Listening for the pitter-patter of tiny metal feet.
Congratulations to everyone at Willow Garage for reaching Milestone 2 in the development of the PR2 robot. 26.2 miles of autonomous indoor navigation, including opening eight doors and plugging in to nine power sockets. We’ve been watching the video in the lab with serious robot envy. Very cool!
Writing in this morning’s FT, Nassim Nicholas Taleb proposes Ten principles for a Black Swan-proof world:
1. What is fragile should break early while it is still small. Nothing should ever become too big to fail. Evolution in economic life helps those with the maximum amount of hidden risks — and hence the most fragile — become the biggest.
…
Then we will see an economic life closer to our biological environment: smaller companies, richer ecology, no leverage.
A sensible plan, but unfortunately Mr. Taleb’s faith in biology is misplaced.
Why the Dinosaurs got so Large
19th-century palaeontologist Edward Drinker Cope noticed that animal lineages tend to get bigger over evolutionary time, starting out small and leaving ever bigger descendants. This process came to be known as Cope’s rule.Getting bigger has evolutionary advantages, explains David Hone, an
expert on Cope’s rule at the Institute of Vertebrate Paleontology and
Paleoanthropology in Beijing, China. “You are harder to predate and it
is easier for you to fight off competitors for food or for mates.” But
eventually it catches up with you. “We also know that big animals are
generally more vulnerable to extinction,” he says. Larger animals eat
more and breed more slowly than smaller ones, so their problems are
greater when times are tough and food is scarce. “Many of the very
large mammals, such as Paraceratherium, had a short tenure in the
fossil record, while smaller species often tend to be more
persistent,” says mammal palaeobiologist Christine Janis of Brown
University in Providence, Rhode Island. So on one hand natural
selection encourages animals to grow larger, but on the other it
eventually punishes them for doing so. This equilibrium between
opposing forces has prevented most land animals from exceeding about 10 tonnes.
Dinosaurs had skewed incentives and took on too much tail risk! If even evolution falls into this trap, God help the bank regulators…
Today’s edition of the New Scientist news feed includes an article about my PhD research. How nice! They called the article ‘Chaos filter stops robots getting lost’. This is kind of a bizarre title – ‘chaos filter’ seems to be a term of their own invention :). Still, they mostly got things mostly right. I guess that’s journalism!
Whatever about the strange terminology, it’s great to see the research getting out there. It’s also nice to see the feedback from Robert Sim, who made a rather impressive vision-only robotic system with full autonomy a few years ago, still quite a rare accomplishment.
For anyone interested in the details of the system, have a look at my publications page. New Scientist’s description more or less resembles how our system works, but many of the specifics are a little wide of the mark. In particular, we’re not doing hierarchical clustering of visual words as the article describes – instead we learn a Bayesian network that captures the visual word co-occurrence statistics. This achieves a similar effect in that we implicitly learn about objects in the world, but with none of the hard decisions and awkward parameter tuning involved in clustering.
I was at a lunch talk today by Nick Bostrom, of Oxford’s Future of Humanity Institute. The institute has an unusual mandate to consider the really big picture: human extinction risks, truly disruptive technologies such as cognitive enhancement, life extension and brain emulation, and other issues too large for most people to take seriously. It was a pleasure to hear someone thinking clearly and precisely, in the manner of a good philosopher, about topics that are usually the preserve of crackpots. Prof Bostrom’s website is a treasure trove of papers. An atypical but perhaps robot-relevant example is the Whole Brain Emulation Roadmap.
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:
Just came across this new video of the Willow Garage PR2 robot. They’re making rapid progress. When they reach their goal of distributing these platforms to research groups around the world, it will be a good day for robotics. One neat package that comes out of the box up many different near-state-of-the-art capabilities. Right now every research group is independently re-creating platforms from scratch, and it’s a huge obstacle to progress.
If you haven’t heard of Willow Garage, I have an overview here.
Update: Another new video, celebrating two successive days of autonomous runs.
When it rains, it pours. Amazon is joining the visual search party, but with a twist.
Today Amazon released an iPhone app with a feature called “Amazon Remembers”. You take a picture of some product you’re interested in and the app uploads it to Amazon. You can then revisit your list later from a browser, e.g. to buy the item. The interesting bit is that Amazon attempts to generate a link to the product page based on your image. Examples here. This isn’t instant, and may take anywhere from five minutes to 24 hours.
Fascinatingly, the back end is apparently not computer vision based. It uses Mechanical Turk, where Amazon is paying people $0.10 per image to generate the links. See here. This is quite amazing to me. I have no idea if Mechanical Turk is deep enough to support this app if it truly becomes popular, but I suppose in that case Amazon could set up a dedicated call-centre type operation to handle the image recognition.
So, visual search companies now have a very direct measure of the value of a correct image search. Judging by the current set of images on Mechanical Turk, a fully automated solution is not possible. However, a hybrid system where some easy categories like books are recognised automatically and harder cases are farmed out to Mechanical Turk would clearly translate into significantly lower costs.
(Of course it’s possible Amazon are already doing this, though I did see several books in the Mechanical Turk requests).
It seems I missed something fairly major in my round up of mobile visual search companies last week. Nokia have a serious project in the area called Point and Find. You can see a demo here. From MSearchGroove:
“Nokia is committed to owning the visual search space and has committed a staff of 30 to build the business and further develop the technology. The business area has the buy-in of Nokia senior execs and “quite large†funding from the company“
The technology comes from an acquisition of a valley startup called Pixto just over a year ago. Nokia’s service is apparently due for launch soon, initially recognising movie posters only.
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.