coolin

joined 1 year ago
[–] [email protected] 1 points 1 year ago (1 children)

I'm pretty sure that guy is a Linux user and making a joke about the perception of Linux

[–] [email protected] 3 points 1 year ago

Up above aliens hover Making home movies for the folks back home Of all these creatures who lock up their spirits Drill holes in themselves and live for their secrets

[–] [email protected] 39 points 1 year ago (4 children)

As a Linux user this has got me very worried. Chromium has so much market share that this change will certainly go through, and I feel like Safari won't care as it benefits them and their ecosystem to have device checks. I feel like Firefox and non standard OSes will almost certainly be blocked on a large range of websites with little impact on total users, not to mention completely blocking ad block and anti-tracking clients.

I think eventually regulators in the US will file an antitrust lawsuit and break chromium off of Google if this actually happens, but until then Fediverse/FOSS and personal websites are going to be the only places untouched by this.

[–] [email protected] 0 points 1 year ago (2 children)

Doesn't matter, America is the only country 🇺🇲

[–] [email protected] 1 points 1 year ago (1 children)

These models are black boxes right now, but presumably we could open it up and look inside to see each and every function the model is running to produce the output. If we are then able to see what it is actually doing and fix things up so we can mathematically verify what it does will be correct, I think we would be able to use it for mission critical applications. I think a more advanced LLM likes this would be great for automatically managing systems and to do science+math research.

But yeah. For right now these things are mainly just toys for SUSSY roleplays, basic customer service, and generating boiler plate code. A verifiable LLM is still probably 2-4 years away.

[–] [email protected] 8 points 1 year ago

I suspect that GPT4 started with a crazy parameter count (rumored 1.8 Trillion and 8x200B expert "sub-models") and distilled those experts down to something below 100B. We've seen with Orca that a 13B model can perform at 88% the level of ChatGPT-3.5 (175B) when trained on high quality data, so there's no reason to think that OpenAI haven't explored this on their own and performed the same distillation techniques. OpenAI is probably also using quantization and speculative sampling to further reduce the burden, though I expect these to have less impact on real world performance.