Software: It Is All In The Details

January 6, 2019 by Al Williams 22 Comments

Who’s the better programmer? The guy that knows 10 different languages, or someone who knows just one? It depends. Programming is akin to math, or perhaps it is that we treat some topics differently than others which leads to misconceptions about what makes a good programmer, mathematician, or engineer. We submit that to be a great programmer is less about the languages you know and more about the algorithms and data structures you understand. If you know how to solve the problem, mapping it to a particular computer language should be almost an afterthought. While there are many places that you can learn those things, there is a lot more focus on how to write the languages, C++ or Java or Python or whatever. We were excited, then, to see [Jeff Erickson] is publishing his algorithms book distilled from teaching at the University of Illinois, Urbana-Champaign for a number of years. The best part? You can read the preprint version online now and it will remain online even after the book goes to print.

When you were in school, you probably learned math in two ways: there was the mechanics (4×4=16) and then there were the word problems (Johnny has 10 candy bars and eats 4, how many are left?). Word problems are usually the bane of the student’s existence, yet they are much more realistic. Your boss has (probably) never come in your office and asked you what 147 divided by 12 is. If she did, you could hand her a calculator. The real value comes in being able to synthesize the right math for the right problem and — if you are lucky — gaining intuition about it (doubling the price will only increase profit by 10%). Software is pretty much the same, for example no one rushes into your cubicle and says “Quick! We need a for loop written!” You get a hazy set of requirements if you are lucky, and you then need to map that into something that computers can do. For that reason, we’ve always been more of a fan of learning about algorithms and data structures rather than specific language features.

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Computer Programming Unplugged For Kids

August 3, 2018 by Al Williams 10 Comments

There was a time when computers were far too expensive to let mere students use them. In those days, we wrote fake programs for fictitious machines and checked them by hand. That wasn’t fun, but it did teach you to think about the algorithm. You weren’t worried about how many tabs to indent code in the editor, or checking your social media feed, or changing the track on your Spotify playlist. Maybe that was the idea behind Computer Science Unplugged. The site is aimed at educators and gives them lesson plans to teach kids about computer concepts through activities that don’t use a computer.

The target ages are from 5 to 14 and topics range from binary numbers, sorting, searching, error detection, and robotics. For example, one exercise has students line up to be bits in a binary number. Each kid holds a card that is blank on one side or has the right number of dots on the other (for example, bit 0 has 1 dot, bit 2 has 4 dots, and so on).

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Algorithms For Visual Learners

May 6, 2018 by Al Williams 10 Comments

Computer programming is a lot like chess. It is fairly simple to teach people the moves. But knowing how the pieces move isn’t the reason you can win. You have to understand how the pieces work together. It is easy to learn the mechanics of a for loop or a Java interface. But what makes programs work are algorithms. There are many books and classes dedicated to algorithms, but if you are a visual learner, you might be interested in a site that shows visualizations of algorithms called VisuAlgo.

The site is from [Dr. Steven Halim] and is meant for students at the National University of Singapore, but it is available “free of charge for Computer Science community on earth.” We suspect if any astronauts or cosmonauts wanted to use it in space, they’d be OK with that, too.

The animations and commentary take you through algorithms ranging from the common — sorting and linked lists — to the obscure — Steiner and Fenwick trees. Each animation frame has some commentary, so it isn’t just pretty pictures. The site is available in many languages, too.

Many of the animations allow you to set up problems and execute them using a C-like pseudo language. When it executes, you can watch the execution pointer and a box comments on the current operation. For example, in the linked list unit, you can create a random doubly linked list and then search it for a particular value. Not only can you see the code, but the graphical representation of the list will update as the code runs.

The site allows you to register for free to get additional features, but we didn’t and it was still a great read about many different data structures. Also, a few of the commentary slides require you to show you are actually a computer science professor — we assume there’s some copyright issue involved because it is only a few.

This site is a great example of how many free educational resources are out there on the web. It isn’t just computer science either. MITx — or more generally, edX — has some great hardware classes and many other topics

Mechanical Wooden Turing Machine

March 8, 2018 by Lara Grant 20 Comments

Alan Turing theorized a machine that could do infinite calculations from an infinite amount of data that computes based on a set of rules. It starts with an input, transforms the data and outputs an answer. Computation at its simplest. The Turing machine is considered a blueprint for modern computers and has also become a blueprint for builders to challenge themselves for decades.

