How to win a fight with God

The breeze whispers of a transformation, a time of great trial and tribulation for mankind. We face an adversary whose complexity is almost unimaginable. Its vast computing power makes the human brain look like a toy. Worse still, it wields against us a powerful nanotechnology, crafting autonomous machines out of thin air. Already, more than one percent of the Earth is under its sway. I’m talking of course about the mighty Amazon rainforest.

But humans have long ago learned to live with our ancient enemies, the plants and the animals. Perhaps we can take some lessons for how to navigate our new friends, the AIs, who may be arriving any day now.

The rainforest is not actively trying to kill us, at least not most of the time. It is locked into a fierce struggle with itself, deploying its vast resources in internal competition. As a side effect, it produces large amounts of oxygen, food and other ecosystem services that are of great benefit to humanity. But the rainforest doesn’t like us, doesn’t care about us, mostly doesn’t even notice us. It exists in a private hell of hyper-competition, honed to a sharp point by the lathe of selection turning for a billion years.

So here is one model for AI safety. Don’t hope for direct control. Don’t dream of singletons. Instead, design a game that locks the AIs in a competition we don’t care about, orthogonal to the human world, perhaps with a few ecosystem services thrown our way as a side-effect. We will only collect scraps from the table of the Gods. But while the Gods are busy with their own games, we can get on with ours.

I think of the Irish proverb: What’s as big as half the Moon? Answer: The other half. Good advice for fighting with God.

Written with assistance from ChatGPT :-)

Google+ Archive

Google is shutting down Google+ in the next few days. I’m archiving my G+ stream here for posterity.

Over 7 years I posted exactly 1,001 times to Google+. I know it was never an especially popular social network, but somewhat to my surprise I found that I enjoyed it a lot. There was a strong set of people on G+ interested in deep learning, robotics and related topics, at least for a period of several years. The unlimited post length meant your could have meaningful conversations in a way you couldn’t on Twitter. For me Google+ was a thoughtful place, with high quality people, interesting content and meaningful discussion. The fact that it was a small, ignored community mostly interested in technical topics provided the conditions for that.

I have now reluctantly moved to Twitter, and I also have this blog for occasional long form content. Twitter is (alas) not much of a substitute for G+. No matter how carefully I curate who I follow, my Twitter stream is invariably full of political anger and culture wars. I am as susceptible to this as anyone else, and a Twitter session is a pretty sure way to make me feel angry and unhappy. I very much wish there was a way to turn down the emotion in my Twitter feed. Unfortunately I am yet to find that setting. Nevertheless, I do still get some useful technical and professional news from Twitter, so I will likely proceed with it and accept the unhappiness tax that it imposes.

So RIP G+, you were not much loved by most, but I will miss you.


Two and a half years ago I left Google and set out to build a new kind of search engine. This may sound a little crazy, but all the best things are like that :-)

We’ve been avoiding the tech press and trying to build things quietly, but this week we’re launched our user-facing app. I’m really proud of what the team has built, so it’s exciting to finally be able to say a bit more about it.

The problem we’ve been working on is finding specific items locally. For example, a light bulb just broke and it’s a strange fitting, where’s the nearest place you can get a new one?  Or you’re half way through a recipe and realise you’re missing an ingredient – where do you get it?



Imagine you’re a road engineer and you’re designing an access road for a new town. The town will soon be built in a previously uninhabited area. You’ve managing the construction project, but unfortunately no one can tell you what the population of the town will be.

Taking your job seriously, you sit down to design the best road that you can build. You settle on constructing a seven lane highway with regular flyovers to minimize traffic. The road will be fully lit with a state-of-the-art LED lighting system. You add crash barriers and regularly spaced emergency telephones. After much consideration you decide to also include a rest area with parking and toilets. This involves designing a self-contained water and sewerage system, but it’s obviously worth it.

With three months to go until launch day, you discover problems with road drainage. After the panic subsides, the construction team agrees to work around the clock to refit a completely new system for surface water management. By a minor miracle, the work is completed on time.

Opening day finally arrives and the excitement is intense. Everyone agrees the finished product is an engineering marvel. The new town will have the best road in the world.

