instagraham

I think it's worth asking, especially with agentic AI tasks that can cost upwards of $20, whether a human could've done a better job on at least one aspect of your project. Fiverr exists.

IDK how much the illustrator was paid (they're Jakarta based and seem like a freelancer), but I think the result was worth it.

The arts have always survived because of patrons, regardless of how favourable or unfavourable the economy was for them. I'm not saying this is the only solution to protecting it from AI, but it's one worth thinking about if you have the resources to do so.

noob here - what's the benefit of this? Will using Rust lead to more optimal LLMs or code or both?

it is amusing that a model from a country that has banned porn would ever be developed. Wouldn't there be hurdles for them to access "data" at every stage?

(I know they must have used a VPN for training but it does seem like an added hoop. OFC the UAE's block is also not particularly thorough.)

You'd assume such models would do better in a laissez-faire nsfw environment than in a walled garden of relatively-less sin.

> A few days later, OpenAI gets back to him, and tells him an internal model found a counterexample for Navier–Stokes

Why is OpenAI chatting with him at all at this stage? Is the discussion along the lines of "hey we used the work you are famous for to do a bigger piece of work, just thought you should know" or "heyyy....so we kinda liked what you were typing in your private chat, and thought we'd develop those ideas a bit. and yeah we solved Navier-Stokes in the process. But it's our finding, so do you want like an honorary acknowledgement or do you want to go to court?"

When people worry about OpenAI stealing their chats and reproducing them elsewhere, I usually view the situation as unlikely - since chats are "trained" upon and not necessarily reproduced verbatim, you can assume that unless your chats depict a foundationally new and effective style of communication or ideation, there would be little need or use thereof of training on your chats.

For eg: "Hey ChatGPT my name is X and I am 6 and a half feet tall. Am I anaemic?" This is a query, and while it might suggest to an AI model that tall people may worry about iron deficiencies, it's not really necessary to include in training. The user may be tall or short, but the idea that one may randomly ask about anaemia is not exclusive to this dataset. At best, this chat is an example of linguistics, not anything else, and the models figured out how to write and answer such questions years ago. It is ignored in training.

But when your work involves solid complex and unique mathematical proofs, the data is suddenly worth training upon. If I understand it correctly, the LLM may view your approach as a brand new path to take to solve an otherwise intractable problem. Its reinforcement training emphasises that it should do this in order to improve. And since it leads to results - large internal teams likely flag the model that reached this stage, the model is rewarded and given compute and attention - it is a desireable outcome both for the model and for OpenAI.

OFC, OpenAI becoming an advertising company will suddenly have incentive to treat all data as valuable. But while they are a "we need to make headlines" company, it's more rational that they view these examples of data as more valuable than others.

I don't doubt that they trained on his chats. This seems like the ideal usecase for "mass surveillance but using training" as a sort of filter.

But even so, one wonders how the model differentiates. If the researcher entered proofs into ChatGPT every day that mentioned "strawberries", while no other math paper on the topic did so, does that mean their chats would be audited?

These are just cases of AI models committing <assumed> illegal activity - without any legal convictions yet. If that's the logic, how is Grok not at the top of the list for deepfaking millions?

Edit: I get that this is about agents, but a lot of these instances are about agents going rogue after the human gave them a task. "inadvertently" breaking the law isn't necessarily a lesser category than "did so on command." If we are ranking alignment, Grok is easily one of the least guardrailed.

Funnily enough, I just ran a task on AI Studio with 3.6 yesterday and got 3.7 to do a similar one today; so it serves as an interesting and quick comparisons between the old and the new (usually, if enough time passes between your use of one model and the next, you'll have a sourer view of it than its actual competence suggests).

It hallucinated in both cases despite being given an API key and building a lot of pipes to access data using this. It was a simple "oh shit" fix moment for the model, but weird how eager it was to hallucinate despite the process being designed for it to be data-driven.

We should move past the idea that benchmarks alone tell us whether a model is getting better. I would've had the same experience a year or two ago with 1.5, and the solution would've been similar (keep prompting). I've been investing time into making system prompts and input prompts more meticulous, but the fundamental "it will make shit up" problem still remains, even though it shouldn't when the job involves calling tools.

I know this sounds like I'm expecting superpowers of it (I'm not), but my point is just that these incremental benchmark gains may not reflect user experience.

I get your gist, but I'm not looking at this as a developer but as a journalist. The reason I specified tasks is it broadens the scope to "What I can use" from "What I must pay for".

For example, most people - and especially in my field where many journalists have old laptops - assume you could never run AI models yourself. Finding out Google Colab exists and has a liberal free tier was kinda crazy.

Another: the most privacy-safe way to transcribe interviews would be Whisper on-device, but the second best might be Whisper in a Colab doc. The uploaded files are deleted after each session and you can terminate the runtime and start a new one. AFAIK the data is relatively safer than other approaches (I prefer running transcription locally myself.) It only takes a line of code and a code block to get Whisper running, but few do this and for them, the space after that - Turboscribe, Notebook, etc - relies on compromising your data in some way or the other.