The old way was to write clever code that could handle every possible outcome. But what if you don’t know exactly what your inputs will look like, or just need a faster route to the final results? The answer is Machine Learning, and we want you to give it a try during the Train All the Things contest!
It’s hard to find a more buzz-worthy term than Artificial Intelligence. Right now, where the rubber hits the road in AI is Machine Learning and it’s never been so easy to get your feet wet in this realm.
From an 8-bit microcontroller to common single-board computers, you can do cool things like object recognition or color classification quite easily. Grab a beefier processor, dedicated ASIC, or lean heavily into the power of the cloud and you can do much more, like facial identification and gesture recognition. But the sky’s the limit. A big part of this contest is that we want everyone to get inspired by what you manage to pull off.
Yes, We Do Want to See Your ML “Hello World” Too!
Wait, wait, come back here. Have we already scared you off? Don’t read AI or ML and assume it’s not for you. We’ve included a category for “Artificial Intelligence Blinky” — your first attempt at doing something cool.
Need something simple to get you excited? How about Machine Learning on an ATtiny85 to sort Skittles candy by color? That uses just one color sensor for a quick and easy way to harvest data that forms a training set. But you could also climb up the ladder just a bit and make yourself a camera-based LEGO sorter or using an IMU in a magic wand to detect which spell you’re casting. Need more scientific inspiration? We’re hoping someday someone will build a training set that classifies microscope shots of micrometeorites. But we’d be equally excited with projects that tackle robot locomotion, natural language, and all the other wild ideas you can come up with.
Our guess is you don’t really need prizes to get excited about this one… most people have been itching for a reason to try out machine learning for quite some time. But we do have $100 Tindie gift certificates for the most interesting entry in each of the four contest categories: ML on the edge, ML on the gateway, AI blinky, and ML in the cloud.
Get started on your entry. The Train All The Things contest is sponsored by Digi-Key and runs until April 7th.
Artificial intelligence is taking the world by storm. Rather than a Terminator-style apocalypse, though, it seems to be more of a useful tool for getting computers to solve problems on their own. This isn’t just for supercomputers, either. You can load AI onto some of the smallest microcontrollers as well. Tensorflow Lite is a popular tool for this, but getting it to work on your particular microcontroller can be a pain, unless you’re using an Espruino.
This project adds support for Tensorflow to this class of microcontrollers without having to fuss around with obtuse build tools. Basically adding a single line of code creates an instance, all without having to compile anything or even reboot. Tensorflow is a powerful software tool for microcontrollers, and having it this accessible now is a great leap forward.
So, what can you do with this tool? The team behind this build is using Tensorflow on an open smart watch that can be used to detect hand gestures and many other things. They also opened up these tools for use in a browser, which allows use of the AI software and emulates an Espruino without needing a physical device. There’s a lot going on with this one, and it’s a bonus that it’s open source and ready to be turned into anything you might need, like turning yourself into a Street Fighter.
The best part about the term “Artificial Intelligence” is that nobody can really tell you what it exactly means. The main reason for this stems from the term “intelligence”, with definitions ranging from the ability to practice logical reasoning to the ability to perform cognitive tasks or dream up symphonies. When it comes to human intelligence, properties such as self-awareness, complex cognitive feats, and the ability to plan and motivate oneself are generally considered to be defining features. But frankly, what is and isn’t “intelligence” is open to debate.
What isn’t open to debate is that AI is a marketing goldmine. The vagueness has allowed for marketing departments around the world to go all AI-happy, declaring that their product is AI-enabled and insisting that their speech assistant responds ‘intelligently’ to one’s queries. One might begin to believe that we’re on the cusp of a fantastic future inhabited by androids and strong AIs attending to our every whim.
In this article we’ll be looking at the reality behind these claims and ponder humanity’s progress towards becoming a Type I civilization. But this is Hackaday, so we’re also going to dig into the guts of some AI chips, including the Kendryte K210 and see how the hardware of today fits into our Glorious Future. Continue reading “How Smart Are AI Chips, Really?”
A fundamental truth about AI systems is that training the system with biased data creates biased results. This can be especially dangerous when the systems are being used to predict crime or select sentences for criminals, since they can hinge on unrelated traits such as race or gender to make determinations.
