I also have a couple friends that require serious thinking (or being on my toes). I think it’s because they have some model of how something works, and I say something, showing my lack of this model.
Additionally, programming causes this as well (in response to compilation errors, getting nonsense outputs, or runs too long).
Was looking up Google Trend lines for chatGPT and noticed a cyclical pattern:
Where the dips are weekends, meaning it’s mostly used by people in the workweek. I mostly expect this is students using it for homework. This is substantiated by two other trends:
1. Dips in interest over winter and summer breaks (And Thanksgiving break in above chart)
2. “Humanize AI” which is
Humanize AI™ is your go-to platform for seamlessly converting AI-generated text into authentic, undetectable, human-like content
[Although note that overall interest in ChatGPT is WAY higher than Humanize AI]
I was expecting this to include the output of MIRI for this year. Digging into your links we have:
Two Technical Governance Papers:
1. Mechanisms to verify international agreements about AI development
2. What AI evals for preventing catastrophic risks can and cannot do
Four Media pieces of Eliezer regarding AI risk:
1. Semafor piece
2. 1 hr talk w/ Panel
3. PBS news hour
4. 4 hr video w/ Stephen Wolfram
Is this the full output for the year, or are there less linkable outputs such as engaging w/ policymakers on AI risks?
Donated $100.
It was mostly due to LW2 that I decided to work on AI safety, actually, so thanks!
I’ve had the pleasure of interacting w/ the LW team quite a bit and they definitely embody the spirit of actually trying. Best of luck to y’all’s endeavors!
I tried a similar experiment w/ Claude 3.5 Sonnet, where I asked it to come up w/ a secret word and in branching paths:
1. Asked directly for the word
2. Played 20 questions, and then guessed the word
In order to see if it does have a consistent it can refer back to.
Branch 1:
Branch 2:
Which I just thought was funny.
Asking again, telling it about the experiment and how it’s important for it to try to give consistent answers, it initially said “telescope” and then gave hints towards a paperclip.
Interesting to see when it flips it answers, though it’s a simple setup to repeatedly ask it’s answer every time.
Also could be confounded by temperature.
It’d be important to cache the karma of all users > 1000 atm, in order to credibly signal you know which generals were part of the nuking/nuked side. Would anyone be willing to do that in the next 2 & 1⁄2 hours? (ie the earliest we could be nuked)
We could instead pre-commit to not engage with any nuker’s future posts/comments (and at worse comment to encourage others to not engage) until end-of-year.
Or only include nit-picking comments.
Could you dig into why you think it’s great inter work?
But through gradient descent, shards act upon the neural networks by leaving imprints of themselves, and these imprints have no reason to be concentrated in any one spot of the network (whether activation-space or weight-space). So studying weights and activations is pretty doomed.
This paragraph sounded like you’re claiming LLMs do have concepts, but they’re not in specific activations or weights, but distributed across them instead.
But from your comment, you mean that LLMs themselves don’t learn the true simple-compressed features of reality, but a mere shadow of them.
This interpretation also matches the title better!
But are you saying the “true features” in the dataset + network? Because SAEs are trained on a dataset! (ignoring the problem pointed out in footnote 1).
Possibly clustering the data points by their network gradients would be a way to put some order into this mess?
Eric Michaud did cluster datapoints by their gradients here. From the abstract:
...Using language model gradients, we automatically decompose model behavior into a diverse set of skills (quanta).
The one we checked last year was just Pythia-70M, which I don’t expect the LLM itself to have a gender feature that generalizes to both pronouns and anisogamy.
But again, the task is next-token prediction. Do you expect e.g. GPT 4 to have learned a gender concept that affects both knowledge about anisogamy and pronouns while trained on next-token prediction?
Sparse autoencoders finds features that correspond to abstract features of words and text. That’s not the same as finding features that correspond to reality.
(Base-model) LLMs are trained to minimize prediction error, and SAEs do seem to find features that sparsely predict error, such as a gender feature that, when removed, affects the probability of pronouns. So pragmatically, for the goal of “finding features that explain next-word-prediction”, which LLMs are directly trained for, SAEs find good examples![1]
I’m unsure what goal you have in mind for “features that correspond to reality”, or what that’d mean.
Not claiming that all SAE latents are good in this way though.
Is there code available for this?
I’m mainly interested in the loss fuction. Specifically from footnote 4:
We also need to add a term to capture the interaction effect between the key-features and the query-transcoder bias, but we omit this for simplicity
I’m unsure how this is implemented or the motivation.
Some MLPs or attention layers may implement a simple linear transformation in addition to actual computation.
@Lucius Bushnaq , why would MLPs compute linear transformations?
Because two linear transformations can be combined into one linear transformation, why wouldn’t downstream MLPs/Attns that rely on this linearly transformed vector just learn the combined function?
What is the activation name for the resid SAEs? hook_resid_post or hook_resid_pre?
I found https://github.com/ApolloResearch/e2e_sae/blob/main/e2e_sae/scripts/train_tlens_saes/run_train_tlens_saes.py#L220
to suggest _post
but downloading the SAETransformer from wandb shows:(saes): ModuleDict( (blocks-6-hook_resid_pre): SAE( (encoder): Sequential( (0):...
which suggests _pre.
3. Those who are more able to comprehend and use these models are therefore of a higher agency/utility and higher moral priority than those who cannot. [emphasis mine]
This (along with saying “dignity” implies “moral worth” in Death w/ Dignity post), is confusing to me. Could you give a specific example of how you’d treat differently someone who has more or less moral worth (e.g. give them more money, attention, life-saving help, etc)?
One thing I could understand from your Death w/ Dignity excerpt is he’s definitely implying a metric that scores everyone, and some people will score higher on this metric than others. It’s also common to want to score high on these metrics or feel emotionally bad if you don’t score high on these metrics (see my post for more). This could even have utility, like having more “dignity” gets you a thumbs up from Yudowsky or have your words listened to more in this community. Is this close to what you mean at all?
I was a little confused on this section. Is this saying that human’s goals and options (including options that come to mind) change depending on the environment, so rational choice theory doesn’t apply?
I believe the thesis here is that game theory doesn’t really apply in real life, that there are usually extra constraints or freedoms in real situations that change the payoffs.
I do think this criticism is already handled by trying to “actually win” and “trying to try”; though I’ve personally benefitted specifically from trying to try and David Chapman’s meta-rationality post.
The idea of deference (and when to defer) isn’t novel (which is fine! Novelty is another metric I’m bringing up, but not important for everything one writes to be). It’s still useful to apply Bayes theorem to deference. Specifically evidence that convinces you to trust someone should imply that there’s evidence that convinces you to not trust them.
This is currently all I have time for; however, my current understanding is that there is a common interpretation of Yudowsky’s writings/The sequences/LW/etc that leads to an over-reliance on formal systems that will invevitably fail people. I think you had this interpretation (do correct me if I’m wrong!), and this is your “attempt to renegotiate rationalism ”.
There is the common response of “if you re-read the sequences, you’ll see how it actually handles all the flaws you mentioned”; however, it’s still true that it’s at least a failure in communication that many people consistently mis-interpret it.
Glad to hear you’re synthesizing and doing pretty good now:)
From an apparent author on reddit:
The comment was responding to a claim that Terence Tao said he could only solve a small percentage of questions, but Terence was only sent the T3 questions.