I think your job in your current form is likely in danger.

SOTA Foundation Models like GPT4 and Gemini Ultra can write code, execute, and debug with special chain of thought prompting techniques, and large acale process verification on synthetic data and RL search for correct outputs will make this 10x better. The silver lining to this is that I expect this to require an absolute shit ton of compute to constantly generate LLM output hundreds of times for each internal prompt over multiple prompts, requiring immense compute and possibly taking longer than an ordinary software engineer to run. I suspect early full stack developer LLMs will mainly be used to do a few very tedious coding tasks and SWEs will be cheaper for a fair length of time.

I expect it will be 2-3 years before this happens, so for that short period I expect workers to be “super-productive” by using LLMs in the coding process, but I expect the crossover point when the LLM becomes better is quite soon, perhaps in the next 5 years as compute requirements go down.

“I use Signal to hide my data from the US government and big tech”

“Wait, you seriously still use Reddit? Everyone switched to the Fediverse!”

“Wow, can’t believe you use Apple! Android is so much better.”

No one who isn’t terminally online understands what these statements mean. If you want people to use something else, don’t make it about privacy and choose something with fancy buttons and cool features that looks close enough to what they have. They do not care about privacy and are literally of the mindset “if I have nothing to hide I have nothing to fear”. They sleep well at night.

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This is another reminder that the anomalous magnetic moment of the muon was recalculated by two different groups using higher precision lattice QCD techniques and wasn’t found to be significantly different from the Brookhaven/Fermilab “discrepancy”. More work needs to be done to check for errors in the original and newer calculations, but it seems quite likely to me that this will ultimately confirm the standard model exactly as we know it and not provide any new insight or the existence of another force particle.

My hunch is that unknown particles like dark matter rely on a relatively simple extension of the standard model (e.g. supersymmetry, axioms, etc.) and the new physics out there that combines gravity and QM is something completely different from what we are currently working on and can’t be observed with current colliders or any other experiments on Earth.

So probably we will continue finding nothing interesting for quite some time until we can get a large ML model crunching every single possible model to check for fit on the data, and hopefully derive some better insight from there.

Though I’m not an expert and I’m talking out of my ass so take this all with a grain of salt.

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.

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.

TBH Minecraft Parkour in the Hypixel server’s housing area are good for this. I usually play one until I starts getting hard/finish then switch to the next one while I’m watching YouTube.