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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.

Based NixOS user

I love NixOS but I really wish it had some form of containerization by default for all packages like flatpak and I didn’t have to monkey with the config to install a package/change a setting. Other than that it is literally the perfect distro, every bit of my os config can be duplicated from a single git repo.

Cool meme but Reuters doesn’t own AP and Rothschild doesn’t own Reuters. It is quite ironic to be pushing against the very real problem of media disinfo/government propaganda trickled down through AP/Reuters, while at the same time spreading misinformation.

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.

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.

The one SIMPLE trick crypto bros HATE: Blockchain -> “Distributed Ledger” NFT -> “Unique Identifier”

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There are some in the research community that agree with your take: THE CURSE OF RECURSION: TRAINING ON GENERATED DATA MAKES MODELS FORGET

Basically the long and short of that paper is that LLMs are inherently biased towards likely responses. The more their training set is LLM generated, and thus contains that bias, the less the LLM will be able to produce unlikely responses, over time degrading the model quality throughout successive generations.

However, I tend to think this viewpoint is probably missing something important. Can you train a new LLM on today’s internet? Probably not, at least without some heavy cleaning. Can you train a multimodal model on video, audio, the chat logs of people talking to it, and even other better LLMs? Yes, and you will get a much higher quality model and likely won’t get the same model collapse implied by the paper.

This is more or less what OpenAI has done. All the conversations with 100M+ users are saved and used to further train the AI. Their latest GPT4 is also trained on video and image recognition, and they have also been exploring ways for LLMs to train new ones, especially to aid in alignment of these models.

Another recent example is Orca, a fine tune of the open source llama model, which is trained by GPT-3.5 and GPT-4 as teachers, and retains ~90% of GPT-3.5’s performance though it uses a factor of 10 less parameters.