alestainer

It matters a lot what kind of tasks AI would be able to solve. Two completely different worlds arise if AI can do what I call a modern PhD grunt work vs it being able to produce genius of Nash/Einstein/Lovelace etc. And the way to test it is trying to produce modern math and science results with those historical LLM models or something similar: https://news.ycombinator.com/item?id=46319826 https://news.ycombinator.com/item?id=47927903 https://news.ycombinator.com/item?id=46319826

Some might say that 2 is as made up as infinity. Let me elaborate a little - your brain together with society made an abstraction "apple", and only by not distinguishing between these "sets" of atoms you can have numbers.

I was in academia in the pre-GPT-3 era and I don't see a difference between the superficial pass-the-criteria understanding of things then and now. People already rely on a ton of sources, putting their faith into it, recent replication crisis in social sciences had nothing to do with any LLMs. The problem of academia lies in the first paragraph of this article - supervisor that has to choose doing incremental, clearly feasible stuff. Currently it's called science, but I like to call it knowledge engineering because you're pretty much following a recipe and there is a clear bound on returns to such activities.

Interesting thing: I've got my internal request that is similar to this pelican. And there was 0 progress on it in the past ~2 years. Which might have at least a couple of explanations. 1. Spillage into the pre-training: some real artist had drawn a pelican riding a bicycle. 2. Seeing it as an important discourse for model intelligence in the training data might affect allocation of compute into solving this problem, either thru engineers or the model itself finding the texts about this challenge.

/e/ docs say it uses Android keyboard, wouldn't that be a source of a data leak?

The study did not add income as a control variable, so the article is basically about how rich people go to the therapist more often.