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?
They've got $400bn+ in annual revenues, with $50bn in free cash flow, and the ability to raise enormous piles of debt if they needed it.
They've not got a pressing need for the cash, so can afford to take a multi-decade view of SpaceX. If you look at other investors who take a view across that time horizon, you'll see some pension funds and huge trusts putting money into SpaceX for the same reason.
It's overpriced today on fundamentals, but in 50 years time, today's price will look cheap.
They invested $900 million[1] in 2015, and it is worth $94.1 billion today. Why sell? They also likely have restrictions on selling post-IPO, but I don't think you can trigger a "bank run" on a stock anyway; the price will just keep adjusting as you sell/buy until it gets absurd.
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.
"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.
why would they sell? because of ridiculously large the valuation is? like, I guess at SpaceX's valuation there's tremendous downside and little upside (x2 growth is unlikely when it's a third of the tech sector)
This. China's copying SpaceX's homework and the public/private system towards that is operating so efficiently, if SpaceX lets off the gas, US is going to fall behind off the next long term gold rushes like asteroid mining, and said space datacenters.
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).
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?
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.
You already do give the image model a prompt to tell it what to do. That’s not something the embedding can use independently of how the model is already using it.
In general an embedding doesn’t have intent or awareness in the way you’re looking for. “Embedding” just means one mathematical structure stuffed inside another. So for example the real number line is embedded into the Cartesian plane as each axis- that’s an embedding.
Now in this case specifically, the embeddings in any kind of transformer model encode the meaning of the thing they represent into vectors (which is what the model itself actually operates on). You can train the embedding to be more useful for a particular task at inference time, which already happens.
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)
Embeddings are used in language models to convert problems which are about words and meaning (which computers suck at) into problems which are about multiplying matrices, (which computers are really good at).
So embeddings are used in vision models to convert problems which are about the content and meaning of images into problems which are about multiplying matrices. The model doesn’t want to work with pixels (that’s what very basic vision models do, but it tends to be limited to special purpose applications) it wants to work with concepts in the image. That’s what the embedding gives it.
I still don’t really know what you mean about giving the embedding model some context. It embeds whatever you want to embed. So if you want to give it just a jpeg, fine. If you want to embed a jpeg and a json blob with some additional metadata/“context”/whatever, that’s also fine. That’s already how embeddings work.
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.
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.
Heh, it's like we all need to collectively read I, Robot yet again, and the myriad of SF books on the subjects. Black and white quickly dithers to grey.
The point of those books is that robots can be perfectly rational, and for a useful robot we would expect them to be.
What they aren't is moral, because of the orthogonality principle: you can't use facts and logic to discover correct moral beliefs. Morality is about values, goals, and the definition of "good". They must be provided to the robot by its creator, and those are things that are very hard to precisely describe in a way that is fully consistent with the speaker's intent in all possible scenarios, and agreeable by all other people.
No, it doesn't. The baby boomers represented an actual demographic change, that's why they're tracked by the Census Bureau. Everything since then is marketing and astrology.
People get used to understand what generation 'name' they got from society, thinktanks and co and its not that hard to know what the younger generations are.
The difference is that named generations don't claim that any differences are due to supernatural influence. Instead they are arguing that the differing circumstances in which people grow up and live influences their experiences and how they see the world. That is a MUCH more defensible position.
It would be defensible if it worked that way. Instead they work backwards: define the groups for marketing purposes, and then try to identify the similarities.
That's irrelevant to what I wrote. Whether it was made up by marketers or anyone else doesn't change the fact that unlike astrology it doesn't make an appeal to magic.
The name of the generation gives you birth years. It also tells you "hey the generation 2010-2020 are getting hit by AI a lot more than the previous ones.
This not astrology at all. Astrology is 'magic thinking'.
BS. While I agree the cutoffs are arbitrary, marking a cohort by, say, "people who graduated highschool before the advent of smart phones" or "people who went through childhood in the early years of social media" are valid groupings.
As a Gen Xer my childhood was soooo different from what kids today experience. And literally every single Gen Xer I know is glad and feels relief (sometimes mixed in with remorse) that our childhood was before the consumer Internet took off.
If you want these cohorts to have statistical significance you have to add more details, such as the geographic location, language, etc. But you can create cohorts with pretty much anything, in itself that doesn’t validate the concept of generations as pitched by marketing agencies
Luckily for us, the cohorts don't need to have statistical significance because people aren't talking about statistics. They are talking about the broad shared experiences of most people born in a given range of years.
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)
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