AI Mistakes Are Different, And That’s A Problem

January 25, 2025 by Donald Papp 59 Comments

People have been making mistakes — roughly the same ones — since forever, and we’ve spent about the same amount of time learning to detect and mitigate them. Artificial Intelligence (AI) systems make mistakes too, but [Bruce Schneier] and [Nathan E. Sanders] make the observation that, compared to humans, AI models make entirely different kinds of mistakes. We are perhaps less equipped to handle this unusual problem than we realize.

The basic idea is this: as humans we have tremendous experience making mistakes, and this has also given us a pretty good idea of what to expect our mistakes to look like, and how to deal with them. Humans tend to make mistakes at the edges of our knowledge, our mistakes tend to clump around the same things, we make more of them when bored or tired, and so on. We have as a result developed controls and systems of checks and balances to help reduce the frequency and limit the harm of our mistakes. But these controls don’t carry over to AI systems, because AI mistakes are pretty strange.

The mistakes of AI models (particularly Large Language Models) happen seemingly randomly and aren’t limited to particular topics or areas of knowledge. Models may unpredictably appear to lack common sense. As [Bruce] puts it, “A model might be equally likely to make a mistake on a calculus question as it is to propose that cabbages eat goats.” A slight re-wording of a question might be all it takes for a model to suddenly be confidently and utterly wrong about something it just a moment ago seemed to grasp completely. And speaking of confidence, AI mistakes aren’t accompanied by uncertainty. Of course humans are no strangers to being confidently wrong, but as a whole the sort of mistakes AI systems make aren’t the same kinds of mistakes we’re used to.

There are different ideas on how to deal with this, some of which researchers are (ahem) confidently undertaking. But for best results, we’ll need to invent new ways as well. The essay also appeared in IEEE Spectrum and isn’t terribly long, so take a few minutes to check it out and get some food for thought.

And remember, if preventing mistakes at all costs is the goal, that problem is already solved: GOODY-2 is undeniably the world’s safest AI.

Turning GLaDOS Into Ted: A Tale Of A Talking Toy

January 14, 2025 by Heidi Ulrich 7 Comments

What if your old, neglected toys could come to life — with a bit of sass? That’s exactly what [Binh] achieved when he transformed his sister’s worn-out teddy bear into ‘Ted’, an interactive talking plush with a personality of its own. This project, which combines the GLaDOS Personality Core project from the Portal series with clever microcontroller tinkering, brings a whole new personality to a childhood favorite.

[Binh] started with the basics: a teddy bear already equipped with buttons and speakers, which he overhauled with an ESP32 microcontroller. The bear’s personality originated from GLaDOS, but was rewritten by [Binh] to fit a cheeky, teddy-bear tone. With a few tweaks in the Python-based fork, [Binh] created threads to handle touch-based interaction. For example, the ESP32 detects where the bear is touched and sends this input to a modified neural network, which then generates a response. The bear can, for instance, call you out for holding his paw for too long or sarcastically plead for mercy. I hear you say ‘but that bear Ted could do a lot more!’ Well — maybe, all this is just what an innocent bear with a personality should be capable of.

Instead, let us imagine future iterations featuring capacitive touch sensors or accelerometers to detect movement. The project is simple, but showcases the potential for intelligent plush toys. It might raise some questions, too.

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Modern AI On Vintage Hardware: LLama 2 Runs On Windows 98

January 13, 2025 by Donald Papp 27 Comments

[EXO Labs] demonstrated something pretty striking: a modified version of Llama 2 (a large language model) that runs on Windows 98. Why? Because when it comes to personal computing, if something can run on Windows 98, it can run on anything. More to the point: if something can run on Windows 98 then it’s something no tech company can control how you use, no matter how large or influential they may be. More on that in a minute.

Ever wanted to run a local LLM on 25 year old hardware? No? Well now you can, and at a respectable speed, too!

What’s it like to run an LLM on Windows 98? Aside from the struggles of things like finding compatible peripherals (back to PS/2 hardware!) and transferring the required files (FTP over Ethernet to the rescue) or even compilation (some porting required), it works maybe better than one might expect.

A Windows 98 machine with Pentium II processor and 128 MB of RAM generates a speedy 39.31 tokens per second with a 260K parameter Llama 2 model. A much larger 15M model generates 1.03 tokens per second. Slow, but it works. Going even larger will also work, just ever slower. There’s a video on X that shows it all in action.

It’s true that modern LLMs have billions of parameters so these models are tiny in comparison. But that doesn’t mean they can’t be useful. Models can be shockingly small and still be perfectly coherent and deliver surprisingly strong performance if their training and “job” is narrow enough, and the tools to do that for oneself are all on GitHub.

This is a good time to mention that this particular project (and its ongoing efforts) are part of a set of twelve projects by EXO Labs focusing on ensuring things like AI models can be run anywhere, by anyone, independent of tech giants aiming to hold all the strings.