Unfortunately, it turns out that the town is a remote settlement with a population of 57. The road is mainly used by an old man and a donkey.

The next year, you are again given a road construction project for another new town. Having learned your lesson, you build a modest single lane road. It’s well constructed but nothing special.

Opening day comes again, and it’s revealed that this time the “town” is in fact a major city with a population of 14 million. There are 50 mile tailbacks for six years before a larger road can be built. Your face appears on wanted posters throughout the nation, and you flee the country in disgrace.

Twitter, I forgive you the Fail Whale. And I hope to always walk the middle *ahem* road.

Epiphenomenalism for Computer Scientists

It’s hard to work on robotics or machine learning and not occasionally think about consciousness.  However, it’s quite easy not to think about it properly! I recently concluded that everything I used to believe on this subject is wrong. So I wanted to write a quick post explaining why.

For a long time, I subscribed to a view on consciousness called “epiphenomenalism”. It just seemed obvious, even necessary. I suspect a lot of computer scientists may share this view. However, I recently had a chance to think a bit more carefully about it, and came upon problems which I now see as fatal. Below I explain briefly what epiphenomenalism is, why it is so appealing to computer scientists, and what convinced me it cannot be right. Everything here is old news in philosophy, but might be interesting for someone coming to the issue from a computer scientist perspective. More

Building a DIY Street View Car

A little blast from the past here. Several years ago I built something very like a Google Street View car to gather data for my PhD thesis. At the time I wrote up a blog post about the experience, as a guide for anyone else who might want to build such a thing. But I never quite finished it. Upgrading WordPress today, I came across this old post sitting in my drafts folder from years ago, and decided to rescue it. So here it is. The making of a DIY StreetView car.


Will the robots take our jobs?

This post is about robots and the economy, but takes some detours first. Bear with me.

Robert Gordon and the End of Growth

There has been a very interesting discussion going on recently, prompted by an article by economist Robert Gordon of Northwestern University. Gordon’s article (“Is US economic growth over?”) makes the case that long-term US economic growth on the scale of the last century was due to one-time events and has run its course, with future growth prospects being much lower. He attributes the growth of the past few centuries to three distinct industrial revolutions. The first, beginning 1750-1830, was due to steam power and railroads. The second, 1870-1900, was due to electrification, internal combustion engines, running water and petroleum. The third, beginning around 1960, was due to the computer and the internet. Gordon makes the case that the second industrial revolution, from 1870-1900, was by far the most important, and that computers and the internet have had far smaller impacts on GDP. Combined with demographic headwinds, he sees much lower rates of growth in the next century.

Martin Wolf summarizes the pessimist’s case succinctly:

Unlimited growth is a heroic assumption. For most of history, next to no measurable growth in output per person occurred. What growth did occur came from rising population. Then, in the middle of the 18th century, something began to stir. Output per head in the world’s most productive economies — the UK until around 1900 and the US, thereafter — began to accelerate. Growth in productivity reached a peak in the two and a half decades after World War II. Thereafter growth decelerated again, despite an upward blip between 1996 and 2004. In 2011 — according to the Conference Board’s database — US output per hour was a third lower than it would have been if the 1950-72 trend had continued (see charts). Prof Gordon goes further. He argues that productivity growth might continue to decelerate over the next century, reaching negligible levels.

Robots to the rescue?

What interests me most is the responses that Gordon’s article has received. His position is very interesting, but likely wrong in one massive aspect.


Highlights of Robotics: Science and Systems 2012

I spent last week at RSS 2012 in Sydney. Here are a few of the papers that caught my attention. This year I went to more talks on manipulation, but I still find myself picking a SLAM paper as my favourite :)

Robust Estimators for SLAM

For me, the most interesting work at the conference were two related papers, one from Ed Olson and another from Niko Sünderhauf.

Figure 1 from Olson and Agarwal 2012

Large Scale Deep Learning at Google

[This blog has been dormant a long time, since I post most of this kind of content on Google+ these days. I’ll still cross-post the occasional longer piece here.]

There’s an important paper at ICML this week, showing results from a Google X project which scaled up deep learning to 16,000 cores. Just by throwing more computation at the problem, things moved substantially beyond the prior state of the art.

