Name Stone Helps You Greet Coworkers

When starting a new job, learning coworkers names can be a daunting task. Getting this right is key to forming strong professional relationships. [Ahad] noted that [Marcos] was struggling with this, so built the Name Stone to help.

The Name Stone consists of some powerful hardware, wrapped up in a 3D printed case reminiscent of the Eye of Agamotto from¬†Doctor Strange. Inside, there’s a Jetson Nano – an excellent platform for any project built around machine learning tasks. This is combined with a microphone and camera to collect data from the environment.

[Ahad] then went about training neural networks to help with basic identification tasks. Video was taken of the coworkers, then the frames used to train a convolutional neural network using PyTorch. Similarly, a series of audio clips were used to again train a network to identify individuals through the sound of their voice, using MFCC techniques. Upon activating the stone, the device will capture an image or a short sound clip, and process the data to identify the target coworker and remind [Marcos] of their name.

It’s a project that could be quite useful, given to new employees to help them transition into the new workplace. Of course, pervasive facial recognition technology does have some drawbacks. Video after the break.

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This Machine Learning Algorithm Is Meta

Suppose you ran a website releasing many articles per day about various topics, all following a general theme. And suppose that your website allowed for a comments section for discussion on those topics. Unless you are brand new to the Internet, you’ll also imagine that the comments section needs at least a little bit of moderation to filter out spam, off topic, or even toxic comments. If you don’t want to employ any people for this task, you could try this machine learning algorithm instead.

[Ladvien] goes through a general overview of how to set up a convolutional neural network (CNN) which can be programmed to do many things, but this one crawls a web page, gathers data, and also makes decisions regarding that data. In this case, the task is to identify toxic comments but the goal is not to achieve the sharpest sword in the comment moderator’s armory, but to learn more about how CNNs work.

Written in Python, the process outlines the code itself and how it behaves, setting up a small server to host the neural network, and finally creating the webservice. As with any machine learning, you need a reliable dataset to use for training and this one came from Wikipedia comments previously flagged by humans. Trolling nuance is thrown aside, as the example homes in on blatant insults and vulgarity.

While [Ladvien] notes that his guide isn’t meant to be comprehensive, but rather to fill in some gaps that he noticed within other guides like this, we find this to be an interesting read. He also mentioned that, in theory, this tool could be used to predict the number of comments following an article like this very one based on the language in the article. We’ll leave that one as an academic exercise for now, probably.

Largest Chip Ever Holds 1.2 Trillion Transistors

We get it, press releases are full of hyperbole. Cerebras recently announced they’ve built the largest chip ever. The chip has 400,000 cores and contains 1.2 trillion transistors on a die over 46,000 square mm in area. That’s roughly the same as a square about 8.5 inches on each side. But honestly, the WSE — Wafer Scale Engine — is just most of a wafer not cut up. Typically a wafer will have lots of copies of a device on it and it gets split into pieces.

According to the company, the WSE is 56 times larger than the largest GPU on the market. The chip boasts 18 gigabytes of storage spread around the massive die. The problem isn’t making such a beast — although a normal wafer is allowed to have a certain number of bad spots. The real problems come through things such as interconnections and thermal management.

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Memristor Computing On A Chip

Memristors have been — so far — mostly a solution looking for a problem. However, researchers at the University of Michigan are claiming the first memristor-based programmable computer that has the potential to make AI applications more efficient and faster.

Because memristors have a memory, they can accumulate data in a way that is common for — among other things — neural networks. The chip has both an array of nearly 6,000 memristors, a crossbar array, along with analog to digital and digital to analog converters. In fact, there are 486 DACs and 162 ADCs along with an OpenRISC processor.

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Gesture Controlled Doom

DOOM will forever be remembered as one of the founding games of the entire FPS genre. It also stands as a game which has long been a fertile ground for hackers and modders. [Nick Bild] decided to bring gesture control to iD’s classic shooter, courtesy of machine learning.

The setup consists of a Jetson Nano fitted with a camera, which films the player and uses a convolutional neural network to recognise the player’s various gestures. Once recognised, an API request is sent to a laptop playing Doom which simulates the relevant keystrokes. The laptop is hooked up to a projector, creating a large screen which allows the wildly gesturing player to more easily follow the action.

The neural network was trained on 3300 images – 300 per gesture. [Nick] found that using a larger data set actually performed less well, as he became less diligent in reliably performing the gestures. This demonstrates that quality matters in training networks, as well as quantity.

Reports are that the network is fairly reliable, and it appears to work quite well. Unfortunately, playability is limited as it’s not possible to gesture for more than one key at once. Overall though, it serves as a tidy example of how to do gesture recognition with CNNs.

If you’re not convinced by this demonstration, you might be interested to learn that neural networks can also be used to name tomatoes. If you don’t want to roll your own pose detection, check out this selfie drone that uses CMU’s OpenPose library. Video after the break.

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Neural Network In Glass Requires No Power, Recognizes Numbers

We’ve all come to terms with a neural network doing jobs such as handwriting recognition. The basics have been in place for years and the recent increase in computing power and parallel processing has made it a very practical technology. However, at the core level it is still a digital computer moving bits around just like any other program. That isn’t the case with a new neural network fielded by researchers from the University of Wisconsin, MIT, and Columbia. This panel of special glass requires no electrical power, and is able to recognize gray-scale handwritten numbers.

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