machine learning

231 Articles

Need To Pick Objects Out Of Images? Segment Anything Does Exactly That

April 14, 2023 by 7 Comments

Segment Anything, recently released by Facebook Research, does something that most people who have dabbled in computer vision have found daunting: reliably figure out which pixels in an image belong to an object. Making that easier is the goal of the Segment Anything Model (SAM), just released under the Apache 2.0 license.

The online demo has a bank of examples, but also works with uploaded images.

The results look fantastic, and there’s an interactive demo available where you can play with the different ways SAM works. One can pick out objects by pointing and clicking on an image, or images can be automatically segmented. It’s frankly very impressive to see SAM make masking out the different objects in an image look so effortless. What makes this possible is machine learning, and part of that is the fact that the model behind the system has been trained on a huge dataset of high-quality images and masks, making it very effective at what it does.

Continue reading “Need To Pick Objects Out Of Images? Segment Anything Does Exactly That” →

ChatGPT Powers A Different Kind Of Logic Analyzer

April 6, 2023 by 17 Comments

If you’re hoping that this AI-powered logic analyzer will help you quickly debug that wonky digital circuit on your bench with the magic of AI, we’re sorry to disappoint you. But if you’re in luck if you’re in the market for something to help you detect logical fallacies someone spouts in conversation. With the magic of AI, of course.

First, a quick review: logic fallacies are errors in reasoning that lead to the wrong conclusions from a set of observations. Enumerating the kinds of fallacies has become a bit of a cottage industry in this age of fake news and misinformation, to the extent that many of the common fallacies have catchy names like “Texas Sharpshooter” or “No True Scotsman”. Each fallacy has its own set of characteristics, and while it can be easy to pick some of them out, analyzing speech and finding them all is a tough job.

Continue reading “ChatGPT Powers A Different Kind Of Logic Analyzer” →

Machine Learning Helps Electron Microscopy

April 3, 2023 by 8 Comments

Machine learning is supposed to help us do everything these days, so why not electron microscopy? A team from Ireland has done just that and published their results using machine learning to enhance STEM — scanning transmission electron microscopy. The result is important because it targets a very particular use case — low dose STEM.

The problem is that to get high resolutions, you typically need to use high electron doses. However, bombarding a delicate, often biological, subject with high-energy electrons may change what you are looking at and damage the sample. But using reduced electron dosages results in a poor image due to Poisson noise. The new technique learns how to compensate for the noise and produce a better-quality image even at low dosages.

Continue reading “Machine Learning Helps Electron Microscopy” →

Combining Acoustic Bioprinting With Raman Spectroscopy For High-Throughput Identification Of Bacteria

March 22, 2023 by 6 Comments

Rapidly analyzing samples for the presence of bacteria and similar organic structures is generally quite a time-intensive process, with often the requirement of a cell culture being developed. Proposed by Fareeha Safir and colleagues in Nano Letters is a method to use an acoustic droplet printer combined with Raman spectroscopy. Advantages of this method are a high throughput, which could make analysis of samples at sewage installations, hospitals and laboratories significantly faster.

Raman spectroscopy works on the principle of Raman scattering, which is the inelastic scattering of photons by matter, causing a distinct pattern in the thus scattered light. By starting with a pure light source (that is, a laser), the relatively weak Raman scattering can be captured and the laser light filtered out. The thus captured signal can be analyzed and matched with known pathogens. Continue reading “Combining Acoustic Bioprinting With Raman Spectroscopy For High-Throughput Identification Of Bacteria” →

Hands-On: NVIDIA Jetson Orin Nano Developer Kit

March 21, 2023 by 35 Comments

NVIDIA’s Jetson line of single-board computers are doing something different in a vast sea of relatively similar Linux SBCs. Designed for edge computing applications, such as a robot that needs to perform high-speed computer vision while out in the field, they provide exceptional performance in a board that’s of comparable size and weight to other SBCs on the market. The only difference, as you might expect, is that they tend to cost a lot more: the current top of the line Jetson AGX Orin Developer Kit is $1999 USD

Luckily for hackers and makers like us, NVIDIA realized they needed an affordable gateway into their ecosystem, so they introduced the $99 Jetson Nano in 2019. The product proved so popular that just a year later the company refreshed it with a streamlined carrier board that dropped the cost of the kit down to an incredible $59. Looking to expand on that success even further, today NVIDIA announced a new upmarket entry into the Nano family that lies somewhere in the middle.

