Scan monster

The beast hath arrive-ed.

After nearly a year of working with 3D scanning, prototype development and related technology, the time has finally come to get serious.

Very serious.

For our automated part scanner, my partner Alex fronted the cash to buy-it-now one gigantic 1500lb welded steel machine platform off Ebay. As with all great Ebay finds, the tiny picture did no justice to the table we now hold in our studio.

Beautifully crafted from 3/16″ tube steel, the top surface is ground and polished smooth, and designed to accept high precision linear bearings. A protective acrylic shield is mounted on one end, and slides part way along the length of the table. The entire table is mounted on 4 seperate air bags to isolate the table contents from room vibration (or isolate table vibration from the room contents). There are shelves built in to mount computer and related electronics, and a set of urethane casters to keep the entire thing mobile.

The table was originally designed to test spinning shafts or spindles in an automotive type application, but it obviously never found it’s way into regular use, as it was as spotlessly clean as the day it was built. The beast will soon be retrofitted to act as an incredibly stiff, stout, and fully isolated table for precision 3D scans. It is, in a nutshell, the perfect platform for our 3D scanner, and it will soon be a powerful tool in our 3D arsenal. Keep an eye on the blog as we put together our latest 10 micron, 2 axis automated high-speed 3D part scanner.

Scannin’ Stuff

I have neglected my duties as a Blogger. For 6 months, I have contributed nothing to my expanding audience of 5 (dare I say 10?) readers. But I have an excuse. I’ve been busy.

I’ve been innovating.

It was on discovery of my work with the laser scanner, and methods for reconstructing real parts in 3D on the computer that a former collegue of mine asked me very plainly, if this technology could be applied to his applications. More specifically, could the work I was doing in 3D be applied to measuring real parts in 3D? Could I then compare the real scanned data to the computer -perfect 3D CAD data used to create the parts in the first place?

My response was neither yes or no. I simply thought about the problem and went to work. Which, I would like to imagine, is what innovators do.

I started by browsing through existing technology. I spent countless hours browsing YouTube, commercial websites and university publications. I read magazines, I contacted vendors. Every existing technology, I felt, had some sort of shortcoming. The most common method of 3D reconstruction relied on a mechanical probe and an automated positioning system. An extraordinarily complex and expensive piece of equipment that could, at best, generate a few measured data points per second. Real 3D surfaces can be mapped using a laser as a reference line, but the laser introduces erroneous data that needed to be filtered out in a secondary operation. You don’t shine lasers in your eyes. They are expensive. Everything took too long to scan. Calibration was a chore. Every option was impossibly expensive.

But more frustrating than anything was the fact that the technology was bundled up and hidden. Buried under proprietary patents and cumbersome software interfaces. There is nothing particularly complicated about 3D data collection. The technology depends on high school trigonometry, a little bit of programming, and a whole lot of time consuming calibration and trial and error. It can benefit from standard programming libraries and the collective experience of 3D visualization which has been developing in leaps and bounds since 3D games first entered the market 15 years ago. So exactly why was it so expensive? Who has $150,000 to experiment with 3D scanning?

The technology, I realized, was kept out of the hands of the many because of the cost. It was inaccessible on purpose. The high cost kept out the curious, and it kept the technology safely hidden behind the curtain. And as such, the options were limited. The technology was useful, but lacked any luster. It was an economy car at a premium price.

But then comes the internet. Along come legions of talented programmers and innovators who understood the power of this technology and begin to develop their own tools. These are the shade tree mechanics of the internet age. Innovators. I found open source tools that began to explore 3D data collection. I found tools that could track the video of a projected laser line, and extract real 3D data. I found open source applications for automating a mechanical touch probe to collect 3D data points. I found experiments with structured light and three phase lighting patterns that promise to capture more data faster and more accuraty than anything on the market. I tested countless tools, and while few could give the reliable results that I was looking for, they all tought me about the technology. They allowed me to browse through the innovation, and pick the one that best fit my needs. I could, for the first time, see how it worked, and understand how to make it better. I found FlexScan 3D, which separated the software from the hardware, and made real 3D data collection a real and accessible technology. It gave me some say in the tools that I was using, and gave me the opportunity to apply those tools in a new and practical way.

Does Ford build the fastest car? Or get the best fuel economy? That kind of refinement always goes to the fanatics, the refiners. The outsiders in pursuit of a single goal. It is my favorite theme, David besting Goliath. Innovation from the bottom up. Make technology available, and it will grow in leaps and bounds. Keep it hidden, and it will stagnate.

So, I’ve spent the last 6 months scannin’ stuff. I grabbed the tools available to me, and began to experiment, in my own all-consuming way with 3D. I now had technology available on which I could test processes and make improvements. I became one of the legions of 3D fanatics that work with this technology to make it better. Countless hours of calibration and experimentation. Better and faster.