Inspired by watching The Imitation Game, a historical drama loosely based on Alan Turing, [Richard J. Ridel] researched Alan Turing and decided to build a Turing machine of his own. During his research, he found most machines were created using electrical parts so he decided to challenge himself by building a purely mechanical Turing machine.

Unlike the machine Alan Turing hypothesized, [Richard J. Ridel] decided on building a machine that accommodated three data elements (0, 1, and “b” for blank) and three states. This was informed by research he did on the minimum amount of data elements and states a machine could have in order to perform any calculation along with his own experimentation and material constraints.

Read more about Richard’s trial and error build development, how his machine works, and possible improvements in the document he wrote linked to above. It’s a great document of process and begs you to learn from it and take on your own challenge of building a Turing machine.

For more inspiration on how to build a Turing machine check out how to build one using readily available electronic components.

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A Two Tapes Turing Machine

February 23, 2018 by Jenny List 6 Comments

Though as with so many independent inventors the origins of computing can be said to have been arrived at through the work of many people, Alan Turing is certainly one of the foundational figures in computer science. His Turing machine was a thought-experiment computing device in which a program performs operations upon symbols printed on an infinite strip of tape, and can in theory calculate anything that any computer can.

In practice, we do not use Turing machines as our everyday computing platforms. A machine designed as an academic abstract exercise is not designed for efficiency. But that won’t stop Hackaday, and to prove that point [Olivier Bailleux] has done just that using readily available electronic components. His twin-tape Turing machine is presented on a large PCB, and is shown in the video below the break computing the first few numbers of the Fibonacci sequence.

The schematic is available as a PDF, and mostly comprises of 74-series logic chips with the tape contents being displayed as two rows of LEDs. The program is expressed as a pluggable diode matrix, but in a particularly neat manner he has used LEDs instead of traditional diodes, allowing us to see each instruction as it is accessed. The whole is a fascinating item for anyone wishing to learn about Turing machines, though we wish [Olivier] had given us a little more information in his write-up.

That fascination with Turing machines has manifested itself in numerous builds here over the years. Just a small selection are one using 3D printing, another using Lego, and a third using ball bearings. And of course, if you’d like instant gratification, take a look at the one Google put in one of their doodles for Turing’s 100th anniversary.

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Hack Your Own Computer Science Degree

January 4, 2018 by Al Williams 38 Comments

We ran across something interesting on GitHub of all places. The “Open Source Society University” has a list of resources to use if you want to teach yourself computer science for free. We found it interesting because there are so many resources available it can be hard to pick and choose. Of course, you can always pick a track from one school, but it was interesting to see what [Eric Douglas] and contributors thought would be a good foundation.

If you dig down, there are really a few potential benefits from going to college. One is you might learn something — although we’ve found that isn’t always a given, surprisingly. The second is you can get a piece of paper to frame that impresses most people, especially those that want to hire you but can’t determine if you know what you are talking about or not. Lastly, if you go to the right school you can meet people that might be useful to know in the future for different reasons.

The Internet has really changed all of those things, you can network pretty easily these days without a class ring, and there are lots of ways to earn accredited diplomas online. If you are interested in what we think is the most important part — the education — there are many options for that too.

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Open Hardware For Open Science – Interview With Charles Fracchia

April 8, 2015 by Aleksandar Bradic 14 Comments

Open Science has been a long-standing ideal for many researchers and practitioners around the world. It advocates the open sharing of scientific research, data, processes, and tools and encourages open collaboration. While not without challenges, this mode of scientific research has the potential to change the entire course of science, allowing for more rigorous peer-review and large-scale scientific projects, accelerating progress, and enabling otherwise unimaginable discoveries.

As with any great idea, there are a number of obstacles to such a thing going mainstream. The biggest one is certainly the existing incentive system that lies at the foundation of the academic world. A limited number of opportunities, relentless competition, and pressure to “publish or perish” usually end up incentivizing exactly the opposite – keeping results closed and doing everything to gain a competitive edge. Still, against all odds, a number of successful Open Science projects are out there in the wild, making profound impacts on their respective fields. HapMap Project, OpenWorm, Sloan Digital Sky Survey and Polymath Project are just a few to name. And the whole movement is just getting started.

While some of these challenges are universal, when it comes to Biology and Biomedical Engineering, the road to Open Science is paved with problems that will go beyond crafting proper incentives for researchers and academic institutions.

It will require building hardware.