November 12, 2014

No wheels and an electric motor

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We now have cars with electric motors, and we also have bikes with electric motors, but what about the boats? I don't know if this thing can be defined as a boat, but at least it behaves like one:

The "boat" you see in the image is a Q2A Electric manufactured by the Slovenian company Quadrofoil. Here are some basic data:
  • Price: From 15,000 Euro ($19,000)
  • Range: 50 km (31 miles)
  • Speed: 30 km/h
  • Seats: 2
There's one limited edition, but this data is from the standard edition. The difference between the two versions is the speed and range and thus the price.

According to the manufacturer, the Quadrofoil (the company name is also the name of the boat) is unsinkable because the hull is hollow. And it also comes with a built-in anti-collision system that absorbs the collision forces. When the Quadrofoil is not moving, the depth of the water you are operating in has to be at least 1 m. But when it's moving, the depth can be much shallower as the draft of the boat is as little as 0.15 m. 

Looks interesting? You have to wait until Q2 2015 before you can buy one.

November 9, 2014

Why you are helping Google each time you sign in

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I've read the book Big Data - A Revolution That Will Transform How We Live, Work, and Think. The book promises to give the reader a brief introduction to the world of big data. While some say big data will revolutionize the world and transform it in ways we've never thought of before, others say that big data is just another bubble. I guess only time will tell us the answer.

The basic idea behind big data is that if we analyze gigantic amounts of data we will discover what we otherwise couldn't have discovered. The book is filled with examples from the world of big data, ranging from how to discover flue trends with the help of what we search for in Google to new alarm systems that analyze the seat-position of a car driver and sends a signal to the police if the car doesn't recognize the driver's position. It also discusses what might be possible in the future. Will we be able to jail criminals before they committed a crime if the data said they would commit the crime in a near future?

The company the book talks the most about is Google - a company that has access to gigantic amounts of data. Each time we search for something in their search engine, they save the data and are improving their systems with the help of that data. But we are also helping Google in other ways each time we sign in. As you probably know, Google has been driving around in their cars and photographed heaven and earth. The result is Street View.  But they have a problem. The images they took are not accurate enough so they can determine the street number of all buildings. That's where you and me are expected to help them each time we sign in somewhere using their system. 

At the height of the tech bubble, in 2000, a guy called Luis von Ahn was tired of automatic computers who wrote "spam" all over the Internet. He came up with the idea to force you and me to write numbers and letters that are difficult for a computer to read automatically. He called the system Captcha. But what if he could use you and me to also do some useful work and not just write random numbers and text each time we sign in. Google had the same idea and acquired the technology from Luis von Ahn's company in 2009. 

So in 2009, we were all hired by Google to translate text. Google has scanned almost all books in the world and they are now searchable at Google Books. But to make them searchable by you and me, Google had to translate the scanned images to text. Their computers could translate some text, but not all, and it would be too expensive to hire translators, so Google began to send out images to you and me each time we signed in. If maybe 5 of us translated the image of text the same way, Google took the translation and added it to the book they scanned.

I believe Google has run out of books to translate, so they now need help to translate street numbers. That's why you and me now have to write a number each time we sign in:

November 6, 2014

Top words in top selling books

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Last year I wrote an article showing the Opening paragraph of top selling books. A while ago I read an article that talked about the top words in top selling books. I've lost that article so I decided to use my data mining skills and make a similar research on my own. The books I've included are a few of the top selling books from the last article:
  • A tale of two cities
  • And then there were none
  • The Hobbit
But since all of the top selling books are not available in the correct format needed to analyze the words, I've also included:

This is the result:

Alice Two cities Hobbit And then Bible Engineer
the the the the the the
and and and a and to
to of of of of a
a to to to to and
she a a and that of
of in he he in in
it his in was he was
said it was said shall he
it that they I unto Elon
in I it it for that

But, as you can see, most of the words in the results above are so-called stop words, so I've also tried to see what happens if these words are removed. This is the result:

Alice Two cities Hobbit And then Bible Engineer
said said said said shall elon
alice mr bilbo lombard unto said
little lorry dwarves blore lord car
know man came vera thou tesla
like defarge like armstrong thy like
went little long rogers god company
thought time thorin mr said electric
queen hand time know ye rocket
time miss great went thee space
did know did little man didnt

If I recall that old article I read on the top words in top selling books, I think the words were "and" and maybe "but." Anyway, it was interesting to see that the words in my own book matched almost all of the words in the other top selling books. But I think we will need more books to come up with any real conclusions.

