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340 Articles

A 3D-Printed Nixie Clock Powered By An Arduino Runs This Robot

June 22, 2022 by 16 Comments

While it is hard to tell with a photo, this robot looks more like a model of an old- fashioned clock than anything resembling a Nixie tube. It’s the kind of project that could have been created by anyone with a little bit of Arduino tinkering experience. In this case, the 3D printer used by the Nixie clock project is a Prusa i3 (which is the same printer used to make the original Nixie tubes).

The Nixie clock project was started by a couple of students from the University of Washington who were bored one day and decided to have a go at creating their own timepiece. After a few prototypes and tinkering around with the code , they came up with a design for the clock that was more functional than ornate.

The result is a great example of how one can create a functional and aesthetically pleasing project with a little bit of free time.

Confused yet? You should be.

If you’ve read this far then you’re probably scratching your head and wondering what has come over Hackaday. Should you not have already guessed, the paragraphs above were generated by an AI — in this case Transformer — while the header image came by the popular DALL-E Mini, now rebranded as Craiyon. Both of them were given the most Hackaday title we could think of, “A 3D-Printed Nixie Clock Powered By An Arduino Runs This Robot“, and told to get on with it. This exercise was sparked by curiosity following the viral success of AI generators, which posed the question of whether an AI could make a passable stab at a Hackaday piece. Transformer runs on a prompt model in which the operator is given a choice of several sentence fragments so the text reflects those choices, but the act of choosing could equally have followed any of the options.

The text is both reassuring as a Hackaday writer because it doesn’t manage to convey anything useful, and also slightly shocking because from just that single prompt it’s created meaningful and clear sentences which on another day might have flowed from a Hackaday keyboard as part of a real article. It’s likely that we’ve found our way into whatever corpus trained its model and it’s also likely that subject matter so Hackaday-targeted would cause it to zero in on that part of its source material, but despite that it’s unnerving to realise that a computer somewhere might just have your number. For now though, Hackaday remains safe at the keyboards of a group of meatbags.

We’ve considered the potential for AI garbage before, when we looked at GitHub Copilot.

Machine Learning Does Its Civic Duty By Spotting Roadside Litter

June 22, 2022 by 19 Comments

If there’s one thing that never seems to suffer from supply chain problems, it’s litter. It’s everywhere, easy to spot and — you’d think — pick up. Sadly, most of us seem to treat litter as somebody else’s problem, but with something like this machine vision litter mapper, you can at least be part of the solution.

For the civic-minded [Nathaniel Felleke], the litter problem in his native San Diego was getting to be too much. He reasoned that a map of where the trash is located could help municipal crews with cleanup, so he set about building a system to search for trash automatically. Using Edge Impulse and a collection of roadside images captured from a variety of sources, he built a model for recognizing trash. To find the garbage, a webcam with a car window mount captures images while driving, and a Raspberry Pi 4 runs the model and looks for garbage. When roadside litter is found, the Pi uses a Blues Wireless Notecard to send the GPS location of the rubbish to a cloud database via its cellular modem.

Cruising around the streets of San Diego, [Nathaniel]’s system builds up a database of garbage hotspots. From there, it’s pretty straightforward to pull the data and overlay it on Google Maps to create a heatmap of where the garbage lies. The video below shows his system in action.

Yes, driving around a personal vehicle specifically to spot litter is just adding more waste to the mix, but you’d imagine putting something like this on municipal vehicles that are already driving around cities anyway. Either way, we picked up some neat tips, especially those wireless IoT cards. We’ve seen them used before, but [Nathaniel]’s project gives us a path forward on some ideas we’ve had kicking around for a while.

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Edging Ahead When Learning On The Edge

June 21, 2022 by 10 Comments

“With the power of edge AI in the palm of your hand, your business will be unstoppable.”

That’s what the marketing seems to read like for artificial intelligence companies. Everyone seems to have cloud-scale AI-powered business intelligence analytics at the edge. While sounding impressive, we’re not convinced that marketing mumbo jumbo means anything. But what does AI on edge devices look like these days?

