had for research.

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.

Am I the only one who, upon hearing the year 2010, imagines some date far off in the future? I think I felt the same way in the weeks before 2000, so I’m sure it will pass. Anyway, another year has gone, indeed another decade, and it’s time for my annual review of predictions. You can find my last annual post here.

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.

Read the rest of this entry »

The third Conference on Artificial General Intelligence will be taking place in Lugano, Switzerland from Friday the 5th to Monday the 8th of March (the picture on the front page of my website is of Lugano). The keynote speaker is the famous reinforcement learning researcher Rich Sutton, and it seems that the inventor of Kolmogorov complexity, Solomonoff induction and universal probability theory, Ray Solomonoff, will also be speaking. The general conference chair is Marcus Hutter, and the local chair is Jürgen Schmidhuber. There will also be Kurzweil Prizes worth $1000 for both the best paper and the best new idea.

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.

Some of you might know about the Lighthill report from 1973 which was deeply critical of progress in AI. This report was the main factor behind cutting the funding of AI research in the UK, and seems to have contributed to the more global cuts around this time known as the “AI winter”. Via Yee Whye Teh I recently came across a BBC debate between James Lighthill and three supporters of AI research: Richard Gregory, John McCarthy and Donald Michie. You can download the televised debate from here, though be warned that it’s 160MB.

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.

The Singularity Summit ‘09 videos are now up and I’ve been asked about the relationship between Itamar Arel’s talk and the neuroscience part of my Halloween talk (1 minute into Part 9, through Part 10, Part 11 and Part 12).

The short answer is: yes, our perspectives are indeed very similar. Essentially, brain-like deep belief networks + brain-like reinforcement learning + powerful computers = AGI quite soon. This similarity isn’t all that surprising: I know a number of people who are thinking along these lines. I actually met Itamar briefly before the conference and mentioned that I did reinforcement learning, but it was only after his talk that I realised how close our perspectives are.

Given the obvious similarities, what are our differences? The main difference seems to be our perspectives on the maturity of deep belief network algorithms. I think these algorithms are quite impressive, but I think that it will take another decade of research before we are ready to even attempt human level AGI, and then something like another half a decade before we will have a realistic chance of getting there. He thinks the technology is more mature and we’ll get there in about half the time.

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.