Im fully on the spectrum, but my first thought when I hear about stuff like this is what would be the moral implication of "recreational gang war" experience. I.e you pay a lot of money, you get guns and armor, you go into gang territory, they shoot arrows at you, you defend yourself. Seems like you would have the right to use deadly force, but you would obviously be at a big advantage.
Not that you've explicitly claimed it's authentic, but the very idea of a pirate hunting tour is a hoax that resurfaces across the Internet every so often in different forms:
Not at all. But the world we live in is pretty fucked up from that perspective. There are ex military contractor guys on youtube talking about their weapons and how they used a certain rifle to fight off invaders in the middle east. Nobody seems to care that the people on the other end of those bullets may have families.
My interest in this is purely philosophical. If you have a what is effectively a lawless country, where native populace has the desire to kill you, to what extent could you engage with that in a moral framework?
but do you have any evidence beyond intuition that those denials differ significantly in reversal rate? one would guess that things that are never going to be covered get denied and then never appealed but one would also guess that things that are never going to be covered don't get submitted in the first place. assuming that the rate of legitimate denials is higher among those claims never appealed, what rate of illegitimate denials is acceptable? why would denying even one person care that they're contractually entitled to receive be okay? why shouldn't insurance companies have to do what they promise to do and accept money for?
CAD is not just drawing. CAD packages are often integrated with other software that does things like stress analysis, BOM, thermal simulation, and so on.
Its an entire ecosystem that streamlines a lot of things, so it makes sense to invest in it.
The reason for Windowsis because a lot of the CAD software utilizes gfx drivers heavily for rendering. You can do things like raytracing to simulate how the product will look.
- In the winter, if there is sun, and you have like black leather interior, it actually helps warm the car up.
-For trucks, having a glass roof is almost essential for seeing if you will fit into a parking garage. Having lived in Seattle, Im glad my truck had one.
- Opening the roof and setting AC to use outside air is the fastest way to vent the car. It doesn't create as many vorticies as opening the windows.
For trucks, having a glass roof is almost essential for seeing if you will fit into a parking garage.
If your truck is that tall, I suggest measuring it and recording the height. After that, you'll find that just about every low-hanging garage, drive-up, bridge, etc. has a height listed. Over 9.5'? Don't try and drive through the Arboretum, as the signs will tell you.
Opening the roof...
That's a sunroof, not a panoramic glass roof being discussed.
Pedantic, but more likely to be a moonroof. A sunroof is opaque, while a moonroof is the clear or mild-to-moderately tinted glass panel we've all grown used to.
I currently live in Seattle and drive a 4WD Toyota Tacoma with a rooftop tent on the back and frequently enter parking garages. I know the height of my truck and was careful to get a setup where the rooftop tent isn't gratuitously high. I think everything is still a little under 6' which is fine in just about every parking garage.
Even if my setup was taller, garages here are pretty good at having the height listed when you enter and anyone with a tall truck really should know exactly how tall it is.
I don't think a sunroof would help me. If I was ever in a situation where it did, I would already be taking too much of a risk of damaging something.
> Turns out that all of the things you listed as examples are stuff that most consumers don’t actually want or will use.
The thing that nobody really realized is that setup is a big factor that isn't worth to a lot of people. For example, to turn off lights with Alexa, you have to do a whole bunch of setup that involves installing special lights, installing app, connecting the app to Alexa. Even though relatively this is simple, its just not worth the effort to a lot of people.
Amazon or Apple could easily overcome this by offering Alexa or Siri connected devices as incentive to builders building houses or apartments. That way, when you plug in Alexa in your new apartment, it can auto detect everything it can control including lights, thermostat appliances, e.t.c.
There are endless posts on HN about how Macs are good for LLMs. Somoene is running some model on their Mac for local inference.
What the posts dont mention is how unusable that experience is with dogshit slow tokens/second. To make a local model usefull you need to run the highest parameter models at 100+ tok/sec, otherwise you are just better off paying for cloud inference. So either all those people are dumb as hell, or Apple is doing clever advertising. And I personally have more faith in the tech sector.
Back in the 2010s, the best, most efficient software engineers were characterized by 3 things
* end to end rough map of the entire space of compute in their head
* ability to search the internet for the right things
* ability to quickly experiment and try things to figure out how to do things
AI hasn't change that, it just made 2 and 3 into a very efficient thing.
The characterization of psychosis is best described by believing AI can do the first thing. No modern LLM can "reason" - otherwise you could give it a task like "make me money", it would ask you all the questions it needs about information that it doesn't know about and needs to know to make you money, then it would set whatever it needs to set up to make you money.
As such, you still need to know the domain entirely to be effective. When you do that, AI is fantastic at getting you to the right solution. Furthermore, its still in large part actually cheaper to higher a developer who then can use AI to build you the product that you need long term.
> No modern LLM can "reason" - otherwise you could give it a task like "make me money", it would ask you all the questions it needs about information that it doesn't know about and needs to know to make you money, then it would set whatever it needs to set up to make you money.
