The Ethics Of When Machine Learning Gets Weird: Deadbots

June 5, 2022 by Donald Papp 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.

Automatic Water Turret Keeps Grass Watered

June 1, 2022 by Bryan Cockfield 11 Comments

Summer is rapidly approaching (at least for those of us living in the Northern Hemisphere) and if you are having to maintain a lawn at your home, now is the time to be thinking about irrigation. Plenty of people have built-in sprinkler systems to care for their turf, but this is little (if any) fun for any children that might like to play in those sprinklers. This sprinkler solves that problem, functioning as an automatic water gun turret for anyone passing by.

This project was less a specific sprinkler build and more of a way to reuse some Khadas VIM3 single-board computers that the project’s creator, [Neil], wanted to use for something other than mining crypto. The boards have a neural processing unit (NPU) in them which makes them ideal for computer vision projects like this. The camera input is fed into the NPU which then directs the turret to the correct position using yaw and pitch drivers. It’s built out of mostly aluminum extrusion and 3D printed parts, and the project’s page goes into great details about all of the parts needed if you are interested in replicating the build.

[Neil] is also actively working on improving the project, especially around the turret’s ability to identify and track objects using OpenCV. We certainly look forward to more versions of this build in the future, and in the meantime be sure to check out some other automated sprinkler builds we’ve seen which solve different problems.

Continue reading “Automatic Water Turret Keeps Grass Watered” →

AI Attempts Converting Python Code To C++

May 28, 2022 by Donald Papp 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.

Natural Language AI In Your Next Project? It’s Easier Than You Think

May 18, 2022 by Donald Papp 25 Comments

Want your next project to trash talk? Dynamically rewrite boring log messages as sci-fi technobabble? Happily (or grudgingly) answer questions? Doing that sort of thing and more can be done with OpenAI’s GPT-3, a natural language prediction model with an API that is probably a lot easier to use than you might think.

In fact, if you have basic Python coding skills, or even just the ability to craft a curl statement, you have just about everything you need to add this ability to your next project. It’s not free in the long run, although initial use is free on signup, but for personal projects the costs will be very small.

Basic Concepts

OpenAI has an API that provides access to GPT-3, a machine learning model with the ability to perform just about any task that involves understanding or generating natural-sounding language.

OpenAI provides some excellent documentation as well as a web tool through which one can experiment interactively. First, however, one must create an account and receive an API key. After that is done, the doors are open.

Creating an account also gives one a number of free credits that can be used to experiment with ideas. Once the free trial is used up or expires, using the API will cost money. How much? Not a lot, frankly. Everything sent to (and received from) the API is broken into tokens, and pricing is from $0.0008 to $0.06 per thousand tokens. A thousand tokens is roughly 750 words, so small projects are really not a big financial commitment. My free trial came with 18 USD of credits, of which I have so far barely managed to spend 5%.

Let’s take a closer look at how it works, and what can be done with it!

Continue reading “Natural Language AI In Your Next Project? It’s Easier Than You Think” →

TapType: AI-Assisted Hand Motion Tracking Using Only Accelerometers

May 14, 2022 by Dave Rowntree 16 Comments

The team from the Sensing, Interaction & Perception Lab at ETH Zürich, Switzerland have come up with TapType, an interesting text input method that relies purely on a pair of wrist-worn devices, that sense acceleration values when the wearer types on any old surface. By feeding the acceleration values from a pair of sensors on each wrist into a Bayesian inference classification type neural network which in turn feeds a traditional probabilistic language model (predictive text, to you and I) the resulting text can be input at up to 19 WPM with 0.6% average error. Expert TapTypers report speeds of up to 25 WPM, which could be quite usable.

Details are a little scarce (it is a research project, after all) but the actual hardware seems simple enough, based around the Dialog DA14695 which is a nice Cortex M33 based Bluetooth Low Energy SoC. This is an interesting device in its own right, containing a “sensor node controller” block, that is capable of handling sensor devices connected to its interfaces, independant from the main CPU. The sensor device used is the Bosch BMA456 3-axis accelerometer, which is notable for its low power consumption of a mere 150 μA.

User’s can “type” on any convenient surface.

