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.
Continue reading →
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.
Posted in Research Review
|
Tagged AGI
|
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.
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.
On Saturday the 31st of October, I’m going to be the speaker at Extrobritannia here in London. I went along to their last meeting and it was totally packed out, nearly a hundred people I believe. Having both Dr. Aubrey de Grey and Dr. Anders Sandberg speaking explains why!
I’ll be covering topics from my PhD thesis, such as the definition of intelligence, Solomonoff’s model of Induction, Hutter’s AIXI and more recent work such as the Monte Carlo approximation of AIXI by Veness et. al. I’ll also include a few thoughts on how recent discoveries in theoretical neuroscience might help guide work towards AGI.
You can find the event on facebook here, and the announcement of the event on the Extrobritannia list here.
Dressing as a witch, wizard, ghost, etc. is optional.
With the summit still fresh in my mind I thought I’d put a bit of a summary together — or perhaps more a collection of random thoughts and observations. For a less personal overview, read the Reason magazine article.
What I will remember most clearly about this summit was Peter Thiel. Firstly, the pre-summit party at his penthouse apartment. That was a treat: a tiny peak into the world of the ultra-rich. His mix of intelligence, focus and energy was quite something to behold and he left a real impression on me. His talk was also among the most engaging in my opinion. No slides, no fluffy stuff, just a straight delivery of ideas and analysis seemingly off the cuff with no notes. In his talk and comments afterwards, the main thing that stuck in my mind was his concern that the singularity wouldn’t arrive quickly enough. Really?
Continue reading →
I got scammed online. I guess it was just a matter of time, but I’d thought that I was smart enough to avoid such things. It’s a pretty slick scam, here’s how it works:
To visit the US from many countries one must now apply online to something called ESTA in order to obtain a so called “visa waiver”. We’ve been doing this for many years on the plane, recently it’s gone online and now you must to do it online before your travel. Knowing this, I googled for US visa waiver and up came a site for applying for US ESTA visa waivers online. I went through the usual process and at the end had to pay a processing fee. A few hours later I went to the site to see if I had been processed. Then I noticed a typo in the word “New Zewland”, weird. Then I saw a grammatical mistake in their faq, a simple mistake, but a mistake nonetheless. Really strange. Oh oh… was this registration site for real?
So I went back to Google and searched again. The EIGHTH link that google returns when searching for “US visa waiver” is in fact the real US government site that you want. The service is free and I was approved in a few seconds. There is even a warning about the scam sites there: of course if you’re reading their warning you must already be on the right site! Anyway, there is now some shady group with money from me, all my credit card details and even my passport details. Bugger. At least I realised my mistake and made a real application and was accepted. It would have been much worse if it had caused me to miss gaining entry permission to the US and messed up my travels.
Continue reading →
While I was visiting Marcus Hutter at ANU a month or so ago, I got talking to one of his students, Joel Veness, who’s working on making computable approximations to AIXI. Joel has a background in writing Go algorithms so is perhaps perfect for the job. I saw recently that the Monte Carlo AIXI paper describing this work is now available online if you want to check it out.
The basic idea goes as follows. In full AIXI you have an extended Solomonoff predictor to model the environment, and an expecti-max tree to compute the optimal action. In order to scale AIXI down and still have something of roughly the same form, you need to find a tractable way to replace both of these two items. Here’s what they did: in the place of extended Solomonoff induction a version of context tree weighting (CTW) is used. CTW has to be extended for this application similar to the way Hutter had to extend Solomonoff induction to active environments for AIXI. In the place of the expecti-max tree search a Monte Carlo tree search is used, similar to that used in Go playing programs: initial selection within the tree, tree expansion, a so called play-out policy, followed by a backup stage to propagate the new information back into the model. You have to be a bit careful here because as the agent imagines different future observations and actions it has to update its hypothetical beliefs to reflect these in order for its analysis and decision making to be consistent. Then, once this possible future has been evaluated, the effect of this on the agent’s model of the world has to be unwound so that the agent doesn’t, in effect, start confusing its fantasies with its present reality.
Continue reading →
Prototyping mathematical code in Python with the Scipy/Numpy libraries and then switching to Cython for speed often works well, but there are limitations. The main problem that has been bugging me recently is the speed of matrix function calls. What happens is that your Cython code needs to compute, say the outer product of two vectors, and so makes a call to Numpy. At this point everything switches to very slow interpreted Python code which does a few checks etc. before calling into the underlying fast BLAS library that does the actual work. For large matrices the cost of this wrapping code wasn’t a big deal, but for small matrices it can be a huge performance hit.
To solve this problem I’ve created a Cython wrapper for many of the more common BLAS functions. It’s called Tokyo: I often name code after cities and both Tokyo and BLAS/LAPACK were big, fast and very foreign to start with! At the moment Tokyo only wraps the BLAS routines for vectors and general matrices with single and double precision. If you want to add other things such as banded matrices, complex numbers or LAPACK calls: just look at what I’ve already done and add the functions you need. I’ve also added a few extra functions that I find useful when doing matrix calculations. The idea is that Tokyo will eventually encompass all of BLAS and LAPACK.
Continue reading →
It was just a matter of time before somebody started working on something like this. Amusement aside, I’m impressed that Prof. Bringsjord managed to make a magazine as serious as Scientific American with this topic. In order to make a “classically evil” AGI, as opposed to a merely “indifferently evil” AGI, I guess you would face some similar issues to the creation of ethical AGI — formalising the concept of maximal evilness is probably pretty hard.