A group of researchers from the Massachusetts Institute of Technology (MIT) CSAIL is working on a solution to “de-bias” data by resampling it to be more balanced. The paper published by PhD students [Alexander Amini] and [Ava Soleimany] describes an algorithm that can learn a specific task – such as facial recognition – as well as the structure of the training data, which allows it to identify and minimize any hidden biases.
Testing showed that the algorithm minimized “categorical bias” by over 60% compared against other widely cited facial detection models, all while maintaining the same precision of detection. This figure was maintained when the team evaluated a facial-image dataset from the Algorithmic Justice League, a spin-off group from the MIT Media Lab.
The team says that their algorithm would be particularly relevant for large datasets that can’t easily be vetted by a human, and can potentially rectify algorithms used in security, law enforcement, and other domains beyond facial detection.
OK, let’s start this one by saying that it’s useful to know how to break security measures in order to understand how to better defend yourself, and that you shouldn’t break into any network you don’t have access to. That being said, if you want to learn about security and the weaknesses within the WPA standard, there’s no better way to do it than with a tool that mimics the behavior of a Tamagotchi.
Called the pwnagotchi, this package of artificial intelligence looks for information in local WiFi packets that can be used to crack WPA encryption. It’s able to modify itself in order to maximize the amount of useful information it’s able to obtain from whatever environment you happen to place it in. As an interesting design choice, the pwnagotchi behaves like an old Tamagotchi pet would, acting happy when it gets the inputs it needs.
This project is beyond a novelty though and goes deep in the weeds of network security. If you’re at all interested in the ways in which your own networks might be at risk, this might be a tool you can use to learn a little more about the ways of encryption, general security, and AI to boot. Of course, if you’re new to the network security world, make sure the networks you’re using are secured at least a little bit first.
Thanks to [Itay] for the tip!
It isn’t often that the world of Hackaday intersects with the world of crafting, which is perhaps a shame because many of the skills and techniques of the two have significant overlap. Crochet for instance has rarely featured here, but that is about to change with [Janelle Shane]’s HAT3000 neural network trained to produce crochet hat patterns.
Taking the GPT-2 neural network trained on Internet text and further training it with a stack of crochet hat patterns, she was able to generate AI-designed hats which her friends on the Ravelry yarn forum set to crochet into real hats. It’s a follow-up to a previous knitting-based project, and instead of producing the hats you might expect it goes into flights of fancy. Some are visibly hat-like while others turn into avant-garde creations that defy any attempt to match them to real heads. A whole genre of hyperbolic progressions of crochet rows produce hats with organic folds that begin to resemble brains, and tax both the stamina of the person doing the crochet and their supply of yarn.
Perhaps most amusingly the neural network retains the ability to produce text, but when it does so it now inevitably steers the subject back to crochet hats. A Harry Potter sentence spawns a passage of something she aptly describes as “terrible crochet-themed erotica“, and such is the influence of the crochet patterns that this purple prose can even include enough crochet instructions to make them crochetable. It would be fascinating to see whether a similar model trained with G-code from Thingiverse would produce printable designs, what would an AI make with Benchy for example?
We’ve been entertained by [Janelle]’s AI work before, both naming tomato varieties, and creating pie recipes.
Thanks [Laura] for the tip.
It doesn’t take long after getting a cat in your life to learn who’s really in charge. Cats do pretty much what they want to do, when they want to do it, and for exactly as long as it suits them. Any correlation with your wants and needs is strictly coincidental, and subject to change without notice, because cats.
[Alvaro Ferrán Cifuentes] almost learned this the hard way, when his cat developed a habit of exploring the countertops in his kitchen and nearly turned on the cooktop while he was away. To modulate this behavior, [Alvaro] built this AI Nerf turret gun. The business end of the system is just a gun mounted on a pan-tilt base made from 3D-printed parts and a pair of hobby servos. A webcam rides atop the gun and feeds into a PC running software that implements the YOLO3 localization algorithm. The program finds the cat, tracks its centroid, and swivels the gun to match it. If the cat stays in the no-go zone above the countertop for three seconds, he gets a dart in his general direction. [Alvaro] found that the noise of the gun tracking him was enough to send the cat scampering, proving that cats are capable of learning as long as it suits them.
We like this build and appreciate any attempt to bring order to the chaos a cat can bring to a household. It also puts us in mind of [Matthias Wandel]’s recent attempt to keep warm in his shop, although his detection algorithm was much simpler.
Continue reading “Keep Pesky Cats At Bay With A Machine-Learning Turret Gun”