Category: Misc

Pointy

Misc

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?

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Startups

Misc

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.

Building a DIY Street View Car

Misc

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.

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The Universal Robotic Gripper

Misc, New Research, Robot News, Robotics Companies

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:

You need to a flashplayer enabled browser to view this YouTube video

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.

Fun with Robots

Misc

It’s no secret that I’m a huge fan of Willow Garage. So as they get ready to ship their first PR2 robots, here’s a gratuitous video of the pre-release testing:

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This second video is a nice overview of what Willow Garage and their open source robotics program is all about:

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Posts of the Year

Misc

As I make arrangements to close down things in the lab and prepare for a bit of turkey and ham, I thought I’d put up some of my favourite blog posts from the last year:

Finally, for a bit of fun, check out the Austrian Hexapod Dance Competition:

You need to a flashplayer enabled browser to view this YouTube video

Silicon Valley Comes to Oxford

Misc

I’ll be at Silicon Valley Comes to Oxford all day today. This has been an excellent event in previous years, and there is a strong line-up again this year. Anyone interested in visual search technology, do come say hello.

Update: I heard some great talks today and met lots of interesting people during coffee. Chris Sacca’s pitch workshop was especially good. No bullshit. The most valuable thing was the perspective – all those bits of knowledge that are obvious from the inside but very hard to come by from the outside. And of course hearing Elon Musk was just fantastic.

For those people who were interested in our lab’s visual search engine, there’s an online demo here (scroll down to where it says Real-time Demo). The demo is actually of some older results from about a year ago by a colleague of mine. Things have gotten even better since then.

Computer Vision in the Elastic Compute Cloud

Misc, Thoughts

In a datacenter somewhere on the other side of the planet, a rack-mounted computer is busy hunting for patterns in photographs of Oxford.  It is doing this for 10 cents an hour, with more RAM and more horsepower than I can muster on my local machine. This delightful arrangement is made possible by Amazon’s Elastic Compute Cloud.

For the decreasing number of people who haven’t heard of EC2, it’s a pretty simple idea. Via a simple command line interface you can “create” a server running in Amazon’s datacenter. You pick a hardware configuration and OS image, send the request and voilà – about 30 seconds later you get back a response with the IP address of the machine, to which you now have root access and sole use.  You can customize the software environment to your heart’s content and then save the disk image for future use. Of course, now that you can create one instance you can create twenty. Cluster computing on tap.

This is an absolutely fantastic resource for research. I’ve been using it for about six months now, and have very little bad to say about it. Computer vision has an endless appetite for computation. Most groups, including our own, have their own computing cluster but demand for CPU cycles typically spikes around paper deadlines, so having the ability to instantly double or triple the size of your cluster is very nice indeed.

Amazon also have some hi-spec machines available. I recently ran into trouble where I needed about 10GB of RAM for a large learning job. Our cluster is 32-bit, so 4GB RAM is the limit. What might have been a serious headache was solved with a few hours and $10 on Amazon EC2.

The one limitation I’ve found is that disk access on EC2 is a shared resource, so bandwidth to disk tends to be about 10MB/s, as opposed to say 70MB/sec on a local SATA hard drive. Disk bandwidth tends to be a major factor in running time for very big out-of-core learning jobs. Happily, Amazon very recently released a new service called Elastic Block Store which offers dedicated disks, though the pricing is a little hard to figure out.

I should mention that for UK academics there is a free service called National Grid, though personally I’d rather work with Amazon.

Frankly, the possibilities opened up by EC2 just blow my mind. Every coder in a garage now potentially has access to Google-level computation. For tech startups this is a dream. More traditional companies are playing too. People have been talking about this idea for a long time, but it’s finally here, and it rocks!

Update: Amazon are keen to help their scientific users. Great!

“But I’m Not Lost!” – Adoption Challenges for Visual Search

Misc, mobile visual search, Robot News

I’m still rather excited about yesterday’s kooaba launch. I’ve been thinking about how long this technology will take to break into the mainstream, and it strikes me that getting people to adopt it is going to take some work.

When people first started using the internet, the idea of search engines didn’t need much promotion. People were very clearly lost, and needed some tool to find the interesting content. Adopting search engines was reactive, rather than active.

Visual search is not like that. If kooaba or others do succeed in building a tool that lets you snap a picture of any object or scene and get information, well, people may completely ignore it. They’re not lost – visual search is a useful extra, not a basic necessity. The technology may never reach usage levels seen by search engines. That said, it’s clearly very useful, and I can see it getting mass adoption. It’ll just need education and promotion. Shazam is great example of a non-essential search engine that’s very useful and massively popular.

So, promotion, and lots of it. What’s the best way? Well, most of the different mobile visual search startups are currently running trail campaigns involving competitions and magazine ads (for example this SnapTell campaign).  Revenue for the startups, plus free public education on how to use visual search. Not a bad deal, easy to see why all the companies are doing it. The only problem is that it may get the public thinking that visual search is only about cheap promotions, not useful for anything real. That would be terrible for long-term usage. I rather prefer kooaba’s demo based on movie posters – it reinforces a real use case, plus it’s got some potential for revenues too.

A Visual Search Engine for iPhone

Misc, mobile visual search, Robot News

Today kooaba released their iPhone client. It’s a visual search engine – you take a picture of something, and get search results. The YouTube clip below shows it in action.  Since this is the kind of thing I work on all day long, I’ve got a strong professional interest. Haven’t had a chance to actually try it yet, but I’ll post an update once I can nab a friend with an iPhone this afternoon to give it a test run.

You need to a flashplayer enabled browser to view this YouTube video

At the moment it only recognises movie posters. Basically it’s current form is more of a technology demo than something really useful. Plans are to expand to recognise other things like books, DVDs, etc. I think there’s huge potential for this stuff. Snap a movie poster, see the trailer or get the soundtrack. Snap a book cover, see the reviews on Amazon. Snap an ad in a magazine, buy the product. Snap a resturant, get reviews. Most of the real world becomes clickable. Everything is a link.

The technology is very scalable – The internals use an inverted index just like normal text search engines. In my own research I’m working with hundreds of thousands of images right now. It’s probably going to be possible to index a sizeable fraction of all the objects in the world –  literally take a picture of anything and get search results. The technology is certainly fast enough, though how the recognition rate will hold up with such large databases is currently unknown.

My only question is – where’s the buzz, and why has it taken them so long?

Update: I gave the app a spin today on a friend’s iPhone, and it basically works as advertised. It was rather slow though – maybe 5 seconds per search. I’m not sure if this was a network issue (though the iPhone had a WiFi connection), or maybe kooaba got more traffic today than they were expecting. The core algorithm is fast – easily less than 0.2 seconds (and even faster with the latest GPU-based feature detection).  I am sure the speed issue will be fixed soon. Recognition seemed fine, my friend’s first choice of movie was located no problem. A little internet sleuthing shows that they currently have 5363 movie posters in their database. Recognition shouldn’t be an issue until the database gets much larger.