And hey, if local AI and the command line is something that’s up your alley, did you know they already exist as single-file, multi-platform, command-line executables?

Running AI Locally Without Spending All Day On Setup

January 8, 2025 by Al Williams 34 Comments

There are many AI models out there that you can play with from companies like OpenAI, Google, and a host of others. But when you use them, you get the experience they want, and you run it on their computer. There are a variety of reasons you might not like this. You may not want your data or ideas sent through someone else’s computer. Maybe you want to tune and tweak in ways they aren’t going to let you.

There are many more or less open models, but setting up to run them can be quite a chore and — unless you are very patient — require a substantial-sized video card to use as a vector processor. There’s very little help for the last problem. You can farm out processing, but then you might as well use a hosted chatbot. But there are some very easy ways to load and run many AI models on Windows, Linux, or a Mac. One of the easiest we’ve found is Msty. The program is free for personal use and claims to be private, although if you are really paranoid, you’ll want to verify that yourself.

What is Msty?

Msty is a desktop application that lets you do several things. First, it can let you chat with an AI engine either locally or remotely. It knows about many popular options and can take your keys for paid services. For local options, it can download, install, and run the engines of your choice.

For services or engines that it doesn’t know about, you can do your own setup, which ranges from easy to moderately difficult, depending on what you are trying to do.

Of course, if you have a local model or even most remote ones, you can use Python or some basic interface (e.g., with ollama; there are plenty of examples). However, Msty lets you have a much richer experience. You can attach files, for example. You can export the results and look back at previous chats. If you don’t want them remembered, you can chat in “vapor” mode or delete them later.

Each chat lives in a folder, which can have helpful prompts to kick off the chat. So, a folder might say, “You are an 8th grade math teacher…” or whatever other instructions you want to load before engaging in chat.

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close up of a TI-84 Plus CE running custom software

Going Digital: Teaching A TI-84 Handwriting Recognition

December 24, 2024 by Heidi Ulrich 4 Comments

You wouldn’t typically associate graphing calculators with artificial intelligence, but hacker [KermMartian] recently made it happen. The innovative project involved running a neural network directly on a TI-84 Plus CE to recognize handwritten digits. By using the MNIST dataset, a well-known collection of handwritten numbers, the calculator could identify digits in just 18 seconds. If you want to learn how, check out his full video on it here.

The project began with a proof of concept: running a convolutional neural network (CNN) on the calculator’s limited hardware, a TI-84 Plus CE with only 256 KB of memory and a 48 MHz processor. Despite these constraints, the neural network could train and make predictions. The key to success: optimizing the code, leveraging the calculator’s C programming tools, and offloading the heavy lifting to a computer for training. Once trained, the network could be transferred to the calculator for real-time inference. Not only did it run the digits from MNIST, but it also accepted input from a USB mouse, letting [KermMartian] draw digits directly on the screen.

While the calculator’s limited resources mean it can’t train the network in real-time, this project is a proof that, with enough ingenuity, even a small device can be used for something as complex as AI. It’s not just about power; it’s about resourcefulness. If you’re into unconventional projects, this is one for the books.

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See What ‘They’ See In Your Photos

December 17, 2024 by Lewin Day 46 Comments

Once upon a time, a computer could tell you virtually nothing about an image beyond its file format, size, and color palette. These days, powerful image recognition systems are a part of our everyday lives. They See Your Photos is a simple website that shows you just how much these systems can interpret from a regular photo.

The website simply takes your image submission, runs it through the Google Vision API, and spits back out a description of the image. I tried it out with a photograph of myself, and was pretty impressed with what the vision model saw:

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Render of life-size robot rat animatronic on blue plane

Robot Rodents: How AI Learned To Squeak And Play

December 9, 2024 by Heidi Ulrich 9 Comments

In an astonishing blend of robotics and nature, SMEO—a robot rat designed by researchers in China and Germany — is fooling real rats into treating it like one of their own.

What sets SMEO apart is its rat-like adaptability. Equipped with a flexible spine, realistic forelimbs, and AI-driven behavior patterns, it doesn’t just mimic a rat — it learns and evolves through interaction. Researchers used video data to train SMEO to “think” like a rat, convincing its living counterparts to play, cower, or even engage in social nuzzling. This degree of mimicry could make SMEO a valuable tool for studying animal behavior ethically, minimizing stress on live animals by replacing some real-world interactions.

For builders and robotics enthusiasts, SMEO is a reminder that robotics can push boundaries while fostering a more compassionate future. Many have reservations about keeping intelligent creatures in confined cages or using them in experiments, so imagine applying this tech to non-invasive studies or even wildlife conservation. In a world where robotic dogs, bees, and even schools of fish have come to life, this animatronic rat sounds like an addition worth further exploring. SMEO’s development could, ironically, pave the way for reducing reliance on animal testing.

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