I’m experimenting with new techniques and new applications. I’m testing hardware and taking notes. I’m building new tools, and exploring alternatives. I’m sharing what I learn with friends and colleagues, and I am applying the technology in new ways to solve old problems. I am, for the first time, able to participate in the innovation on which I have based so many of my past articles. I am creating something new and helping shape the future. In 3D.

Johnny Lee and the GDP

 I had stumbled across Johnny Lee in some early searches for information on Arduino and do it yourself electronics projects. Johnny Lee has created something of an Internet  following for his work with the Nintendo Wii controller, when he realized that the controllers were cheap and housed some pretty sophisticated electronics.  Using his own time and skills, he developed a few fantastic projects using the $40 controllers as a source of electronic hardware, and published his procedures free of charge online on YouTube. He also posted some related software and detailed information on a website, so others could develop similar projects based on his research.

I was just sent a link to a recent TED conference video that showcased Johnny Lee and his findings, and it struck a chord. No so much because of the novelty of his invention, but because of the fundamental shift in innovation and social behavior that I believe it represents.

I was probably thinking about grand social context because I had just finished reading Dan Ariely’s book Predictably Irrational, which highlights a series of experiments and findings of the sometime irrational behavior of humans in social situations. While this may seem far removed from Johnny Lee and his Wii experiments, have patience and keep reading.

 Dan had identified two basic exchange formats on which we as humans interact on a daily basis. The first and most familiar to the good ole USA is the traditional market exchange, in which we exchange our time for money, or our money for goods and services. Beaurocracies love this type of market because the total value can be tracked, documented and taxed. The second is a social exchange, in which we share goods and services with no money exchanged. Tough to tax.

What is surprising is how fundamentally different these exchanges are, and how we are motivated to contribute to each. In one experiment, Dan Ariely staged an incident in which a researcher was attempting to move a couch from moving van. The researcher would ask for help from people walking by, and most would gladly pitch in. Kind of asking for a favor. This is an example of a social exchange in which we are willing to give our time away free to strangers because it sustains some sort of invisible, untrackable social exchange. Give a little, get a little. Maybe somewhere in the back of our minds, we imagine getting similar help from a stranger when we find ourselves in need.

What was interesting (and a common theme of Predictably Irrational) was as soon as the researcher offered money for the same assistance, fewer people were willing to help. For all we have been taught about work, GDP, and the almighty American dollar, we are generally better motivated by social exchanges than market ones, and often deliver better results. This was due to the fact that applying a dollar value to a social service suddenly makes us think in terms of dollars. $2 to help move a couch isn’t even minimum wage, we may think. But we would do it for free.

Irrational, right?

This sort of open exchange is exactly what I’ve been trying to identify in all of my ramblings. Open source software development, YouTube and all of the people that have chipped in to create the Arduino and similar open source projects is exactly the kind of things that can be created under a free market exchange of social favors. The reason open source technology has the potential to be so much better than traditional market driven development is because is completely motivated by these social forces, with no dollars involved. Kind of a primitive drive that transcends money, and leaves everyone more satisfied with their contribution to the world.

O.k, so Johnny published his findings online for free. Under a traditional market approach, he would have sold his idea to a developer and the technology would have been kept under wraps until it was sold to the market. But as a social exchange, Johnny published his findings on YouTube and got to sleep at night knowing he had helped a lot of people develop the technology for their own use. Try to put a dollar value on that kind of development, and it get’s kind of foggy. So Johnny gave it away, and in a sense, he has become a hero, a celebrity, and with his recent TED conference interview, an inspiration for the futurists and builders of tomorrow.

Considering our dire economic market, it is kind of inspiring to realize that there is a stronger market underneath, that never really went away. The untrackable, untaxable, and often overlooked social market is something to consider. This is the same market that kept people thriving during great economic depression of the 30’s. Considering the social problems that have become more prevalent in the last 20 years, I suspect we may be coming out of a social depression, which is vastly more catastrophic to our outlook and well being than a falling Gross Domestic Product.

It is surprising how satisfying it can be to help anyone with just about anything, without considering dollars, market forces, time equivalents or any of the other drivel we have been taught to relate to. It’s just as suprising to find out who steps in to help when you really need it most.

So hats off to Johnny Lee and all the future contributors to the new social GDP.

Art Fish All In Tell Lee Gents?

Artificial intelligence doesn’t relate exclusively to computers acting human, though that would be the popular belief. Artificial intelligenc really defines an approach to problem solving that uses techniques and tricks similar to the human brain.

The human brain is pretty well known to be the most amazing device on the planet, so it stands to reason that it would make a good model for aspiring computing devices. The human brain isn’t particularly fast; we make something like 200 calculations per second for any given neuron, where your computer may be capable of making 3 billion calculations per second. But the human brain is extremely good at performing complex calculations that computers have yet to make any progress. A computer can memorize a phonebook, but it can’t create a poem or easily distinguish between two types of apples. These are processes of pattern recognition, and they are unique strengths of the network of neurons in the human brain.

