foota

"A bank run happens when everyone tries to withdraw all their money at the same time and the bank runs out of cash. Not really possible in the stock market where companies literally can create/destroy shares and there is a whole secondary pricing layer to it." Sure, it's not possible for stocks to become insolvent in the same way, but if a large shareholder sells it could trigger a panic and greatly decrease the price.

I don't think so. Large surface area helps with convective cooling I think by increasing the surface area that participates in heat exchange with the air (or other thermally conducting material), radiative cooling wouldn't benefit from this because you can't concentrate light beyond the source that it's emitted from (etendue).

Though I do wonder if it would be possible to have some kind of internal heat pump driven by electrical power to juice up the temperature of the radiators to increase the power being radiated away? E.g., run a heat pump to increase the temperature of a working fluid and then run high temperature radiators? I think it would work and I don't immediately see that it would violate the laws of thermodynamics? (this is ignoring all practically, I'm sure the engineering would be devilishly hard, although if you're already shooting for the moon you might as well throw in some artificial gravity to boot, it's not like the robots get motion sickness)

Getting this all up into orbit it obviously the hard part, but if you're already building so much solar capacity the cooling actually doesn't seem unreasonable?

It's a little crazy they haven't tried to sell it off yet. Maybe they don't want to trigger a bank rush or they're limited in their ability to do so?

I have a bit of an obsession with trying to find hot springs. Washington state, for all of its mountains, is relatively devoid of hot springs. I feel like there must be some that remain unknown. I'd like to someday use a thermal camera on a drone of some kind to try and find one :) Maybe go along fault lines that have other hot springs?

Economic background: profit maximizing businesses would like to extract the most value out of every transaction. Not everyone derives the same value from the same thing. E.g., someone might be willing to pay $5 for a burger, whereas others might only pay $4. Price too high and you lose the people that will only pay (or can only afford) less. Price too low and you don't charge people as much as they would have been willing to pay and lose out on profit. The economic POV is that voluntary transactions happen because both sides benefit (or at least don't lose out) or they wouldn't happen. Therefore every voluntary transaction produces some economic benefit in the form of producer and consumer surplus. The amount of these is determined by the gap between the price and the consumer's WTP (willingness to pay, i.e., the highest price they would be willing to pay) and the price and the producer's WTA (willingness to accept, i.e., the lowest price they would accept at). Together these form the economic surplus of a transaction.

Profit maximizing businesses want to capture as much of the economic surplus of transactions as possible by optimizing the price they charge. When businesses offer a single price their ability to do so is limited because some people with a lower WTP that is still above the producers WTA don't elect to purchase and on the flip side some people who have a higher WTP would be willing to pay more and don't.

To increase their profits therefore businesses can attempt to do what's referred to as "price discrimination" which is when they offer different prices to people based on the person's perceived WTP (there are different means of doing so, such as geographically based pricing, etc.,) and when they offer exactly the customer's WTP to every unique customer it's called perfect price discrimination, because they're capturing the entire value of all transactions.

In competitive markets, businesses ability to price discriminate is reduced, but not entirely eliminated.

Now... this surveillance pricing is basically a form of price discrimination. However, the interesting part is that while price discrimination in net is beneficial for businesses, it actually can also benefit lower income/lower WTP consumers by allowing them to buy at a lower price (since they wouldn't have bought at a higher price -- both the consumer and the business benefit here) but hurts customers with a higher income/WTP since the business can charge them more.

This is interesting because this is a somewhat rare regressive (hurts lower income people more than higher income people) anti-business policy. Generally, I think most anti-business policies are also progressive (well, except for the idiotic ones like broad tariffs) but in this case banning the ability of price discrimination through personalized pricing hurts businesses and lower income people while benefiting higher income people (the surveillance aspect of it could be thought of as an externality, which hurts everyone).

If you're highly anti-surveillance you might argue that it's net positive for everyone because lower income people wouldn't be surveilled in the same way (well, at least it wouldn't be applied, I don't think it would actually change the surveillance side of things) but that requires a normative position on whether surveillance is bad.

In theory this policy could probably be made non-redistributive (benefitting higher and lower income people equally) by adding a grocery tax that would be used to offset the impact to lower income people, but in practice it seems like it would be difficult to administer (especially in Seattle, which doesn't collect city taxes from people directly today, not to mention the opportunities for arbitrage).

The idea that I'm proposing basically isn't an embedding (which is context independent) but rather combining the embedding model with the context of the LLM. It sounds like the embedding model here is normally a "vision transformer" that maps image chunks into tokens with positional embeddings for both the position in the context as well as position within the image. Maybe it could be given the ability to consume the context and decide to emit multiple tokens for a single chunk? So for example, let's say that I ask a question "How many blades of grass are in this image" (a hard question for traditional image embeddings in models since the embedding won't contain the information). If the proposed architecture was both aware of the context and able to "decide" to emit multiple tokens for an image, then for the above it could emit tokens that represent the answer to the question posed, instead of just being the embedding of the image. Or you could ask something like "How many green pixels are there" and again I think it would work better under this architecture than it would otherwise.

