I forget where I originally heard this idea, but I always explain to people that LLMs are (affectionately) "bullshitters." Terms like "lying" or "hallucinating" imply that it's trying to tell the truth, but actually it doesn't care if what it says is true or not at all save for the fact that true text is slightly more plausible than false text.
Not really? There's a webcam, an indicator LED, an ambient light sensor, and a lot of empty space. As far as I can tell, the MacBook notch is wide just to make it look like the iPhone notch.
There is also the mounting hardware. It’s not an unreasonable size for what it contains. If they were to really redesign the module they might shave a little off of it but how impactful would that reduction really be?
> The MacBook notch is wide just to make it look like the iPhone notch.
I'm convinced this is true, at least partially.
The iPhone notch is branding and a visual differentiator from the competition, which is Apple's forte, and carrying over that very distinctive design element to other product lines seems right in Apple's playbook.
In other words: glass slab in your hand? Who knows. Glass slab with a black notch? iPhone.
Person typing on metallic laptop in a cafe? Who knows. Ah, but the screen has a notch? MacBook.
Partners can choose to disable all types/placements of ads ("skippable video ads", "non-skippable video ads", "pre-roll ads", "mid-roll ads", and "post-roll ads") except for "display ads" (that is, banner ads). As for whether those options actually work, I can only assume so, but the article you linked is specifically about non-partners.
This annoyed me particularly because I pay for YouTube premium... but I can't sign into my Google account on my work computer. So if they block ad blockers, paying for YouTube isn't even enough to get rid of the ads for me.
>it couldn't possibly understand how to spell "platoggle" if it's treating it just as a single, never-before-seen, opaque token
That's not how the tokenizer works. A novel word like "platoggle" is decomposed into three separate tokens, "pl", "at", and "oggle". You can see for yourself how prompts are tokenized: https://platform.openai.com/tokenizer
They do, e.g. "gvqbkpwz" is tokenised into individual characters. Actually it was a bit tricky to construct that, since I needed to find letter combinations that are very low probability in tokeniser's training text (e.g. "gv").
So notice it doesn't contain any vowels, since almost all consonant-vowel pairs are sufficiently frequent in the training text as to be tokenised at least as a pair. E.g. "guq" is tokenised as "gu" + "q", since "gu" is common enough.
(Compare "gun" which is just tokenised as a single token "gun", as it's common enough in the training set as a word on its own, so it doesn't need to tokenise it as "gu"+"n".)
The only exceptions I found with consonant-vowel pairs being tokenised as pairs were ones like "qe", tokenised as "q" + "e". Or "qo" as "q"+"o". Which I guess makes sense, given these will be low-frequency pairings in the training text -- compare "qu" just tokenised as "qu".
(Though I didn't test all consonant-vowel pairs, so there may be more).
I might as well take this opportunity to plug my clone of Semantle which uses a tSNE visualization. I've heard many people say this helps them visualize the chains of logic in their guesses.
Yours has the best interface I've seen so far, but the words still feel kind of obscure when I'm clicking the hint button. Ideally those should be limited to maybe the top 10,000 english words.
I think there's a really approachable game somewhere in this space, but it needs to implement something along the lines of an auto hinting system.
I imagine that for every guess, you could get a word or two that is similar (to help you understand what part of the word's context is important), and maybe words that are further away to help understand what isn't important?
My partner and I were consumed by this for a while. There's something very satisfying about hopping from cluster to cluster and recognizing the various meanings of a word.
Makes me wonder what 3D (or even 4D) version would be like.
- You can generate grids on multiple devices with a seed
- If the phone displaying the grid falls over, it automatically hides the grid.
I don't know if the grids in the app are truly random or if they follow some constraints though.