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.

Wearable Sensor Trained To Count Coughs

November 15, 2022 by Bryan Cockfield 5 Comments

There are plenty of problems that are easy for humans to solve, but are almost impossibly difficult for computers. Even though it seems that with modern computing power being what it is we should be able to solve a lot of these problems, things like identifying objects in images remains fairly difficult. Similarly, identifying specific sounds within audio samples remains problematic, and as [Eivind] found, is holding up a lot of medical research to boot. To solve one specific problem he created a system for counting coughs of medical patients.

This was built with the idea of helping people with chronic obstructive pulmonary disease (COPD). Most of the existing methods for studying the disease and treating patients with it involves manually counting the number of coughs on an audio recording. While there are some software solutions to this problem to save some time, this device seeks to identify coughs in real time as they happen. It does this by training a model using tinyML to identify coughs and reject cough-like sounds. Everything runs on an Arduino Nano with BLE for communication.

While the only data the model has been trained on are sounds from [Eivind], the existing prototypes do seem to show promise. With more sound data this could be a powerful tool for patients with this disease. And, even though this uses machine learning on a small platform, we have seen before that Arudinos are plenty capable of being effective machine learning solutions with the right tools on board.

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!

Laser Zaps Cockroaches Over One Meter

October 3, 2022 by Chris Lott 40 Comments

You may have missed this month’s issue of Oriental Insects, in which a project by [Ildar Rakhmatulin] Heriot-Watt University in Edinburgh caught our attention. [Ildar] led a team of researchers in the development of an AI-controlled laser that neutralizes moving cockroaches at distances of up to 1.2 meters. Noting the various problems using chemical pesticides for pest control, his team sought out a non-conventional approach.

The heart of the pest controller is a Jetson Nano, which uses OpenCV and Yolo object detection to find the cockroaches and galvanometers to steer the laser beam. Three different lasers were used for testing, allowing the team to evaluate a range of wavelengths, power levels, and spot sizes. Unsurprisingly, the higher power 1.6 W laser was most efficient and quicker.

The project is on GitHub (here) and the cockroach machine learning image set is available here. But [Ildar] points out in the conclusion of the report, this is dangerous. It’s suitable for academic research, but it’s not quite ready for general use, lacking any safety features. This report is full of cockroach trivia, such as the average speed of a cockroach is 4.8 km/h, and they run much faster when being zapped. If you want to experiment with cockroaches yourself, a link is provided to a pet store that sells the German Blattela germanica that was the target of this report.

If this project sounds familiar, it is because it is an improvement of a previous project we wrote about last year which used similar techniques to zap mosquitoes.

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Self-Driving Laboratories Do Research On Autopilot

September 29, 2022 by Lewin Day 16 Comments

Scientific research is a messy business. The road to learning new things and making discoveries is paved with hard labor, tough thinking, and plenty of dead ends. It’s a time-consuming, expensive endeavor, and for every success, there are thousands upon thousands of failures.

It’s a process so inefficient, you would think someone would have automated it already. The concept of the self-driving laboratory aims to do exactly that, and could revolutionize materials research in particular.

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Tesla’s Dojo Is An Interesting CPU Design

September 6, 2022 by Matthew Carlson 29 Comments

What do you get when you cross a modern super-scalar out-of-order CPU core with more traditional microcontroller aspects such as no virtual memory, no memory cache, and no DDR or PCIe controllers? You get the Tesla Dojo, which Chips and Cheese recently did a deep dive on.

It starts with a comparison to the IBM Cell processors. The Cell of the mid-2000s featured something called the SPE (Synergistic Processing Elements). They were smaller cores focused on vector processing or other specialized types of workloads. They didn’t access the main memory and had to be given tasks by the fully featured CPU. Dojo has 1.25MB of SRAM that it can use as working memory with five ports, but it has no cache or virtual memory. It uses DMA to get the information it needs via a mesh system. The front end pulls RISC-V-like (heavily MIPS-inspired) instructions into a small instruction cache and decodes eight instructions per cycle. Continue reading “Tesla’s Dojo Is An Interesting CPU Design” →