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For the love of God please stop posting the same story about AI model collapse. This paper has been out since May, been discussed multiple times, and the scenario it presents is highly unrealistic.

Training on the whole internet is known to produce shit model output, requiring humans to produce their own high quality datasets to feed to these models to yield high quality results. That is why we have techniques like fine-tuning, LoRAs and RLHF as well as countless datasets to feed to models.

Yes, if a model for some reason was trained on the internet for several iterations, it would collapse and produce garbage. But the current frontier approach for datasets is for LLMs (e.g. GPT4) to produce high quality datasets and for new LLMs to train on that. This has been shown to work with Phi-1 (really good at writing Python code, trained on high quality textbook level content and GPT3.5) and Orca/OpenOrca (GPT-3.5 level model trained on millions of examples from GPT4 and GPT-3.5). Additionally, GPT4 has itself likely been trained on synthetic data and future iterations will train on more and more.

Notably, by selecting a narrow range of outputs, instead of the whole range, we are able to avoid model collapse and in fact produce even better outputs.

We have no moat and neither does OpenAI is the leaked document you’re talking about

It’s a pretty interesting read. Time will tell if it’s right, but given the speed of advancements that can be stacked on top of each other that I’m seeing in the open source community, I think it could be right. If open source figured out scalable distributed training I think it’s Joever for AI companies.

I don’t know what type of chatbots these companies are using, but I’ve literally never had a good experience with them and it doesn’t make sense considering how advanced even something like OpenOrca 13B is (GPT-3.5 level) which can run on a single graphics card in some company server room. Most of the ones I’ve talked to are from some random AI startup that have cookie cutter preprogrammed text responses that feel less like LLMs and more like a flow chart and a rudimentary classifier to select an appropriate response. We have LLMs that can do the more complex human tasks of figuring out problems and suggesting solutions and that can query a company database to respond correctly, but we don’t use them.

This makes sense for any other company but OpenAI is still technically a non profit in control of the OpenAI corporation, the part that is actually a business and can raise capital. Considering Altman claims literal trillions in wealth would be generated by future GPT versions, I don’t think OpenAI the non profit would ever sell the company part for a measly few billions.

Lmao Twitter is not that hard to create. Literally look at the Mastodon code base and “transform” it and you’re already most of the way there.

I used to be on GrapheneOS, but the drama with the developer plus mainly not being able to put my university ID on the wallet, forced me back on stock Android.

Besides Android, I use Google Play Store, YouTube, and Maps. For YouTube I’ve technically degoogled, using Invidious and NewPipe, but that’s obviously still using Google services.

I really wish that digital payment didn’t rely on two proprietary services (Google Wallet and Apple Wallet). It would be so much easier for phone companies to ship privacy friendly versions of Android if there was a FOSS alternative directly integrated into AOSP. I also wish apps didn’t have to use Google service framework just to function, it seems stupid af. I don’t think this will ever improve, so I’ll probably end up on a true Linux phone whenever those catch up (2030 YEAR OF THE LINUX PHONE???)

We also need open collaboration on mapping. There is the OpenStreetMaps and Overture maps from Linux foundation, but those aren’t really there yet unfortunately.

My bad you are correct I’m just talking out of my ass

The natural next place for people to go to once they can’t block ads on YouTube’s website is to go to services that exploit the API to serve free content (NewPipe, Invidious, youtube-dl, etc.). If that happens at a large scale, YouTube might shut off its API just like Reddit did and we’ll end up in scenario where creators are forced to move to Peertube, and, given how costly hosting is for video streaming, it could be much worse than Reddit->Lemmy+KBin or Twitter->Mastodon. Then again, YouTube has survived enshittiffication for a long time, so we’ll have to wait and see.

FediSearch I guess is similar to your idea, though I think the goal would be to make a new and open search index specifically containing fediverse websites instead of just using Google. I also feel like the formatting should be more like Lemmy, with the particular post title and short description showing instead of the generic search UI.

The idea of a fediverse search is really cool though. If things like news and academic papers ever got their own fediverse-connected service, I could see a FediSearch being a great alternative to the AI sludge of Google.

I can’t think of a time he’s said any slur, but there is a particular video I would be interested to see it

This isn’t an actual problem. Can you train on post-ChatGPT internet text? No, but you can train on the pre-ChatGPT common crawls, the millions of conversations people have with the models and on audio, video and images. As we improve training techniques and model architectures, we will need even less of this data to train even more performant models.