In this post, I proclaim/endorse forum participation (aka commenting) as a productive research strategy that I've managed to stumble upon, and recommend it to others (at least to try). Note that this is different from saying that forum/blog posts are a good way for a research community to communicate. It's about individually doing better as researchers.
This is the ninth post in my series on Anthropics. The previous one is The Solution to Sleeping Beauty.
There are some quite pervasive misconceptions about betting in regards to the Sleeping Beauty problem.
One is that you need to switch between halfer and thirder stances based on the betting scheme proposed. As if learning about a betting scheme is supposed to affect your credence in an event.
Another is that halfers should bet at thirders odds and, therefore, thirdism is vindicated on the grounds of betting. What do halfers even mean by probability of Heads being 1/2 if they bet as if it's 1/3?
In this post we are going to correct them. We will understand how to arrive to correct betting odds from both thirdist and halfist positions, and...
Throughout your comment you've been saying a phrase "thirders odds", apparently meaning odds 1:2, not specifying whether per awakening or per experiment. This is underspecified and confusing category which we should taboo.
Yeah, that was sloppy language, though I do like to think more in terms of bets than you do. One of my ways of thinking about these sorts of issues is in terms of "fair bets" - each person thinks a bet with payoffs that align with their assumptions about utility is "fair", and a bet with payoffs that align with different assumptions...
On 16 March 2024, I sat down to chat with New York Times technology reporter Cade Metz! In part of our conversation, transcribed below, we discussed his February 2021 article "Silicon Valley's Safe Space", covering Scott Alexander's Slate Star Codex blog and the surrounding community.
The transcript has been significantly edited for clarity. (It turns out that real-time conversation transcribed completely verbatim is full of filler words, false starts, crosstalk, "uh huh"s, "yeah"s, pauses while one party picks up their coffee order, &c. that do not seem particularly substantive.)
ZMD: I actually have some questions for you.
CM: Great, let's start with that.
ZMD: They're critical questions, but one of the secret-lore-of-rationality things is that a lot of people think criticism is bad, because if someone criticizes you, it hurts your...
...aZMD: Looking at "Silicon Valley's Safe Space", I don't think it was a good article. Specifically, you wrote,
In one post, [Alexander] aligned himself with Charles Murray, who proposed a link between race and I.Q. in "The Bell Curve." In another, he pointed out that Mr. Murray believes Black people "are genetically less intelligent than white people."
End quote. So, the problem with this is that the specific post in which Alexander aligned himself with Murray was not talking about race. It was specifically talking about whether specific programs
Here's a very neat twitter thread: the author sends various multimodal models screenshots of the conversation he's currently having with them, and asks them to describe the images. Most models catch on fast: the author describes this as them passing the mirror test.
I liked the direction, so I wanted to check if ChatGPT could go from recognising that the images are causally downstream of it to actually exercising control over the images. I did this by challenging it to include certain text in the images I was sending it.
And the answer is yes! In this case it took three images for ChatGPT to get the hang of it.
OpenAI doesn't support sharing conversations with images, but I've taken screenshots of the whole conversation below: it took three images...
The only way ChatGPT can control anything is by writing text, so figuring out that it should write the text that should appear in the image seems pretty straightforward. It only needs to rationalize why this would work.
This is my personal opinion, and in particular, does not represent anything like a MIRI consensus; I've gotten push-back from almost everyone I've spoken with about this, although in most cases I believe I eventually convinced them of the narrow terminological point I'm making.
In the AI x-risk community, I think there is a tendency to ask people to estimate "time to AGI" when what is meant is really something more like "time to doom" (or, better, point-of-no-return). For about a year, I've been answering this question "zero" when asked.
This strikes some people as absurd or at best misleading. I disagree.
The term "Artificial General Intelligence" (AGI) was coined in the early 00s, to contrast with the prevalent paradigm of Narrow AI. I was getting my undergraduate computer science...
No, I was talking about the results. lsusr seems to use the term in a different sense than Scott Alexander or Yann LeCun. In their sense it's not an alternative to backpropagation, but a way of constantly predicting future experience and to constantly update a world model depending on how far off those predictions are. Somewhat analogous to conditionalization in Bayesian probability theory.
I haven't watched the LeCun interview you reference (it is several hours long, so relevant time-stamps to look at would be appreciated), but this still does not make sen...
Lots of people already know about Scott Alexander/ACX/SSC, but I think that crossposting to LW is unusually valuable in this particular case, since lots of people were waiting for a big schelling-point overview of the 15-hour Rootclaim Lab Leak debate, and unlike LW, ACX's comment section is a massive vote-less swamp that lags the entire page and gives everyone equal status.
It remains unclear whether commenting there is worth your time if you think you have something worth saying, since there's no sorting, only sifting, implying that it attracts small numbers of sifters instead of large numbers of people who expect sorting.
Here are the first 11 paragraphs:
...Saar Wilf is an ex-Israeli entrepreneur. Since 2016, he’s been developing a new form of reasoning, meant to transcend normal human bias.