Neurons in a neural network function sort of like this. Lets say you have 10 neurons connected to the nerves on the tip of your finger. If you touch something warm, 6 of those neurons send a signal out to anything they happened to be connected to. Maybe some are connected to the pain networks in your brain, maybe others are connected to temperature sensing regions. If you touch something REALLY hot, maybe all 10 send a signal; more signals are sent to the pain sensors in the brain, maybe one is connected to a childhood memory. Each of those areas of the brain that receive the signals act in the exact same way. The pain networks in the brain, for example, may be receiving lots of pain signals from the nerves, plus they are getting signals from the eyes, childhood memories, and any number of other networks that all send a bundle of logic signals (either on or off) all over the place. If the brain gets enough signals, it may pull your finger away from the heat. Kind of a criss cross of sensors connected all over the place, but only where they make sense. Imagine a wired telephone system where any two houses in the world that would want to talk with one another are connected together with a direct line, but not to anyone else. Very messy and impractical to build, but the basic model for how our brain works.

The key to getting all of those connections to make sense, however, is the most unique thing about a neural network. We aren’t born with all of these sensors and connections in place. The nerves in the finger don’t necessarily connect to the right place immediately after birth. Our brains start with a clean slate, and we spent the earliest part of our development making all of those connections. Training the network, so to speak.

 Human babies don’t really do much to start with. They lie around and stare at things. They babble and drool, and stuff things into their mouths. In time they roll around, crawl around and toddle on two feet. Its a process that we are all familiar with, but what they are doing during this time is absolutely astounding. All of these strange behaviors are crucial and assist in training the network of billions neurons in their brain.

Consider the following.

A baby is lying in his crib, staring at the ceiling. The baby brain sends a random signal, and the arm moves. He sees it with his eyes. Bingo. The brain makes a neuron connection. ‘arm move’ neuron is now connected to the eye neurons and to the rest of the brain. A baby brain doesn’t necessarily know what an arm is to start. It doesn’t necessarily know that babies come with their own arms, or what they can be used for, but in time, as more and more connections are made, the brain begins to map out the function of the arm, the muscles, and all of the nerves that were not defined at birth.

Baby brain sends a second random signal and a toe wiggles. It doesn’t show up to the eyes, so no connection is made. ‘ ‘toe wiggle’ is left disconnected for now. Later on, when the ‘move leg’ network is trained, and the ‘grab with fingers’ network is trained, the baby reaches out with it’s hands and grabs it’s legs. Another random signal wiggles the toes, and Bingo, ‘toe wiggle’ is linked to the rest of the brain.

During the training process, the brain is generating lots of random signals, and making as many connections as possible. Bad connections are constantly being removed and replaced, good connections are strengthened, and in time, our brains become extremely efficient at processing the chaos of the environment in which we live. In time the signals become less random and more likely to get a result; we get better at making connections, and we make better connections. It is why babies and developing children do lots of things that don’t make sense to adults, but become more sensible over time. It is about training, and learning. It makes us adaptable, and it makes us unique.

Now bear in mind, there was no complex programing to start with. Just a simple program that says good connections stay, and bad connections are rejected, which is the very basis of artificial intelligence, and the future of computing technology and robotics.

And that is where we are today.

Computers historically used brute force to accomplish tasks.  A computer can make on/off (logic) calculations nearly a million times faster than the human brain. Type ‘apple pie’ into a search engine, and the computer searches through millions of pages of data for an exact fit. Brute force.

 Ask grandma, and she instantly recalls the recipe. ‘Apple pie’ connects directly to the memory of the recipe. Human intelligence, above all else, is a model in efficiency.

Which is exactly where we are heading with computing technology, and why artificial intelligence represents such a fascinating breakthrough for the future.

Artificial intelligence is being used to run computers in a completely different way. Rather than programming a computer to accomplish a specific task, programmers are developing software to learn to accomplish a certain task, much in the same way that the neuron networks are trained in the brain. Artificial networks of neurons are created and told to accomplish a goal, rather than follow a routine. The computer is then allowed to run on its own to train the synthetic network (learn) and come up with it’s own approach to solving a problem. It writes it’s own program, in a sense, based on input from the user and a simple set of rules.

Take a toaster, for example. A traditional program would dictate to heat the toast to 300 degrees for 5 minutes and stop. The same program every time. Sometimes it burns, sometimes it doesn’t.

An artificial intelligence approach would seek to make perfect toast (golden brown). It would accomplish this by constantly experimenting with the variables (temperature and time) while watching the results (color, smell). Over time, with a series of successes and failures, the toaster would create a network of succesful toasting techniques,. It would learn to recognize patterns (raisin bread and bagels, for example), and eventually learn to make excellent toast, regardless of the bread type. The toaster may depend on input from you, the user, but in time it would be trained to make it the way you like it. A personal butler, in a sense, and a subtle shift from dumb appliances to intelligent ones.