If that's your bar for reasoning, then most people can't reason either.
The thing about these math proofs is that they really add nothing of value. Its a cool tech demonstration on how LLMS can search the trained space, but fundamentally, LLMS haven't "discovered" anything groundbreaking.
For NS equations, they are trying to model something that is discrete (i.e molecules colliding) in a continuous manner. You can easily think of a condition where they fail - imagine a vaccum where there is sufficient space between air molecules, so that collisions aren't always possible. NS won't be able to predict the state of the fluid in every single point in space.
In practice, when you do CFD, no package uses direct differential simulation of NS equations, you usually have simpler approximations that are good enough for the space you are working for. And if you want accuracy, you usually do something like LBM which simulates particle collisions using probability distributions.
I can't wait to tell my pure maths professors that their most of their research adds nothing of value. I mean I am sure most of them would agree to some extent, but like, dude, have some more faith in the utility of pure maths, esp. centuries down the line
There is plenty of work that goes on in the math field that is super important. But any field has research that is irrelevant. So far, all the proofs done by ai have been largely irrelevant.
sure I get that, but like, my field has plenty of counterexample as proofs. we have had non-constructive proofs like probabilistic arguments. i don't think we can play the game of "oh this proof is useful that proof is not useful" well
Generally, the proofs that are in the form of "here is a single contradiction to an established statement that proves that its not always true", are generally useless. We can prove that newtons laws don't apply when you start considering relativity, but because they still apply for a large domain, they are still used.
Same with NS equations. Who cares if you can find a singularity.
And if you want an example of something novel that is worth pursuing - Its highly likely that the modern transformer architecture is sub optimal, you probably don't need to do full matrix multiplies in the transformers. There potentially could be a higher level mathematical formulation of minimal math operations that are needed without having to do trial and error - especially because all of the math involves linear combination passed through smooth activation functions.
But coincidentally, there hasn't been any research in terms of point LLMS to self optimize in this way, because there isn't enough human math literature on the LLMs to train on.
i really think we are opening a can of worms with these “who cares if you find a single counter example as disproof” arguments. i think the better version is “ok any lemmas or techniques we can generalize from this” or “what did we learn about maths through this” and use this as a basis to say LLM proofs are not useful
like say if god lets me find a single counter example to P=NP and thus disproving it — I think we can learn tons about complexity theory from this counter example by studying it. we should not have the hubris of assuming “oh a single counterexample is generally useless” — why, how. this is the same hubris imo that produced like “number theory is useless” until it is not
P vs NP is a more fundamental problem that if proven, will have insane consequences, perhaps more than anything else out there. For starters, you would be insantly able to design an an actuall all knowing AGI.
The NS equations are far,far,far less meaningful. Like I mentioned earlier, if you actually want accurate CFD, you dont even use them.
i am not a complexity theorist but I am a CS academic by training (I never was a good one, but welp), and during my PhD it is often said that maybe P vs. NP an initial proof/disproof to the statement is not that practically important, e.g., if P=NP, maybe the NP -> P reduced algorithm is very very cosmic. P=NP by itself hardly proves that one would instantly design an AGI whatsoever. Often the downstream potential theoretical/practical insights/results seems more exciting;
> The NS equations are far,far,far less meaningful. Like I mentioned earlier, if you actually want accurate CFD, you dont even use them.
Sure. Consider this: in algorithm research often the most optimal algorithm in big-O is not the one used IRL; examples are numerous: matrix multiplication, LCA data structures, many variants of shortest paths.
An academic can work two years on faster-in-theory matrix multiplication that no one expects to be used in practice (in our currently imaginable univese). Do you consider that less meaningful than working on faster matmul kernels?
Essentially, every task that AGI can do is the same as the traveling salesman problem - you have some system with dynamics where you can make decisions and to get to the right outcome, you are trying to find an optimal path. The best it can do at this moment is to create parallel simulations (i.e AlphaZero/MuZero MCTS - the tree represents the dynamics and evolution of the game board and it effectively does a simulation of the game prior to making a move). To do this with something as complex as the world, including all the humans within it, most likely means that you have to have more compute than reality allows - otherwise you are taking a hit in accuracy, which given chaotic effects creates problems where you can be way off on the predictions.
On the flip side if P=NP, that means that instead of dedicating compute to running branching simulations, An AGI can dedicate compute to just solving directly the actions it needs to do for any given outcome. This is a shortcut to reality, which means that reality in itself is compressible.
There still has to be some logic behind what is the meaning of a proof. Generally, its pointless to explore random equations of numbers and try to prove that this equation holds for every value. That equation needs to have some use, whether its cryptography, or description of a physical process.
For example, look at Poincare conjecture proof. As cool as it is, can you name one area where the derivation of that proof or the proof itself has been used (without asking an LLM)?. Note that the core concept, Ricci flow, is used in lots of places, but the application of the proof is largely irrelevant - the homeomorphism of any 3d shape (say like a surface in Blender) to a sphere can be determined in other ways, more efficiently than what Pointcare conjecture states (i.e that every loop can be tightened to a point).
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