Age limits do exist: you have to be at least 35 to run for President, at least 30 for Senator, and 25 for Representative. This automatically adds a decade or two to your candidates.
James Camacho
Karma: 39
In earlier times, I spent an incredible amount of my mental capacity trying to accurately model those around me. I can count on zero hands the number of people that reciprocated. Even just treating me as real as I treated them would fit on one hand. On the other hand, nearly everyone I talk to does not have “me” as even a possibility in their model.
It just takes a very long time in practice, see “Basins of Attraction” by Ellison.
I’ve been thinking about something similar, and might write a longer post about it. However, the solution to both is to anneal on your beliefs. Rather than looking at the direct probabilities, look at the logits. You can then raise the temperature, let the population kind of randomize their beliefs, and cool it back down.
See “Solving Multiobjective Game in Multiconflict Situation Based on Adaptive Differential Evolution Algorithm with Simulated Annealing” by Li et. al.
Perhaps “fit”, from the Latin fio (come about) + English fit (fit). An object must fit, survive, and spread.
To see how much the minimal point contributes to the integral we can integrate it in its vicinity
I think you should be looking at the entire stable island, not just integrating from zero to one. I expect you could get a decent approximation with Lie transform perturbation theory, and this looks similar to the idea of macro-states in condensed matter physics, but I’m not knowledgeable in these areas.
−N∑i=1logp(yi|xi,w)
You have a typo, the equation after Free Energy should start with
Also the third line should be , not minus.
Also, usually people use for model parameters (rather than ). I don’t know the etymology, but game theorists use the same letter (for “types” = models of players).
Also sometimes when I explain what a hyperphone is well enough for the other person to get it, and then we have a complex conversation, they agree that it would be good. But very small N, like 3 to 5.
It’s difficult to understand your writing, and I feel like you could improve in general at communication based on this quote. The concept of a hyperphone isn’t that complex—the ability to branch in conversations—so the modifiers “well enough”, “complex”, and “very small N” make me believe it’s only complex because you’re unclear.
For example, the blog post you linked to is titled “Hyperphone”, yet you never define a hyperphone. I can infer from the section on streaming what you imagine, but that’s the second-to-last section!
There’s the automorphism
which turns a switchy distribution into a sticky one, and vice versa. The two have to be symmetric, so your conclusion cannot be correct.
This means the likelihood distribution over data generated by Steady is closer to the distribution generated by Switchy than to the distribution generated by Sticky.
Their KL divergences are exactly the same. Suppose Baylee’s observations are . Let be the probability if there’s a chance of switching, and similar for . By the chain rule,
In particular, when either or is equal to one half, this divergence is symmetric for the other variable.
The problem with etching specific models is scale. It costs around $1M to design a custom chip mask, so it needs to be amortized over tens or hundreds of thousands of chips to become profitable. But no companies need that many.
Assume a model takes 3e9 flops to infer the next token, and these chips run as fast as H100s, i.e. 3e15 flops/s. A single chip can infer 1e6 tokens/s. If you have 10M active users, then 100 chips can provide each user a token every 10ms, around 600wpm.
Even OpenAI would only need hundreds, maybe thousands of chips. The solution is smaller-scale chip production. There are startups working on electron beam lithography, but I’m unaware of a retailer Etched could buy from right now.
EDIT: 3 trillion flops/token (similar to GPT-4) is 3e12, so that would be 100,000 chips. The scale is actually there.
so
It should be .
如果需要更長的時間來理解，那麼效率就很低。
Graph Utilitarianism:
People care about others, so their utility function naturally takes into account utilities of those around them. They may weight others’ utilities by familiarity, geographical distance, DNA distance, trust, etc. If every weight is nonnegative, there is a unique global utility function (Perron-Frobenius).
Some issues it solves:
Pascal’s mugging.
The argument “utilitarianism doesn’t work because you should care more about those around you”.
Big issue:
In a war, people assign negative weights towards their enemies, leading to multiple possible utility functions (which say the best thing to do is exterminate the enemy).
Did you check if there was a significant age difference between the two groups? I would expect proto-rationalists to be younger, so they would have less money and fewer chances to have signed up for cryonics.
There’s a recent paper doing something similar: https://arxiv.org/pdf/2305.13304.pdf. They tell GPT to create long- and short-term memory, then use semantic search to look up the long-term memory.
James Camacho
Have you considered that signaling could play a large part into this? A European friend of mine once said, “people in the US try to do everything in high school.” Because, to get into a top undergraduate program, Americans have to signal very hard. Worse, a master’s degree is quickly becoming the new high school diploma, due to signaling to employers.
When kids are spending their lives trying to signal stronger, it’s a lot harder to balance it with friends. It used to be dating as an undergraduate made sense—people would actually get married during or out of college! Now, it makes less sense to date for a year or two and try to maintain a long-distance relationship as you split off into different PhD programs.
I think the correct reasoning is, if you didn’t get the job you didn’t pray hard enough. You weren’t faithful enough to be rewarded. Or maybe you were, and this is just a trial of your faith. It’s easy to have faith when faith seems to work, it’s only when all experiments you perform seem to contradict your faith that it is really tested.
The computer vision researchers just chose the wrong standard. Even the images they train on come in [pixel_position, color_channels] format.