you have to do a lot of squinting to accept this take.

so his wins were copying competitors, and even those products didn’t see success until they were completely revolutionized (Bing in 2024 is a Ballmer success? .NET becoming widespread is his doing?). one thing Nadela did was embrace the competitive landscape and open source with key acquisitions like GitHub and open sourcing .NET, and i honestly don’t have the time to fully rebuff this hot take. but i don’t think the Ballmer haters are totally off base here. even if some of the products started under Ballmer are now successful, it feels disingenuous to attribute their success to him. it’s like an alcoholic dad taking credit for his kid becoming an actor. Microsoft is successful despite him

All programs were developed in Python language (3.7.6). In addition, freely available Python libraries of NumPy (1.18.1) and Pandas (1.0.1) were used to manipulate data, cv2 (4.4.0) and matplotlib (3.1.3) were used to visualize, and scikit-learn (0.24.2) was used to implement RF. SqueezeNet and Grad-CAM were realized using the neural network library PyTorch (1.7.0). The DL network was trained and tested using a DL server mounted with an NVIDIA GeForce RTX 3090 GPU, 24 Intel Xeon CPUs, and 24 GB main memory

it’s interesting that they’re using pretty modest hardware (i assume they mean 24 cores not CPUs) and fairly outdated dependencies. also having their dependencies listed out like this is pretty adorable. it has academic-out-of-touch-not-a-software-dev vibes. makes you wonder how much further a project like this could go with decent technical support. like, all these talented engineers are using 10k times the power to work on generalist models like GPT that struggle at these kinds of tasks, while promising that it would work someday and trivializing them as “downstream tasks”. i think there’s definitely still room in machine learning for expert models; sucks they struggle for proper support.

i haven’t personally had trouble with that since early 2023, but it depends on your dependencies

i feel like if you’re not sat stationary at a workstation (who is these days) what you want is a laptop that’s good at being a laptop. 99% of the software developers i work with (not a small number) use Macbook Pros. they are well built, have good components, have best in class battery life (we’ll see how things shake out with Qualcomm), and are BSD based and therefore Unix compatible. my servers and gaming/CUDA PC? Linux all day. my laptop? Macbook. i’m not ideological enough to have range anxiety every time i step away from my desk. plus any decent sized org is going to have to administrate these machines, from scientists to administrators, and catering to .4% of your users is not a good ROI if your software vendors struggled for 8 years to get their Windows 98 based specialty sensor software to run on Mac.

that .4% is likely not 0 because they are nerds.

seriously tho if Qualcomm chips can make a Linux book that lasts all day i would happily make the switch

i’m not really here to advocate for Rust in the kernel. i will say that i work on Rust professionally at a Fortune 100 company that is in the process of adopting it, which may skew my perception of it as mainstream, just to get the bias out of the way.

it is part of the project though, no? drivers still need to be interfaced with. so the people working on driver interfaces should be comfortable with it, again at least to preserve basic builds and do basic code review. this is specifically in reference to the issue that this thread is ostensibly started from: a kernel dev was getting worked up about “having to learn Rust”. so no, i don’t think it’s a strawman to point out the real people denying or frustrating patches just because they don’t understand the language. overly harsh maybe but not a total mischaracterization.

i can definitely see it as a “hostile takeover” of sorts, but this is something the project has decided on, for better or worse. i can understand not wanting to learn a new language that you may not like or agree with, but that means you will have to divest yourself from a project that adopts that language to a certain extent. Rust is—again for better or worse—something Linus thinks is good for the project, and thus learning Rust at least enough to not break the builds is a requirement for the project. i can’t imagine working on a software team where a chunk of people refuse to take part in a major portion of it simply because they’re not immediately familiar with it. that does sound like old crotchety behavior. on the other hand it’s tragic that so many people with all this experience are being forced into a design decision that arguably may have been made hastily and that they had little say in.

that makes this definitely an old guard vs new issue. and maybe it is an olive branch for the old guard to say “let’s just take our time with this.” but we have crossed a threshold where seeing a new project in C is the oddity while new projects in Rust are commonplace. Rust is mainstream now, and “i don’t want to learn this” is a dogshit technical justification.

i was mostly making a joke about how this absolutely is not a common problem on any platform, not to this degree. and at least when my Arch and Nix systems go down i don’t have anyone to blame but myself. sure, systems have update issues, but a kernel level meltdown that requires a safe mode rescue? that’s literally never happened to me unless it was my fault

damn i haven’t used Windows in over a decade. are y’all ok?

language is intrinsically tied to culture, history, and group identity, so any concept that is expressed through a certain linguistic system is inseparable from its cultural roots

i feel like this is a big part of it. it reminds me of the Sapir Whorf Hypothesis. search results and neural networks are susceptible to bias just like a human is; “garbage in garbage out” as they say.

the quote directly after mentions that newer or more precise searches produce more coherent results across languages. that reminds me of the time i got curious and looked up Marxism on Conservapedia. as you might expect, the high level descriptions of Marxism are highly critical and include a lot of bias, but interestingly once you dig down to concepts like historical materialism etc it gets harder to spin, since popular media narratives largely ignore those details and any “spin” would likely be blatant falsehood.

the author of the article seems to really want there to be a malicious conspiratorial effort to suppress information, and, while that may be true in some cases, it just doesn’t seem feasible at scale. this is good to call out, but i don’t think these people who concern their lives with the research and advancement of language concepts are sleeping on the fact that bias exists.

it’s super weird that people think LLMs are so fundamentally different from neural networks, the underlying technology. neural network architectures are constantly improving, and LLMs are just a product of a ton of research and an emergence after the discovery of the transformer architecture. what LLMs have shown us is that we’re definitely on the right track using neural networks to solve a wide range of problems classified as “AI”

most Zionists i’ve met are white Protestants, and most Jews i’ve met aren’t Zionists…

simply not true. they’re no angels or open source champions, but come on.

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.

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.

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.