231–240 of 268 posts
Everyone's asking tech details and "how", but I wonder about the "why". Do we want LLMs to always write for us, or whisper in our ear what to say? By design LLMs tend toward the most commonplace, mainstream ideas and ways of saying things. They're not much for originality or human idiosyncracy. Are we engineering a bland world full of pablum?
iPhone’s auto complete and form fills drive me absolutely mad. It’s honestly just basic stuff that should have just worked a decade ago. I’m not even begging them for LLMs or some fancy ML, just learn basic words I type out all the time. For example my full email address? how about my last name? This seems like basic stuff
My iPhone has always suggested those things...
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Because at work I’m typing the same bland things all the time in documents and communications. I appreciate stuff like the predictive word stuff in Google Docs. It’s helpful because business language is expected to be normalized and boring. On the other side of that token, the average language abilities of the average American office worker are pretty low so I’m assuming they view this as an enhanced AutoCorrect and…
> Because at work I’m typing the same bland things all the time in documents and communications. This problem can be solved without text prediction by building a personal knowledge base with hyperlinking[0] and backlinking[1] for discovery, and transclusion[2] for automating writing the same bland things. How I org in 2023 by Nick Anderson[3] goes over a great workflow for this. The advantage of this is that all of t…
Everyone's asking tech details and "how", but I wonder about the "why". Do we want LLMs to always write for us, or whisper in our ear what to say? By design LLMs tend toward the most commonplace, mainstream ideas and ways of saying things. They're not much for originality or human idiosyncracy. Are we engineering a bland world full of pablum?
The example at the end made me wonder if Apple's model is actually better than GPT2 for text prediction. It generated garbage, but all that garbage made somewhat sense in the context of only the word "Today". Whereas GPT2 hallucinated random stuff about the US government. A text prediction model should predict what the user wanted to type, so if you evaluate the models based on that, GPT2 actually performed horribly,…
"Today is a good day for you to be able to do it more than i just a few weeks to get a new."
The example at the end made me wonder if Apple's model is actually better than GPT2 for text prediction. It generated garbage, but all that garbage made somewhat sense in the context of only the word "Today". Whereas GPT2 hallucinated random stuff about the US government. A text prediction model should predict what the user wanted to type, so if you evaluate the models based on that, GPT2 actually performed horribly,…
Earlier quoted context omitted.
> Because at work I’m typing the same bland things all the time in documents and communications. This problem can be solved without text prediction by building a personal knowledge base with hyperlinking[0] and backlinking[1] for discovery, and transclusion[2] for automating writing the same bland things. How I org in 2023 by Nick Anderson[3] goes over a great workflow for this. The advantage of this is that all of t…
What an obtuse way of saying use links and quote replies
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How does Snapchat use it?
https://qz.com/1005879/snapchat-quietly-revealed-how-it-can-...
Do you have another source that goes into Apple's implementation?
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On the other side of that token, the average language abilities of the average American office worker are pretty low so I’m assuming they view this as an enhanced AutoCorrect and they appreciate it because it makes them look less dumb. Awfully presumptuous, don’t you think?
I would also assume this to be largely true, mainly because of how language in media has gone from being formal and informative to casual and less expressive.