I'm not sure how practical it is to train that architecture though or whether there would be performance issues.

Isn't think only true if getting enough vitamin D is as important to you as cancer?

This /looks/ at least more official. Most unofficial Google projects have a disclaimer in the repo.

I think what I'm saying is that I don't understand why the embedding exists. I assume it's some kind of training and inference cost issue? But why can't the Gemma architecture linked above just learn to represent pixels in the LLM model's embedding space directly, rather than having the embedding from 48 x 48 pixel chunks? Or rather, give the embedding model some context to produce the embedding? (Which, as you note, wouldn't really be an embedding anymore, but seems like it would better understand fine detail)

I'm not an ML expert, but I was thinking of a sort of "guided" embedding. E.g., give the image model some prompt for what it's trying to do? I don't understand why multimodal models generate an embedding that doesn't understand what the model is trying to "figure out".

I think this is similar to how Gemma 4 12B is implemented, but even then I don't think the single layer image embedding is "aware" of the context.

I feel like multimodal models that can read images should work differently than they do. My understanding is that multimodal models basically first generate an image embedding and then the model is trained to interpret that embedding, but in the same way that text is lossy, it seems like the embedding would be as well. Why don't multimodal models learn to interpret images themselves without an embedding? Or e.g., by passing some "prompt" to the embedding model?

Jokes aside, I think the idea is that the law is simple to code, the proof that it holds is where the agent is responsible. This probably becomes less true though as you try to express more complicated laws.

A generation is a time that people were born between and useful because it embodies much of the cultural context that people share.

Funny enough I was thinking about something very similar to this based on the Jev model posted yesterday.

Just curious, why? Is this true even if you did something like a per-CPU histogram that uses atomic ops to increment?

You got an article off by one error, I think you meant to post on https://news.ycombinator.com/item?id=49558685 :)

Ah this is interesting. Essentially the idea is that the compute can try and move into positions that it can evaluate but humans might have trouble evaluating because of the board state's complexity?

That was a lot of snark :-) I know what you're saying. It's not clear to what degree the film industry would be viable in its current form though without IP rights. If everyone can watch the latest marvel film at home for free, are they going to be willing to pay to see it in theaters?

There would likely still be some demand for the experience, but the tickets would need to be more expensive to recoup the costs of making the film. At that point: it does seem like people pooling together their funds to fund the movie itself isn't insane. The point is that the "investors" wouldn't be investing in the hopes of a financial return, they'd be paying for the creation of the film.

I spent years learning logic and doing puzzles. I don't think a baby or even average kid could solve these.

I think they meant more like "people band together to pay Christophe Nolan to create some new film" ala crowdfunding. Is that a sustainable model for the world economy? Unclear.

> Ban offline training/pretraining. Models must train from scratch after submission Previously this was considered impossible so rule. My model shows this is possible Guarantees no synthetic data can be used It makes the comparison fair across differet models. Otherwise some models like LLMs can benchmaxx ARC by using ungodly amounts of offline training. (Since the benchmark has been around a long time, many ARC-like datasets have been created)

I'm not an ML researcher, so YMMV, but... how could a model learn to answer these ARC-AGI questions without training beforehand?

> I agree that its rare to see to face problem sets in real life where every problem is given at once. Even if it is (like an exam), humans can usually only attempt one at a time

Just one small snippet that I thought was interesting. I would always read through ~the entire exam before starting. Both so that I could find the problems most approachable to me, but also because sometimes it helps me figure out the rest of the questions :-)

Ah shoot, I War of Worlds' myself. I didn't realize this was fiction until the end.

Why can't effort just be at the end of the prompt? My understanding is that that is how modes and other "dynamic updates" like the time are handled.

Calling China a country that "bred like rabbits" over the past few decades is first of all offensive and second of all stupid for a country that has one of the biggest problems with their coming population pyramid and limited couples to a single child for most of the last 40 years.

China is at a higher installed capacity by 6x from overall and 30% by per capita capacity. They're growing installed capacity from your source at 45% y/y as compared to 20% in the US.

Sure, there might be more transmission constraints or whatever, but those will certainly be solved in time (transmission capacity is more distributed so I'm not surprised it's lagging capacity).

The quote you pulled for China is conveniently 3 years old and missing the global context that it was in response to supply issues with other fossil fuels, not a reflection in meeting demand.

These charts address your points:

https://ourworldindata.org/profile/energy/china https://ourworldindata.org/profile/energy/united-states https://ourworldindata.org/profile/energy/india

India is a bit more concerning for a number of reasons (lower per capita income means they are lower on per-capita emissions currently and hence have more room to grow, and they don't have the resources that China does to build out renewables) but criticizing China from a renewables perspective (there are a whole lot of other valid reasons to criticize them) is insane.

Hmmm... Why wouldn't this be handled like other end of prompt things like the current mode?

Ah, so it's more that "LLMs just bypass Rust's safety" rather than "LLMs write perfect C"? That's a more fair argument.