Why didn't the robot cross the road? Because he couldn't see.

In the economist's Babbage podcast from October 23 (their first), there was a discussion on computer vision. They mention that the reason we don't see more robots in our everyday lives is that they really aren't able to see where they are going. While they can be trained to interact with specific types of objects, developing an algorithm for computer vision has its own challenges. They mention that the most successful computer vision algorithms out there begin with edge detection and try to build up to larger objects.

There is an enormous community working on the problem of computer vision, so it seems like the Economist's coverage cannot give a very complete overview of the state of research in this area. What I think they can do is talk about some of the most complicated tasks that computer vision can and cannot accomplish.

Shake, rattle and roll

I heard this beautiful example of an application of mathematics. How fast does a dog need to shake in order to dry off? This is an excellent example where it is both surprising that you can apply mathematics to solve that problem and it is also surprising that the answer ends up being relatively simple that you can summarize it in a short story.

This interview appeared on NPR but there were several news organizations which carried the story which includes video: Discover magazine, Wired Science, Guardian, Physorg.com. You may also be interested in the paper that was posted under the arxiv and the corresponding video (a repository of math and physics papers).

Manufacturing is driven by technical innovation

In a story about manufacturing in the U.S., the Planet Money of NPR put together a remarkable story about why manufacturing in the U.S. succeeds. They mention that nearly 22% of all goods are manufactured in the U.S. This fact seemed quite counterintuitive to (my) conventional wisdom about the direction of the U.S. economy and the idea that manufacturing is moving or has all moved to China. While a lot of manufacturing is leaving the U.S., there is a reason why U.S. companies can compete and keep their manufacturing in the U.S.: innovation!

The story is presented by comparing two different factories, one that is failing because it produces something that can be made elsewhere for a lot less money, and a second factory that is succeeding because they produce a product that requires a large amount of innovation. This second factory makes electronic connectors and they need to come up with a new product nearly every year to stay ahead of the curve. There are engineers who work at this factory and talk to other engineers at other companies to try to solve their technical issues.

One line about this story made me sit up and take notice: "...if your parents can understand the product you make, there is a good chance that someone in China is making it a lot cheaper." The U.S. and Canadian economy is being driven by individuals who are able to come up with the new ideas to solve engineering and technical problems and invent new products. This requires mathematical and scientific skills that distinguish U.S. students. "This idea of the one guy in the back, having ideas, and then that fueling all the profits that allow a manufacturing business to succeed. That really is the model for U.S. manufacturing to succeed. U.S. manufactures that succeed make things, sure, but they really make their money by coming up with new ideas."

Welcome message

I sometimes come across stories that make me think "This is why people need to be more math literate!" I am a mathematician and I sometimes teach classes where I can bring stories like these up for discussion. But if I don't record them somewhere I will forget them.

I am looking for material such as news stories that demonstrate why math is important (not necessarily education related, but not excluding these stories either). Reporters of economics, science and related news often need to avoid a math discussion in their reporting in order to keep the story interesting for a wide audience. I don't think that they are necessarily 'dumbing it down,' but are trying to make it more appealing to people who are not interested in math. I hope to find those stories and highlight why math, science and technical innovation is important to the story and to our society as a whole.

About me: I am a professor at York University in Toronto, Canada. I grew up and went to school in various parts of the U.S. (mostly San Diego and WDC area). I often teach math classes 'for teachers' and students who want to become teachers and the topic of this blog is an extension of some of the things I think about for my classes.