Amazon Echo Show

Back in May, Amazon announced the Echo Show, its new version of Alexa with a 7 inch touchscreen. The Echo Show is an interesting device, but will the great unwashed masses pony up $229 to buy the show? That’s $50 more than the original Echo, or $180 more than the Echo Dot. With 5.2 million units sold in 2016, Echo has been a resounding success. This has been in part due to Amazon’s open approach to the API. Anyone can build an Alexa compatible device using a Raspberry Pi. Google has (finally) followed suit with their Home device.

It’s not just the hardware that is accessible. Skills Kit, the programmer interface for extending Echo’s functionality, is also open. At CES this year, Alexa was the belle of the ball. Third party devices are being introduced from all corners, all of them connecting to Amazon’s cloud and responding to the “Alexa” keyword.

The Echo Show takes the family in a new direction. Adding a touch screen gives the user a window on the the world not available with voice interactions. Echo Show also includes a camera, which opens up a whole new set of privacy and security questions. Amazon touts it as a device for viewing security cameras, watching YouTube videos, and making video calls. This puts Echo Show dangerously close to the internet appliance category, essentially a barren wasteland littered with the corpses of previous devices. Does anyone remember when Palm tried this with the 3Com Ergo Audrey? How about the i-Opener? Will Alexa persevere and succeed where others have failed? A lot of it will depend on the third party developers, and how Amazon treats them.

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DIY Raspberry Neural Network Sees All, Recognizes Some

As a fun project I thought I’d put Google’s Inception-v3 neural network on a Raspberry Pi to see how well it does at recognizing objects first hand. It turned out to be not only fun to implement, but also the way I’d implemented it ended up making for loads of fun for everyone I showed it to, mostly folks at hackerspaces and such gatherings. And yes, some of it bordering on pornographic — cheeky hackers.

An added bonus many pointed out is that, once installed, no internet access is required. This is state-of-the-art, standalone object recognition with no big brother knowing what you’ve been up to, unlike with that nosey Alexa.

But will it lead to widespread useful AI? If a neural network can recognize every object around it, will that lead to human-like skills? Read on. Continue reading “DIY Raspberry Neural Network Sees All, Recognizes Some”

The Red Special: Brian May’s Handmade Guitar

Guitarists are a special breed, and many of them have a close connection with the instruments they play. It might be a specific brand of guitar, or a certain setup required to achieve the sound they’re looking for. No one has a closer bond with an instrument than Brian May to his Red Special. The guitar he toured with and played through his career with Queen and beyond had very humble beginnings. It was built from scratch by Brian and his father Harold May.

A young Brian May playing the brand new Red Special. Note the disk magnets of the original handmade pickups

It was the early 1960’s and a young teenaged Brian May wanted an electric guitar. The problem was that the relatively new instruments were still quite expensive — into the hundreds of dollars. Well beyond the means of the modest family’s budget. All was not lost though. Brian’s father Harold was an electrical engineer and a hacker of sorts. He built the family’s radio, TV, and even furniture around the house. Harold proposed the two build a new electric guitar from scratch as a father-son project. This was the beginning of a two-year odyssey that resulted in the creation of one of the world’s most famous musical instruments.

Brian was already an accomplished guitarist, learning first on his dad’s George Formby Banjo-ukulele, and graduating to an Egmond acoustic guitar. Brian’s first forays into electric guitars came from experimenting with that Egmond. If you look close, you can even see the influence it had on the final design of the Red Special.

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How Smart is the Grid?

Marketing and advertising groups often have a tendency to capitalize on technological trends faster than engineers and users can settle into the technology itself. Perhaps it’s no surprise that it is difficult to hold back the motivation to get a product to market and profit. Right now the most glaring example is the practice of carelessly putting WiFi in appliances and toys and putting them on the Internet of Things, but there is a similar type of fiasco playing out in the electric power industry as well. Known as the “smart grid”, an effort is underway to modernize the electric power grid in much the same way that the Internet of Things seeks to modernize household appliances, but to much greater and immediate benefit.

