Overengineering A Smart Doorbell

September 12, 2021 by Jenny List 19 Comments

Fresh from the mediaeval splendour of the Belgian city of Gent, we bring you more from the Newline hacker conference organised by Hackerspace Gent. [Victor Sonck] works at the top of his house, and thus needed a doorbell notifier. His solution was unexpected, and as he admits over engineered, using machine learning on an audio stream from a microphone to detect the doorbell’s sound.

Having established that selling his soul to Amazon with a Ring doorbell wasn’t an appropriate solution, he next looked at his existing doorbell. Some of us might connect directly to its power to sense when the button was pressed, but we’re kinda glad he went for the overengineered route because it means we are treated to a run-down how machine learning works and how it can be applied to audio. The end result can sometimes be triggered by a spoon hitting a cereal plate, but since he was able to demonstrate it working we think it can be called a success. Should you wish to dive in further you can find more in his GitHub repository.

How would you overengineer a doorbell? Use GNU radio and filters? Or maybe a Rube Goldberg machine involving string and pulleys? As always, the comments are open.

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Flamethrower weedkiller mounted on a robot arm riding a tank tracked base

Don’t Sleep On The Lawn, There’s An AI-Powered, Flamethrower-Wielding Robot About

September 11, 2021 by Dave Rowntree 25 Comments

You know how it goes, you’re just hanging out in the yard, there aren’t enough hours in the day, and weeding the lawn is just such a drag. Then an idea just pops into your head. How about we attach a gas powered flamethrower to a robot arm, drive it around on a tank-tracked robotic base, and have it operate autonomously with an AI brain? Yes, that sounds like a good idea. Let’s do that. And so, [Dave Niewinski] did exactly that with his Ultimate Weed Killing Robot.

And you thought the robot overlords might take a more subtle approach and take over the world one coffee machine at a time? No, straight for the fully-autonomous flamethrower it is then.

This build uses a Kinova Robots Gen 3 six-axis arm, mounted to an Agile-X Robotics Bunker base. Control is via a Connect Tech Rudi-NX box which contains an Nvidia Jetson Xavier NX Edge AI computing engine. Wow that was a mouthful!

Connectivity from the controller to the base is via CAN bus, but, sadly no mention of how the robot arm controller is hooked up. At least this particular model sports an effector mount camera system, which can feed straight into the Jetson, simplifying the build somewhat.

To start the software side of things, [Dave] took a video using his mobile phone while walking his lawn. Next he used RoboFlow to highlight image stills containing weeds, which were in turn used to help train a vision AI system. The actual AI training was written in Python using Google Collaboratory, which is itself based on the awesome Jupyter Notebook (see also Jupyter Lab on the main site. If you haven’t tried that yet, and if you do any data science at all, you’ll kick yourself for not doing so!) Collaboratory would not be all that useful for this by itself, except that it gives you direct, free GPU access, via the cloud, so you can use it for AI workloads without needing fancy (and currently hard to get) GPU hardware on your desk.

Details of the hardware may be a little sparse, but at least the software required can be found on the WeedBot GitHub. It’s not like most of us will have this exact hardware lying around anyway. For a more complete description of this terrifying contraption, checkout the video after the break.

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AI Powered Coffee Maker Knows A Bit Too Much About You

September 9, 2021 by Dave Rowntree 20 Comments

People keep warning that Skynet and the great robot uprising is not that far away, what with all this recent AI and machine-learning malarky getting all the attention lately. But we think going straight for a terminator robot army is not a very smart approach, not least due to a lack of subtlety. We think that it’s a much better bet to take over the world one home appliance at a time, and this AI Powered coffee maker might just well be part of that master plan.

Raspberry Pi Zero sitting atop the custom nixie tube driver PCB — PCB stackup with Pi Zero sat atop the driver / PSU PCBs

[Mark Smith] has taken a standard semi-auto espresso maker and jazzed it up a bit, with a sweet bar graph nixie tube the only obvious addition, at least from the front of the unit. Inside, a Raspberry Pi Zero sits atop his own nixie tube hat and associated power supply. The whole assembly is dropped into a 3D printed case and lives snuggled up to the water pump.

The Pi is running a web application written with the excellent Flask framework, and also an additional control application written in python. This allows the user to connect to the machine via Ethernet and see its status. The smarts are in the form of a simple self-grading machine learning algorithm, that takes a time series as an input (in this case when you take your shots of espresso) and after a few weeks of data, is able to make a reasonable prediction as to when you might want it in the future. It then automatically heats up in time for you to use the machine, when you usually do, then cools back down to save energy. No more pointless wandering around to see if the machine is hot enough yet – as you can just check the web page and see from the comfort of your desk.

