Noisy Keyboards Sink Ships

August 6, 2023 by Al Williams 40 Comments

Many of us like a keyboard with a positive click noise when we type. You might want to rethink that, though, in light of a new paper from the UK that shows how researchers trained an AI to decode keystrokes from noise on conference calls.

The researchers point out that people don’t expect sound-based exploits. The paper reads, “For example, when typing a password, people will regularly hide their screen but will do little to obfuscate their keyboard’s sound.”

The technique uses the same kind of attention network that makes models like ChatGPT so powerful. It seems to work well, as the paper claims a 97% peak accuracy over both a telephone or Zoom. In addition, where the model was wrong, it tended to be close, identifying an adjacent keystroke instead of the correct one. This would be easy to correct for in software, or even in your brain as infrequent as it is. If you see the sentence “Paris im the s[ring,” you can probably figure out what was really typed.

We’ve seen this done before, but this technique raises the bar. As sophisticated as keyboard listening was back in the 1970s, you can only imagine what the three-letter agencies can do these days.

In the meantime, the mitigation for this particular threat seems obvious — just start screaming whenever you type in your password.

The AI Engine That Fits In 100K

August 2, 2023 by Al Williams 14 Comments

Running your own AI models is possible, but it requires a giant computer, right? Maybe not. Researchers at NVidia are showing off Perfusion, a text-to-image model they say is 100KB in size and takes four minutes to train. The model specializes in customizing a photo. For example, the paper shows a picture of a teddy bear and a prompt to dress it as a wizard. In all fairness, the small size and quick training are a little misleading, we think, because the results are still using the usual giant model. What’s small and fast is the customization of the existing model.

Customizing models is a common task since you often want to work with something the model doesn’t contain. For example, you might want to alter a picture of your face or your pet, which probably isn’t in the original model. You can create a special keyword and partially train the model for what you want using something called textual inversion. The problem the researchers identified is that creating textual inversions often causes the new training to leak to unintended areas.

They describe “key locking,” a technique to avoid overfitting when fine-tuning an existing model. For example, suppose you want to add a specific dog picture to the model. With typical techniques, a special keyword like dog* will indicate the custom dog image, but the keyword has no connection with generic dogs, mammals, or animals. This makes it difficult for the AI to work with the image. For example, the prompts “a man sitting” and “a dog sitting” require very different image generations. But if we train a specific dog as “dog*” there’s no deeper understanding that “dog*” is a type of “dog” that the model already knows about. So what do you do with “dog* sitting?” Key locking makes that association.

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Self-Hosted Chatbot Focuses On Privacy

July 30, 2023 by Bryan Cockfield 6 Comments

Large language models (LLMs) have been all the rage lately, assisting from all kinds of tasks from programming to devising Excel formulas to shortcutting school work. They’re also relatively easy to access for the most part, but as the old saying goes, if something on the Internet is free the real product is you (and your data). Luckily there are ways of hosting LLMs on your own to avoid your personal data getting harvested, as well as taking advantage of open-source solutions, but building these systems takes a little bit of effort. [Stephen] and a team from Mozilla walk us through this process and show us a number of options currently available.

Working from the ground up, the group first decides on hosting, which (unsurprisingly) involves using Mozilla hosting services. The choice of runtime environment was a little bit more challenging. The project was time constrained, so they looked at two options here: Hugging Face and llama.cpp. Eventually deciding to move forward with llama.cpp largely due to its ability to run on more consumer-oriented hardware (especially Apple silicon) and the fact that it doesn’t need a powerful GPU, the next task was to choose the model. Settling on the LLaMa model that Facebook recently open-sourced, this model works well with the runtime environment and is essentially the only one that does.

From there, the team at Mozilla wanted to make sure their chat bot would be able to provide other Mozilla employees with information more readily pertinent to their jobs, so they trained their model with some internal Mozilla data as well as other more generic information. This doesn’t mean the job is done, though, there are a number of other factors that went in to designing this system before it was finally complete. Even then, since they built this in a week it’s not perfect; there are some issues with non-permissive licensing of some of the components and many of the design choices may not have been ideal. It’s impressive what’s out there if you’re hosting your own system, though, and while this might be a little more advanced for a self-hosted project, take a look at some other more beginner-friendly projects you can try if you’re just starting out on the self-hosted path.

The Right Benchmark For GPT

July 29, 2023 by Elliot Williams 41 Comments

Dan Maloney wanted to design a part for 3D printing. OpenSCAD is a coding language for generating 3D objects. ChatGPT can write code. What could possibly go wrong? You should go read his article because it’s enlightening and hilarious, but the punchline is that it ran afoul of syntax errors, but also gave him enough of a foothold that he could teach himself enough OpenSCAD to get the project done anyway. As with many people who have asked the AI to create some code, Dan finds that it’s not as good as asking someone who knows what they’re doing, but that it’s also better than nothing.

