No one cares

It's the same on out of distribution problems, It will just confidently say false things, I will tell it what is wrong and explain why and it will correct code making it wrong/not working correctly in a different way. I recently build a thing and you can't find anything similar to it anywhere in open source and you can't find any tutorial/solution to this problem online and ChatGPT failed to deliver.

At the end of the day it's just statistics based on all available knowledge on the internet.

I guess I could have worded it better. What I mean to say is that once they’ve output something, it’s in the record. There’s no pausing to think and go through a few different iterations of the sentence, or evaluating if what they’re about to say has faults. They just output directly, instead of reading what they’re about to output and vetting it.

Time to rewatch Westworld Season 1

it's not. it's giving you answers to appear intelligent, many times in almost magical ways, but it doesn't "think" - especially in steps.

The MSFT paper notes that this is one of its clearest shortcomings - it can't do long range planning. At least not yet. But i think this is partially people expecting way too much of a single model.

> like ingesting a book

Interestingly, currently LLMs can't naturally ingest a book, since it doesn't fit in the prompt (they can fit 32K tokens that's about 24k words). This is where GPTs differ fundamentally from the human brain. GPTs always produce one token at a time, given the full prompt. There is no state kept between token generation steps other than the prompt which grows one token at a time. The human brain on the other hand has a state, and it is continuously evolving. In the case of a book, our brain state will be affected by the content of the book as we read it.

LLMs need to be able to hold more state to get to the next level. Perhaps get augmented with some sort of LSTM architecture where state can be built up from a theoretically infinite amount of input, or have another compressed/non-human-readable prompt that gets read before generating the token and gets updated after generating the token.

> Moreover, I doubt any human programmer will have troubles with the "Beginner" problems, regardless of their specialization.

FWIW, I think you overestimate humans. Particularly those who haven't actively been practicing leetcode-style coding. E.g., many of the problems are specified in "competition language", not "human-friendly language" (where "human-friendly", e.g., is something you'd be happy to see in a design doc). (Should that matter to GPT-4? I dunno.)

I do think it is fair though to say that, with some baseline level of practice (which is potentially the relevant comparison point), a lot of people would probably nail the "beginner" tests.

Yea, I agree with this, but I still don’t see what advantage the state of the art providers receive by adhering to an open protocol. If anything doing so would (on the margin) push users towards open source models when they might have been willing to pay for a more advanced model just to access certain plugins.

That being said, I do think that a standardized approach to a plugin ecosystem will arise. I just think it’s silly to expect any of the foundation model providers to participate.

No explicit purpose. other than to respond with “yes I am ready”

I'm learning the very basics of clustering and classification algorithms. From my understanding, these use hard cutoffs to set boundaries between the groups in the outputs. My question is - do modern algorithms allow for smoothing or "adding weight" to the boundaries, so they are not just hard cutoffs? And if so, are there any applications where you've seen this done?

It can, but I think it's something about how it selects what to do. There needs to be more logic to it. I find it does really well you tell it to use reasoning. For example i give it code and ask for it to draw inspiration. It does really well at this. Really, it needs a good prompt engineer. But there's no set strategies yet but there will be. I can almost get it to generate anything if I prompt it right. So it's more I need to figure out how it thinks of stuff and try to present my problem to it that way

> Do you mean re-prompt it asking to correct its mistakes?

Well, re-prompt + asking it to bake test cases upfront and continuously analyze how failures line up with the test cases.

I don't see how it is possible to not end up just memorizing the internet, which is full of enough questions and discussions to simulate convincing Q&As. Consider if a team had invented an algorithm or heuristic to avoid data contamination (https://aisnakeoil.substack.com/p/gpt-4-and-professional-benchmarks). Then what you have is something that can separate content into logically similar, but orthogonal realizations. That would be an incredibe tool and worth a prize in its own right.

> and won’t read its own output as it’s outputting

This is literally what transformer decoders do, unless I've strongly misunderstood your statement.

You mean, like continuously refining your google searches until you find the right stackexchange answer?

[removed]

>Apparently it cannot solve coding problems which require any amount of thinking.

Not yet, and this is not surprising.

First, GPT-4 can solve many coding problems on the first try. Yes, these small programs may be simple, but how many developers can write code that directly runs? Maybe in 1-2 languages, and even then only in the problem domain that they are very familiar with. Also, since LLMs can write code in more languages and frameworks than most developers, LLMs can actually solve more coding problems than most of the programmer out there... So LLMs already contain vast amounts of "knowledge" and "intuitive ability". But intuition is not enough to solve larger or more complex problems.

So, finally, coming to the thinking part. What challenging problems can be solved by humans by "off-the-cuff"? We also, scribble, draw diagrams, try out a few things, see if things run and work as expected, do web searches, talk to stake holders, sleep on the problem, etc. In other words, in any non-trivial problem solving, we also rely heavily on feedback between our brains and the external world.

Frankly, I don't see this as a problem of LLMs, they can be effectively used as foundation models. One could have another layer, on top of LLMs to solve problems end-to-end. For example one could build a meta-model, where multiple instances work together in an actor-critic fashion. The actor is the one interacting with the user, the critic can be prompted (and perhaps) fine-tuned with with general problem solving strategies, with the main prompt being to second-guess and try to find flaws in the reasoning of the actor. Just as reinforcement learning (RL) was used to improve the general usability of ChatGPT, RL could be used to fine-tune such a meta-model (or maybe just fine-tune the critic). ...thinking fast, thinking slow

P.S. I think LLMs also need some sort of memory, so that not everything needs to be in the prompt to work on a problem.

cue video of my boss who left computing in the 90s waving his hands about

“It’S jUsT fAnCy aUtOcOmPlEtE!!!!11111!!! I KnOw bEcAuSe i’M a PrOgRaMmER”

To be fair, he was instrumental in getting the internet where it is today. He also assumes tech stopped evolving when he stopped developing it.

It’s pretty amazing how many shortcomings of that architecture could be summarized by “It only outputs when directly prompted to output, and won’t read its own output as it’s outputting”. Once these things can continuously take input and output, we’ll probably see quite the rush of advancement.

What is the point of the "do you understand?" At the end? Does the model confirming that it understand add some sort of emphasis or something?

Or confirmation bias and we get a computer Alex Jones

well, i was able to use ChatGPT to generate a novel, functional, complete software library for me, including a test suite, tutorial, and announcement blog post. crazy idea: maybe you just need to get a bit more creative with your prompting or anticipate that there might need to be multi-stage prompts (or god forbid: back and forth dialogue and iteration) for certain applications.

If the worst thing we have to worry about stemming from this is YouTubing by committee, we’re in a pretty good spot.

My mistake, correct you are:

https://www.beren.io/2022-08-06-The-scale-of-the-brain-vs-machine-learning/

This post recons we using 800b parameters for language processing.

morons/boomers. same people that believe the news cuz it's printed.