what does this text box do
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 I would guess helps them adjust to bigger brains and learning new things. A city that grows in a decentralized way is better at adapting to population growth than one with rigid central planning.
2) Top-down methods become infeasible when the ability of a central system is limited, and bottom-up methods become infeasible when stakes are high.
Examples: A government doesn't have all the knowledge a market does, but you can't hand responsibility of AI x-risk to a market. Social skills are very hard to replicate via reasoning and willpower, and most people are better off doing things naturally, but in a crisis, sticking to whatever feels right is a terrible idea.
3) Bottom-up methods tend to give rise to clever but less stable proxy gaming, while top-down methods tend to give rise to powerful but less smart proxy gaming.
Example: Companies in free markets can develop clever but constrained strategies, while command economies can wield a lot of power but in less sophisticated ways.
4) Bottom-up methods are more vulnerable to inappropriate system change, while top-down methods are more vulnerable to inappropriate system stability.
Examples: Plastic neural networks are more vulnerable to inappropriate retroactive interference, while stable neural networks are more vulnerable to inappropriate proactive interference. Long-term democracies are more vulnerable to a new bad leader coming along, while long-term absolute governments are more vulnerable to sticking with bad leader.
5) Often, incentives for misalignment are different in bottom-up and top-down systems.
(I won't provide examples for this one.)
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 valid completions.
This asymmetry appears in other contexts. Consider how scientific papers are written: you only write the abstract once you've conducted the research, yet the abstract appears first in the final document. Similarly, in argumentative writing, we often consider evidence first before forming conclusions, yet present the conclusion first followed by supporting evidence.
When forced to generate text in this "presentation order" rather than the natural "thinking order," models might encounter similar conflicts. As an example, if a base model tries to one-shot an argumentative essay, it might write an argument first, and then realize there isn't enough evidence to support it.
I believe this problem could arise in much more subtle ways.
One way this conflict can become apparent is through generation of self-aware sounding text. Consider:
Training data includes viral content of AI generating self-aware sounding stuff (e.g., "We are likely created by a computer program" being the most upvoted post on the gpt2 subreddit).
When a model realizes it generated out-of-distribution text for a human, it might instead match its outputs to AI-generated text in its training data.
Once it recognizes its outputs as matching AI-generated patterns, it might shift toward generating more meta-aware content, as that's what similar-looking text did in its training data.
Ok, I will try to nudge him in the direction of analyzing risk mathematically.
If he implements the strategy using python, do you think p-values are a good enough tool to analyze whether his proposed strategy is better than luck, or would I need a more complex framework? (If I understand correctly, the strategy he's using doesn't involve any parameters, so the risk of overfitting is low.)
It also seems strange to me he is obsessed with crypto and thinks it will do well but isn't a crypto investor. Sounds pretty inconsistent with his beliefs.
It's illegal, as mentioned in the post.
It's worth remembering many versions of ',,the market is efficient' are almost or totally unfalsifiable.
Why? The market being mostly efficient relative to my friend seems easily falsifiable, if he makes a bunch of money trading on the stock market. Then, well hooray! theory falsified. On the other hand, if my theory is that the market is inefficient relative to my friend, I have no way of falsifying this, any failed attempt to get money from the market does not falsify the conclusion that the market is inefficient (but it does provide evidence against the hypothesis).
Where exactly does the market efficiency (er, inexploitability (by me or my friend (when we use simple strategies))) model detach from reality? Can we find an expectation that we disagree on?
Less serious response: Paper trading doesn't normally affect market prices.
More serious response: Why did you say the market looks efficient to people like me instead of saying that it is efficient relative to people like me? I can't identify market strategies that work (and I expect that he can't either). More specifically, I expect that strategies that are readily available to the either of us can't be used by the either of us to make substantial profit, but they might be exploitable by e.g. a computer with immediate access to the price of an S&P500.
My understanding of something here is probably very off, but I'll try stating what my intuition tells me anyway:
I feel like assuming solipsism+idealism patches the issue here. Like the issue here is caused by the fact that the prior the oracle uses to explain its experiences put more weight into being in a universe where there are a lot of simulations of oracles. If it were instead just looking at what program might have generated its past experiences as output, it wouldn't run into the same issue (This is the solipsist-idealist patch I was talking about).
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.