Mathing with Theo

While I was in Africa this summer I worked out how I could aim several mirrors at my old water heater.  I had already stripped off half the casing and foam insulation on the water and heater and painted it black knowing that I wanted to acheive some sort of solar water heating but had never really worked out how to position the mirrors to make it really hot!

So, being a mathematician I set about working out the requisite formulas which would help me to space out the mirrors and determine at which angle I should keep them.

It turns out that instead of tracking the sun it’s actually quite simple to place the water heater perpendicular to the sun, and array the mirrors facing north.  Aligning the mirrors in the center with the long edge running perpendicular to the sun means that at midday there will be an additional 8 sq. ft of solar power to heat the water that will eventually be contained in the heater.

In the first picture we can also see that there are 2 additional mirrors placed at 45 degrees in either direction to the middle mirrors.  These mirrors are testing my hypothesis that at about 11 am (a little past quarter day) and 3pm there will be some additional sunlight that could be caught.

Once I get a little more funding for this project I’ll add a few more mirrors (4-10) more and also start work on a smaller concentrator for my home office.

Later I’ll connect up some actual water pipes, and circulate the hot water into the house for our new radiant heating system that I’m hoping to install this fall.

If you’d like to code for your own project please contact me.

I recently figured out how to do city level Geo targetting with OpenX ad server and spent many hours trying to find documentation on this feature.

I’ve summarized the process below.

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I’m currently doing some work on Mutli-Agent Algorithms for a company in the UK and I thought it might make a nice topic for a post.

A multi-agent algorithm is an algorithm that attempts to simulate a complex phenomenon and thereby solve or make useful predictions and strategies for the phenomenon using millions of simulated autonomous agents.

For example, consider the stock market. The price of each individual stock may seem quite random when viewed on a large scale. However, upon further inspection we can see that the current price of a stock is the result of thousands of interactions over time between a set of traders. Each trader brings his knowledge, his expectations and his goals to the trading floor causing small shifts in price.

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I used to work at DoubleClick… now Google… as a Statistical Analyst. In my short career there I learned that internet ads are very rarely clicked on. In fact a rate of 1 in 1000 clicks/deliveries would be considered pretty decent.

Lately I’ve noticed that life seems to be a bit like an Internet Advertisement.

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Back in high school we learned that any right triangle inscribed inside of a circle has vertices on a point on the circumference and two points on the opposite ends of the diameter (if the right angle vertex is on the circumference).

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One of the hard parts about life is choosing one option at the expense of another future opportunity

For instance. When looking for a job it’s rare that you get three competing offers at one time. It’s more likely that you can get one interview/offer this week, and a different possibly better offer next week. If we take the current possibly sub-optimal offer we forgo next weeks potential offer for the security of an immediate pay check. However if we don’t take today’s offer and wait for next week we may both have greater financial security and greater job satisfaction… or we may have nothing at all.

In Artificial Intelligence the same applies, every decision made is a decision that commits the program/robot to a course of action which forgoes future options.

The questions to ask are: How do I design a program/robot to make decisions when faced with choices? How do I allow the program to learn from mistakes and make generalizing principles to help guide it when faced with a decision next time?

Of course we might also ask ourselves these very same questions: How do I make decisions? What are my generalizing and guiding principles?

Mathematicians and Statisticians are often asked, and usually in a highly dubious tone, “so…. what are you going to do with that advanced stats/math degree?”. Sometimes it’s followed with, “teach?”

So many people must have asked this question that the department of Statistics at Colombia University put on a whole conference devoted to the subject. The conference included panelists from both Academia and Industry.

A Postdoc from the department, commented on the conference.

I thought the following was a good observation:

There is not as much flexibility in industry as with academia (research must be in the companies interests), however, the compensation is usually much better.

All industry panelists agreed that statisticians must be excited by data.

It’s true, to be a good mathematical/statistical researcher you must be excited by data!

Since greentheo.scroggles.com was heading in a more green/political/philosophical direction I thought I would start another blog mostly for sharing code snippets, discussing Artificial Intelligence, Machine Learning and other things Math related.

You might say that this is an Applied Math blog. (Yikes!)

So to kick it off here is an interesting article I found today about a company who provides a handwriting analysis service for potential employers.

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I predict that in the future there will be few powerful personal computers. While the personal computer slows its growth in terms of performance, the internet will become increasingly ubiquitous. We will eventually transfer almost all of our computing needs to giant serving and computing farms. Perhaps we will even logon to a server via the internet to access our software instead of loading it into our personal computers.

Maybe in the end it is more efficient to centralize. And with the right security measures it could be reasonably safe. But maybe it’s a human tendency to put power in the hands of the few.

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Ever wanted to know what portions of your webpage or picture attract the most attention from the human eye? You have two options…

1.) Run a very expensive eye tracking hardware/software test with multiple users.

2.) Go to Feng-Gui and get a simulated eye tracking analysis done for free in a matter of seconds. The technology replicates and simulates how human eyes tend to look at things. This site will even give you a heat map (red is the most intensely looked at portion) free to dowload!

Here’s what the latest version of Scroggles! looks like to the average person. Do you find the result correct?