1,068 karma · joined November 27, 2018
(And also principles #6, #7, #9 to some extent.)
Like I said, I'm not a moral relativist at all. Murder is still wrong in China, imprisoning people not convicted of any crime is still wrong in China, lying is still wrong in China. But I just don't see how copyright infringement is universally an immoral act.
Geoff Hinton is not a billionaire! And the field of AI is much wider than LLMs, despite what it may seem like from news headlines. Eg. the sub-field of reinforcement learning focuses on building agents, which are capable of acting autonomously.
https://www.strangeloopcanon.com/p/agi-strange-equation
(Or maybe it's an independent reinvention of the same idea, and something's just in the water.)
Ironically Banach-Tarski would have been better suited for a movie about infinity. Poincare recurrence is relevant for amounts of time that are merely exponentially large, not infinite.
I agree with Wadge that the movie was not very good, but I'm not actually sure what his objection is to the bit with the orange in a box, it seems fairly reasonable based on our current understanding of physics.
Say the worker refuses that order and their boss says "okay, you're fired". Would we also give that worker legal protection? Would we also hold accountable the higher ups who gave that order and set the policies for how trains are to be run? If not, then you now understand why the accident happened.
The author of this article must either really love the NSA or really hate Snowden if he's willing to undermine his entire thesis just to get that jab in.
cos((x - t)) + cos(0.5877852522924731*y + 0.8090169943749475*(x - t)) + cos(0.9510565162951535*y + 0.30901699437494745*(x - t)) + cos(0.9510565162951536*y - 0.30901699437494734*(x - t)) + cos(0.5877852522924732*y - 0.8090169943749473*(x - t)) = z
10.0*(pow(sin(0.1*(x*x-y*y)), 2.0) + pow(sin(0.2*x*y), 2.0))/sqrt(x*x + y*y) = z
x*x+y*y+z*z=2.0+cos(t+10.0*x+24.0*y)+cos(t - 10.0*x+24.0*y+16.0*z)+sin(t+x+y+2.0*z)+cos(3.0*t+y-z)+sin(2.0*t+x*y)+cos(t+3.0*x*y)+sin(0.1*t+y*z + 7.0*x*y)+cos(0.5*t+x*y*z)+cos(5.0*z)+cos(1.618*t+7.0*z)+cos((x+2.0*y)*(2.0\*z-y))> Disclaimer: This essay was generated by StableDiffusion (the image) and ChatGPT (the text). It is based on my tweetstorm, which can be found here: https://twitter.com/IntuitMachine/status/1600804191731036160
Alcohol or vinegar from humans intentionally fermenting things?
A metallic aluminum alloy?
Sulfuric acid (which sometimes occurs naturally)?
Turpentine?
Soda-lime glass?
I get that it generally refers to substances that are more on the very artificial side, requiring advanced knowledge of chemistry to produce, and to have a connotation of harmfulness / toxicity. But it's not at all obvious what the speaker would consider to be a "chemical" because that varies from speaker to speaker.
Lots of interesting things I'd want to try here. Eg, the formulation of thinking of your probability distribution as a charge distribution opens up the possibility of adding negative charges. So you could have a regular positive data examples, but also some negative examples that the generative model tries extra hard to avoid.
I'd also be interested to see what happens when you "add the fields" for two different distributions over the same space. Seems like it would be interestingly different than regular superposition, and I'd really like to see what happens when you add together a dog image field to a cat image field.
I guess the current formulation where you only get the direction of the electric field and not the magnitude would cause problems there. Seems like an improvement might be to predict the actual potential instead of the field and then take the gradient. That would also guarantee that the field is curl-free, which it should be, but it doesn't seem to be enforced in the current paper. I think they don't try this because it's hard to get NNs to produce numbers across a wide range of orders of magnitude. Maybe separately predicting the normalized direction and something like the log of the magnitude would help here.
Is that actually true, though? It's true that a higher fraction of the people in that community give credence to the intelligence explosion hypothesis than pretty much anywhere else. (This is what one would expect, since part of the purpose of LessWrong is to be a forum for discussions about super-intelligent AI.) But even if the intelligence explosion is a terrible, absolutely-wrong theory, that doesn't prevent the people who hold it from being open-minded and tolerant of independent thought. Willingness to consider new and different ideas is something the LessWrong community claims to value, so it would be a little bit weird if they were doing way worse than average at it. And AFAICT, it seems like they're doing fine. Some examples:
- Here [1] is a post critical of the intelligence explosion theory. It has 81 upvotes as of this writing, and the highest upvoted comment goes like: "thanks for writing this post, it makes a lot of good arguments. I agree with these things you wrote" (list of things) "here are some points where I disagree" (list of things). This may even be the original post you were talking about in your comment, except that it doesn't start with an apology.
- LW has 2 different kinds of voting: "Regular upvotes" provide an indication of the quality of a post or comment, and "agree/disagree votes" let people express how much they agree or disagree with a particular comment. Down-voting a high quality comment just because you disagree (instead of giving it a disagree-vote) would be against the culture on LW.
If you're already sure that LW is wrong about superintelligence, and you're trying to explain how they became wrong, then "those LW people were too open minded and fell for that intelligence explosion BS" makes more sense to me than anything about suppression of independent thought.
[1] https://www.lesswrong.com/posts/zB3ukZJqt3pQDw9jz/ai-will-ch...
> We're ahead of even the most optimistic timelines — what's happening with transformers is taking everyone by surprise.
I admit that I was surprised by how good GPT-2 was. But after that, you expect a lot of effort to go into development and for things to improve further. I haven't found any of the subsequent developments too surprising. Also, while LLMs do make a good example of how capable neural networks can be if you're willing to spend a load of compute, the kinds of models that have the potential to be dangerous are reinforcement learning models, and the state of the art there is a lot more primitive than the state of the art in next-token prediction.
> Humans are famously bad at dealing with exponentials
AI research is not an exponential process. It's too discrete and jumpy. It's better to mainly think of it as a series of discoveries that can only be made once.
> I was chatting with a researcher from OpenAI the other day who told me he intellectually understood AGI risks, but couldn't feel the fear emotionally, because he was so close to the problem and saw how hard it was to get these models to do anything.
This should be reassuring. An expert in the field is saying that we probably have a lot of time before these models are at the point of becoming dangerous, based on his direct experience with them. It's reasonable to worry that AI researchers will find it hard to believe that the models they are working on are dangerous because their job depends on them not understanding the risk. That's not what's going on in this case, though.
> The only thing that gives me solace is that, historically, new life forms don't extinguish previous ones.
If we were to stop developing AI while continuing to develop all other technology, it's plausible that we'd eventually build a Dyson sphere around the sun. If we build an AI with alien desires, then we should expect it to want to gather resources so that it can achieve its alien goals. A very important resource in the universe is energy, so an AI would probably also want to build a Dyson sphere. An AI indifferent to human life wouldn't have a reason to leave any sunlight for us.
[1] https://www.lesswrong.com/posts/K9ZaZXDnL3SEmYZqB/ends-don-t...
Related to the topics of AI apocalypse and putting things off because they seem far away: I personally find it amusing to contemplate a strong AGI being developed in 2035 and wiping out humanity. Why? Because it would eliminate the Y2038 problem, retroactively vindicating all the engineers who only allocated 32 bits to timestamps. It would mean the justification of "oh, it's so far off, the world will have changed unimaginably by then", would for once turn out to be correct!