E-Paper News Feed Illustrates The Headlines With AI-Generated Images

It’s hard to read the headlines today without feeling like the world couldn’t possibly get much worse. And then tomorrow rolls around, and a fresh set of headlines puts the lie to that thought. On a macro level, there’s not much that you can do about that, but on a personal level, illustrating your news feed with mostly wrong, AI-generated images might take the edge off things a little.

Let us explain. [Roy van der Veen] liked the idea of an e-paper display newsfeed, but the crushing weight of the headlines was a little too much to bear. To lighten things up, he decided to employ Stable Diffusion to illustrate his feed, displaying both the headline and a generated image on a 7.3″ Inky 7-color e-paper display. Every five hours, a script running on a Raspberry Pi Zero 2W fetches a headline from a random source — we’re pleased the list includes Hackaday — and composes a prompt for Stable Diffusion based on the headline, adding on a randomly selected prefix and suffix to spice things up. For example, a prompt might look like, “Gothic painting of (Driving a Motor with an Audio Amp Chip). Gloomy, dramatic, stunning, dreamy.” You can imagine the results.

We have to say, from the examples [Roy] shows, the idea pretty much works — sometimes the images are so far off the mark that just figuring out how Stable Diffusion came up with them is enough to soften the blow. We’d have preferred if the news of the floods in Libya had been buffered by a slightly less dismal scene, but finding out that what was thought to be a “ritual mass murder” was really only a yoga class was certainly heartening.

Two white Chevy Bolt hatchbacks sit side-by-side, immobilized in the street, their roofs festooned with sensors and an orange cone on their hoods like a snowman's nose pointed toward the sky.

Coning Cars For Fun And Non-Profit

Self-driving cars are being heralded as the wave of the future, but there have been many hiccups along the way. The newest is activists showing how autonomous vehicles are easy to hack with a simple traffic cone.

As we’ve discussed before, self-driving cars aren’t actually that great at driving, and there are a number of conditions that can cause them to fail safe and stop in the middle of the road. Activist group Safe Street Rebel is exploiting this vulnerability by “coning” Waymo and Cruise vehicles in San Francisco. By placing a traffic cone on the vehicle’s hood in the way of the sensors and cameras used to navigate the streets, the vehicles are rendered inoperable. Continue reading “Coning Cars For Fun And Non-Profit”

BingGPT Brings AI Chat To The Desktop

Interested in AI, but sick of using everything in a browser? Miss clicking on a good old desktop icon to open a local bit of software? In that case, BingGPT could be just the thing for you.

It’s nothing too crazy—just a desktop application that gives you access to Bing’s AI-powered chatbot. It’s available on a range of platforms, from Windows, to Apple, and Linux, and binaries are available for Intel, Apple Silicon, and ARM processors.

Using BingGPT is simple. Sign in with your Microsoft account, and away you go. There’s no need to use Microsoft Edge or any ugly browser plugins, and you can export your conversations to Markdown, PNG, and PDF for sharing beyond the program. It’s also complete with a range of keyboard shortcuts to speed your interaction with the large language model when it gets off track. There’s also the Compose button which can actually go ahead and write stuff for you.

Fundamentally, all the cool stuff is still coming in via the web, but it’s nice to be able to use Bing’s chatbot without having to succumb to the horrors of a Microsoft browser. It’s interesting to see how large language models are becoming an all-pervasive tool of late. If you’re building your own nifty projects in this area, don’t hesitate to let us know!

Programming A Poker Game With GPT Help

Although ChatGPT generated a huge amount of hype around replacing white collar workers completely when it was first released to the public, the general consensus now is that it won’t outright replace anyone yet, but rather people who know how to use it as a tool will replace those who don’t. Getting started with it is not too hard, either, but you’ll of course need a project to work on to familiarize yourself with the tool. [Volos Projects] gave himself the challenge of writing a poker game using ChatGPT not as the opposing player, but as a co-designer in order to learn more about it as an assistant.

