There were recently many articles that beef meat has a negative effect on the environment. I have no clue if it is true or not, but as beef meat is still available, we probably did not convert newest findings into practice. I also don't think we necessarily need to find a replacement for beef if it goes there, but rather invent new types of meals which have a better impact on the environment, taste well and are healthy? Do we really need something that tastes like beef but isn't? In the meantime, you can still make ground beef rice with kidney beans: You make rice and beef separately and mix at the end: onions red kidney beans (from cans) tomatoes ketchup for taste salt, pepper, red paprika ground beef red wine (optional) brown rice (healthier?)
So I was wondering who invented linear regression: Francis Galton Isn't it amazing that in 5 seconds you can receive answer and read more about history on Wikipedia? I was also surprised he is behind log-normal distribution as well.
Publication On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? caused some stir. I understand frustrations, etc. I understand the publication is trying to point out what can go wrong. I just do not like the way it was done. From the paper: "In the case of US and UK English, this means that white supremacist and misogynistic, ageist, etc. views are overrepresented in the training data, not only exceeding their prevalence in the general population but also setting up models trained on these datasets to further amplify biases and harms." This sentence is not supported by any data (is it really overrepresented, or is it underrepresented, or about the same?). Research publications should stick to facts or close to facts as much as possible and there is no way for me to know if this sentence is correct or not. It might be dependent also on dataset we create (as you are pointing out) and I can imagine this percentage being underrepresented as well. If I was a