Bonus!
As a bonus, I've also calculated the average words per sentence in each book. This is the result:
  • Alice: 27
  • Two cities: 18
  • Hobbit: 20
  • And then: 9
  • Bible: 28
  • Engineer: 20
...and average length per word:
  • Alice: 4.09
  • Two cities: 4.33
  • Hobbit: 4.17
  • And then: 4.21
  • Bible: 3.94
  • Engineer: 4.46

October 30, 2014

Why you need a better recommendation system

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I've just finished the first version of a new project currently called August Shield 743. The idea behind the name is simple: it was generated by Google when I created the API-key I needed to complete the project. But as the name sounds like a secret Navy Seals operation I decided to keep it.

August Shield 743 is a blog-article-recommendation-system and it took about a week to complete the first live version. The idea behind a recommendation system is to recommend x based on a,b,c,... where x could be a movie, a book, or as in this case a blog article. If you have visited Amazon and at at the bottom of the page saw the heading "Customers Who Bought This Item Also Bought," you will know what I'm talking about. For example, if you want to buy the Steve Jobs book, you will notice that other people also bought the book Einstein and Benjamin Franklin. Amazon is finding these suggestions with the help of a recommendation system. 

Last year I read the book The Everything Store, which is a biography on Jeff Bezos, who founded Amazon. The section from the book I remember the most is the section where the author talks about the first version of Amazon's recommendation system. It goes like this:
Eric Benson took about two weeks to construct a preliminary version that grouped together customers who had similar purchasing histories and then found books that appealed to the people in each group. That feature, called Similarities, immediately yielded a noticeable uptick in sales and allowed Amazon to point customers toward books that they might not otherwise have found. Greg Linded, and engineer who worked on the project, recalls [Jeff] Bezos coming into his office, getting downed on his hand and knees, and joking, "I'm not worthy."
According to the book Big Data, a third of all of Amazon's sales are said to result from its recommendation and personalization systems.

So a recommendation system is a really powerful tool you can use to increase sales. With this thought in my mind I had earlier installed another recommendation system om this blog called LinkWithin. I've used that system for a while, but I realized that it didn't give any good recommendations. You don't need just any recommendation system - you also need a recommendation system that generates good results. 

Improving a recommendation system is actually very complicated. When Netflix wanted to improve their recommendation system they decided to make a competition out of it called Netflix Prize, where the winning team would win 1 million USD. Despite the reward, it took no less than 3 years before the competitors had developed an improved system.

But improving LinkWithin took about a week, and the solution looks like this:
  1. Read the blogger-data with JavaScript
  2. Clean the data to remove strange characters and unneeded words
  3. Find recommendations by using a similarity measure called Jaccard distance. I've here found the distances between the title, text, and labels and then added them together, so articles can get a score between 0 and 3, where 3 means that it's the same article. The system is general and will work for all blogs because I didn't specialize it to just this blog 
  4. Store the data in a database
  5. Read the data and add recommendations to blog articles

This was the result:

Recommended articles with the LinkWithin system:
  • The Random Show with Kevin Rose and Tim Ferriss (has nothing to do with the article)
  • Experiments with Blender (has a little bit to do with the article because I developed the car model in Blender)
  • Quote: Edward Tufte (has nothing to do with the article)
Recommended articles with the August Shield 743 system:
  • Tesla Motors Simulator Update (has everything to do with the article)
  • Tesla Motors Simulator (has everything to do with the article)
  • Tesla Motors Test Track Simulator (has everything to do with the article)
  • Catacomb Snatch 3D  (has a little bit to do with the article because both Catacomb Snatch 3D and the simulator were developed in Unity)
  • The Engineer Update 3 (has a little bit to do with the article because The Engineer is a biography on Elon Musk, who co-founded Tesla Motors)

If you are interested in learning how to develop your own recommendation system, you should read the following books:

October 21, 2014

Tesla Motors Simulator Update - GTA Style

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I've updated my Tesla Motors Simulator. Before this update you could only drive the red Model S, but now you can change car like in Grand Theft Auto. I've also improved the performance by using custom collision meshes. Next step is probably to add another model, maybe the Roadster or the Model X?

Looks interesting? You can test it here: Tesla Motors Simulator