Which represents the next major leap in technology. When computers learn to think for themselves, to make decisions based on their environment rather than their programming, we will suddenly find ourselves with an incredible tool for solving even more problems, and creating more solutions. A whole new genre of technology that can help our lives, and bring a little In Tell Lee Gents into our world.

The Stupidest Thing I Have Ever Done

I think everyone that seeks to go out on their own begins to doubt themselves and their abilities. It seems to be a common occurrence among entrepreneurs; more importantly it seems to be a common occurrence among successful entrepreneurs. So after 4 months on my own, with no income, and no clear path to success, I began interviewing for another job. Not just any job, but what I would have considered my perfect job opportunity. It involved tinkering, and robots, and electronics, and making cool stuff. It was a job I could be passionate about, and successful in. It was a lifeline in a faltering economy, and likely my only real opportunity to go back to work.

Which I think was part of the problem. I could see the rest of my life in that job, working late every day.  Taking 2 week vacations every year. Having health care and a steady income. It was a great job, but it was a job nonetheless. It wasn’t me, and it wasn’t how I was going to live my life. I had only been on my own for 4 months, and really hadn’t had enough time to fail (or succeed). More importantly I was giving up on my dream of working for myself, and I haven’t really given up on anything before. It didn’t sit right, and I walked away from the opportunity.

Which may have been The Stupidest Thing I have Ever Done.

Which was the tagline that embedded itself in my head.

In part because doubt crept in, and I begin to question my own abilities. Exactly how long am I going to live on my savings? What happens when they are gone? Shouldn’t I see some results by now?

The thought of The Stupidest Thing I Have Ever Done anchored itself in my brain, and I couldn’t imagine success anymore. It was a distraction from my goals, and it prevented me from moving forward. I began frantically searching for other jobs. I considered selling the equipment that I had accumulated. I immediately put my car up for sale. Worst of all, I stopped dreaming. I stopped progressing, and I nearly gave up.

Every entrepreneur makes no money the first year, and everyone doubts their own abilities at some point. Many successes are after 2-3 dismal failures, which is a testament to the die hard spirit of  those that finally succeed. Those failures, I believe, are key to developing strong character, and are viewed by those that succeed as invaluable learning opportunities; if it were easy, everyone would be doing it.

The world has a tendency to balance good and bad, yin-yang, or whatever your philosophy. Enduring hardship gives us the opportunity to appreciate the good things, and makes us all healthier. If life is too easy, we become selfish and forget what really matters. Society keeps itself in check.

I had been at it 4 months. Robert Rodriguez had to sell blood to raise enough capitol to finish the movie El Mariachi, which became an international success. I hadn’t had to sell anything yet, and I still have two kidneys. James Dyson spent 5 years in a carriage house perfecting a vacuum that later became a global success. I’ve spent 4 month working on projects that verge on invention; building the tools that I need with no instructions and no guidebooks, and attempting to build a business at the same time. It’s a tough place to be, and it will take way more than 4 months to decide if it will succeed. I’m not in debt, I’m not facing bankrupcy, and I hadn’t come close to failure. Not yet. I just came close to giving up.

Which would have been the Stupidest Thing I have Ever Done.

Arduino Online

Or why you should name your child (or children) Arduino.

I’ve started my first tests in automated machine control, using the Arduino open source controller to automate the travel of a line laser for my 3D laser digitizer. I salvaged the carriage and stepper motors from an old (and well built) flatbed photo scanner, discarded all of the original electronics and replaced them with an Arduino controller and an EasyDriver step motor board. For the example video, I was able to download a simple routine from the Internet that someone else had created, and use it to control the stepper motor attached to the laser carriage. I changed some of the parameters in the program to adjust speed and overall travel and installed it on the tiny blue Arduino. The total cost of all of the components shown in the video is less than $50.

So the scanner was now completely under my command. And that is pretty cool.

Ok, I’ll start with some definitions.

A micro controller is a small, simple computer designed to follow a program. Similarly, a logic controller is a tiny computer that uses computer friendly digital signals. A logic controller may be used to operate a digital thermostat in your home, automating the task of turning on the heat at 6:00 am and regulating the temperature to 68 degrees. Most controllers are designed to read electronic inputs from a source, such as a thermostat and output signals (such as turning the furnace on) as a response based on a simple program permanently stored in the memory.  Controllers such as these are generally built to do one job, and cannot be easily modified to do anything else.

Programmable logic controllers (or PLC), on the other hand, are designed to run programs written in a common computer programming language, which can then be transferred to the memory of the controller. This allows a controller to be custom programmed by a designer to perform a specific task. It can also be reprogrammed at any time, which makes it more versatile. PLC’s are common in industry, but up until recently, they were expensive, difficult to program and generally out of the hands of the hobbyist.

Enter the Arduino, and a new aproach to digital electronics.

