“The chatbot gave wildly different answers to the same math problem, with one version of ChatGPT even refusing to show how it came to its conclusion.”
It’s getting worse. And because it’s a black box model they don’t know why. The computer science professor here likens it to how human students make mistakes… but human students make mistakes because they don’t have perfect recall, mishear things being told to them, are tired and/or not paying attention… A bunch of reason that basically relate to having a human body that needs food, rest and water. A thing a computer does not have.
The only reason ChatGPT should be getting math wrong is that it’s getting inputs that are wrong, but without view into it they can’t figure out where it’s getting it wrong and who told it the wrong info.
I mean an “average” computer would require a pretty beefy set of hardware. I think most of the average local llama’s would run fairly decently on a MacBook without issue nowadays (that m3 is going to be a pretty awesome beast). But the quality is pretty reduced even compared to something like 3.5 which most people thought wasn’t all that great.
But really, I’m excited about researchers have access to more computer for smaller amounts (see this https://www.chatgptguide.ai/2023/07/20/worlds-largest-supercomputer-for-ai-training-is-out/) currently we have 1T models that are good, but we could pretty soon have 100T models from the open source community. Let’s see whether we can scale the hardware needs with the parameter growth so we don’t need A100s to run a decent model.