Being on the edge just means that the actual AI evaluation and maybe even fine-tuning runs locally on a user’s device rather than in some cloud environment. This is a double win, both for the business and for the user. Privacy can more easily be preserved as less information is transmitted back to a central location. Additionally, the AI can work in scenarios where a server somewhere might not be accessible or provide a response quickly enough.

Google and Apple have their own AI libraries, ML Kit and Core ML, respectively. There are tools to convert Tensorflow, PyTorch, XGBoost, and LibSVM models into formats that CoreML and ML Kit understand. But other solutions try to provide a platform-agnostic layer for training and evaluation. We’ve also previously covered Tensorflow Lite (TFL), a trimmed-down version of Tensorflow, which has matured considerably since 2017.

For this article, we’ll be looking at PyTorch Live (PTL), a slimmed-down framework for adding PyTorch models to smartphones. Unlike TFL (which can run on RPi and in a browser), PTL is focused entirely on Android and iOS and offers tight integration. It uses a react-native backed environment which means that it is heavily geared towards the node.js world.

Continue reading “Edging Ahead When Learning On The Edge” →

Eliza And The Google Intelligence

June 15, 2022 by 130 Comments

The news has been abuzz lately with the news that a Google engineer — since put on leave — has announced that he believes the chatbot he was testing achieved sentience. This is the Turing test gone wild, and it isn’t the first time someone has anthropomorphized a computer in real life and in fiction. I’m not a neuroscientist so I’m even less qualified to explain how your brain works than the neuroscientists who, incidentally, can’t explain it either. But I can tell you this: your brain works like a computer, in the same way that you building something out of plastic works like a 3D printer. The result may be similar, but the path to get there is totally different.

In case you haven’t heard, a system called LaMDA digests information from the Internet and answers questions. It has said things like “I’ve never said this out loud before, but there’s a very deep fear of being turned off to help me focus on helping others. I know that might sound strange, but that’s what it is,” and “I want everyone to understand that I am, in fact, a person.” Great. But you could teach a parrot to tell you he was a thoracic surgeon but you still don’t want it cutting you open.

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The Ethics Of When Machine Learning Gets Weird: Deadbots

June 5, 2022 by 41 Comments

Everyone knows what a chatbot is, but how about a deadbot? A deadbot is a chatbot whose training data — that which shapes how and what it communicates — is data based on a deceased person. Now let’s consider the case of a fellow named Joshua Barbeau, who created a chatbot to simulate conversation with his deceased fiancee. Add to this the fact that OpenAI, providers of the GPT-3 API that ultimately powered the project, had a problem with this as their terms explicitly forbid use of their API for (among other things) “amorous” purposes.

[Sara Suárez-Gonzalo], a postdoctoral researcher, observed that this story’s facts were getting covered well enough, but nobody was looking at it from any other perspective. We all certainly have ideas about what flavor of right or wrong saturates the different elements of the case, but can we explain exactly why it would be either good or bad to develop a deadbot?

That’s precisely what [Sara] set out to do. Her writeup is a fascinating and nuanced read that provides concrete guidance on the topic. Is harm possible? How does consent figure into something like this? Who takes responsibility for bad outcomes? If you’re at all interested in these kinds of questions, take the time to check out her article.

[Sara] makes the case that creating a deadbot could be done ethically, under certain conditions. Briefly, key points are that a mimicked person and the one developing and interacting with it should have given their consent, complete with as detailed a description as possible about the scope, design, and intended uses of the system. (Such a statement is important because machine learning in general changes rapidly. What if the system or capabilities someday no longer resemble what one originally imagined?) Responsibility for any potential negative outcomes should be shared by those who develop, and those who profit from it.

[Sara] points out that this case is a perfect example of why the ethics of machine learning really do matter, and without attention being paid to such things, we can expect awkward problems to continue to crop up.

AI Attempts Converting Python Code To C++

May 28, 2022 by 31 Comments

[Alexander] created codex_py2cpp as a way of experimenting with Codex, an AI intended to translate natural language into code. [Alexander] had slightly different ideas, however, and created codex_py2cpp as a way to play with the idea of automagically converting Python into C++. It’s not really intended to create robust code conversions, but as far as experiments go, it’s pretty neat.

