Should you wish to try high-quality voice recognition without buying something, good luck. Sure, you can borrow the speech recognition on your phone or coerce some virtual assistants on a Raspberry Pi to handle the processing for you, but those aren’t good for major work that you don’t want to be tied to some closed-source solution. OpenAI has introduced Whisper, which they claim is an open source neural net that “approaches human level robustness and accuracy on English speech recognition.” It appears to work on at least some other languages, too.
If you try the demonstrations, you’ll see that talking fast or with a lovely accent doesn’t seem to affect the results. The post mentions it was trained on 680,000 hours of supervised data. If you were to talk that much to an AI, it would take you 77 years without sleep!
There are about one million known species of insects – more than for any other group of living organisms. If you need to determine which species an insect belongs to, things get complicated quick. In fact, for distinguishing between certain kinds of species, you might need a well-trained expert in that species, and experts’ time is often better spent on something else. This is where CNNs (convolutional neural networks) come in nowadays, and this paper describes a CNN doing just as well if not better than human experts.
How do the potatoes in that sack keep from sprouting on their long trip from the field to the produce section? Why don’t the apples spoil? To an extent, the answer lies in varying amounts of irradiation. Though it sounds awful, irradiation reduces microbial contamination, which improves shelf life. Most people can choose to take it or leave it, but in some countries, they aren’t overly concerned about the irradiation dosages found in, say, animal feed. So where does that leave non-vegetarians?
If that line of thinking makes you want to Hulk out, you’re not alone. [kutluhan_aktar] decided to build an IoT food irradiation detector in an effort to help small businesses make educated choices about the feed they give to their animals. The device predicts irradiation dosage level using a combination of the food’s weight, color, and emitted ionizing radiation after being exposed to sunlight for an appreciable amount of time. Using this information, [kutluhan_aktar] trained a neural network running on a Beetle ESP32-C3 to detect the dosage and display relevant info on a transparent OLED screen. Primarily, the device predicts whether the dosage falls into the Regulated, Unsafe, or just plain Hazardous category.
[kutluhan_aktar] lets this baby loose on some uncooked pasta in the short demo video after the break. The macaroni is spread across a load cell to detect the weight, while [kutluhan_aktar] uses a handheld sensor to determine the color.
Recently, we’ve stumbled upon the extensive effort that is the BirdNET research platform. BirdNET uses a neural network to identify birds by the sounds they make, and is a joint project between the Cornell Lab of Ornithology and the Chemnitz University of Technology. What strikes us is – this project is impressively featureful and accessible for a variety of applications. No doubt, BirdNET is aiming to become a one-stop shop for identifying birds as they sing.
There’s plenty of ways BirdNET can help you. Starting with likely the most popular option among us, there are iOS and Android apps – giving the microphone-enabled “smart” devices in our pockets a feature even the most app-averse hackers can respect. However, the BirdNET team also talks about bringing sound recognition to our browsers, Raspberry Pi and other SBCs, and even microcontrollers. We can’t wait for someone to bring BirdNET to a RP2040! The code’s open-source, the models are freely available – there’s hardly a use case one couldn’t cover with these.
About that Raspberry Pi version! There’s a sister project called BirdNET-Pi – it’s an easy-to-install software package intended for the Raspberry Pi OS. Having equipped your Pi with a USB sound card, you can make it do 24/7 recording and analysis using a “lite” version of BirdNET. Then, you get a web interface you can log into and see bird sounds identified in real-time. Not just that – BirdNET-Pi also processes the sounds and creates spectrograms, keeps the sound in a database, and can even send you notifications.
The BirdNET-Pi project is open, too, of course. Not just that – the BirdNET-Pi team emphasizes everything being fully local, unless you choose otherwise, and perhaps decide to share it with others. Many do make their BirdNET-Pi instances public, and there’s a lovely interactive map that shows bird sounds all across the world!
