Smart Bike Suspension Tunes Your Ride On The Fly

January 23, 2023 by Dan Maloney 23 Comments

Riding a bike is a pretty simple affair, but like with many things, technology marches on and adds complications. Where once all you had to worry about was pumping the cranks and shifting the gears, now a lot of bikes have front suspensions that need to be adjusted for different riding conditions. Great for efficiency and ride comfort, but a little tough to accomplish while you’re underway.

Luckily, there’s a solution to that, in the form of this active suspension system by [Jallson S]. The active bit is a servo, which is attached to the adjustment valve on the top of the front fork of the bike. The servo moves the valve between fully locked, for smooth surfaces, and wide open, for rough terrain. There’s also a stop in between, which partially softens the suspension for moderate terrain. The 9-gram hobby servo rotates the valve with the help of a 3D printed gear train.

But that’s not all. Rather than just letting the rider control the ride stiffness from a handlebar-mounted switch, [Jallson S] added a little intelligence into the mix. Ride data from the accelerometer on an Arduino Nano 33 BLE Sense was captured on a smartphone via Arduino Science Journal. The data was processed through Edge Impulse Studio to create models for five different ride surfaces and rider styles. This allows the stiffness to be optimized for current ride conditions — check it out in action in the video below.

[Jallson S] is quick to point out that this is a prototype, and that niceties like weatherproofing still have to be addressed. But it seems like a solid start — now let’s see it teamed up with an Arduino shifter.

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Giving An Old Typewriter A Mind Of Its Own With GPT-3

January 15, 2023 by Dan Maloney 27 Comments

There was an all-too-brief period in history where typewriters went from clunky, purely mechanical beasts to streamlined, portable electromechanical devices. But when the 80s came around and the PC revolution started, the typewriting was on the wall for these machines, and by the 90s everyone had a PC, a printer, and Microsoft Word. And thus the little daisy-wheel typewriters began to populate thrift shops all over the world.

That’s fine with us, because it gave [Arvind Sanjeev] a chance to build “Ghostwriter”, an AI-powered automatic typewriter. The donor machine was a clapped-out Brother electronic typewriter, which needed a bit of TLC to bring it back to working condition. From there, [Arvind] worked out the keyboard matrix and programmed an Arduino to drive the typewriter, both read and write. A Raspberry Pi running the OpenAI Python API for GPT-3 talks to the Arduino over serial, which basically means you can enter a GPT writing prompt with the keyboard and have the machine spit out a dead-tree version of the results.

To mix things up a bit, [Arvind] added a pair of pots to control the creativity and length of the response, plus an OLED screen which seems only to provide some cute animations, which we don’t hate. We also don’t hate the new paint job the typewriter got, but the jury is still out on the “poetry” that it typed up. Eye of the beholder, we suppose.

Whatever you think of GPT’s capabilities, this is still a neat build and a nice reuse of otherwise dead-end electronics. Need a bit more help building natural language AI into your next project? Our own [Donald Papp] will get you up to speed on that.

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Machine Learning Makes Sure Your LOLs Are Genuine

January 4, 2023 by Dan Maloney 13 Comments

There was a time not too long ago when “LOL” actually meant something online. If someone went through the trouble of putting LOL into an email or text, you could be sure they were actually LOL-ing while they were typing — it was part of the social compact that made the Internet such a wholesome and inviting place. But no more — LOL has been reduced to a mere punctuation mark, with no guarantee that the sender was actually laughing, chuckling, chortling, or even snickering. What have we become?

To put an end to this madness, [Brian Moore] has come up with the LOL verifier. Like darn near every project we see these days, it uses a machine learning algorithm — EdgeImpulse in this case. It detects a laugh by comparing audio input against an exhaustive model of [Brian]’s jocular outbursts — he says it took nearly three full minutes to collect the training set. A Teensy 4.1 takes care of HID duties; if a typed “LOL” correlates to some variety of laugh, the initialism is verified with a time and date stamp. If your LOL was judged insincere – well, that’s on you. See what you think of the short video below — we genuinely LOL’d. And while we’re looking forward to a ROTFL verifier, we’re not sure we want to see his take on LMAO.

Hats off to [Brian] for his attempt to enforce some kind of standards online. You may recall his earlier attempt to make leaving Zoom calls a little less awkward, which we also appreciate.

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Shopping Cart Does The Tedious Work For You

December 29, 2022 by Bryan Cockfield 36 Comments

Thanks to modern microcontrollers, basic home automation tasks such as turning lights on and off, opening blinds, and various other simple tasks have become common DIY projects. But with the advent of artificial intelligence and machine learning the amount of tasks that can be offloaded to computers has skyrocketed. This shopping cart that automates away the checkout lines at grocery stores certainly fits into this category.

The project was inspired by the cashierless Amazon stores where customers simply walk into a store, grab what they want, and leave. This is made possible by the fact that computers monitor their purchases and charge them automatically, but creator [kutluhan_aktar] wanted to explore a way of doing this without a fleet of sensors and cameras all over a store. By mounting the hardware to a shopping cart instead, the sensors travel with the shopper and monitor what’s placed in the cart instead of what’s taken from a shelf. It’s built around the OpenMV Cam H7, a microcontroller paired with a camera specifically designed for these types of tasks, and the custom circuitry inside the case also includes WiFi connectivity to make sure the shopping cart can report its findings properly.