The wristband units themselves appear to be a combination of a main PCB hosting the BLE chip and supporting circuit, connected to a flex PCB with a pair of the accelerometer devices at each end. The assembly was then slipped into a flexible wristband, likely constructed from 3D printed TPU, but we’re just guessing really, as the progression from the first embedded platform to the wearable prototype is unclear.

What is clear is that the wristband itself is just a dumb data-streaming device, and all the clever processing is performed on the connected device. Training of the system (and subsequent selection of the most accurate classifier architecture) was performed by recording volunteers “typing” on an A3 sized keyboard image, with finger movements tracked with a motion tracking camera, whilst recording the acceleration data streams from both wrists. There are a few more details in the published paper for those interested in digging into this research a little deeper.

The eagle-eyed may remember something similar from last year, from the same team, which correlated bone-conduction sensing with VR type hand tracking to generate input events inside a VR environment.

Continue reading “TapType: AI-Assisted Hand Motion Tracking Using Only Accelerometers” →

A putter with an Arduino attached to its shaft

This Golf Club Uses Machine Learning To Perfect Your Swing

May 10, 2022 by Robin Kearey 2 Comments

Golf can be a frustrating game to learn: it takes countless hours of practice to get anywhere near the perfect swing. While some might be lucky enough to have a pro handy every time they’re on the driving range or putting green, most of us will have to get by with watching the ball’s motion and using that to figure out what we’re doing wrong.

Luckily, technology is here to help: [Nick Bild]’s Golf Ace is a putter that uses machine learning to analyze your swing. An accelerometer mounted on the shaft senses the exact motion of the club and uses a machine learning algorithm to see how closely it matches a professional’s swing. An LED mounted on the club’s head turns green if your stroke was good, and red if it wasn’t. All of this is driven by an Arduino Nano 33 IoT and powered by a lithium-ion battery.

The Golf Ace doesn’t tell you what part of your swing to improve, so you’d still need some external instruction to help you get closer to the ideal form; [Nick]’s suggestion is to bundle an instructor’s swing data with a book or video that explains the important points. That certainly looks like a reasonable approach to us, and we can also imagine a similar setup to be used on woods and irons, although that would require a more robust mounting system.

In any case, the Golf Ace could very well be a useful addition to the many gadgets that try to improve your game. But in case you still end up frustrated, you might want to try this automated robotic golf club.

Continue reading “This Golf Club Uses Machine Learning To Perfect Your Swing” →

A man performing push-ups in front of a PC

Machine Learning Helps You Get In Shape While Working A Desk Job

April 22, 2022 by Robin Kearey 9 Comments

Humans weren’t made to sit in front of a computer all day, yet for many of us that’s how we spend a large part of our lives. Of course we all know that it’s important to get up and move around every now and then to stretch our muscles and get our blood flowing, but it’s easy to forget if you’re working towards a deadline. [Victor Sonck] thought he needed some reminders — as well as some not-so-gentle nudging — to get into the habit of doing a quick workout a few times a day.

To this end, he designed a piece of software that would lock his computer’s screen and only unlock it if he performed five push-ups. Locking the screen on his Linux box was as easy as sending a command through the network, but recognizing push-ups was a harder task for which [Victor] decided to employ machine learning. A Raspberry Pi with a webcam attached could do the trick, but the limited processing power of the Pi’s CPU might prove insufficient for processing lots of raw image data.

[Victor] therefore decided on using a Luxonis OAK-1, which is a 4K camera with a built-in machine-learning processor. It can run various kinds of image recognition systems including Blazepose, a pre-trained model that can recognize a person’s pose from an image. The OAK-1 uses this to send out a set of coordinates that describe the position of a person’s head, torso and limbs to the Raspberry Pi through a USB interface. A second machine-learning model running on the Pi then analyzes this dataset to recognize push-ups.

[Victor]’s video (embedded below) is an entertaining introduction into the world of machine-learning systems for video processing, as well as a good hands-on example of a project that results in a useful tool. If you’re interested in learning more about machine learning on small platforms, check out this 2020 Remoticon talk on machine learning on microcontrollers, or this 2019 Supercon talk about implementing machine vision on a Raspberry Pi.

Continue reading “Machine Learning Helps You Get In Shape While Working A Desk Job” →