BirdNET is, undoubtedly, a high-effort project – and a shining example of what a dedicated research team can do with a neural network and an admirable goal in mind. For many of us who feel joy when we hear birds outside, it’s endearing to know that we can plug a USB sound card into our Pi and learn more about them – even if we can’t spot them or recognize them by sight just yet. We’ve covered bird sound recognition on microcontrollers before – also using machine learning.
Mosquitoes tend to be seen as an almost universal negative, at least in the lives of humans. While they serve as a food source for plenty of other animals and may even pollinate some plants, they also carry diseases like malaria and Zika, not to mention the itchy bites. Various mosquito deterrents have been invented over the years to solve some of these problems, but one of the more interesting ones is this project by [Ildaron] which attempts to build a mosquito-tracking laser.
The device uses a neural learning algorithm to identify mosquitoes flying nearby. Once a mosquito is detected, a laser is aimed at it and activated in order to “thermally neutralize” the pest. The control system as well as the neural network and machine learning are hosted on a Raspberry Pi and Jetson Nano which give it plenty of computing power. The only major downside with this specific project is that the high-powered laser can be harmful to humans as well.
Ideally, a market for devices like these would bring the price down, perhaps even through the use of something like an ASIC specifically developed for these mosquito-targeting machines. In the meantime, [Ildaron] has made this project available for replication on his GitHub page. We have also seen similar builds before which are effective against non-flying insects, so it seems like only a matter of time before there is more widespread adoption — either that or Judgement day!
[Dave Niewinski] clearly knows a thing or two about robots, judging from his YouTube channel. Usually the projects involve robot arms mounted on some sort of wheeled platform, but this time it’s the tune of some pretty famous yellow robot legs, in the shape of spot from Boston Dynamics. The premise is simple — tell the robot what snacks you want, entirely by voice command, and off he goes to fetch. But, we’re not talking about navigating to the fridge in the same room. We’re talking about trotting out the front door, down the street and crossing roads to visit favorite restaurant. Spot will order the snacks and bring them back, fully autonomously.
There are multiple things going here, all of which are pretty big computational tasks. Firstly, there is no cloud-based voice control, ala Google voice or Alexa. The robot works on the premise of full autonomy, which means no internet connectivity for any aspect. All voice recognition, voice-to-text, and speech synthesis are performed locally using the NVIDIA Riva GPU-based AI speech SDK, running on the local NVIDIA Jetson AGX Orin carried on Spot’s back. A front-facing webcam supplies the audio feed for this. The voice recognition application listens for the wake phrase, then turns the snack order into text, for later replay when it gets to the destination. Navigation is taken care of with a Microstrain RTK GNSS module, which has all the needed robustness, such as dual antennas, and inertial fallback for those regions with a spotty signal. Navigation is no use out in the real world on its own, which is where Spot’s depth sensor cameras come in. These enable local obstacle avoidance, as per the usual spot behavior we’ve all seen before. But what about crossing the road without getting tens of thousands of dollars of someone else’s hardware crushed by a passing truck? Spot’s onboard streaming cameras are fed into the NVIDIA dash cam net AI platform which enables real-time recognition of moving obstacles such as cars, humans and anything else that might be wandering around and get in the way. All in all a cool project showing the future potential of AI in robotics for important tasks, like fetching me a beer when I most need it, even if it comes from the local corner shop.
Neural networks have become a hot topic over the last decade, put to work on jobs from recognizing image content to generating text and even playing video games. However, these artificial neural networks are essentially just piles of maths inside a computer, and while they are capable of great things, the technology hasn’t yet shown the capability to produce genuine intelligence.
Cortical Labs, based down in Melbourne, Australia, has a different approach. Rather than rely solely on silicon, their work involves growing real biological neurons on electrode arrays, allowing them to be interfaced with digital systems. Their latest work has shown promise that these real biological neural networks can be made to learn, according to a pre-print paper that is yet to go through peer review. Continue reading “Researchers Build Neural Networks With Actual Neurons”→