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.

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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.

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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.

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