The poker game is being built on an ESP32 board with a built-in AMOLED screen. Five buttons are wired to the microcontroller to allow the player to select which cards to discard and which to keep. The bet for each hand can be raised or lowered much like the tabletop poker games often seen in bars and restaurants. To program it, though, ChatGPT was used to help design the code at each step of the way, first describing the overall goal and then building each function one-by-one like shuffling the deck, dealing the hand, and then replacing and dealing new cards.

For anyone who hasn’t yet explored using ChatGPT to help design their programming projects, this effort goes a long way to showing just how useful a tool it can be. For more complex tasks, though, it does take a little bit of knowledge on the part of the user because ChatGPT can often turn out nonsense or factually inaccurate information, but at least in a programming environment you’ll generally find out quickly when that happens. It’s not just a useful tool for writing programs, either. It can accomplish a lot of ancillary tasks related to programming as well, even if it’s not writing the code directly.

Thanks to [Peter] for the tip!

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Here’s Why GPUs Are Deep Learning’s Best Friend

If you have a curiosity about how fancy graphics cards actually work, and why they are so well-suited to AI-type applications, then take a few minutes to read [Tim Dettmers] explain why this is so. It’s not a terribly long read, but while it does get technical there are also car analogies, so there’s something for everyone!

He starts off by saying that most people know that GPUs are scarily efficient at matrix multiplication and convolution, but what really makes them most useful is their ability to work with large amounts of memory very efficiently.

Essentially, a CPU is a latency-optimized device while GPUs are bandwidth-optimized devices. If a CPU is a race car, a GPU is a cargo truck. The main job in deep learning is to fetch and move cargo (memory, actually) around. Both devices can do this job, but in different ways. A race car moves quickly, but can’t carry much. A truck is slower, but far better at moving a lot at once. Continue reading “Here’s Why GPUs Are Deep Learning’s Best Friend”

High Quality 3D Scene Generation From 2D Source, In Realtime

Here’s some fascinating work presented at SIGGRAPH 2023 of a method for radiance field rendering using a novel technique called Gaussian Splatting. What’s that mean? It means synthesizing a 3D scene from 2D images, in high quality and in real time, as the short animation shown above shows.

Neural Radiance Fields (NeRFs) are a method of leveraging machine learning to, in a way, do what photogrammetry does: synthesize complex scenes and views based on input images. But NeRFs work in a fraction of the time, and require only a fraction of the source material. There are different ways to go about this and unsurprisingly, there tends to be a clear speed vs. quality tradeoff. But as the video accompanying this new work seems to show, clever techniques mean the best of both worlds.

A short video summary is embedded just below the page break. Interested in deeper details? The research PDF is here. The amount of development this field has seen is nothing short of staggering, and certainly higher in quality than what was state-of-the-art for NeRFs only a year ago.

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Re-Creating Pink Floyd In The Name Of Speech

For people who have lost the ability to speak, the future may include brain implants that bring that ability back. But could these brain implants also allow them to sing? Researchers believe that, all in all, it’s just another brick in the wall.

In a new study published in PLOS Biology, twenty-nine people who were already being monitored for epileptic seizures participated via a postage stamp-sized array of electrodes implanted directly on the surface of their brains. As the participants were exposed to Pink Floyd’s Another Brick In the Wall, Part 1, the researchers gathered data from several areas of the brain, each attuned to a different musical element such as harmony, rhythm, and so on. Then the researchers used machine learning to reconstruct the audio heard by the participants using their brainwaves.

First, an AI model looked at the data generated from the brains’ responses to components of the song, like the changes in rhythm, pitch, and tone. Then a second model rejiggered the piecemeal song and estimated the sounds heard by the patients. Of the seven audio samples published in the study results, we think #3 sounds the most like the song. It’s kind of creepy but ultimately very cool. What do you think?

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