I don't really think this is the case. The rules feel very consistently applied. Training is fine. Output may still be copyright infringing in some cases (but difficult to prove and none of these tech companies have…
From this comment it isn't clear if you previously thought it stood for polynomial and non-polynomial and have been corrected or if you still have this misconception, but just in case: it actually stands for polynomial…
It just seems like we're missing some pretty important stuff. The amount of "training data" humans process in out lifetimes is nowhere near enough to achieve the abilities we have, so clearly the bulk of our brains…
[dead]
It isn't actually Jevon's Paradox, but nearly all products behave that way.
In some circumstances you might not need to anti alias if you are using pixel art. In some circumstances you do. Either way it largely won't hurt.
I somewhat doubt it. Having many people, even at your own company, know your private key seems like a problem of it's own. In fact it's likely to me that no human has ever seen it until now.
I quite like how i2p does it. Public keys are part of addresses, human readable names are user defined and shared but static.
I guess you could do some SNARK like proof. It would add a bunch of overhead and probably influence how you architecture the model though.
I'm kinda sick of people making shittier emulators and then calling it not an emulator
How is the compute of the model weighed against the compute that it saves?
I have a hard time coming up with a definition of RSI where it matters at all or where we haven't already reached it. It feels like compute and training data are far bigger bottlenecks than researchers. My thinking is…
I don't know that people in 1000 years will feel the obligation to follow through with the leap hour at all.
Got the West where? Massively successful and prosperous?
Even if you are a world-class cryptographer, you have to trust that the algorithms are sound (but now it isn't even a person you are trusting).
Models generally are capable of counting
I wouldn't consider it an unreasonable goal when it's been the standard nearly all previous pre-ai re projects have held themselves to.
The vulnerability to watch out for would be the model being trained to not redact some specific information.
The line is blurry, but with most quants you still get the benefit of basing off the higher precision weights. I don't see how that's possible with extra layers. Then again, I wouldn't have thought it possible with…
I mean I somewhat agree that only the formalized results should be taken seriously, but I don't really think that openai has an obligation to do so.
I don't really think this is the case. The rules feel very consistently applied. Training is fine. Output may still be copyright infringing in some cases (but difficult to prove and none of these tech companies have…
From this comment it isn't clear if you previously thought it stood for polynomial and non-polynomial and have been corrected or if you still have this misconception, but just in case: it actually stands for polynomial…
It just seems like we're missing some pretty important stuff. The amount of "training data" humans process in out lifetimes is nowhere near enough to achieve the abilities we have, so clearly the bulk of our brains…
[dead]
It isn't actually Jevon's Paradox, but nearly all products behave that way.
In some circumstances you might not need to anti alias if you are using pixel art. In some circumstances you do. Either way it largely won't hurt.
[dead]
I somewhat doubt it. Having many people, even at your own company, know your private key seems like a problem of it's own. In fact it's likely to me that no human has ever seen it until now.
I quite like how i2p does it. Public keys are part of addresses, human readable names are user defined and shared but static.
I guess you could do some SNARK like proof. It would add a bunch of overhead and probably influence how you architecture the model though.
I'm kinda sick of people making shittier emulators and then calling it not an emulator
How is the compute of the model weighed against the compute that it saves?
I have a hard time coming up with a definition of RSI where it matters at all or where we haven't already reached it. It feels like compute and training data are far bigger bottlenecks than researchers. My thinking is…
I don't know that people in 1000 years will feel the obligation to follow through with the leap hour at all.
Got the West where? Massively successful and prosperous?
[dead]
Even if you are a world-class cryptographer, you have to trust that the algorithms are sound (but now it isn't even a person you are trusting).
[dead]
Models generally are capable of counting
I wouldn't consider it an unreasonable goal when it's been the standard nearly all previous pre-ai re projects have held themselves to.
[dead]
The vulnerability to watch out for would be the model being trained to not redact some specific information.
[dead]
The line is blurry, but with most quants you still get the benefit of basing off the higher precision weights. I don't see how that's possible with extra layers. Then again, I wouldn't have thought it possible with…
I mean I somewhat agree that only the formalized results should be taken seriously, but I don't really think that openai has an obligation to do so.