We’ve all come to terms with a neural network doing jobs such as handwriting recognition. The basics have been in place for years and the recent increase in computing power and parallel processing has made it a very practical technology. However, at the core level it is still a digital computer moving bits around just like any other program. That isn’t the case with a new neural network fielded by researchers from the University of Wisconsin, MIT, and Columbia. This panel of special glass requires no electrical power, and is able to recognize gray-scale handwritten numbers.
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.
Whilst we patiently wait for the day that Womble-shaped robots replace human tennis players at Wimbledon, we can admire the IBM powered AI technology that the organisers of the Wimbledon tennis tournament use to enhance the experience for TV and phone viewers.
As can be expected, the technology tracks the ball, analyses player gestures, crowd cheers/booing but can’t yet discern the more subtle player behaviour such as serving an ace or the classic John McEnroe ‘smash your racket on the ground’ stunt. Currently a large number of expert human side kicks are required for recording these facets and manually uploading them into the huge Watson driven analytics system.
Phone apps are possibly the best places to see the results of the IBM Slammtracker system and are perfect for the casual tennis train spotter. It would be interesting to see the intrinsic AI bias at work – whether it can compensate for the greater intensity of the cheer for the more popular celebrities rather than the skill, or fluke shot, of the rank outsider. We also wonder if it will be misogynistic – will it focus on men rather than women in the mixed doubles or the other way round? Will it be racist? Also, when will the umpires be replaced with 100% AI?
Finally, whilst we at Hackaday appreciate the value of sport and exercise and the technology behind the apps, many of us have no time to mindlessly watch a ball go backwards and forwards across our screens, even if it is accompanied by satisfying grunts and the occasional racket-to-ground smash. We’d much rather entertain ourselves with the idea of building the robots that will surely one day make watching human tennis players a thing of the past.
Anyone with a cat knows that the little purring ball of fluff in your lap is one tiny step away from turning into a bloodthirsty serial killer. Give kitty half a chance and something small and defenseless is going to meet a slow, painful end. And your little killer is as likely as not to show off its handiwork by bringing home its victim – “Look what I did for you, human! Are you not proud?”
As useful as a murder-cat can be, dragging the bodies home for you to deal with can be – inconvenient. To thwart his adorable serial killer [Metric], Amazon engineer [Ben Hamm] turned to an AI system to lock his prey-laden cat out of the house. [Metric] comes and goes as he pleases through a cat flap, which thanks to a solenoid and an Arduino is now lockable. The decision to block entrance to [Metric] is based on an Amazon AWS DeepLens AI camera, which watches the approach to the cat flap. [Ben] trained three models: one to determine if [Metric] was in the scene, one to determine whether he’s coming or going, and one to see if he’s alone or accompanied by a lifeless friend, in which case he’s locked out for 15 minutes and an automatic donation is made to the Audubon Society – that last bit is pure genius. The video below is a brief but hilarious summary of the project for an audience in Seattle that really seems quite amused by the whole thing.
So your cat isn’t quite the murder fiend that [Metric] is? An RFID-based cat door might suit your needs better.
Even though machine learning AKA ‘deep learning’ / ‘artificial intelligence’ has been around for several decades now, it’s only recently that computing power has become fast enough to do anything useful with the science.
However, to fully understand how a neural network (NN) works, [Dimitris Tassopoulos] has stripped the concept down to pretty much the simplest example possible – a 3 input, 1 output network – and run inference on a number of MCUs, including the humble Arduino Uno. Miraculously, the Uno processed the network in an impressively fast prediction time of 114.4 μsec!
Whilst we did not test the code on an MCU, we just happened to have Jupyter Notebook installed so ran the same code on a Raspberry Pi directly from [Dimitris’s] bitbucket repo.
He explains in the project pages that now that the hype about AI has died down a bit that it’s the right time for engineers to get into the nitty-gritty of the theory and start using some of the ‘tools’ such as Keras, which have now matured into something fairly useful.
In part 2 of the project, we get to see the guts of a more complicated NN with 3-inputs, a hidden layer with 32 nodes and 1-output, which runs on an Uno at a much slower speed of 5600 μsec.
This exploration of ML in the embedded world is NOT ‘high level’ research stuff that tends to be inaccessible and hard to understand. We have covered Machine Learning On Tiny Platforms Like Raspberry Pi And Arduino before, but not with such an easy and thoroughly practical example.
In 2019, using AI to evaluate artwork is finally more productive than foolish. We all hope that someday soon our Roomba will judge our living habits and give unsolicited advice on how we could spruce things up with a few pictures and some natural light. There is already an extensive amount of Deep Learning dedicated to photo recognition but a team in Croatia is adapting them for use on fine art. It makes sense that everything is geared toward cameras since most of us have a vast photographic portfolio but fine art takes longer to render. Even so, the collection on Wikiart.org is vast and already a hotbed for computer classification work, so they set to work there.
As they modify existing convolutional neural networks, they check themselves by comparing results with human ratings to keep what works and discard what flops. Fortunately, fine art has a lot of existing studies and commentary, whereas the majority of photographs in the public domain have nothing more than a file name and maybe some EXIF data. The difference here is that photograph-parsing AI can say, “That is a STOP sign,” while the fine art AI can say, “That is a memorable painting of a sign.” Continue reading “AI And Art Appreciation”
If you’ve got a working Model 33 Teletype, every project starts to look like an excuse to use it. While the hammering, whirring symphony of a teleprinter going full tilt brings to mind a simpler time of room-sized computers and 300 baud connections, it turns out that a Teletype makes a decent AI conversationalist, within the limits of AI, of course.
The Teletype machine that [Hugh Pyle] used for this interesting project, a Model 33 ASR with the paper tape reader, is a nostalgia piece that figures prominently in many of his projects. As such, [Hugh] has access to tons of Teletype documentation, so when OpenAI released their GPT-2 text generation language model, he decided to use the docs as a training set for the model, and then use the Teletype to print out text generated by the model. Initial results were about as weird as you’d expect for something trained on technical docs from the 1960s. The next step was obvious: make a chat-bot out of it and stream the results live. The teletype can be seen clattering away in the recorded stream below, using the chat history as a prompt for generating text responses, sometimes coherent, sometimes disturbing, and sometimes just plain weird.
Alas, the chat-bot and stream are only active a couple of times a week, so you’ll have to wait a bit to try it out. But it looks like a fun project, and we appreciate the mash-up of retro tech and AI. We’ve seen teleprinters revived for modern use before, both for texting and Tweeting, but this one almost has a mind of its own.