But that’s not all [Mark] has done. He also improved the temperature control of the water boiler, and added an interlock that prevents the machine from producing a shot until the water temperature is just so. Water level is indicated by the glorious bar graph nixie tube, which also serves a few other user indication duties when appropriate. All in all a pretty sweet build, but we do add a word of caution: If your toaster starts making an unreasonable number of offers of toasted teacakes, give it a wide berth.

video of someone pushing the button to generate new art

AI Generating Paintings Off To A Flying Art

August 27, 2021 by Matthew Carlson 14 Comments

The philosophical question of “What is art?” has an ethereal, transient quality to it. A definition seems to slip away as you get close to an answer. Embracing that quality, [Max Fischer] has created an AI-powered painting that paints a new piece of art at the push of a button. When the button below the screen is pushed, a new image is generated and the old one is forever lost, which in a way, makes the frame a piece of art itself.

The really makes this project stand is the sheer quality of documentation on the GitHub repo. The instructions are incredibly detailed. Everything from setting up the Jetson to building the control box out of half-inch MDF (12mm for the sane part of the world) is laid out with copious pictures. Despite the ease of generating images ahead of time, [Max] took the ~~hard route~~ Hackaday route and did all inference locally and in real-time. To handle the processing requirements, an Nvidia Jetson Xavier NX single-board computer was used. He trained StyleGAN with high-resolution abstract art that gets generated whenever the button below the screen is pushed. To prevent screen burn-in, a PIR was added to turn the screen off when no one is around.

Here at Hackaday, we’ve seen several projects putting old laptop screens or monitors into a nice wooden case and mounting them to the wall. Since 32″ laptops are rather hard to find, [Max] opted to take a different approach and instead got a 32″ Samsung Frame for relatively cheap.

For all their detail, [Max] did leave one thing out of the readme: the AI that generates the art. [Max] hints that he wants others to create their picture frames, but with their own art generation. So what are you waiting for? Go make some art.

GitHub Copilot And The Unfulfilled Promises Of An Artificial Intelligence Future

August 2, 2021 by Maya Posch 36 Comments

In late June of 2021, GitHub launched a ‘technical preview’ of what they termed GitHub Copilot, described as an ‘AI pair programmer which helps you write better code’. Quite predictably, responses to this announcement varied from glee at the glorious arrival of our code-generating AI overlords, to dismay and predictions of doom and gloom as before long companies would be firing software developers en-masse.

As is usually the case with such controversial topics, neither of these extremes are even remotely close to the truth. In fact, the OpenAI Codex machine learning model which underlies GitHub’s Copilot is derived from OpenAI’s GPT-3 natural language model, and features many of the same stumbles and gaffes which GTP-3 has. So if Codex and with it Copilot isn’t everything it’s cracked up to be, what is the big deal, and why show it at all?

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Ostrich Robot Machine-Learns Itself To 5K

August 1, 2021 by Jenny List 43 Comments

Ever since humanity has grasped the idea of a robot, we’ve wanted to imagine them into walking humanoid form. But making a robot walk like a human is not an easy task, and even the best of them end up with the somewhat shuffling gait of a Honda Asimo rather than the graceful poise of a balerina. Only in recent years have walking robots appeared to come of age, and then not by mimicking the human gait but something more akin to a bird.

We’ve seen it in the Boston Dynamics models, and also now in a self-balancing two-legged robot developed at Oregon State University that has demonstrated its abilities by completing an unaided 5 km run having used its machine learning skills to teach itself to run from scratch. It’s believed to be the first time a robot has achieved such a feat without first being programmed for the specific task.

The university’s PR piece envisages a time in which walking robots of this type have become commonplace, and when humans interact with them on a daily basis. We can certainly see that they could perform a huge number of autonomous outdoor tasks that perhaps a wheeled robot might find to be difficult, so maybe they have a bright future. Decide for yourself, after watching the video below the break.

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Smart Camera Based On Google Coral

June 30, 2021 by Bryan Cockfield 8 Comments

As machine learning and artificial intelligence becomes more widespread, so do the number of platforms available for anyone looking to experiment with the technology. Much like the single board computer revolution of the last ten years, we’re currently seeing a similar revolution with the number of platforms available for machine learning. One of those is Google Coral, a set of hardware specifically designed to take advantage of this new technology. It’s missing support to work with certain hardware though, so [Ricardo] set out to get one working with a Raspberry Pi Zero with this smart camera build based around Google Coral.

The project uses a Google Coral Edge TPU with a USB accelerator as the basis for the machine learning. A complete image for the Pi Zero is available which sets most of the system up right away including headless operation and includes a host of machine learning software such as OpenCV and pytesseract. By pairing a camera to the Edge TPU and the Raspberry Pi, [Ricardo] demonstrates many of its machine learning capabilities with several example projects such as an automatic license plate detector and even a mode which can recognize whether or not a face mask is being worn, and even how correctly it is being worn.

For those who want to get into machine learning and artificial intelligence, this is a great introductory project since the cost to entry is so low using these pieces of hardware. All of the project code and examples are available on [Ricardo]’s GitHub page too. We could even imagine his license plate recognition software being used to augment this license plate reader which uses a much more powerful camera.