sure it does. it won’t tell you how to build a bomb or demonstrate explicit biases that have been fine tuned out of it. the problem is McDonald’s isn’t an AI company and probably is just using ChatGPT on the backend, and GPT doesn’t give a shit about bacon ice cream out of the box.
not sure what you mean by expensive. i run language models on my laptop that are pretty good at this type of task. and, yes, these models are infinitely easier and cheaper ultimately than trying to change the human proclivity for attention seeking behavior.
you’ve not seen the type of email chains i get at work. personally i think it should be illegal to respond-all to an email chain with hundreds of people with “Great job team!!! 🎉”. but it would be great to have a LM to read it near instantaneously for me to be like “oh yeah there was a product release and here’s a few relevant metrics”. doesn’t matter if it’s 100% in on every subtle detail, and a decent summary could tell me where or if i even should dig into details.
a lot of things are unknown.
i’d be very surprised if it doesn’t have an opt out.
a point i was trying to make is that a lot of this info already exists on their servers, and your trust in the privacy of that is what it is. if you don’t trust them that it’s run on per user virtualized compute, that it’s e2e encrypted, or that they’re using local models i don’t know what to tell you. the model isn’t hoovering up your messages and sending them back to Apple unencrypted. it doesn’t need to for these features.
all that said, this is just what they’ve told us, and there aren’t many people who know exactly what the implementation details are.
the privacy issue with Recall, as i said, is that it collects a ton of data passively, without explicit consent. if i open my KeePass database on a Recall enabled machine, i have little assurance that this bot doesn’t know my Gmail password. this bot uses existing data, in controlled systems. that’s the difference. sure maybe people see Apple as more trustworthy, but maybe sociology has something to do with your reaction to it as well.
i mean, you’re right. i’m just saying it’s a little silly to ship a Python interpreter when there are easier, better supported ways to do the same thing.
looks like tesseract provides C bindings which are probably being utilized in those apps.
people generally probably hate the iOS integration just because it’s another AI product, but they’re fundamentally different. the problem with Recall isn’t the AI, it’s the trove of extra data that gets collected that you normally wouldn’t save to disk whereas the iOS features are only accessing existing data that you give it access to.
from my perspective this is a pretty good use case for “AI” and about as good as you can do privacy wise, if their claims pan out. most features use existing data that is user controlled and local models, and it’s pretty explicit about when it’s reaching out to the cloud.
this data is already accessible by services on your phone or exists in iCloud. if you don’t trust that infrastructure already then of course you don’t want this feature. you know how you can search for pictures of people in Photos? that’s the terrifying cLoUD Ai looking through your pictures and classifying them. this feature actually moves a lot of that semantic search on device, which is inherently more private.
of course it does make access to that data easier, so if someone could unlock your device they could potentially get access to sensitive data with simple prompts like “nudes plz”, but you should have layers of security on more sensitive stuff like bank or social accounts that would keep Siri from reading it. likely Siri won’t be able to get access to app data unless it’s specified via their API.
no need for Python. there’s a Google SDK, ML Kit, that will do the heavy lifting on this. if that’s not acceptable, TensorFlow, PyTorch, and ONNX support Android, albeit not as nicely integrated.
your image processing pipeline will be imageSource -> RGB encoding -> OCR -> profit. your OCR just needs an RGB encoded image. doesn’t matter if that’s a JPEG or YUV video feed at the source.
as for if there’s an app that fits OP’s exact use case, dunno.
same as with crypto. the software community started using GPUs for deep learning, and they were just meeting that demand
tbh this research has been ongoing for a while. this guy has been working on this problem for years in his homelab. it’s also known that this could be a step toward better efficiency.
this definitely doesn’t spell the end of digital electronics. at the end of the day, we’re still going to want light switches, and it’s not practical to have a butter spreading robot that can experience an existential crisis. neural networks, both organic and artificial, perform more or less the same function: given some input, predict an output and attempt to learn from that outcome. the neat part is when you pile on a trillion of them, you get a being that can adapt to scenarios it’s not familiar with efficiently.
you’ll notice they’re not advertising any experimental results with regard to prediction benchmarks. that’s because 1) this actually isn’t large scale enough to compete with state of the art ANNs, 2) the relatively low resolution (16 bit) means inputs and outputs will be simple, and 3) this is more of a SaaS product than an introduction to organic computing as a concept.
it looks like a neat API if you want to start messing with these concepts without having to build a lab.
this data is not the world
i think most ML researchers are aware that the data isn’t perfect, but, crucially, it exists in a digestible form.
i mean, i’ve worked in neural networks for embedded systems, and it’s definitely possible. i share you skepticism about overhead, but i’ll eat my shoes if it isn’t opt in
there are language models that are quite feasible to run locally for easier tasks like this. “local” rules out both ChatGPT and Co-pilot since those models are enormous. AI generally means machine learned neural networks these days, even if a pile of if-else used to pass in the past.
not sure how they’re going to handle low-resource machines, but as far as AI integrations go this one is rather tame
if it’s easier to pay, people spend more
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IBM then. or, i don’t know, the British Royal Family?
the reality of talking about extremist economics is no one knows how it would work out in the long term. but regardless, if it happened tomorrow we already have a Microsoft to deal with.
“taxation is theft” “wage labour is exploitation”
sometimes things are subtle and complicated and can’t be practically boiled down to absolutes.
“we don’t know how” != “it’s not possible”
i think OpenAI more than anyone knows the challenges with scaling data and training. anyone working on AI knows the line: “a baby can learn to recognize elephants from a single instance”. reducing training data and time is fundamental to advancement. don’t get me wrong, it’s great to put numbers to these things. i just don’t think this paper is super groundbreaking or profound. a bit clickbaity and sensational for Computerphile
honestly 8 space indents always felt a bit ridiculous to me. i usually use 4 since it’s more conventional in most languages but could also be happy with 2.
weird hill to die on. use default setting unless you have a good reason not to. the argument itself is a waste of time on projects that want to get things done.
gotem!
seriously tho, you don’t think OpenAI is tracking this? architecural improvements and training strategies are developing all the time
i really want to like Nix.
gave it a shot a few years ago, but i felt like documentation and community support wasn’t really there yet. this was long before Nix surpassed Arch in terms of number of available packages. now people still complain about documentation, especially of the Nix language. i see a lot of package authors using it, and that kind of tempts me to start using at least the package manager. but a lot of packages don’t. the allure of GitOpsing my entire OS is very tempting, but then there’s been these rumors (now confirmed) of new forks, while Guix splintered off much earlier. for something that’s ostensibly supposed to be the most stable OS, that makes me nervous. it also seems to have some nontrivial overhead—building packages, retaining old packages, etc.
the pitch for Nix is really appealing, but with so much uncertainty it’s hard to pull the trigger on migrating anything. heck, if i could pull off some PoCs, i think my enterprise job might consider adopting it, but it’s a hard recommend for me today as it was 5 years ago.
simply not true. they’re no angels or open source champions, but come on.