When you say it’s not yet practical, are we missing some key steps, or could it be done at high enough cost with current technology but can’t scale?
I imagine a startup which cured rich people’s cancers on a case by case basis would have a lot of customers, which would help drive prices down as the technology improved.
My grandmother suffered from Dementia. For a period of a couple of years I would call her every Friday, and we would have literally the exact same conversation each time, including her making the same jokes at the same points in the conversation, using the same phrasing. I concluded that people are in fact pretty deterministic, even over the long term.
You’re intuition is correct when the jet has already passed ahead—those are very hard to catch and shoot down. But usually you detect an aircraft when it’s heading towards you, and all the missile has to do is intercept. It doesn’t even have to be faster than the jet (unless the jet detected it in time and does a 180).
But I discussed that in the post. All you need are enough cameras + processing power. Both are cheap.
To be honest, this just feels like the Euthyphro Dilemma all over again. “Good” is defined by what God does. God chooses to run the laws of physics. Laws of physics are “Good”. Who gives a damn?
Also this is directly contradictory to Christianity, since the core beliefs of Christianity all assume some level of non-natural intervention in the world (e.g. resurrection of Christ). Same for almost all other religions. So who is this even for?
Lens and CCD technology is not trivial at those speeds and insane angular resolution.
But we can easily capture a picture of a fighter jet when it’s close. And the further it is the higher the angular resolution required, but also the lower the angular speed, so do those cancel out to make it not much harder, or it doesn’t work like that?
Note you don’t even need high resolution in all directions, just high enough to see whether it’s worth zooming in/switching to a better camera.
Why would you need large telescopes?
Naked eye has angular resolution of 30m at 100km, you need something slightly better. A small lense should do it. Cameras + zoom lens are well understood mass produced components. And this is a highly parallelizable task.
But then no need for stealth at all?
I wasn’t referring to the A10, but the use of e.g. f-35s in ground support roles—as heavily practised by the IDF for example.
Let’s rephrase: if this was a major issue for the f-35, the USA wouldn’t have invested trillions of dollars in stealth without addressing optical camouflaging. All f-35s would have camouflage paint. They’d be a lot of research into how to reduce visibility of aircraft, just like there is for reducing RCS. Given they don’t do this, clearly they don’t think optical detection is a major concern.
Aircraft already often fly low, which also works well against radar, but makes them vulnerable to cheaper and more numerous MANPADS. Flying high shouldn’t work particularly well given the setup I’ve described here, since we have a range of about 100km, an order of magnitude higher than the f35 can fly.
Night is an obvious point! Should have thought of that!
Still this is relevant when the stealth aircraft is providing tactical support, rather than bombing operations, when you can’t necessarily pick the time to provide aerial support. But tactical support is usually from behind the front lines, rather than over enemy territory, where anti aircraft weapons are less effective anyway.
What if you are not a person, but a computer, converting a string into an integer? In that case, having a simpler and faster algorithm is important, having to start with only the beginning of a string (what the user has typed so far) is plausible, and knowing the number’s approximate value is useless. So in this case the little-endian algorithm is much better than the big-endian one.
For the most part this is irrelevant. If you are a computer with only partial input, recieving the rest of the input is so much slower than parsing it that it literally does not matter which algorithm you use. If you have all the input, either is equally fast. Also, most of the complexity is going to be validating the input and handling edge cases (e.g commas Vs decimal points), not keeping track of the total.
There could be some weird edge cases, where you’re reading a huge number, for some reason stored as a string, in a known format, don’t require validation, from an extremely fast storage mechanism, e.g. RAM or an SSD. In that case you might care about performance of keeping track of the running total, but weird edge cases shouldn’t decide which format to use (and in practice I guess they’d both be essentially identically fast here too).
Computers also need to decide whether to use big Indian or little Indian for their native representation of numbers. But either way they’ll be using specialized hardware to operate on them, and both will be equally fast (and for the most part invisible to the user).
As Gregor Samsa awoke one morning from uneasy dreams he found his wife transformed in his bed into a gigantic insect...
...but he concluded that since ontologically this insect was not his wife, his marriage vows no longer applied. He squashed it under his boot as he walked out.
Might this be an issue even for people without this gene? What is the risk that constantly producing low levels of ethanol can cause oral cancer for ordinary people?
I think this is a really interesting question since it seems like it should neatly split the “LLMs are just next token predictors” crows from the “LLMs actually display understanding” crowd.
If in order to make statements about chairs and tables an LLM builds a model of what a chair and a table actually are, and to answer questions about fgeyjajic and chandybsnx it builds a model of what they are, it should be able to notice that these models correspond. At the very least it should be surprising if it can’t do that.
If it can’t generalize beyond stuff in the training set, and doesn’t display any ‘true’ intelligence, then it would be surprising if it can translate between two languages where it’s never seen any examples of translation before.
Taking a thirder position:
The reason you don’t usually accept a bet which you can only accept at most once is that you have a 2/3s credence the coin is tails, but given that the coin is tails there’s a 50% chance the bet is fake (since you’ve already accepted it the first time you woke up). This halves your expected earnings, so the bet is net negative.
But this gives you a way to break the symmetry. You only take the bet if the room is blue, which means your expected takings are now = 1⁄3 * -$200 + 2⁄3 * $100 = $0. So you still don’t take the bet, but would if slightly better odds are available.
Taking a halfer position:
When you wake up and see the wall is blue, your credence that the coin was tails must remain %50 (by conservation of expected evidence—you knew the wall would be either red or blue before you opened your eyes). So why should you take the bet?
Makes sense thanks!
I imagine a startup of this ilk could be based in Prospera, which wouldn’t be a problem for the wealthy few to travel there for personalised treatment.
I also imagine that with a lighter regulatory regime, no need to scale up production, and no need for lengthy trials, developing a monoclonal antibody would be much quicker and cheaper. Consider how quickly COVID vaccines were found compared to when they were ready for use.
The other hurdles sound significant though.