The Arduino is a cheap, reliable PLC, and an experiment in open source hardware. In 2005, an Italian teacher named Mossimo Banzi decided that he wanted a simple, affordable logic controller for his students to develop their own technical projects. What made this particular project unique was the completely open development process, and the long term commitment to providing the Arduino platform as completely open source. No patents, no profits. Just great technology made available to anyone that wants it. The technical details of the Arduino board are available to anyone online, and anyone that wants to build their own board is free to do so. If you want to build it and sell it for a profit, you are free to do that as well. If you want to make automated products using the Arduino as a controller, you can do that too. All free, and without the cost and hassle of licensing. A hugely generous gift to the world, and the inspiration for hundreds of large scale open source development projects all over the web.

Open source projects such as the Arduino are inspirational simply for the incredible speed in which they are embraced and expanded by the global community. Even more important is the vast amount of information shared among its users, and the huge libraries of programs and tutorials written by individuals to be used by all. People see  these new projects, and are inspired to contribute their own time and skills, or create projects of their own. The effect is a radical develoment of new ideas and new technology, based purely on the voluntary contributions of users all over the world.

And so the Arduino has inspired creativity in digital electronics. Of course creativity doesn’t always lead to sensible developments. But it certainly leads to interesting ones.

There is an Arduino based device that will call you on the telephone when your plants need water. Arduino powers a trampoline that shoots balls of flame proportionate to the height of your jump. There is a mood lamp that can change colors by punching a bag, a harp with lasers instead of strings, turn signals for a bicyclists jacket, home made Segway scooters, and no shortage of gigantic digital clocks, glowing multicolor interactive light displays, noise generators, robots and strange machines that interact with people in odd and sometimes pointless ways. Arduino has turned technology into an art form, and opened up a flood of inspired and less than inspired projects all over the world.

And creative art always leads our culture down new paths, and ultimately finds it way into our daily lives. So keep an eye out for new developments, and watch the Arduino developers for a taste of products to come. Many of these devices may someday find their way into your home.

O.k, maybe not the trampoline.

A Waste of Times to Come

O.k, so exactly what is the point of this video clip, you may ask.
It would appear that someone put together a robot with the sole purpose of playing a video game.
More importantly, someone obviously invested a great deal of money and time to build a sophisticated machine with no meaningful purpose whatsoever. I mean, it’s not exactly feeding the orphans of the world, is it?

Allow me to step back in time, for a moment. Back to the dawn of the computer age. Technology emerging from behind the fortified walls of high tech, military, and industry. It started with the geeks, the high tech do-it-yourselfers that built thier own machines on the kitchen table. The troopers that soldered diodes ad nauseum, wrapped wires onto tiny fragile terminals, and singed their knuckle hair with their soldering irons. They glared at green monochrome monitors, and programmed in BASIC. There was no Internet, and no real communication link. They embraced the technology because there was something yet to come. The first computers were a novelty, but they ushered in a new generation of technology. The first users were outcast, because no one understood what they were trying to accomplish. They believed that computers had a place in the future, and had no idea as to the extent in which computers would ultimately affect all of our lives.

Personal computers first entered the mainstream market late in the 1970’s, and became more prevalent into the 80’s. Eventually we saved up our cash, and forked over $2500 for an Apple II of our own. The bright advertisements showed happy families doing the taxes together, or typing a letter to grandma. We brought them into our homes, sold on the idea of a new way of doing things. And what did we do with them?

We played games.

We sat for hours under the radioactive green glow and typed commands to navigate the fantasy world of a text adventure. We pounded on the arrow keys to control the ever growing worm, and shook our fists in fury when the worm finally devoured itself and ended the game. We blasted away space invaders that looked almost as good as the ones in the video arcade, only for free, and for hours on end. It was a pointless waste of time, but it signified the transition of computer technology from work and industry into our personal lives.

And in a sense, it wasn’t a waste of time. What these games were doing was engaging us with the new technology. It engaged our brains in how to communicate with the computer. It engaged our fingers to the keyboard. It got kids using them in schools, and established a legion of programmers who thrived on the idea of creating their own software tools. It was warming us up to the expansion of computer technology, and it did by giving us a reason to use them, no matter how mundane that reason.

Which is exactly what this game playing robot signifies. Microsoft founder Bill Gates, a keen observer of new technologies, is convinced that robotics stands to take the world by storm today, much as the personal computer took the world in the early 80’s. The computer didn’t take the world because of some leap in technology, but rather, because of some change in public perception and behavior. Robotics, and mechanized electronic devices, it would seem, are coming into this new public perception, and it starts with games.

Or in this case, a game playing robot.

The robot is really an assembly of established technology. A machine vision camera, often used in industry for identifying objects on an assembly line (in this case, looking for colored dots moving down the video screen) is placed in front of the game screen. The images picked up by the camera are collected and analyzed on the computer next to it. The computer looks for patterns, such as a red dot, or a green dot. If the pattern is distinctive enough (such as a BIG red dot) the computer will trigger the correct button on the guitar robot to press the corresponding red button on the guitar. This is more or less what our brain should be doing to move our fingers, but as we are all human and prone to mistakes, we can never achieve 100% accuracy. Neither can the computer, but the results are significantly better than any human player would hope to achieve. This is all standard technology in industry, but an example such as this shows just how soon we may be bringing robotics into our homes.