His
Way back in 2020 there was an article A Proposed Origin For SARS-COV-2 and the COVID-19 Pandemic, which I read after George Church tweeted it (!) (without comment or explanation). Their proposal (they call it "Mojiang Miner Passage" theory) in brief was that it WAS a lab leak but NOT gain-of-function. Rather, in April 2012, six workers in a "Mojiang mine fell ill from a mystery illness while removing bat faeces. Three of the six subsequently died." Their symptoms were a perfect match to COVID, and two were very sick for more than four months.
The proposal i...
If it’s worth saying, but not worth its own post, here's a place to put it.
If you are new to LessWrong, here's the place to introduce yourself. Personal stories, anecdotes, or just general comments on how you found us and what you hope to get from the site and community are invited. This is also the place to discuss feature requests and other ideas you have for the site, if you don't want to write a full top-level post.
If you're new to the community, you can start reading the Highlights from the Sequences, a collection of posts about the core ideas of LessWrong.
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The Open Thread tag is here. The Open Thread sequence is here.
Unsure if there is normally a thread for putting only semi-interesting news articles, but here is a recently posted news article by Wired that seems.... rather inflammatory toward Effective Altruism. I have not read the article myself yet, but a quick skim confirms the title is not only to get clickbait anger clicks, the rest of the article also seems extremely critical of EA, transhumanism, and Rationality.
I am going to post it here, though I am not entirely sure if getting this article more clicks is a good thing, so if you have no interest in read...
Hi, I’d like to share my paper that proposes a novel approach for building white box neural networks.
The paper introduces semantic features as a general technique for controlled dimensionality reduction, somewhat reminiscent of Hinton’s capsules and the idea of “inverse rendering”. In short, semantic features aim to capture the core characteristic of any semantic entity - having many possible states but being at exactly one state at a time. This results in regularization that is strong enough to make the PoC neural network inherently interpretable and also robust to adversarial attacks - despite no form of adversarial training! The paper may be viewed as a manifesto for a novel white-box approach to deep learning.
As an independent researcher I’d be grateful for your feedback!
Thanks, this is very interesting.
I wonder if this approach is extendable to learning to predict the next word from a corpus of texts...
The first layer might perhaps still be embedding from words to vectors, but what should one do then? What would be a possible minimum viable dataset?
Perhaps, in the spirit of PoC of the paper, one might consider binary sequences of 0s and 1s, and have only two words, 0 and 1, and ask what would it take to have a good predictor of the next 0 or 1 given a long sequence of those as a context. This might be a good starting point, and then one might consider different examples of that problem (different examples of (sets of) sequences of 0 and 1 to learn from).
Summary: The post describes a method that allows us to use an untrustworthy optimizer to find satisficing outputs.
Acknowledgements: Thanks to Benjamin Kolb (@benjaminko), Jobst Heitzig (@Jobst Heitzig) and Thomas Kehrenberg (@Thomas Kehrenberg) for many helpful comments.
Imagine you have black-box access to a powerful but untrustworthy optimizing system, the Oracle. What do I mean by "powerful but untrustworthy"? I mean that, when you give an objective function as input to the Oracle, it will output an element that has an impressively low[1] value of . But sadly, you don't have any guarantee that it will output the optimal element and e.g. not one that's also chosen for a different purpose (which might be dangerous for many reasons, e.g. instrumental convergence).
What questions can you safely ask the Oracle? Can you use it to...
First thought: The oracle is going to choose to systematically answer or not answer the queries we give it. This represents a causal channel of one bit per query it can use to influence the outside world[1]. Can you conquer the world in one awkwardly delivered kilobyte or less? Maybe.
Maybe we can stop that by scrapping every Oracle that doesn't answer and training a new one with presumably new goals? Or would the newly trained Oracles just cooperate with the former dead ones in one long-term plan to break out, take control, and reward all the dead Oracles ...
On the 3rd of October 2351 a machine flared to life. Huge energies coursed into it via cables, only to leave moments later as heat dumped unwanted into its radiators. With an enormous puff the machine unleashed sixty years of human metabolic entropy into superheated steam.
In the heart of the machine was Jane, a person of the early 21st century.
From her perspective there was no transition. One moment she had been in the year 2021, sat beneath a tree in a park. Reading a detective novel.
Then the book was gone, and the tree. Also the park. Even the year.
She found herself laid in a bathtub, immersed in sickly fatty fluids. She was naked and cold.
The first question Jane had for the operators and technicians who greeted her...
I was ultimately disappointed by it - somewhat like Umineko, there is a severe divergence from reader expectations. Alexander Wales's goal for it, however well he achieved it by his own lights, was not one that is of interest to me as a reader, and it wound up being less than the sum of its parts for me. So I would have enjoyed it better if I had known from the start to read it for its parts (eg. revision mages or 'unicorns' or 'Doris Finch').