priansh

Signed up and saw extraction can take several days — is this done manually? I assumed scanning would be done purely automatically but I can see why there would need to be a manual review component.

I’d love to see a lightweight, flexible recommendation system at a low level, specifically the scoring portion. There are a few flexible ones (Apache has one) but none are lightweight and require massive servers (or often clusters). It also can’t be bundled into frontend applications which makes it difficult for privacy-centric, own-your-data applications to compete with paid, we-own-your-data-and-will-exploit-it applications.

Can you say that about any other task in ML? When Inceptionv3 came out I was able to run the model pretty comfortable on a 1060. Even pix2pix and most GANs fit comfortably in commercial compute, and the top of the line massive models can still run inference on a 3090. It’s so unbelievably ironic that one of the major points Transformers aimed to solve when introduced was the compute inefficiency of recurrent networks, and it’s devolved into “how many TPUs can daddy afford” instead.

I can understand from an ease of use perspective to have the bridge in between but it wouldn’t be worth subjecting a codebase to commercial licensing IMO. It’s not a whole lot more work to use process calls instead there so it seems an odd choice to commercialize that aspect in particular.

1 post on Facebook might reach 100s, 1000s of people. Rarely a page with large enough following may hit 100,000s of people.

1 post on TikTok from a moderate influencer can hit millions.

That’s not to say other social media forms haven’t had bad trends in the past, but TikTok has had far more and in a far shorter amount of time. People just don’t interact with other social content the way they buy into TikTok, which is dangerous and leads to far more misinformation — it was maybe not even 3 weeks ago I had a bunch of my friends sending me a bunch of popular TikTok videos and asking about how AI Transformers were here and taking over the world (they are, but transformers in AI have nothing in common with the movies).

The FB app ID is also one of the most common meta tags behind Open Graph.

A nice small example of what this can do can be seen with Clearbit [0] which does a good job of telling you where someone works based off of nothing but their IP address. Imagine that but with the exponentially larger data warehouses of Facebook or Google, paired with referrer tags (FB has CLIDs that allow them but not you to match clicks to actual users) and meta tags (FB can tell exactly what app, page etc a website is associated with and use that data to advertise to users).

[0] https://clearbit.com

Keep in mind there are also closed source alternatives: for example, AI21’s Jurassic-1 models are comparable, cheaper, and technically larger (albeit somewhat comically, 178B instead of 175B parameters).

If you’re a hobbyist or an academic or starting a totally new project that won’t have dependencies/doesn’t need a large community of modules, then I recommend deno. Otherwise I highly recommend sticking to node until deno comes out with something to make the switch more appealing.

Also, I would scream if some opinionated dev gone crazy with their linter added pylint disables and comments explaining it every time we use a while loop in our codebase.

Why does the linter rule not, instead, check if the while loop is unbounded and warn the user of that? Surely screaming fire when there's an actual fire is better than screaming fire at the first sign of smoke.

Could you write them as a for loop? Possibly, but why? Is the new 2021 programming fad hating on while loops? Who do I contact to exchange my "js bad" t-shirts for "while bad" laptop stickers?

The logic behind disapproving of while loops in the article seems to stem from the author assuming while loops are "unbounded." This is absolutely, completely, totally false and there are entire branches of reasoning dedicated towards observing the bounded-ness of while loops. Most colleges include this in their curriculums and you usually have to learn to prove (via induction) the variants and invariants surrounding your loop before you can pass basic classes. It's even more ridiculous that, in the same breath, the author assumes for loops are guaranteed to complete execution. I can disprove this in two lines of code and two braincells:

  for x in infinite_generator():
    do_something()

I'll cede that it's easier to write unbounded code with while loops than for loops, but this is programmer error that can be pretty easily avoided by simply being a teensy tiny bit careful (and can be easily corrected). I'd also argue that the majority of these cases where while loops can be substituted by for loops, are also cases where writing a while loop is much simpler than trying to convert into a for loop. (Besides, you could make any of these arguments about recursion.)

> You know what else doesn’t have unbounded loops? Excel.

This is also a lie, Excel absolutely has infinite loops, it just yells at you when you do it. The equivalent of this in programming is a linter. Instead of just screaming fire every time the user writes a perfectly fine while loop, why not concentrate your efforts on identifying unbounded loops and introducing a linter rule for that?

Honestly, it's absolutely terrifying to me that someone contributing to the predominant linter for a major programming language not only wholeheartedly believes while loops are always unbounded and usually evil, but also managed to get this introduced (and presumably approved) by reviewers of this linter, and then proceeded to gloat about it on HackerNews. It comes as no shock to me that most people I've worked with disable pylint in their editors if they're this unreliable with their review process.

NextCloud's engine doesn't seem to be very friendly/lightweight in this regard as it's been built specifically for their frontend :/ I was hoping there was some library that accomplished the same thing while being more lightweight/having friendly bindings.

Any hope for a Chrome extension?

I can see a really huge impact for this in allowing those who are less abled to create art. It's hard to express yourself when you physically can't but technology like this can make it possible to create art in many different forms!

^^ If you're getting started, just following the steps here can get you set up really fast. IIRC there's also an HTTP endpoint at /recognize you can use instead of WebSocket if you're transcribing audio files so it's pretty cool!

I definitely think it's a big step in the right direction; it's easily 100x faster than DeepSpeech for us.

If I could have anything I wanted for xmas, I'd ask for a speech to text system that is fast enough to work in browser thru wasm or something.

The article quotes DeepSpeech, wavenets, LSTMs of all sorts; essentially, all the neural networks that scale terribly. DeepSpeech for example is pretty heavy and requires a decent GPU to get anywhere near a realtime factor of 1.

Meanwhile ASR through HMM's consistently hits realtime factors sub-1 and can run on small CPUs. e.g. the default models Kaldi ships with outperform DeepSpeech on a lot of modern examples, and are exponentially faster.

Moreover, training these HMM's is something that is feasible for a normal developer. Training newer models requires data of scale and quality (iirc Mozilla's models are trained on Common Speech which is an enormous crowd sourced dataset, and Google's wavenet models use an internal dataset of very high quality and quantity).

Until the models get more practically achievable, ASR for average people will probably continue to be dominated by Kaldi, Sphinx etc