Don’t want a game playing robot? What about a car that perceives the road and drives for you? Robotic controls don’t drink lattes and fall victim to fits of road rage; machine vision could make safer decisions with the same visual input to get you safely to your destination. What about cooking? Couldn’t this same vision technology soon be making us dinner at home? It is a lot to forecast, but really, who would have envisioned a global phenomenon such as the Internet while assembling blocks in a game of Tetris?

I share these innovative bits of technology because they represent the shape of times to come. Even if it may look like a waste of time.

Quality and Consumerism

My computer mouse broke.

That little plastic wheel stopped wheeling. Maybe a minor inconvenience for some, but the 3D modeling software I use every day uses the little wheel to zoom in and out of 3D views. It is as vital to navigating the 3D world as a keypad is to dialing a phone. I was stranded and irritated.

I immediately took the thing apart, believing that it could be fixed, and I could get back to work. What I found was of no surprise. The little axle for the little wheel had sheared off close to the hub, and no longer engaged the encoder. The tiny plastic axle, no more than 2mm in diameter had broken under normal operation. This was especially compelling when I took apart a 10 year old mouse of similar design, and found a steel axle, and an indestructible design. It was obviously designed to last forever, and worked flawlessly despite my best efforts to destroy it.

This mouse failure, I felt, was a design flaw; a complete lack of proper design with no testing to ensure safe and reliable operation. It was completely and absolutely the fault of the manufacturer.

Or was it?

I paid $15.00 for the wireless mouse at a large electronics retailer. I purchased it new, and picked it out from among 50 or more similar devices of varying price. The price of this mouse was on the low side for the features it offered. It was cheap and appealing, and I bought it under the assumption that it would work as advertised. I ignored other brand names and fancy features. It was a tool that I needed, and I spent the least amount of dollars to fill that need.

Subconsciously, I made the following assumptions.

1. The mouse was properly designed and tested to operate under normal use.

2. If the mouse failed, I could return it for a refund or replacement.

3. The manufacturer had resolved any design issues in response to consumer returns and complaints.

O.k., so here is where things aren’t what they used to be. For (1), the mouse was not properly designed and tested in the first place. It didn’t need to be, because (2) it would never be returned for replacement because it was too cheap to bother the effort, and (3) the manufacturer would never be informed of any design issues because nobody would bother to return it or complain. Things look sunny in Mouse Manufactoryville, so they keep churning out faulty mice.

The truth is, I tossed the faulty mouse in my garbage. I couldn’t be bothered the hour it would take me to return for a refund and replacement, and I needed to get back to work. I went to the local Goodwill and picked up an old indestructible replacement, and got on with my life.

By doing this, I had personally contributed to the erosion of product quality in our marketplace.

In my role as an engineer, my primary goal was to deliver parts at the lowest possible cost. For all of the hype about quality and design, my single most important design target was to provide additional revenue for the company in which I worked. I was also responsible for reducing cost on existing designs. This is not entirely bad, and if done properly, many cost reductions can improve the quality as well. But without accountability and feedback from the costumer, the trend is to continue trimming away until there is no cost left. If there were no customers, I would eventually reduce my cost to the bare minimum. I would build parts out of paper mache so long as they survived long enough to taken off the showroom floor. But since customers do complain on occasion, and vote for their product preference with their dollars, I was held accountable to deliver a minimum level of quality to meet the customers needs, and as such, chose to build my parts out of more durable materials.

What I realized with this mouse incident, was my own contribution to the erosion of quality in consumer products. The mouse didn’t break because it was made in China. The mouse broke because no one bothered to complain about the faulted design. The truth is, if EVERY broken mouse were returned to the store, the American company that imports the mouse would be held accountable, suffer financially, and quickly be compelled to resolve the problem in order to stop the financial bleeding. That notion of accountability is what defines the quality of American goods. But if a company remains profitable with a poor quality product, and no accountability, there is no incentive to change.

This cycle is partly to blame for the nasty and wasteful consumerist culture that we know today. We buy goods because they are cheap. We throw them away because they are cheap. Cheap is cheap, but quality never factors in to the equation. This really is a wasteful way to live.

As a consumer, I am an odd one. For every new item I purchase that breaks (which I find to be far too often), I replace it with something old or second hand. Old because I find many goods manufactured before 1980 to better quality (this is especially true of tools and appliances), or second hand because the sting of paying $5 for a drill that eventually breaks is much less than the sting of paying $50 for a drill that breaks. Occasionally I build things myself, with mixed results.  Strange, I know, but you would be surprised at just how much traditional, well built goods out perform poorly designed ones, and how often we are mislead to believe that new technology is a guaranteed improvement.

