Computers haven’t done much for the quality of our already poor handwriting. However, a man paralyzed by an accident can now feed input into a computer by simply thinking about handwriting, thanks to work by Stanford University researchers. Compared to more cumbersome systems based on eye motion or breath, the handwriting technique enables entry at up to 90 characters a minute.
Currently, the feat requires a lab’s worth of equipment, but it could be made practical for everyday use with some additional work and — hopefully — less invasive sensors. In particular, the sensor used two microelectrode arrays in the precentral gyrus portion of the brain. When the subject thinks about writing, recognizable patterns appear in the collected data. The rest is just math and classification using a neural network.
If you want to try your hand at processing this kind of data and don’t have a set of electrodes to implant, you can download nearly eleven hours of data already recorded. The code is out there, too. What we’d really like to see is some easier way to grab the data to start with. That could be a real game-changer.
Mind control might seem like something out of a sci-fi show, but like the tablet computer, universal translator, or virtual reality device, is actually a technology that has made it into the real world. While these devices often requires on advanced and expensive equipment to interpret brain waves properly, with the right machine learning system it’s possible to do things like this mind-controlled flame thrower on a much smaller budget. (Video, embedded below.)
[Nathaniel F] was already experimenting with using brain-computer interfaces and machine learning, and wanted to see if he could build something practical combining these two technologies. Instead of turning to an EEG machine to read brain patterns, he picked up a much less expensive Mindflex and paired it with a machine learning system running TensorFlow to make up for some of its shortcomings. The processing is done by a Raspberry Pi 4, which sends commands to an Arduino to fire the flamethrower when it detects the proper thought patterns. Don’t forget the flamethrower part of this build either: it was designed and built entirely by [Nathanial F] as well using gas and an arc lighter.
While the build took many hours of training to gather the proper amount of data to build the neural network and works as the proof of concept he was hoping for, [Nathaniel F] notes that it could be improved by replacing the outdated Mindflex with a better EEG. For now though, we appreciate seeing sci-fi in the real world in projects like this, or in other mind-controlled projects like this one which converts a prosthetic arm into a mind-controlled music synthesizer.
Have you ever noticed that people in old photographs looks a bit weird? Deep wrinkles, sunken cheeks, and exaggerated blemishes are commonplace in photos taken up to the early 20th century. Surely not everybody looked like this, right? Maybe it was an odd makeup trend — was it just a fashionable look back then?
Not quite — it turns out that the culprit here is the film itself. The earliest glass-plate emulsions used in photography were only sensitive to the highest-frequency light, that which fell in the blue to ultraviolet range. Perhaps unsurprisingly, when combined with the fact that humans have red blood, this posed a real problem. While some of the historical figures we see in old photos may have benefited from an improved skincare regimen, the primary source of their haunting visage was that the photographic techniques available at the time were simply incapable of capturing skin properly. This lead to the sharp creases and dark lips we’re so used to seeing.
Of course, primitive film isn’t the only thing separating antique photos from the 42 megapixel behemoths that your camera can take nowadays. Film processing steps had the potential to introduce dust and other blemishes to the image, and over time the prints can fade and age in a variety of ways that depend upon the chemicals they were processed in. When rolled together, all of these factors make it difficult to paint an accurate portrait of some of history’s famous faces. Before you start to worry that you’ll never know just what Abraham Lincoln looked like, you might consider taking a stab at Time-Travel Rephotography.
Amazingly, Time-Travel Rephotography is a technique that actually lives up to how cool its name is. It uses a neural network (specifically, the StyleGAN2 framework) to take an old photo and project it into the space of high-res modern photos the network was trained on. This allows it to perform colorization, skin correction, upscaling, and various noise reduction and filtering operations in a single step which outputs remarkable results. Make sure you check out the project’s website to see some of the outputs at full-resolution.
We’ve seen AI upscaling before, but this project takes it to the next level by completely restoring antique photographs. We’re left wondering what techniques will be available 100 years from now to restore JPEGs stored way back in 2021, bringing them up to “modern” viewing standards.
IBM has come up with an automatic debating system called Project Debater that researches a topic, presents an argument, listens to a human rebuttal and formulates its own rebuttal. But does it pass the Turing test? Or does the Turing test matter anymore?
