This would also effectively ban the use of any research produced by a Chinese national. Any papers which cite the work of Chinese labs (most of them) would be illegal, as this could be interpreted as aiding Chinese AI research.

The llama-1 paper acknowledged the use of the books dataset, libgen isn’t mentioned in any of the papers so this is new info.

Tech bros have ruined the prestige of a lot of titles. Software “Engineer”, Systems “Architect”, Data “Scientist”, Computer “Wizard”, etc.

For a 16k context window using q4_k_s quants with llamacpp it requires around 32GB. You can get away with less using smaller context windows and lower accuracy quants but quality will degrade and each chain of thought requires a few thousand tokens so you will lose previous messages quickly.

Perfect AI boyfriends are the bigger threat to young men

Now everyone gets to hand over their ids to the tech companies.

If everyone has access to the model it becomes much easier to find obfuscation methods and validate them. It becomes an uphill battle. It’s unfortunate but it’s an inherent limitation of most safeguards.

Of course it was political retribution and not the whole unregistered securities and gambling market thing.

Anthropic released an api for the same thing last week.

This is actually pretty smart because it switches the context of the action. Most intermediate users avoid clicking random executables by instinct but this is different enough that it doesn’t immediately trigger that association and response.

All signs point to this being a finetune of gpt4o with additional chain of thought steps before the final answer. It has exactly the same pitfalls as the existing model (9.11>9.8 tokenization error, failing simple riddles, being unable to assert that the user is wrong, etc.). It’s still a transformer and it’s still next token prediction. They hide the thought steps to mask this fact and to prevent others from benefiting from all of the finetuning data they paid for.

The role of biodegradable materials in the next generation of Saw traps

It’s cool but it’s more or less just a party trick.

How many times is this same article going to be written? Model collapse from synthetic data is not a concern at any scale when human data is in the mix. We have entire series of models now trained with mostly synthetic data: https://huggingface.co/docs/transformers/main/model_doc/phi3. When using entirely unassisted outputs error accumulates with each generation but this isn’t a concern in any real scenarios.

Based on the pricing they’re probably betting most users won’t use it. The cheapest api pricing for flux dev is 40 images per dollar, or about 10 images a day spending $8 a month. With pro they would get half that. This is before considering the cost of the language model.

About a dozen methods they could use https://arxiv.org/pdf/2312.07913v2

New record for most buzz words in a headline.

I feel like they should at least provide them with a laptop If they’re going to do unpaid promotion.

The model does have a lot of advantages over sdxl with the right prompting, but it seems to fall apart in prompts with more complex anatomy. Hopefully the community can fix it up once we have working trainers.

The names missing from the list say more about the board’s purpose than the names on it.