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Apple’s new Transformer-powered predictive text model

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Re: Apple’s new Transformer-powered predictive text model

#221

Post by olah_1 »

kahnclusions wrote: post #101
blindriver wrote: post #17

I don't want better predictive text. I need better autocorrect. Something happened about 6 years ago where the quality of the autocorrect fell off the roof, and it's been absolutely terrible since then. I spend too much of my time fighting with either mispelling, flipping to the wrong word even though I spelled the word properly, etc. It has made typing on my iPhone an unpleasant experience and I need it to change. T…

Totally agree. I love my iPhone but autocorrect is absolutely the most painful and most infuriating part of the experience. I am constantly fighting with the autocorrect… sometimes repeating the same word 3 times because I’ve typed it correctly but iPhone really wants to change it. Same with proper nouns. If I deliberately start a word with a capital letter it’s probably a proper noun and I know how it’s spelled, don…

Seconding that autocorrect is inexcusably bad on iPhone.

Re: Apple’s new Transformer-powered predictive text model

#222

Post by kkielhofner »

lern_too_spel wrote: post #188
kkielhofner wrote: post #157

Earlier quoted context omitted.

Apple put neural in their silicon a full four years before Google did: https://www.apple.com/newsroom/2017/09/the-future-is-here-ip... https://blog.research.google/2021/11/improved-on-device-ml-o... Apple has just been more methodical to the rest of the ecosystem - essentially waiting to understand use-cases before fully embracing it across their ecosystem from Apple Silicon in Mac with neural and `device=mps`, CoreM…

> Apple put neural in their silicon a full four years before Google did: You're confusing when Google started designing its own mobile SOC with when Android devices (including Google's) first started using neural network accelerators, which happened months earlier on Android than on iPhones. https://www.qualcomm.com/news/onq/2017/01/tensorflow-machine... As for GP's claim, Android has indeed had what the iPhone is no…

> You're confusing when Google started designing its own mobile SOC with when Android devices (including Google's) first started using neural network accelerators, which happened months earlier on Android than on iPhones.

Apple (famously) doesn't announce until new functionality/devices are actually available. When they announce real people in the real world get it within a week if not less.

A press release from Qualcomm that came a few months before Apple actually got it in people's hands only further demonstrates that Apple was working on this long before Qualcomm included it in the chipsets that lag on the market from Android device manufacturers.

> As for GP's claim, Android has indeed had what the iPhone is now getting for two years

You meant to say that Pixel had a variant at that time with Gboard. I'm not going to bother to do the research on Gboard on iOS but I suspect it was also available on iOS with Gboard at the same time or soon thereafter. If not that's on the Gboard team at Google for not supporting neural on iOS and Apple hardware that came far, far sooner.

On a somewhat-negative Apple take they are infamous for being, ummm, "inspired by" successful apps and add-ons in terms of what makes it to iOS. I wouldn't be surprised in the least if Gboard vs built-in iOS keyboard is another one of these cases.

Re: Apple’s new Transformer-powered predictive text model

#223

Post by kkielhofner »

flangola7 wrote: post #166
kkielhofner wrote: post #157

Earlier quoted context omitted.

Apple put neural in their silicon a full four years before Google did: https://www.apple.com/newsroom/2017/09/the-future-is-here-ip... https://blog.research.google/2021/11/improved-on-device-ml-o... Apple has just been more methodical to the rest of the ecosystem - essentially waiting to understand use-cases before fully embracing it across their ecosystem from Apple Silicon in Mac with neural and `device=mps`, CoreM…

How does Snapchat use it?

https://qz.com/1005879/snapchat-quietly-revealed-how-it-can-...

Re: Apple’s new Transformer-powered predictive text model

#224

Post by ezfe »

r0m4n0 wrote: post #216

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...

Re: Apple’s new Transformer-powered predictive text model

#225

Post by harshaw »

_a_a_a_ wrote: post #220
runjake wrote: post #208

Earlier quoted context omitted.

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 business language is expected to be normalized and boring why?

I might say it a bit differently. The amazon writing style is to avoid flowery prose, weasel words (maybe, perhaps, etc), give precise dates, use data, etc. I'd love a model that I could hand to engineers to help them write in this style.

Re: Apple’s new Transformer-powered predictive text model

#226

Post by quadcore »

QuadrupleA wrote: post #167

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?

Those who still carefully craft their text messages will stand out.

Re: Apple’s new Transformer-powered predictive text model

#227

Post by dsco »

vore wrote: post #212
runjake wrote: post #208

Earlier quoted context omitted.

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…

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.

Re: Apple’s new Transformer-powered predictive text model

#228

Post by Zambyte »

runjake wrote: post #208
QuadrupleA wrote: post #167

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?

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 the words that you share are actually words that you wrote, instead of sharing words that Google suggested you share.

[0] https://en.wikipedia.org/wiki/Hyperlink [1] https://en.wikipedia.org/wiki/Backlink [2] https://en.wikipedia.org/wiki/Transclusion [3] https://cmdln.org/2023/03/25/how-i-org-in-2023/

Re: Apple’s new Transformer-powered predictive text model

#229

Post by nojs »

WirelessGigabit wrote: post #182
crazygringo wrote: post #178

Earlier quoted context omitted.

It's not LLM's writing for us, it's just autocomplete. If the suggestion doesn't match what you were already planning on saying, you just ignore it. The human desire to be original and authentic is always going to be stronger. (It's much less effort to ignore it and keep typing your original thought, than it is to think about it, compare with what you were going to say, decide its version is better, and then accept i…

2 problems with suggestions: 1) there is a sort order to them. And we don't know how the 'recommendations' work. What is a recommendation? Why is Google recommending something over something else? 2) it bogs down creativity. You end up not thinking and accepting the suggestion as 'good' enough.

> it bogs down creativity. You end up not thinking and accepting the suggestion as 'good' enough.

This is why I stopped using copilot autocomplete in my IDE. Once you see a suggestion, you can’t make your brain un-see it.

Re: Apple’s new Transformer-powered predictive text model

#230

Post by tuukkah »

kayodelycaon wrote: post #162
tuukkah wrote: post #155

Earlier quoted context omitted.

In general so that what you write is not mutilated by software that doesn't know the words you use. If there are typos, it's not difficult for the receiver to "autocorrect" while reading, but when autocorrect miscorrects, it's not easy for the writer to notice how the message changed nor for the reader to guess what was there before the "fixes".

That assumes your typing and spelling accuracy is high and the other person reading has significant proficiency in your language. My typing and spelling accuracy is horrendous to the point I use autocorrect on my laptop. For me, it’s an important accessibility feature. I also use dictation for single words.

Normal spellcheck still exists, highlights typos and lets you pick from suggested fixes.

Anyway, I'm not saying you shouldn't use whatever works best for you but one size doesn't fit all.

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