What’s The Deal With AI Art?

September 28, 2024 by Elliot Williams 103 Comments

A couple weeks ago, we had a kerfuffle here on Hackaday: A writer put out a piece with AI-generated headline art. It was, honestly, pretty good, but it was also subject to all of the usual horrors that get generated along the way. If you have played around with any of the image generators you know the AI-art uncanny style, where it looks good enough at first glance, but then you notice limbs in the wrong place if you look hard enough. We replaced it shortly after an editor noticed.

The story is that the writer couldn’t find any nice visuals to go with the blog post, with was about encoding data in QR codes and printing them out for storage. This is a problem we have frequently here, actually. When people write up a code hack, for instance, there’s usually just no good image to go along with it. Our writers have to get creative. In this case, he tossed it off to Stable Diffusion.

Some commenters were afraid that this meant that we were outsourcing work from our fantastic, and very human, art director Joe Kim, whose trademark style you’ve seen on many of our longer-form original articles. Of course we’re not! He’s a genius, and when we tell him we need some art about topics ranging from refining cobalt to Wimshurst machines to generate static electricity, he comes through. I think that all of us probably have wanted to make a poster out of one or more of his headline art pieces. Joe is a treasure.

But for our daily blog posts, which cover your works, we usually just use a picture of the project. We can’t ask Joe to make ten pieces of art per day, and we never have. At least as far as Hackaday is concerned, AI-generated art is just as good as finding some cleared-for-use clip art out there, right?

Except it’s not. There is a lot of uncertainty about the data that the algorithms are trained on, whether the copyright of the original artists was respected or needed to be, ethically or legally. Some people even worry that the whole thing is going to bring about the end of Art. (They worried about this at the introduction of the camera as well.) But then there’s also the extra limbs, and AI-generated art’s cliche styles, which we fear will get old and boring after we’re all saturated with them.

So we’re not using AI-generated art as a policy for now, but that’s not to say that we don’t see both the benefits and the risks. We’re not Luddites, after all, but we are also in favor of artists getting paid for their work, and of respect for the commons when people copyleft license their images. We’re very interested to see how this all plays out in the future, but for now, we’re sitting on the sidelines. Sorry if that means more headlines with colorful code!

Building AI Models To Diagnose HVAC Issues

August 8, 2024 by Bryan Cockfield 10 Comments

HVAC – heating, ventilation, and air conditioning – can account for a huge amount of energy usage of a building, whether it’s residential or industrial. Often it’s the majority energy consumer, especially in places with extreme climates or for things like data centers where cooling is a large design consideration. When problems arise with these complex systems, they can go undiagnosed for a time and additionally be difficult to fix, leading to even more energy losses until repairs are complete. With the growing availability of platforms that can run capable artificial intelligences, [kutluhan_aktar] is working towards a system that can automatically diagnose potential issues and help humans get a handle on repairs faster.

The prototype system is designed for hydronic (water-based) systems and uses two separate artificial intelligences, one to analyze thermal imagery of the system and look for problems like leaks, hot spots, or blockages, and the other to listen for anomalous sounds especially relating to the behavior of cooling fans. For the first, a CNC-like machine was built to move a thermal camera around a custom-built model HVAC system and report its images back to a central system where they can be analyzed for anomalies. The second system which analyses audio runs its artificial intelligence on a XIAO ESP32C6 and listens to the cooling fans running in the model.

One problem that had to be tackled before any of this could be completed was actually building an open-source dataset to train the AI on. That’s part of the reason for the HVAC model in this project; being able to create problems to train the computer to detect before rolling it out to a larger system. The project’s code and training models can be found on its GitHub page. It seems to be a fairly robust solution to this problem, though, and we’ll be looking forward to future versions running on larger systems. Not everyone has a hydronic HVAC system, though. As heat pumps become more and more popular and capable, you’ll need systems to control those as well.

AI Image Generator Twists In Response To MIDI Dials, In Real-time

July 30, 2024 by Donald Papp 29 Comments

MIDI isn’t just about music, as [Johannes Stelzer] shows by using dials to adjust AI-generated imagery in real-time. The results are wild, with an interactivity to them that we don’t normally see in such things.

[Johannes] uses Stable Diffusion‘s SDXL Turbo to create a baseline image of “photo of a red brick house, blue sky”. The hardware dials act as manual controls for applying different embeddings to this baseline, such as “coral”, “moss”, “fire”, “ice”, “sand”, “rusty steel” and “cookie”.

By adjusting the dials, those embeddings are applied to the base image in varying strengths. The results are generated on the fly and are pretty neat to see, especially since there is no appreciable amount of processing time required.

The MIDI controller is integrated with the help of lunar_tools, a software toolkit on GitHub to facilitate creating interactive exhibits. As for the image end of things, we’ve previously covered how AI image generators work.

Peering Into The Black Box Of Large Language Models

July 3, 2024 by Donald Papp 35 Comments

Large Language Models (LLMs) can produce extremely human-like communication, but their inner workings are something of a mystery. Not a mystery in the sense that we don’t know how an LLM works, but a mystery in the sense that the exact process of turning a particular input into a particular output is something of a black box.

This “black box” trait is common to neural networks in general, and LLMs are very deep neural networks. It is not really possible to explain precisely why a specific input produces a particular output, and not something else.

Why? Because neural networks are neither databases, nor lookup tables. In a neural network, discrete activation of neurons cannot be meaningfully mapped to specific concepts or words. The connections are complex, numerous, and multidimensional to the point that trying to tease out their relationships in any straightforward way simply does not make sense.

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Torment Poor Milton With Your Best Pixel Art

June 25, 2024 by Donald Papp 7 Comments

One of the great things about new tech tools is just having fun with them, like embracing your inner trickster god to mess with ‘Milton’, an AI trapped in an empty room.

Milton is trapped in a room is a pixel-art game with a simple premise: use a basic paint interface to add objects to the room, then watch and listen to Milton respond to them. That’s it? That’s it. The code is available on the GitHub repository, but there’s also a link to play it live without any kind of signup or anything. Give it a try if you have a few spare minutes.

Under the hood, the basic loop is to let the user add something to the room, send the picture of the room (with its new contents) off for image recognition, then get Milton’s reaction to it. Milton is equal parts annoyed and jumpy, and his speech and reactions reflect this.

The game is a bit of a concept demo for Open Souls whose “thing” is providing AIs with far more personality and relatable behaviors than one typically expects from large language models. Maybe this is just what’s needed for AI opponents in things like the putting game of Connect Fore! to level up their trash talking.

Hackaday Links: June 16, 2024

June 16, 2024 by Dan Maloney 16 Comments

Attention, slackers — if you do remote work for a financial institution, using a mouse jiggler might not be the best career move. That’s what a dozen people learned this week as they became former employees of Wells Fargo after allegedly being caught “simulating keyboard activity” while working remotely. Having now spent more than twice as many years working either hybrid or fully remote, we get it; sometimes, you’ve just got to step away from the keyboard for a bit. But we’ve never once felt the need to create the “impression of active work” during those absences. Perhaps that’s because we’ve never worked in a regulated environment like financial services.

For our part, we’re curious as to how the bank detected the use of a jiggler. The linked article mentions that regulators recently tightened rules that require employers to treat an employee’s home as a “non-branch location” subject to periodic inspection. More than enough reason to quit, in our opinion, but perhaps they sent someone snooping? More likely, the activity simulators were discovered by technical means. The article contains a helpful tip to avoid powering a jiggler from the computer’s USB, which implies detecting the device over the port. Our guess is that Wells tracks mouse and keyboard activity and compares it against a machine-learning model to look for signs of slacking.

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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!