“Don Knuth Plays with ChatGPT” but with ChatGPT-4

>The logic failure in the above statement

And which alleged logic failure is that?

Yes, the models are not constantly learning. They only update their knowledge when they are retrained, which is pretty infrequently (I think the base GPT models have not been retrained, but the chat laters on top might).

I much prefer the attitude of the chap that made the video "GPT 4 is smarter than you think" https://youtu.be/wVzuvf9D9BU

Instead of nit-picking flaws in what is a very early iteration of a revolutionary technology, he instead immediately started exploring ways of making it better and more useful.

Even with minimal effort that was essentially just copy-pasting some text around, he was able to show that the current way we use LLMs like GPT 4 is not the be-all and end-all of this type of technology.

I'm entirely convinced that we're just scratching the surface. It's like the first transistor, which was a crude, ugly, useless thing: https://images.computerhistory.org/siliconengine/1947-1-1.jp...

Just in the last two weeks(!), I've read about the following still-experimental methods for enhancing LLMs:

1. Plugging in "calculators" like Wolfram Alpha.

2. Adding vision input so they can understand equations, graphs, etc...

3. Filtering the output probability vector for certain allowed terms only ("YES", "NO", "MAYBE"), making them more useful in programmatically-invoked scenarios.

4. Similarly, filtering the output token list for syntax-validity, such as "valid JSON", "valid XML", etc... That is, instead of a purely random selection between to "top-n" output tokens, only valid tokens can be chosen, based on contextual syntax.

5. Storing embeddings in a vector database, giving LLMs medium-term memory, and the ability to index and reference sources precisely.

6. Efficient fine-tuning through Low-Rank Adaptation (LoRA), which allows desktop GPUs to tune a model overnight! This overcomes the "stale long-term memory" issue of ChatGPT, which only knows things up to September 2021. It could now read the news daily and "keep up".

7. External script harnesses that run multiple LLMs in parallel, with different prompts and/or different system messages. Some optimised for "idea generation", some optimised for "task completion", and then finally models tuned for "review and verification". Almost like a human team, multiple ideas can be generated, merged, reviewed, planned out, and then actioned. Check out "smol developer", which utilises Anthropic's 100K context window for this: https://www.youtube.com/watch?v=UCo7YeTy-aE

This is just the beginning. Chat GPT 4 hasn't even been available for 3 months yet, and practically all of the above experimentation has been done with weaker models because GPT 4 still doesn't have generally-available API access! Similarly, the 32K context window version of the GPT 4 model isn't available to anyone except a lucky few.

What will 2024 bring!? Heck... what will H2 2023 bring?

O.K., less than half know that the ballet is scheduled at appropriate times so the friends of the girls can get some bar time in without undue hassle.

I’d be curious to know if someone could get a more “valiant effort” version of those first two questions with some prompt engineering. E.g. if it was asked to roleplay a conversation with the proper disclaimers to override its objection to not knowing what they actually think.