Repurposing Old AMD APUs For AI Work

The BC250 is what AMD calls an APU, or Accelerated Processing Unit. It combines a GPU and CPU into a single unit, and was originally built to serve as the heart of certain Samsung rack mount servers. If you know where to find cheap surplus units of the BC250, you can put them to good use for AI work, as [akandr] demonstrates.

The first thing you’ll have to figure out is how to take an individual BC250 APU and get it up and running. It’s effectively a full system-on-chip, combining a Zen 2 CPU with a Cyan Skillfish RDNA 1.5 GPU. However, it was originally intended to run inside a rackmount server unit rather than a standalone machine. To get it going, you’ll need to hook it up with power and some kind of cooling solution.

From there, it’s a matter of software. [akandr] explains how to get AI workflows running on the BC250 using Ollama and Vulkan, while noting useful hacks to improve performance like disabling the GUI and tweaking the CPU governor. The hardware can be used with a wide range of different models depending on what you’re trying to achieve, it just takes some careful management of the APU’s resources to get the most out of it. Thankfully, that’s all in the guide on GitHub.

We’ve already seen these AMD APUs repurposed before for gaming use. Unfortunately the word is out already  about their capabilities, so prices have risen significantly in response to demand. Still, if you manage to score a BC250 and do something cool with it yourself, be sure to let us know on the tipsline!

This is an image that would have been difficult to chroma key by hand.

CorridorKey Is What You Get When Artists Make AI Tools

You may not have noticed, but so-called “artificial intelligence” is slightly controversial in the arts world. Illustrators, graphics artists, visual effects (VFX) professionals — anybody who pushes pixels around are the sort of people you’d expect to hate and fear the machines that trained on stolen work to replace them. So, when we heard in a recent video that [Niko] of Corridor Digital had released an AI VFX tool, we were interested. What does it look like when the artist is the one coding the AI?

It looks amazing, both visually and conceptually. Conceptually, because it takes one of the most annoying parts of the VFX pipeline — cleaning up chroma key footage — and automates it so the artists in front of the screen can get to the fun parts of the job. That’s exactly what a tool should do: not do the job for them, but enable them to enjoy doing it, or do it better. It looks amazing visually, because as you can see in the embedded video, it works very, very well.

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AI Picks Outfits With Abandon

Most of us choose our own outfits on a daily basis. [NeuroForge] decided that he’d instead offload this duty to artificial intelligence — perhaps more for the sake of a class project than outright fashion.

The concept involved first using an AI model to predict the weather. Those predictions would then be fed to a large language model (LLM), which would recommend an appropriate outfit for the conditions. The output from the LLM would be passed to a simple alarm clock which would wake [NeuroForge] and indicate what he should wear for the day. Amazon’s Chronos forecasting model was used for weather prediction based on past weather data, while Meta’s Llama3.1 LLM was used to make the clothing recommendations. [NeuroForge] notes that it was possible to set all this up to work without having to query external services once the historical weather data had been sourced.

While the AI choices often involved strange clashes and were not weather appropriate, [NeuroForge] nonetheless followed through and wore what he was told. This got tough when the outfit on a particularly cold day was a T-shirt and shorts, though the LLM did at least suggest a winter hat and gloves be part of the ensemble. Small wins, right?

We’ve seen machine learning systems applied to wardrobe-related tasks before. One wonders if a more advanced model could be trained to pick not just seasonally-appropriate clothes, but to also assemble actually fashionable outfits to boot. If you manage to whip that up, let us know on the tipsline. Bonus points if your ML system gets a gig on the reboot of America’s Next Top Model.

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Why LLMs Are Less Intelligent Than Crows

The basic concept of human intelligence entails self-awareness alongside the ability to reason and apply logic to one’s actions and daily life. Despite the very fuzzy definition of ‘human intelligence‘, and despite many aspects of said human intelligence (HI) also being observed among other animals, like crows and orcas, humans over the ages have always known that their brains are more special than those of other animals.

Currently the Cattell-Horn-Carroll (CHC) theory of intelligence is the most widely accepted model, defining distinct types of abilities that range from memory and processing speed to reasoning ability. While admittedly not perfect, it gives us a baseline to work with when we think of the term ‘intelligence’, whether biological or artificial.

This raises the question of how in the context of artificial intelligence (AI) the CHC model translate to the technologies which we see in use today. When can we expect to subject an artificial intelligence entity to an IQ test and have it handily outperform a human on all metrics?

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A Bird Watching Assistant

When AI is being touted as the latest tool to replace writers, filmmakers, and other creative talent it can be a bit depressing staring down the barrel of a future dystopia — especially since most LLMs just parrot their training data and aren’t actually creative. But AI can have some legitimate strengths when it’s taken under wing as an assistant rather than an outright replacement.

For example [Aarav] is happy as a lark when birdwatching, but the birds aren’t always around and it can sometimes be a bit of a wild goose chase waiting hours for them to show up. To help him with that he built this machine learning tool to help alert him to the presence of birds.

The small device is based on a Raspberry Pi 5 with an AI hat nested on top, and uses a wide-angle camera to keep an eagle-eyed lookout of a space like a garden or forest. It runs a few scripts in Python leveraging the OpenCV library, which is a widely available machine learning tool that allows users to easily interact with image recognition. When perched to view an outdoor area, it sends out an email notification to the user’s phone when it detects bird activity so that they can join the action swiftly if they happen to be doing other things at the time. The system also logs hourly bird-counts and creates a daily graph, helping users identify peak bird-watching times.

Right now the system can only detect the presence of birds in general, but he hopes to build future versions that can identify birds with more specificity, perhaps down to the species. Identifying birds by vision is certainly one viable way of going about this process, but one of our other favorite bird-watching tools was demonstrated by [Benn Jordan] which uses similar hardware but listens for bird calls rather than looking for the birds with a vision-based system.

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Dual-Arm Mobile Bot Built On IKEA Cart Costs Hundreds, Not Thousands

There are many incredible open-source robotic arm projects out there, but there’s a dearth of affordable, stable, and mobile robotic platforms with arms. That’s where XLeRobot comes in. It builds on the fantastic LeRobot framework to make a unit that can be trained for autonomous tasks via machine learning, as well as operated remotely.

XLeRobot, designed by [Vector Wang], has a pretty clever design that makes optimal use of easy to obtain parts. In addition to the mostly 3D-printed hardware, it uses an IKEA cart with stacked bin-like shelves as its main frame.

The top bin holds dual arms and a central stalk with a “head”. There’s still room left in that top bin, a handy feature that gives the robot a place to stow or carry objects.

The bottom of the cart gets the three-wheeled motion unit. Three omnidirectional wheels provide a stable base while also allowing the robot to propel itself in any direction and turn on a dime. The motion unit bolts to the bottom, but because the IKEA cart’s shelf bottoms are a metal mesh, no drilling is required.

It’s all very tidy, and results in a mobile robotics platform that is cheap enough for most hobbyists to afford, while being big enough to navigate indoor environments and do useful tasks.

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Pong Cloned By Neural Network

Although not the first video game ever produced, Pong was the first to achieve commercial success and has had a tremendous influence on our culture as a whole. In Pong’s time, its popularity ushered in the arcade era that would last for more than two decades. Today, it retains a similar popularity partially for approachability: gameplay is relatively simple, has hardwired logic, and provides insights about the state of computer science at the time. For these reasons, [Nick Bild] has decided to recreate this arcade classic, but not in a traditional way. He’s trained a neural network to become the game instead.

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