While the $499 price tag of the Jetson Orin Nano Developer Kit may be a bit steep for hobbyists, there’s no question that you get a lot for your money. Capable of performing 40 trillion operations per second (TOPS), NVIDIA estimates the Orin Nano is a staggering 80X as powerful as the previous Nano. It’s a level of performance that, admittedly, not every Hackaday reader needs on their workbench. But the allure of a palm-sized supercomputer is very real, and anyone with an interest in experimenting with machine learning would do well to weigh (literally, and figuratively) the Orin Nano against a desktop computer with a comparable NVIDIA graphics card.

We were provided with one of the very first Jetson Orin Nano Developer Kits before their official unveiling during NVIDIA GTC (GPU Technology Conference), and I’ve spent the last few days getting up close and personal with the hardware and software. After coming to terms with the fact that this tiny board is considerably more powerful than the computer I’m currently writing this on, I’m left excited to see what the community can accomplish with the incredible performance offered by this pint-sized system.

Continue reading “Hands-On: NVIDIA Jetson Orin Nano Developer Kit” →

Voice Without Sound

March 15, 2023 by 14 Comments

Voice recognition is becoming more and more common, but anyone who’s ever used a smart device can attest that they aren’t exactly fool-proof. They can activate seemingly at random, don’t activate when called or, most annoyingly, completely fail to understand the voice commands. Thankfully, researchers from the University of Tokyo are looking to improve the performance of devices like these by attempting to use them without any spoken voice at all.

The project is called SottoVoce and uses an ultrasound imaging probe placed under the user’s jaw to detect internal movements in the speaker’s larynx. The imaging generated from the probe is fed into a series of neural networks, trained with hundreds of speech patterns from the researchers themselves. The neural networks then piece together the likely sounds being made and generate an audio waveform which is played to an unmodified Alexa device. Obviously a few improvements would need to be made to the ultrasonic imaging device to make this usable in real-world situations, but it is interesting from a research perspective nonetheless.

The research paper with all the details is also available (PDF warning). It’s an intriguing approach to improving the performance or quality of voice especially in situations where the voice may be muffled, non-existent, or overlaid with a lot of background noise. Machine learning like this seems to be one of the more powerful tools for improving speech recognition, as we saw with this robot that can walk across town and order food for you using voice commands only.

Continue reading “Voice Without Sound” →

Tiny Machine Learning On As Little As 2 KB Of RAM

February 24, 2023 by 6 Comments

All of the machine language stuff coming out lately doesn’t affect you if you are developing with embedded microcontrollers, right? Perhaps not. Microsoft Research India wants you to use their EdgeML tool to do machine learning tasks such as gesture recognition in tiny devices like an Arduino Uno. According to the developers, you might need as little as 2 KB of RAM. There’s no network connection required and the work is using Tensorflow underneath, so it is compatible with much of what you’ll find for bigger computers.

If you add processing power, you can get more capability. For example, one of the demonstrations is a wake-word recognizer on a Raspberry Pi Zero (although the page for that demo seems to be missing at the moment; try the GesturePod, instead).

The system generally uses Python, but there are efficient C++ implementations for selected algorithms. The code lives on GitHub. There are also a number of research papers about each tool that you can find on the GitHub page. There’s also a recent paper on MinUn, an attempt to make things even more efficient for ARM microcontrollers. In particular, MinUn can store approximate numbers to save space, allows for variable precision of tensors, and tries to reduce memory fragmentation, an important feature for CPUs that don’t have memory management units.

If you haven’t studied TensorFlow yet, start here. Why use something like this with a microcontroller? How about smarter robots?