[kutluhan_aktar] also built the entire software stack from the ground up and trained the model on a set of common products as a proof-of-concept. The idea was to allow smaller stores to operate more efficiently without needing a full suite of Amazon hardware and software backing it up, and this prototype seems to work pretty well to that end. If you want to develop a machine vision project on your own with more common hardware, take a look at this project which uses the Raspberry Pi instead.

On Getting A Computer’s Attention And Striking Up A Conversation

November 14, 2022 by Maya Posch 17 Comments

With the rise in voice-driven virtual assistants over the years, the sight of people talking to various electrical devices in public and in private has become rather commonplace. While such voice-driven interfaces are decidedly useful for a range of situations, they also come with complications. One of these are the trigger phrases or wake words that voice assistants listen to when in standby. Much like in Star Trek, where uttering ‘Computer’ would get the computer’s attention, so do we have our ‘Siri’, ‘Cortana’ and a range of custom trigger phrases that enable the voice interface.

Unlike in Star Trek, however, our virtual assistants do not know when we really desire to interact. Unable to distinguish context, they’ll happily respond to someone on TV mentioning their trigger phrase. This possibly followed by a ludicrous purchase order or other mischief. The realization here is the complexity of voice-based interfaces, while still lacking any sense of self-awareness or intelligence.

Another issue is that the process of voice recognition itself is very resource-intensive, which limits the amount of processing that can be performed on the local device. This usually leads to the voice assistants like Siri, Alexa, Cortana and others processing recorded voices in a data center, with obvious privacy implications.

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How The Image-Generating AI Of Stable Diffusion Works

October 24, 2022 by Donald Papp 40 Comments

[Jay Alammar] has put up an illustrated guide to how Stable Diffusion works, and the principles in it are perfectly applicable to understanding how similar systems like OpenAI’s Dall-E or Google’s Imagen work under the hood as well. These systems are probably best known for their amazing ability to turn text prompts (e.g. “paradise cosmic beach”) into a matching image. Sometimes. Well, usually, anyway.

‘System’ is an apt term, because Stable Diffusion (and similar systems) are actually made up of many separate components working together to make the magic happen. [Jay]’s illustrated guide really shines here, because it starts at a very high level with only three components (each with their own neural network) and drills down as needed to explain what’s going on at a deeper level, and how it fits into the whole.

Spot any similar shapes and contours between the image and the noise that preceded it? That’s because the image is a result of removing noise from a random visual mess, not building it up from scratch like a human artist would do.

It may surprise some to discover that the image creation part doesn’t work the way a human does. That is to say, it doesn’t begin with a blank canvas and build an image bit by bit from the ground up. It begins with a seed: a bunch of random noise. Noise gets subtracted in a series of steps that leave the result looking less like noise and more like an aesthetically pleasing and (ideally) coherent image. Combine that with the ability to guide noise removal in a way that favors conforming to a text prompt, and one has the bones of a text-to-image generator. There’s a lot more to it of course, and [Jay] goes into considerable detail for those who are interested.

If you’re unfamiliar with Stable Diffusion or art-creating AI in general, it’s one of those fields that is changing so fast that it sometimes feels impossible to keep up. Luckily, our own Matthew Carlson explains all about what it is, and why it matters.

Stable Diffusion can be run locally. There is a fantastic open-source web UI, so there’s no better time to get up to speed and start experimenting!

Render Yourself Invisible To AI With This Adversarial Sweater Of Doom

October 20, 2022 by Dan Maloney 57 Comments

Ugly sweater season is rapidly approaching, at least here in the Northern Hemisphere. We’ve always been a bit baffled by the tradition of paying top dollar for a loud, obnoxious sweater that gets worn to exactly one social event a year. We don’t judge, of course, but that’s not to say we wouldn’t look a little more favorably on someone’s fashion choice if it were more like this AI-defeating adversarial ugly sweater.

The idea behind this research from the University of Maryland is not, of course, to inform fashion trends, nor is it to create a practical invisibility cloak. It’s really to probe machine learning systems for vulnerabilities by making small changes to the input while watching for changes in the output. In this case, the ML system was a YOLO-based vision system which has little trouble finding humans in an arbitrary image. The adversarial pattern was generated by using a large set of training images, some of which contain the objects of interest — in this case, humans. Each time a human is detected, a random pattern is rendered over the image, and the data is reassessed to see how much the pattern lowers the object’s score. The adversarial pattern eventually improves to the point where it mostly prevents humans from being recognized. Much more detail is available in the research paper (PDF) if you want to dig into the guts of this.

The pattern, which looks a little like a bad impressionist painting of people buying pumpkins at a market and bears some resemblance to one we’ve seen before in similar work, is said to work better from different viewing angles. It also makes a spiffy pullover, especially if you’d rather blend in at that Christmas party.