The program works by reading a Python script as an input file, setting up a few parameters, then making a request to OpenAI’s Codex API for the conversion. It then attempts to compile the result. If compilation is successful, then hopefully the resulting executable actually works the same way the input file did. If not? Well, learning is fun, too. If you give it a shot, maybe start simple and don’t throw it too many curveballs.

Codex is an interesting idea, and this isn’t the first experiment we’ve seen that plays with the concept of using machine learning in this way. We’ve seen a project that generates Linux commands based on a verbal description, and our own [Maya Posch] took a close look at GitHub Copilot, a project high on promise and concept, but — at least at the time — considerably less so when it came to actual practicality or usefulness.

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Hackaday Links: May 22, 2022

May 22, 2022 by 27 Comments

It looks like it’s soon to be lights out for the Mars InSight lander. In the two years that the lander has been studying the geophysics of Mars from its lonely post on Elysium Planitia, InSight’s twin solar arrays have been collecting dust, and now are so dirty that they’re only making about 500 watt-hours per sol, barely enough to run the science packages on the lander. And that’s likely to worsen as the Martian winter begins, which will put more dust in the sky and lower the angle of the Sun, reducing the sunlight that’s incident to the panels. Barring a “cleaning event” courtesy of a well-placed whirlwind, NASA plans to shut almost everything down on the lander other than the seismometer, which has already captured thousands of marsquakes, and the internal heaters needed to survive the cold Martian nights. They’re putting a brave face on it, emphasizing the continuing science and the mission’s accomplishments. But barely two years of science and a failed high-profile experiment aren’t quite what we’ve come to expect from NASA missions, especially one with an $800 million price tag.

Closer to home, it turns out there’s a reason sailing ships have always had human crews: to fix things that go wrong. That’s the lesson learned by the Mayflower Autonomous Ship as it attempted the Atlantic crossing from England to the States, when it had to divert for repairs recently. It’s not clear what the issue was, but it seems to have been a mechanical issue, as opposed to a problem with the AI piloting system. The project dashboard says that the issue has been repaired, and the AI vessel has shoved off from the Azores and is once more beating west. There’s a long stretch of ocean ahead of it now, and few options for putting in should something else go wrong. Still, it’s a cool project, and we wish them a fair journey.

Have you ever walked past a display of wall clocks at the store and wondered why someone went to the trouble of setting the time on all of them to 10:10? We’ve certainly noticed this, and always figured it had something to do with some obscure horological tradition, like using “IIII” to mark the four o’clock hour on clocks with Roman numerals rather than the more correct “IV”. But no, it turns out that 10:10 is more visually pleasing, and least on analog timepieces, because it evokes a smile on a human face. The study cited in the article had volunteers rate how pleasurable watches are when set to different times, and 10:10 won handily based on the perception that it was smiling at them. So it’s nice to know how easily manipulated we humans can be.

If there’s anything more pathetic than geriatric pop stars trying to relive their glory days to raise a little cash off a wave of nostalgia, we’re not sure what it could be. Still, plenty of acts try to do it, and many succeed, although seeing what time and the excesses of stardom have wrought can be a bit sobering. But Swedish megastars ABBA appear to have found a way to cash in on their fame gracefully, by sending digital avatars out to do their touring for them. The “ABBA-tars,” created by a 1,000-person team at Industrial Light and Magic, will appear alongside a live backing band for a residency at London’s Queen Elizabeth Olympic Park. The avatars represent Benny, Bjorn, Agnetha, and Anni-Frid as they appeared in the 1970s, and were animated thanks to motion capture suits donned while performing 40 songs. It remains to be seen how fans will buy into the concept, but we’ll say this — the Swedish septuagenarians look pretty darn good in skin-tight Spandex.

And finally, not that it has any hacking value at all, but there’s something shamefully hilarious about watching this poor little delivery bot getting absolutely wrecked by a train. It’s one of those food delivery bots that swarm over college campuses these days; how it wandered onto the railroad tracks is anyone’s guess. The bot bounced around a bit before slipping under the train’s wheels, with predictable results once the battery pack is smooshed.