Giant Brains, Or Machines That Think

June 15, 2024 by Elliot Williams 8 Comments

Last week, I stumbled on a marvelous book: “Giant Brains; or, Machines That Think” by Edmund Callis Berkeley. What’s really fun about it is the way it sounds like it could be written just this year – waxing speculatively about the future when machines do our thinking for us. Except it was written in 1949, and the “thinking machines” are early proto-computers that use relays (relays!) for their logic elements. But you need to understand that back then, they could calculate ten times faster than any person, and they would work tirelessly day and night, as long as their motors keep turning and their contacts don’t get corroded.

But once you get past the futuristic speculation, there’s actually a lot of detail about how the then-cutting-edge machines worked. Circuit diagrams of logic units from both the relay computers and the brand-new vacuum tube machines are on display, as are drawings of the tricky bits of purely mechanical computers. There is even a diagram of the mercury delay line, and an explanation of how circulating audio pulses through the medium could be used as a form of memory.

All in all, it’s a wonderful glimpse at the earliest of computers, with enough detail that you could probably build something along those lines with a little moxie and a few thousands of relays. This grounded reality, coupled with the fantastic visions of where computers would be going, make a marvelous accompaniment to a lot of the breathless hype around AI these days. Recommended reading!

AI Kayak Controller Lets The Paddle Show The Way

June 10, 2024 by Dan Maloney 6 Comments

Controlling an e-bike is pretty straightforward. If you want to just let it rip, it’s a no-brainer — or rather, a one-thumber, as a thumb throttle is the way to go. Or, if you’re still looking for a bit of the experience of riding a bike, sensing when the pedals are turning and giving the rider a boost with the motor is a good option.

But what if your e-conveyance is more of the aquatic variety? That’s an interface design problem of a different color, as [Braden Sunwold] has discovered with his DIY e-kayak. We’ve detailed his work on this already, but for a short recap, his goal is to create an electric assist for his inflatable kayak, to give you a boost when you need it without taking away from the experience of kayaking. To that end, he used the motor and propeller from a hydrofoil to provide the needed thrust, while puzzling through the problem of building an unobtrusive yet flexible controller for the motor.

His answer is to mount an inertial measurement unit (IMU) in a waterproof container that can clamp to the kayak paddle. The controller is battery-powered and uses an nRF link to talk to a Raspberry Pi in the kayak’s waterproof electronics box. The sensor also has an LED ring light to provide feedback to the pilot. The controller is set up to support both a manual mode, which just turns on the motor and turns the kayak into a (low) power boat, and an automatic mode, which detects when the pilot is paddling and provides a little thrust in the desired direction of travel.

The video below shows the non-trivial amount of effort [Braden] and his project partner [Jordan] put into making the waterproof enclosure for the controller. The clamp is particularly interesting, especially since it has to keep the sensor properly oriented on the paddle. [Braden] is working on a machine-learning method to analyze paddle motions to discern what the pilot is doing and where the kayak goes. Once he has that model built, it should be time to hit the water and see what this thing can do. We’re eager to see the results.
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Hackaday Links: June 2, 2024

June 2, 2024 by Dan Maloney 6 Comments

So you say you missed the Great Solar Storm of 2024 along with its attendant aurora? We feel you on that; the light pollution here was too much for decent viewing, and it had been too long a day to make a drive into the deep dark of the countryside survivable. But fear not — the sunspot that raised all the ruckus back at the beginning of May has survived the trip across the far side of the sun and will reappear in early June, mostly intact and ready for business. At least sunspot AR3664 seems like it’s still a force to be reckoned with, having cooked off an X-class flare last Tuesday just as it was coming around from the other side of the Sun. Whether 3664 will be able to stir up another G5 geomagnetic storm remains to be seen, but since it fired off an X-12 flare while it was around the backside, you never know. Your best bet to stay informed in these trying times is the indispensable Dr. Tamitha Skov.

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Tabletop Handybot Is Handy, And Powered By AI

May 21, 2024 by Lewin Day 15 Comments

Decently useful AI has been around for a little while now, and robotic arms have been around much longer. Yet somehow, we don’t have little robot helpers on our desks yet! Thankfully, [Yifei] is working towards that reality with Tabletop Handybot.