Odd consumer that I am,  I throw away fewer and fewer faulty products, and I save money in the process. My house is more or less free from plastic clutter, and what I no longer need can be sold or used by someone else rather than tossed in the garbage. I share with friends and neighbors, and I find myself more and more willing to pay for high quality goods, even if that means purchasing less stuff.

So that is what this is all about. My own contribution to the society in which I live, and the behaviors I need to change in order to shape the world for the better. In order to stop consuming resources, we all need to consider our own behaviors, and consider a new way of behaving. It starts with what we buy.

Daniel Ferguson

Navigating the future

If you have ever been curious about what the future will look like, take a look at the TED lecture series at www.ted.org The ideas that are being developed are beyond imagination, and it shapes a future unlike anything we could have predicted.

Take the Seadragon project for example :

Under development from Microsoft, Seadragon was designed simply as a new way to look at electronic images and image data. The software was designed to tackle the baffling task of navigating incredible amounts of image data quickly and efficiently. It may not sound like much of an accomplishment, but imagine storing entire books of photographs, with text and magazines on your mobile phone. Imagine browsing through the data effortlessly on your Iphone. Imagine it being easier to navigate and more convenient than a good old fashioned magazine. It is actually quite daunting, and despite our best efforts with technology, nothing has come close to replacing printed media.

But in a sense, replacing it isn’t what this software intends to do. By completely rethinking the way we navigate visual media, there may be an opportunity to improve on it. As with any technological leap, it is only when something better comes along that we willingly shift our preferences to embrace something new.

But it didn’t stop with navigating visual media. Seadragon had developed sophisticated algorithms for managing image data, and one aspect of its functionality was to sort and match similar images. Imagine, for example, if you took a picture of a cookie. Imagine if you took lots of pictures of lots of cookies. Maybe you took lots of pictures of potatoes and trees as well. Now imagine if you could use your computer to search for ‘cookie’ and find only images of cookies. Sounds easy, right?

Well not really.

Up until now, it takes a human brain to associate the image of a cookie with the word ‘cookie’. Even Google images relies on typed captions under photographs to provide a description; it really can’t tell what a photograph is unless some human brain recognized the image and wrote caption for it. Our brains, extraordinary as they are, are capable of matching patterns and making associations that computers struggle with, and even a database as powerful as Google needs a lot of human brains to sort the images.

So here is where Seadragon accomplished something extraordinary. Seadragon developed the capacity to make sense of all of these images. The software was able to recognize some of the key features of image data, to recognize patterns and make associations with other images. The software couldn’t recognize an image of a cookie, per se, but it could compare two images that looked similar, and make a connection. It could scan thousands of images, and make associations that previously took a human brain. So now one human brain labels one cookie, and the software can make the connection to thousands of images of cookies.

Now apply this to the entire web. Imagine the vast amounts of image data available online; photographs, drawings, maps, paintings. The truth is, only a small percentage  of the image data currently available on the web is searchable, because up until now, the only way to search anything with a computer was to rely on text descriptions. But imagine if we could use images as a search tool? Imagine browsing for long lost friends from a photograph. Imagine sketching the shape of a particular flower and finding it instantly over the internet. Imagine a completely new way of navigating data that no longer relies on a keyboard; a fundamental shift in how we index and navigate data.

But why stop with images? What about sounds? Music? Enter your favorite song at www.pandora.com and it generates a non-stop stream of music from similar artists; music you might like. It does this by gathering input from all of its users. Don’t like a suggested song? Click on the website to skip it. The software depends on this user input to make associations between music and preferences; essentially associating musical preferences based on the collective input of thousands of users. Pandora is one of the new developments in interactive internet that gather input from its users, essentially gathering intelligence and becoming more effective and efficient. An extremely powerful approach to organizing data that relies on the most powerful computer of all.

The human brain.

Daniel Ferguson

Change

Over the course of a year, in what signifies the beginning of the great depression of 1929, the Dow Jones industrial average dropped to 68% of its peak value. That means if you had $100 invested before the crash, you now only had $68. That began the greatest time of economic hardship in our recent history. I say recent because it was by no means the worst economic time historically for the United States, but it is the time we remember today, as remembered by our parents, our grandparents, and our great-grand parents. It was a time when we worried about food and our future, and it still lingers in our memories today.

Over the course of a year, between March of 2008 and March of 2009, the Dow Jones industrial average dropped to 55% of its original value. You now have $55 of that original $100. That is a big deal. Over the course of a year, which can safely filter out some speculative peaks and troughs, the indexed value of our business economy dropped significantly more than before the great depression. That is the reality of our current situation. For all of the predictions of economists, investors, and the news of the state of things to come, that is where we are right now. Faced with the facts, those that do not learn from history are destined to repeat it.

So what went wrong?

It’s actually surprisingly simple. We all got greedy.