A Cutler-Hammer industrial breaker ominously predicts the coming confusion in the smart grid arena.
Photo by Bryan Cockfield

To that end, if there’s anything in need of modernization it’s the electric grid. Often still extensively using technology that was pioneered in the 1800s like synchronous generators and transformers (not to mention metering and billing techniques that were perfected before the invention of the transistor), there is a lot of opportunity to add oversight and connectivity to almost every part of the grid from the power plant to the customer. Additionally, most modern grids are aging rapidly at the same time that we are asking them to carry more and more electricity. Modernization can also help the aging infrastructure become more efficient at delivering energy.

While the term “smart grid” is as nebulous and as ill-defined as “Internet of Things” (even the US Government’s definition is muddied and vague), the smart grid actually has a unifying purpose behind it and, so far, has been an extremely useful way to bring needed improvements to the power grid despite the lack of a cohesive definition. While there’s no single thing that suddenly transforms a grid into a smart grid, there are a lot of things going on at once that each improve the grid’s performance and status reporting ability.

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From 50s Perceptrons To The Freaky Stuff We’re Doing Today

Things have gotten freaky. A few years ago, Google showed us that neural networks’ dreams are the stuff of nightmares, but more recently we’ve seen them used for giving game character movements that are indistinguishable from that of humans, for creating photorealistic images given only textual descriptions, for providing vision for self-driving cars, and for much more.

Being able to do all this well, and in some cases better than humans, is a recent development. Creating photorealistic images is only a few months old. So how did all this come about?

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Reading Bingo Balls with Microcontrollers

Every once in a while a project comes along with that magical power to consume your time and attention for many months. When you finally complete it, you feel sorry that you don’t have to do anything more.

What is so special about this Bingo ball reader? It may seem like an ordinary OCR project at first glance; a camera captures the image and OCR software recognizes the number. Simple as that. And it works without problems, like every simple gadget should.

But then again, maybe it’s not that simple. Numbers are scattered all over the ball, so they have to be located first, and the best candidate for reading must be selected. Then, numbers are painted onto a sphere rather than a flat surface, sometimes making them deformed to the point where their shape has to be recovered first. Also, the angle of reading is not fixed but somewhere on a 360° scale. And then we have the glare problem to boot, as Bingo balls are so shiny that every light source reflects as a saturated bright spot.

So, is that all of it? Well, almost. The task is supposed to be performed by an embedded microcontroller, with limited speed and memory, yet the recognition process for one ball has to be fast — 500 ms at worst. But that’s just one part of the process. The project includes the pipelined mechanism which accepts the ball, transports it to be scanned by the OCR and then shot by the public broadcast camera before it gets dumped. And finally, if the reading was not reliable enough, the ball has to be subtly rotated so that the numbers would be repositioned for another reading attempt.

Despite these challenges I did manage to build this system. It’s fast and reliable, and I discovered some very interesting tricks along the way. Take a look at the quick demo video below to get a feel for the speed, and what the system “sees”. Then join me after the break to dive into the details of this interesting embedded build.

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The IP Of The Infinite Build Volume 3D Printer

Last week, the Blackbelt 3D printer launched on Kickstarter. What makes the Blackbelt 3D printer different than any other 3D printer on Kickstarter? This printer has an infinite build volume. It’s built for continuous production. As long as you have a large enough spool of filament, this printer will keep producing plastic parts with no downtime in between. The Blackbelt is a truly remarkable and innovative machine. Yes, it’s a bit expensive, but it’s designed for production and manufacturing, not some guy tinkering in his garage.

However, the Blackbelt 3D website includes two words that have sent the 3D printer community into an uproar. ‘Patent Pending’ is something no one in the community wants to see given the history of the industry and a few poor decisions from the first movers during the great 3D printer awakening of 2010. The idea of an infinite build volume printer that allows for continuous production is not new; we saw one last March at the Midwest RepRap Festival. The question, therefore, is what is covered by the upcoming Blackbelt patents, what is the prior art, and is it still possible to build an Open Source printer that uses these innovative techniques?

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