What [Yifei] has developed is a robotic arm that accepts voice commands. The robot relies on a Realsense D435 RGB-D camera, which provides color vision with depth information as well. Grounding DINO is used for object detection on the RGB images. Segment Anything and Open3D are used for further processing of the visual and depth data to help the robot understand what it’s looking at. Meanwhile, voice commands are interpreted via OpenAI Whisper, which can feed prompts to ChatGPT for further processing.

[Yifei] demonstrates his robot picking up markers on command, which is a pretty cool demo. With so many modern AI tools available, we’re getting closer to the ideal of robots that can understand and execute on general spoken instructions. This is a great example. We may not be all the way there yet, but perhaps soon. Video after the break.

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Generative AI Hits The Commodore 64

May 15, 2024 by Lewin Day 5 Comments

Image-generating AIs are typically trained on huge arrays of GPUs and require great wads of processing power to run. Meanwhile, [Nick Bild] has managed to get something similar running on a Commodore 64. (via Tom’s Hardware).

A figure generated by [Nick]’s C64. We shall name him… “Sword Guy”!

As you might imagine, [Nick’s] AI image generator isn’t churning out 4K cyberpunk stills dripping in neon. Instead, he aimed at a smaller target, more befitting the Commodore 64 itself. His image generator creates 8×8 game sprites instead.

[Nick’s] model was trained on 100 retro-inspired sprites that he created himself. He did the training phase on a modern computer, so that the Commodore 64 didn’t have to sweat this difficult task on its feeble 6502 CPU. However, it’s more than capable of generating sprites using the model, thanks to some BASIC code that runs off of the training data. Right now, it takes the C64 about 20 minutes to run through 94 iterations to generate a decent sprite.

8×8 sprites are generally simple enough that you don’t need to be an artist to create them. Nonetheless, [Nick] has shown that modern machine learning techniques can be run on slow archaic hardware, even if there is limited utility in doing so. Video after the break.

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How AI Large Language Models Work, Explained Without Math

May 15, 2024 by Donald Papp 24 Comments

Large Language Models (LLMs ) are everywhere, but how exactly do they work under the hood? [Miguel Grinberg] provides a great explanation of the inner workings of LLMs in simple (but not simplistic) terms that eschews the low-level mathematics of how they work in favor of laying bare what it is they do.

At their heart, LLMs are prediction machines that work on tokens (small groups of letters and punctuation) and are as a result capable of great feats of human-seeming communication. Most technical-minded people understand that LLMs have no idea what they are saying, and this peek at their inner workings will make that abundantly clear.

Be sure to also review an illustrated guide to how image-generating AIs work. And if a peek under the hood of LLMs left you hungry for more low-level details, check out our coverage of training a GPT-2 LLM using pure C code.

The Perfect Desktop Kit For Experimenting With Self Driving Cars

May 14, 2024 by Lewin Day 1 Comment

When we think about self-driving cars, we normally think about big projects measured in billions of dollars, all funded by major automakers. But you can still dive into this world on a smaller scale, as [jmoreno555] demonstrates.

The build consists of a small RC car—an HSP 94123, in fact. It’s got a simple brushed motor inside, driven by a conventional speed controller, and servo-driven steering. A Raspberry Pi 4 is charged with driving the car, but it’s not alone. It’s outfitted with a Google Coral USB stick, which is a machine learning accelerator card capable of 4 trillion operations per second. The car also has a Wemos D1 onboard, charged with interfacing distance sensors to give the car a sense of its environment. Vision is courtesy of a 1.2-megapixel camera with a 160-degree lens, and a stereoscopic camera with twin 75-degree lenses. Software-wise, it’s early days yet. [jmoreno555] is exploring the use of Python and OpenCV to implement basic lane detection and other self driving routines, while using Blender as a simulator.

The real magic idea, though, is the treadmill. [jmoreno555] realized that one of the frustrations of working in this space is in having to chase a car around a test track. Instead, the use of a desktop treadmill allows the car to be programmed and debugged with less fuss in the early stages of development.

If you’re looking for a platform to experiment with AI and self-driving, this could be an project to dive in to. We’ve covered some other great builds in this space, too. Meanwhile, if you’ve cracked driving autonomy and want to let us know, our tipsline is always standing by!