Isn’t $\beta$ proportional to the inverse temperature, and so should be smaller now (with easier, more frequent trading)?
joseph_c
Karma: 147
A math textbook leaving certain results as an exercise for the reader?
I think this is usually actually one of (1) the author not wanting to write out the proof (because it’s boring/tedious) or (2) a proof that would make a good exercise because it is easy enough if you understand the big ideas (and coming up with good exercises is not always easy).
Gradual/Sharp
I recently came across Backpack Language Models and wanted to share it in case any AI interpretability people have not seen it. (I have yet to see this posted on LessWrong.)
The main difference between a backpack model and an LLM is that it enforces a much stricter rule to map inputs’ embeddings to output logits. Most LLMs allow the output logits to be an arbitrary function of the inputs’ embeddings; a backpack model requires the output logits to be a linear transformation of a linear combination of the input embeddings. The weights for this linear combination are parameterized by a transformer.
The nice thing about backpack models is that they are somewhat easier to interpret/edit/control: The output logits are a linear combination of the inputs’ embeddings, so you can directly observe how changing the embeddings changes the outputs.
joseph_c’s Shortform
Most students, 48 percent, claimed to be Native American on their application....
According to Intelligent.com Managing Editor Kristen Scatton, the prevalence of applicants who claim Native American ancestry is possibly due to the popular narrative that for many Americans, a small percentage of their DNA comes from a Native American tribe.
Maybe these students are purposely misinterpreting “Native American” to be someone who was born and raised in the United States, perhaps with ancestors born and raised in the US as well. This is actually the older sense of the term “Native American”, found, for example, in the name of the Native American Party back in the mid-1800s.
includeIt is written in More Dakka:
If something is a good idea, you need a reason to not try doing more of it.
Taken at face value, it implies the contrapositive:
If something is a bad idea, you need a reason to not try doing less of it.
This is not the contrapositive. It is not even the opposite.
Parfit’s Hitchhiker: You’re stranded in the desert and Omega comes up. It will give you a ride out of the desert iff it predicts you’d give it 10,000 dollars upon reaching civilization again. You get a ride. When in civilization again, do you go over to the bank and withdraw some money? Well, policies which pay up in this specific situation get (value of a life − 10,000 dollars) more than policies which don’t pay in this specific situation, which just die.
Why is this called Parfit’s Hitchhiker? Who is the Parfit it is referring to? Where was this scenario first written up? (I’m trying to dig up the original reference.)
Not for Mormons. They don’t believe in an omnipresent God.
Well, what are your actual steps? Or is this just advertisement?
Do you still live in Utah?
Did your family cut you off?
Do you know about [r/exmormon](https://old.reddit.com/r/exmormon/)?
Maybe try controlling for age? I think young people are both less likely to have signed up for cryonics (because they have less money and are less likely to die) and also have higher probabilities of cryonics working for them (because cryonics will improve by the time they need it).
This graph seems to match the rise of the internet. Here’s my alternate hypothesis: Most people are irrational, and now it’s more reasonable to call them crazy/stupid/fools because they have much greater access to knowledge that they are refusing/unable to learn from. I think people are just about as empathetic as they used to be, but incorrect people are less reasonable in their beliefs.
The trick here is that both equations contain which is the hardest to calculate, and that number drops out when we divide the equations.
You have a couple typos here. The first centered equation should not have a $P(\bar H H | X)$ but instead have $P(\bar H | X)$, and the inline expression should be $P(D | X)$, not $P(D | H)$.
A few things to note:
GPT-4′s release was delayed by ~8 months because they wanted to do safety testing before releasing it. If you take this into account your graph looks much less steep.
The employees at OpenAI know about prediction markets.
They also have incentives to manipulate them to look like GPT-5 will come out later than it actually will. They don’t want to set off an AI arms race.
I think most people view “All people are equal” as a pronouncement of a moral belief they hold, not as a statement of fact. When they say, “All people are equal”, they mean they believe “all people should be treated equally”, or “everyone should have to obey the same laws” or “everyone’s needs have equal importance”.
This moral pronouncement is also consistent with a utilitarian pronouncing “All people are equal to me”, as in that all people’s lives hold equal weight in his utility function.
I think the old meaning of “bigot” is very close to this. From the 1828 Websters Dictionary:
BIG’OT, noun
1. A person who is obstinately and unreasonably wedded to a particular religious creed, opinion, practice or ritual. The word is sometimes used in an enlarged sense, for a person who is illiberally attached to any opinion, or system of belief; as a bigot to the Mohammedan religion; a bigot to a form of government.
2. A venetian liquid measure containing the fourth part of the amphor, or half the boot.
How much more advantageous would this be than a “head only” option? To get to the brain, wouldn’t you have to cut open the head anyways?
In case it’s useful for others, a more direct link is https://podcasters.spotify.com/pod/show/planecrash/episodes/How-to-Read-Glowfic-e21k2pq.
I’m a little confused about which computation you’re trying to sparsify. The paper seems to be written in the context of the technique where one uses sparse autoencoders to extract “features” from the embedding space of large language models which are hopefully interpretable. (Please correct me if I’m wrong about that!)
The goal would seem to be, then, to sparsify the computation of the language model. However, the method in your paper seems to sparsify the computation of the autoencoders themselves, not the language model. Shouldn’t the goal be to sparsify the language model’s computation? If so, why not use weight pruning? What is JSAE better at?