The United States economy is considered one of the most stable in the world, due almost exclusively to the concept of private home ownership. That’s it. We own our homes and we pay our taxes. It’s as simple as that, and as a collective group of homeowners, we are stable, reliable, and a lucrative source of income for the United States government. So the government raises additional capitol for big projects, like roads, buildings, military operations by issueing Treasury Bills to anyone that wants to buy them, knowing that we, the stable Americans, will provide the income to the government to pay them back in the future at a nominal interest rate.

But we the stable Americans, weren’t so stable. Despite record economic growth, our collective savings rate dipped below zero for the first time since the middle of the great depression. Despite having one of the highest per capita incomes in the entire world, on average, we were unable to save for a rainy day. Our other savings account, home equity, was being tapped to fund new cars, consumer electronics and vacations. We were living a luxurious lifestyle and stretching our incomes to the very edge. A precarious situation to be in, when there is a chance things could go wrong.

And then there were the homes. Investors, weary of the dot com bust of then new millenium, began looking to real estate as a stable place to earn back their money. It was no help that a global ‘Big Pool of Money’ was suddenly dumped into the hands of our lending institutions, a strange result of formerly poor countries like China and India suddenly becoming rich from American consumerism and wondering what the heck to do with all of that cash. This was a lot of money, and everyone wanted to put it somewhere safe. Like, say, the American homeowner?

So the banks started lending money. They suddenly had more money than they knew what to do with. They lent it to everyone with a pulse. And we bought houses. We bought blocks of houses. And we collected funds and built condos and buildings and gigantic rows of houses for miles on end. And there was no end in sight. Investors wanted houses. Retirees wanted investment houses. We wanted two houses.

Soon enough, they were all bought up, and the number of available houses went down. So the prices rose. Up 10%, 15%, 30% in the course of a year. But we still needed to live somewhere. The young couples, the renter, we all wanted to own our own home, but they were too expensive. Twenty percent down on a traditional mortgage. This may be reasonable with a $80,000 home. Does that work on a $600,000 house? Does anyone have $120,000 in savings? What about $40,000?

The median household income in the United States is about $50,000. That means that we can safely afford a home worth approximately $180,000, roughly what the median home price was in 2003. By 2007, the price was $262,000. Our income hadn’t changed. These were homes we could not afford. That was $82,000 we didn’t have and should not have spent. We should have stopped there, the banks should have stood by their guns and demanded 20% down for every home. If there were no qualified buyers, then the home prices would eventually settle down.

But the banks got creative with the loans. Variable interest rates, interest only. They continued to lend to people outside of their means. The banks had a huge pool of money, and needed to make it earn interest. They sold monthly payments instead of long term responsibility. They got the loans out to anybody and everybody. Some were good, some were risky, and some were destined for disaster. And those risky loans? The 2 years of absurdly low interest to be re-adjusted after two years? Interest only on a million dollar home? Who was responsible for those? The banks?

Well sort of. You see the banks are pretty smart with their cash, and they had this brilliant idea to take all of those loans (their safe and stable, remember?) and sell them to investors as a ‘safe’ investments. They were bundled together as mortgage backed securities and sold to investors. You know, 401k, pensions, and the like. As stable as the U.S government, right? Safe investments for stable investors. The American home owner, the most stable economic contributor in history.

Uh yeah, stable.

So now the average price of homes drops back to what it should have been in the first place. Let’s say $180,000. Which, coincidentally is 68% of the original value (the same loss in value of the crash of 1929). And those risky loans? The interest only loans on million dollar homes? Well, it turns out that the homeowners aren’t real keen on staying there when the value drops. Suddenly you owe $200,000 more than the value of the home. Add that burden to the already low savings rate and you have a recipe for bankruptcy. It’s easier, you will find, to simply walk away. So the banks get the house back, and are suddenly responsible for selling to recoup whatever money they can from the investment. A 20% loss isn’t exactly a stable investment now, is it?

It’s a downward spiral. Banks get scared, they stop lending. Businesses can’t get funds, they fail. Failed business lay off their employees. Unemployement leads to bankrupcy, bankrupcy leads to more forelosures, and more foreclosures are a drain on the finances of the banks. A bad progression that can easily spiral out of control.

So here we are, all wondering what will happen next.

The truth is, I don’t know. We are facing, above and beyond anything else, a massive change in how we function as a country. Our collective greed has driven the economy to shambles and we all want it to return, to bounce back to what it was. I wanted my house to go up in value because I was greedy. I was tempted by the economy. But then what good is economy? Does it make us happy or secure? Despite being taught that money can’t buy happiness, we were drawn to its luxuries. We love to be part of a rich nation for the privilege it entitles us, and the thought of that changing is frightening and unsettling.

So let’s get our priorities straight. Write down the things you truly love. Look at the list.
Is economy on there? What about your job, the one you might lose? Is that on there? Strangly enough, neither was on my list.

What I do know is that we are facing a massive change in our economic foundation. It does not mean we are facing a loss of the things we love. Change. That’s it.

Daniel Ferguson