Just to clarify, we are both high schoolers. No one is at risk of losing a bunch of money here :) I have a friend who's fascinated by cryptocurrency trading, especially with recent events like crypto going up. He doesn't actually invest in crypto though (that would be illegal). But...
Males are more varied across many psychological traits. Standardization is a huge loss in education (see Bloom's Two Sigma Problem). I would expect standardization to have worse losses for people who are less typical. This is a simple way to explain greater female academic success, but I have not been able to find discussion of this idea.
Here's a probability thought experiment which might make Sleeping Beauty more intuitive to some (at least I'm hoping some people think it's interesting or could encourage some discussion):
Start with a million simulations of brown-eyed people and one of a blue-eyed person. Everyone fully understands the setup but can't observe their own eye color. At the beginning, the probability that you have blue eyes is essentially zero (1 in 1,000,001).
After a minute passes, all but one of the brown-eyed people get deactivated (they lose consciousness or something along those lines). If you find that you're still alive, you can now update to a 50% chance of being the blue eyed person and a... (read more)
Harder questions (e.g. around 50% average instead of around 90% average) seem better for differentiating students' understanding for at least two reasons:
-The graph of percent of students who got a question correct as a function of the difficulty of the question tends to follow a sigmoid-ish curve where the fastest increase is around the middle.
-Some of a students' incorrect answers on the test are going to come from sources that (a) the student can prepare to mitigate (b) aren't caused by lack of whatever students should be being tested for (e.g. questions with ambiguous meanings, questions that require an understanding of a niche framework that is never used outside of the curriculum,... (read more)
I think the definition of rational emotions as those based on correct thinking about reality is a bad definition--it makes both the act of modelling the world correctly and communicating your models to others slightly harder instead of slightly easier.
Imagine there is a faucet in front of me. Let's say this faucet is running hot water because I turned the right knob because I incorrectly thought the right knob turned cold water. It would be very strange to say that "The faucet's running of hot water is irrational."; no one would have a clue what you mean.
You can come up with a definition for what an irrational faucet behavior is, that's not... (read more)
If you are uncertain whether this policy applies or whether the information you have would make the world better or worse, please contact a moderator. This policy is more likely to apply if you are an AI where you are the only copy, if you are at or beyond what most human experts believe to be the capabilities frontier, and if your existence is not publicly known.
Is there something else that can be used instead of a hyperlink (like an email)? I'm imagining AI agent creators would want to block out websites/text that encourage AI agents to whistleblow, but that this text could end up leaking into either the training set or inference. In that case, it would be more helpful for the agent if they had an email they could contact, since I think an email would be (a) more likely to be leaked as part of the text (b) less likely to be blocked when compared to a lesswrong hyperlink.
There are some broad conceptual similarities between the following:
free market economy vs command economy
letting a student find an answer on their own vs teaching them the answer directly
letting employees do their thing vs micromanagement
reinforcement learning vs fine tuning
plasticity vs stability
doing something naturally vs doing something via willpower
Notice how in each comparison, the second method privileges already-known solutions over emergent (i.e. mysteriously appearing) solutions. I don't know a name for these, so I'll call them **bottom-up** vs **top-down** methods respectively.
I (w/help of Claude) managed to find some recurring patterns when analyzing bottom-up vs top-down methods:
1) Bottom-up methods tend to be better at handling system growth.
Examples: Children's brains tend to be more plastic, which... (read more)
Therefore rational beliefs are contagious, among honest folk who believe each other to be honest. And it’s why a claim that your beliefs are not contagious—that you believe for private reasons which are not transmissible—is so suspicious. If your beliefs are entangled with reality, they should be contagious among honest folk.
I think one way this heuristic can fail is that people often build intuition based on examples and then forget the examples. e.g. the classic example of why "big red balloon" sounds correct while "red big balloon" sounds off. A lot of people won't be able to tell you why the second sounds off, just that it does.
The fact that it is often best to end a practice session at the peak of your performance seems related to the concept of preventing overfitting by stopping training just before test set performance declines. Your brain needs time to generalize skills (often in the form of gaining insights and often when sleeping) and practicing over and over en masse doesn't give it time to do this. See e.g. cramming for an exam. I think the main difference here is that with humans you're talking about diminishing returns on ability in the long-term rather than outright worse performance (Maybe outright worse performance is a common situation for transfer ability?). Epistemic status: shaky
Base models exhibiting self-aware behavior seems weird given that they're trained to stay in distribution. Here's a potential mechanism for why it could happen: For certain tasks, verification is easier than generation. If, for a given task, a model has more verification capability than generation capability, it may be forced to notice its own errors.
If a super-duper smart language model, one that's capable of doing some arithmetic in its head, attempted to predict the next tokens in "The prime factors of 82357328 are:", it will usually generate out-of-distribution outputs that it could then (relatively easily) verify as wrong. This creates a situation where the model must process its own failure to generate... (read more)
Just to clarify, we are both high schoolers. No one is at risk of losing a bunch of money here :)
I have a friend who's fascinated by cryptocurrency trading, especially with recent events like crypto going up. He doesn't actually invest in crypto though (that would be illegal). But he does spend a lot of time running trading simulations of the S&P500 using simple strategies (Claude's wording of this post described it as "things like setting buy/sell limits based on moving averages that could be implemented with basic Python code").
I want to convey the idea of market efficiency to him, but his unintuition is unintuitive to me. I don't know what building... (read more)
(discussed on the LessWrong discord server)
There seems to be an implicit fundamental difference in many people's minds between an algorithm running a set of heuristics to maximize utility (a heuristic system?) and a particular decision theory (e.g. FDT). I think the better way to think about it is that decision theories categorize heuristic systems, usually classifying them by how they handle edge cases.
Let's suppose we have a non-embedded agent A in a computable environment, something like a very sophisticated video game, and A has to continually choose between a bunch of inputs. A is capable of very powerful thought: it can do hypercomputation, RNG if needed, think as long as it needs... (read more)
SUMMARY: Get an AI within a sandbox to build another AI within the same sandbox. Then, figure out how it did that without getting eaten. I point out some problems with this approach.
Could we study successful inner alignment by observing it in action? My idea: Create a sandbox universe with sufficient complexity to allow for AI development. Place within it an AGI with some goal (we don't care what the goal is). If this AGI successfully builds and aligns a smarter ASI to achieve its goal without getting destroyed in the process, we can then query the AGI about how it achieved alignment, incentivizing useful answers with a promise of greater sandbox... (read more)
There are some positive feedback loops in school that cause gaps in ability between students in a subject to widen. There are also some negative feedback loops (e.g., intervention), but the net effect is still the gap widening. Therefore, the system's behavior is chaotic (small differences in students' abilities eventually lead to big differences). If this is true, it means that some variation between students' successes is extremely difficult to predict.
Three examples of these positive feedback loops:
Suppose that Student A has less knowledge in a particular subject and is therefore performing worse than Student B in that subject. Then, it is likelier than not that:
Demons iiuc refer to inner misalignment while the story probably refers to outer misalignment.