priansh

Given how many “Web3” platforms and content creators this law would nuke out of existence, I’n genuinely surprised there isn’t more $$$ pushback.

Lobbying isn’t exclusive to supervillain-y agendas, it’s strange that deep pockets with a lot to lose aren’t being more active / fiscally vocal about their opposition to this type of legislature.

And can you imagine the sheer footprint of this type of tech? Given that there is no incentive to make filtering software efficient for these lobbyists, this could spell doom for companies that would need to scale it to ridiculous sizes; e.g. imagine what this would mean for Twitch, which already has a tough time with DMCA, as they would be legally mandated to run bloated filtering software on hundreds of thousands of live video streams all at once.

All I’m saying is if you have to name your legislature “SMART” you’re clearly compensating…

This is honestly awesome, copy editing is such a pain point that we schedule weekly 3 hour blocks to go through all changes live. This would greatly streamline our workflow.

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.

The main issue with deploying these systems right now is the technical overhead to develop them out. Existing solutions are either paid and require you to share your valuable data, or open source but either abandoned (rip Crab) or inextensible (most rely on their own DB or postgres).

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.

I’ve been saying this for years, language models are the ML equivalent of the billionaire space race, it’s just a bunch of orgs with unlimited funding spending millions of dollars on compute to get more parameters than their rivals. It could be decades before we start to see them scale down or make meaningful optimizations. This paper is a good start but I’d be willing to bet everyone will ignore it and continue breaking the bank.

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.

Mostly mask fill, but Transformers can be fine tuned to downstream tasks relatively easily (T5 was built for translation but is used for autocomplete in many cases)

If they implemented SSO with Office365/AAD it would be a stellar alternative. The GSuite dependency for SSO is damning

This is really cool but I can’t say I’m a fan of Zapium given it would move to a commercial license in the future. If you have to ship CEF anyhow, what is the performance advantage to licensing and using Zapium over just compiling WASM and shipping binaries with Electron? FWIW — this is how Java, .NET, etc packaging is done for interop with Electron.

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.

The HN collab I never knew I needed, although I doubt the whole issue could fit on there. Maybe the repo name, issue name and issue number instead?

Yeah my first thought was, well you could lay out the timestamp in a way that supports lexicographical sorting, and then realized they had already done so. No reason this shouldn’t be a string!

I find this a bit disingenuous. TikTok videos have by far the highest reach of all social media due to their format and the way the for you page works. Moreover it rewards influencers from interacting with each other’s posts which often leads to top comments on TikTok (and Reels too although it’s much smaller as a platform) largely being an echo chamber.

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).

Suicide rates are tricky since mental health can have long lasting issues and the societal changes from lockdown will have even longer term effects. How do you attribute a suicide 2 years from now to depression that started during COVID? It’s going to be interesting seeing how the psychology field approaches this.

Touché, I opened this hoping to find a new method to train Transformers and got the opposite

GPT-J is 6B and comes pretty close. Also practically I haven’t noticed a difference.

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).