The Turing test was first introduced in 1950, often cited as year-one for AI research. It asks, “Can machines think?”. Today we’re more interested in machines that can intelligently make restaurant recommendations, drive our car along the tedious highway to and from work, or identify the surprising looking flower we just stumbled upon. These all fit the definition of AI as a machine that can perform a task normally requiring the intelligence of a human. Though as you’ll see below, Turing’s test wasn’t even for intelligence or even for thinking, but rather to determine a test subject’s sex.
The rumor mill has recently been buzzing about Nintendo’s plans to introduce a new version of their extremely popular Switch console in time for the holidays. A faster CPU, more RAM, and an improved OLED display are all pretty much a given, as you’d expect for a mid-generation refresh. Those upgraded specifications will almost certainly come with an inflated price tag as well, but given the incredible demand for the current Switch, a $50 or even $100 bump is unlikely to dissuade many prospective buyers.
But according to a report from Bloomberg, the new Switch might have a bit more going on under the hood than you’d expect from the technologically conservative Nintendo. Their sources claim the new system will utilize an NVIDIA chipset capable of Deep Learning Super Sampling (DLSS), a feature which is currently only available on high-end GeForce RTX 20 and GeForce RTX 30 series GPUs. The technology, which has already been employed by several notable PC games over the last few years, uses machine learning to upscale rendered images in real-time. So rather than tasking the GPU with producing a native 4K image, the engine can render the game at a lower resolution and have DLSS make up the difference.
The implications of this technology, especially on computationally limited devices, is immense. For the Switch, which doubles as a battery powered handheld when removed from its dock, the use of DLSS could allow it to produce visuals similar to the far larger and more expensive Xbox and PlayStation systems it’s in competition with. If Nintendo and NVIDIA can prove DLSS to be viable on something as small as the Switch, we’ll likely see the technology come to future smartphones and tablets to make up for their relatively limited GPUs.
But why stop there? If artificial intelligence systems like DLSS can scale up a video game, it stands to reason the same techniques could be applied to other forms of content. Rather than saturating your Internet connection with a 16K video stream, will TVs of the future simply make the best of what they have using a machine learning algorithm trained on popular shows and movies?
We wouldn’t be where we are today without Mrs. Coldiron’s middle school typing class. Even though she may have wanted to, she never did use negative reinforcement to improve our typing speed or technique. We unruly teenagers might have learned to type a lot faster if those IBM Selectrics had been wired up for discipline like [3DPrintedLife]’s terrifying, tingle-inducing typist trainer keyboard (YouTube, embedded below).
This keyboard uses capsense modules and a neural network to detect whether the user is touch-typing or just hunting and pecking. If you’re doing it wrong, you’ll get a shock from the guts of a prank shock pen every time you peck the T or Y keys. Oh, and just for fun, there’s a 20 V LED bar across the top that is supposed to deter you from looking down at your hands with randomized and blindingly bright strobing light.
Twenty-four of the keys are connected in groups of three by finger usage — for example Q, A, and Z are wired to the same capsense module. These are all wired up to a Raspberry Pi Zero along with the light bar. [3DPrintedLife] was getting a lot of cross-talk between capsense modules, so they solved the problem in software by training a TensorFlow model with a ton of both proper and improper typing data.
We love the little meter on the touchscreen that shows at a glance how you’re doing in the touch typing department. As the meter inches leftward, you know you’re in for a shock. [3DPrintedLife] even built in some games that use pain to promote faster and more accurate typing. Check out the build video after the break, but don’t say we didn’t warn you about the strobing lights.
Going from a microcontroller blinking an LED, to one that blinks the LED using voice commands based on a data set that you trained on a neural net work is a “now draw the rest of the owl” problem. Lucky for us, Shawn Hymel walks us through the entire process during his Tiny ML workshop from the 2020 Hackaday Remoticon. The video has just now been published and can be viewed below.
This is truly an end-to-end Hello World for getting machine learning up and running on a microcontroller. Shawn covers the process of collecting and preparing the audio samples, training the data set, and getting it all onto the microcontroller. At the end of two hours, he’s able to show the STM32 recognizing and responding to two different spoken words. Along the way he pauses to discuss the context of what’s happening in every step, which will help you go back and expand in those areas later to suit your own project needs.