Who is "Humans"? This stuff is done by a handful of tech companies and megalomaniacal billionaires who are pretending they represent the entirety of the human race. It is not done by "us humans".
AI models don't train themselves. The vast majority of even just the US population is deeply skeptical of this stuff, even if they use it a lot. You can see in the whole data center debate how little people are willing to support even just inference. And now we're seriously claiming those people would want to have ever-accelerating model training and recursive self-improvement?
I don't see what unions would have against automated emergency stop systems. Workplace safety is a core issue of unions, so if anything, shouldn't they demand those systems?
Obviously it’s impossible to know unless you’re sitting in on union discussions, but there is a perverse incentive at play. If one fears automation taking their job, I could see resistance to allowing any automation “foot in the door” so to speak.
> make_more_likely is, of course, doing a heroic amount of work here.
Indeed it is, and so is even just the inference method. I think it's worth remembering that both involve running the input tokens through a gargantuan neural network with (often) billions of parameters that only gain semantic meaning during the training process itself.
> it is trained to predict next tokens as they occur in its training data.
What I found important to understand is that not even the pretrainig is a deterministic process that only depends on the training data - as you would expect if the model just captured statistical properties of the data.
Gradient descent starts by setting all the parameters of the neural network to some initial values - usually by setting them at random, according to some distribution. Then during training, it gradually nudges them towards values that somehow make them useful to calculate the desired outcome of the network.
This means that by taking the exact same trainset and the exact same model architecture, you can still get models with different internal structure. The result doesn't just depend on the training data, but also on the order of examples, learning rate, the parameter initialization, etc etc.
Maybe I'm out of the loop, but wasn't AGI the full-on scifi version of AI, where the AI is a persistent, conscious entity? I don't see how task benchmark scores are relevant for that.
Yeah, apparently they both (used to) offer unbounded registrations of 3LDs and unbounded registrations of 2LDs? So if I see j.doe.name, the only way to find out if "doe.name" is a public suffix or not, i.e. if I should (not) be able to set a cookie on it, would be to email the registrar?
So does that mean that in practice, .name domains were always treated by browsers like regular 2LDs, meaning the cookie and origin protection was always broken for those domains?
Doesn't sound like good news for the guy in the OP...
> which in no way challenge the established view that these bots are next-token predictors.
I mean, of course they are, that's literally what the inference loop does. You can look at the source of your favorite model runner and you'll see exactly that.
What I find misleading about this term is that it focuses attention on the "next token" part and glosses over the "prediction" part as some sort of unspecified "statistical algorithm" - even though this is where most of the work happens and where the interesting questions are.
There are other next token prediction algorithms such as markov chains or HMMs that also "fit the same interface", but are vastly simpler than what LLMs use.
I've seen various posters talk about "stochastic parrots" or about how LLMs were just "using very simple word statistics" to get their results, which sounded to me very much as if they thought of LLMs simply as glorified markov chains. I would consider that a big misunderstanding.
"In machine learning, the term stochastic parrot is a metaphor that frames large language models as systems that statistically mimic text without real understanding."
https://en.wikipedia.org/wiki/Stochastic_parrot
Personally, I like if not everything is going through my phone.
It can be distracting. You have social media, messages and apps on the same device you need to login. Already makes it harder to put away the phone for a while.
It can never be "cold": You have your phone always with you, which means the risk of it being stolen or lost is higher than with something you can keep in your desk drawer for 99% of the time.
It's a single point of failure: If you should lose it, you potentially have a huge problem: You're right that a thief couldn't make much use of it and we have sophisticated tech to remote-lock and locate phones now. But that won't help you if you need your phone to log into your Google or Apple account to access that tech in the first place.
(Yes, I know there alternative ways to regain access to Apple or Google accounts, but those all rely on you having something... not on your phone)
I don't really own the phone. I know it's paranoid, but I don't like the idea of some update being able to change the way authentication is done. (Even if this is improbable in practice)
At least FIDO2 firmware has a fixed interface.
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