Building High-level Features Using Large Scale Unsupervised Learning
Quoc V. Le, Marc’Aurelio Ranzato, Rajat Monga, Matthieu Devin, Kai Chen, Greg S. Corrado, Jeffrey Dean, and Andrew Y. Ng

Learned feature detectors

I think this is a really important paper, so let me give some background. Since starting at Google, there have been five projects which I’ve seen internally that blew me away with their obvious potential to truly change the world. A few of those are now public: (1) Self-driving cars, (2) Project Glass, (3) Knowledge Graph. Number four is this paper. It might grab fewer headlines than the first three, but in the long term this is by far the most important.

What this paper demonstrates (or at least convinced me) is that raw computation is now the key limiting factor for machine learning. That is a huge. For the last twenty years or more, it was not really the case. The field was dominated by SVMs and Boosting. Progress didn’t really have much to do with Moore’s Law. If machines got a million times faster, it wasn’t really clear that we had any good way to use the extra computation. There certainly wasn’t a viable path to animal-level perceptual abilities. Now I would like to stick my neck out and say that I think that position has changed. I think we now have a research program that has a meaningful chance of arriving at learning abilities comparable to biological systems.
That doesn’t mean that if someone gifted us a datacenter from 2050 we could solve machine learning immediately. There is a lot of algorithmic progress still to be made [1]. Unlike SVMs, the training of these systems still owes a lot to black magic. There are saturation issues that I think nobody has really figured out yet, to name one of a hundred problems [2]. But, the way seems navigable. I’ve been optimistic about this research ever since I saw Geoff Hinton’s talk on RBMs back in 2007, but it was a cautious optimism back then. Now that Google has shown you can scale the methods up by orders of magnitude and get corresponding performance improvements, my level of confidence has gone up several notches.

Returning to the present, here are a few cool aspects of the current paper:

1) Without supervision, the model learns complex, generalizable features (see the human face and cat face detectors below). To say that again, there is no labelled training data. Nobody told the model to detect faces. The face feature simply emerges naturally as a compact way for the network to reconstruct its inputs. We’ve seen that before for low level features like edges and edge junctions, but to see it for high level concepts is a result.

2) “Control experiments show that this feature detector is robust not only to translation but also to scaling and out-of-plane rotation.”

This is important too. It’s been known for a while that most current approaches used in computer vision don’t really learn any meaningful invariance to transformations which are not explicitly hand-designed into the features. e.g. See this paper from the DiCarlo lab: Comparing State-of-the-Art Visual Features on Invariant Object Recognition Tasks

3) “Starting with these learned features, we trained our network to obtain 15.8% accuracy in recognizing 20,000 object categories from ImageNet, a leap of 70% relative improvement over the previous state-of-the-art.”

It works! Well – it still fails 85% of the time (on a very very hard test set), but it’s big progress. These techniques apply to everything from speech recognition to language modeling. Exciting times.


[1]: I saw a talk by Geoff Hinton just yesterday which contained a big advance which he called “dropout”. No paper available yet, but check his website in the next few days. Lots happening right now.

[2] Or the embarrassing fact that models that achieve close to record performance on MNIST totally fail on 1 – MNIST (i.e. just invert the colours and the model fails to learn). Another example is the structural parameters (how many layers, how wide) which are still picked more or less arbitrarily. The brain is not an amorphous porridge, in some places the structure is important and the details will take years for us to figure out.

The Universal Robotic Gripper

I just saw a video of device that consists of nothing more than a rubber balloon, some coffee grounds and a pump. I’m pretty sure it’s going to change robotics forever. Have a look:

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It’s a wonderful design. It’s cheap to make. You don’t need to position it precisely. You need only minimal knowledge of the object you’re picking up. Robotic grasping has always been too hard to be really practical in the wild. Now a whole class of objects just got relatively easy.

Clearly, the design has it’s limitations. It’s not going to allow for turning the pages of a book, making a cheese sandwich, tying a dasiy chain, etc. But for relatively straightforward manipulation of rigid objects, it’s a beautiful solution. This one little idea could help start a whole industry.

The research was a collaboration between Chicago, Cornell and iRobot, with funding from DARPA. It made the cover of PNAS this month. The research page is here.