This post is an overview of all the companies developing mobile image recognition search engines. For people to whom that means nothing, see these posts. My robotics readers will have to forgive me for my sudden obsession with this topic, but I work on visual search engines for my PhD, and I’m giving serious thought to joining this industry in one form or another in the next six months, so I’m rather excited to see commercial applications taking off. More
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.
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!
In common with half of YouTube, I was mesmerized by the BigDog videos from Boston Dynamics earlier in the year, though I couldn’t say much about how the robot worked. For everyone hungry for some more technical details, check out the talk by Marc Raibert at Carnegie Mellon’s Field Robotics 25 event. There’s some interesting discussion of the design of the system, where’s it’s headed, and more great video.
There are a bunch of other worthwhile talks from the event. I particularly enjoyed Hugh Durrant-Whyte’s description of building a fully automated container terminal “without a graduate student in 1000km”.