ChatGPT – Observations from Uppsala

(Local) AI, Please Write some Code

My previous blog post in this series tested the ability of a range of large language models to analyze a piece of C code and determine what a mystery function did. That was interesting and entertaining, but possibly not a particularly “fair” test of the models’ capabilities. Most of time, I think people use “AI” to help write code, not to understand some tricky piece of algorithmic code. Thus, I turn the problem around and ask the models to write code for the algorithm I previously asked them to analyze.

(Local) AI, Please Explain This Code

Continuing my exploration of what a local AI model can do, I decided to test them on the task of code analysis. It would be so nice to have an AI model that is tuned and trained on a particular tool or programming system, and that can be distributed for users to run on their own on their local machine, server, or cloud VM. Avoiding the need to run and charge for a custom cloud service and ensuring confidentiality and availability.

Updated 2024-12-12 with Llama-3.3-70B

RI.SE AI Day – More on LLMs (and some)

The Swedish research institute RI.SE hosted an “Artificial Intelligence and Computer Science day” (AI and CS day) last week. RI.SE has a long tradition of hosting interesting open houses, both as RI.SE and in their previous guide as SiCS. The day was a mix of organized talks in the morning, and an open house where RI.SE researchers showed off their work in the afternoon. Most of the AI discussions were related to large language models (LLMs), but not all. I got some new insights about LLMs in general and using LLMs for coding in particular.

ChatGPT and Legal

In previous three blog posts (1,2,3) about ChatGPT in particular and large language models in general, I touched on what they can do, what they cannot do, what they seem not to do, how they fall down in funny ways, and why I think they are fundamentally flawed for many applications. There is one more aspect left to consider – the legal and licensing side. I am not a lawyer, I am not an expert, but it seems obvious that there is a huge problem. There are also clear questions about business morals and what the right thing to do would be. I also doubt the business viability of LLMs in the way they are currently trained.

ChatGPT and Critique

ChatGPT and other transformer-based models like Dall-E are technologically very impressive. They do things that seemed totally impossible just a few years ago. However, they are not really generally intelligent, and there are innumerable problems with how they work, what they do, what people think they do, ethics, and legal and licensing issues. This is my third post about ChatGPT, where I present my critique of and reflections on the technology. The previous posts were about ChatGPT and Simics and Coding using ChatGPT.

ChatGPT and Code

In my previous blog post about ChatGPT and Simics, I tested it on its knowledge and abilities with a fairly niche subject. Not unsurprisingly it did not do all that well. However, one area where ChatGPT appears to really work well is when dealing with program code. This seems more practically useful as well, especially as a generator of starting points and boiler-plate code. It can also sometimes do a decent job explaining code, subject to quite common bizarre mistakes and errors. Update: Part 3, a critique of ChatGPT has been published.

ChatGPT and Simics

It is an understatement to say that ChatGPT has been a hot topic since it was launched a few months back. Everyone seems to be seeing what it can do in their favorite domain, so I had to try it on what I work with, Simics and virtual platforms. The results did not live up to the hype some people think the technology deserves, but it was very impressive and a little scary nevertheless. This is the first post in what looks like it will be a series about ChatGPT. Update: Part 2, ChatGPT and Code, is now out. Update to the update: Part 3, a critique of ChatGPT has been published.