And this is where I start grumbling. When you type your desires into the word-follower machine, your alternative isn’t nothing. Your alternative is to fire up a search engine instead and type “openscad tutorial”. That, for nearly any human endeavor, will get you a few good guides, written by humans who are probably expert in the subject in question, and which are aimed at teaching you the thing that you want to learn. It doesn’t get better than that. You’ll be up and running with your design in no time.

Indeed, if you think about the relevant source material that the LLM was trained on, it’s exactly these tutorials. It can’t possibly do better than the best of them, although the resulting average tutorial might be better than the worst you’ll find. (Some have speculated on what happens when the entire Internet is filled with these generated texts – what will future AIs learn from?)

In Dan’s case, though, he didn’t necessarily want to learn OpenSCAD – he just wanted the latch designed. But in the end, he had to learn enough OpenSCAD to get the AI code compiling without error. He spent an hour learning OpenSCAD and now he’s good to go on his next project too.

So the next time you hear someone say that they got an answer back from a large language model that wasn’t perfect, but it was “better than nothing”, think critically if “nothing” is really the right benchmark.

Do you really want to learn nothing? Do you really have no resources to get started with? I would claim that we have the most amazing set of tutorial resources the world has ever known at our fingertips. Compared to the ability to teach millions of humans to achieve their own goals, that makes the LLM party tricks look kinda weak, in my opinion.

GETMusic Uses Machine Learning To Generate Music, Understands Tracks

July 27, 2023 by Donald Papp 10 Comments

Music generation guided by machine learning can make great projects, but there’s not usually much apparent control over the results. The system makes what it makes, and it’s an achievement if the results are not obvious cacophony. But that’s all different with GETMusic which allows for a much more involved approach because it understands and is able to create music by tracks. Among other things, this means one can generate a basic rhythm and melody first, then add additional elements to those existing ones, leaving the previous elements unchanged.

GETMusic can make music from scratch, or guided from examples, and under the hood uses a diffusion-based approach similar to the method behind AI image generators like Stable Diffusion. We’ve previously covered how Stable Diffusion works, but instead of images the same basic principles are used to guide the model from random noise to useful tracks of music.

Just a few years ago we saw a neural network trained to generate Bach, and while it was capable of moments of brilliance, it didn’t produce uniformly-listenable output. GETMusic is on an entirely different level. The model and code are available online and there is a research paper to accompany it.

You can watch a video putting it through its paces just below the page break, and there are more videos on the project summary page.

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Text-to-Speech Model Can Do Music, Background Noises, And Sound Effects

July 24, 2023 by Donald Papp 8 Comments

Bark is a universal text-to-audio model that can not only create realistic speech, it can incorporate music, background noises, and sound effects. It can even include non-speech sounds like laughter, sighs, throat clearings, and similar elements. But despite the fact that it can deliver such complex results, it’s important to understand some of the peculiarities.

The model takes a prompt and generates the resulting sound from scratch. Results might sometimes be unexpected.

Bark is not a conventional text-to-speech program, and how it works has a lot more in common with large language model AI chatbots. This means that results can deviate from expectations, and outputs aren’t necessarily going to be studio-quality speech. As the project’s README points out, “(generated outputs can) be anything from perfect speech to multiple people arguing at a baseball game recorded with bad microphones.” That being said, there is some support for voice presets as a way to help guide the model with some consistency.

Bark was designed by a company called Suno for research purposes and is available under the MIT License. It can be installed and run locally, and has some demos available as well as an online implementation.

The ability to install and run Bark locally is promising territory for incorporating it into projects. And should you be more interested in speech-to-text instead, don’t forget about this plain C/C++ implementaion of AI-powered speech recognition.

When Is Open Source AI Not Open Source AI?

July 21, 2023 by Jenny List 18 Comments

The world of AI is abuzz, or at least parts of it are, at the news of Meta’s release of Llama 2. This is an AI text model which is thought to surpass ChatGPT in capabilities, and which the social media turned VR turned own all your things company wants you to know is open to all. That’s right, the code is open source and you can download the model, and Meta want you to feel warm and fuzzy about it. Unfortunately all is not as it seems, because of course the model isn’t open-source and is subject to a licensing restriction which makes it definitely not free of charge for larger users. This is of course disappointing to anyone hoping for an AI chatbot without restrictions, but we’re guessing Meta would prefer not to inadvertently enable a competitor.

Happily for the open source user large or small who isn’t afraid of a little work there’s an alternative in the form of OpenLLaMA, but we understand that won’t be for all users. Whichever LLM you use though, please don’t make the mistake of imagining that it possesses actual intelligence.

Thanks to the CoupledAI team for the tip!