If you ignore the fact that soup consists of discrete molecules, the answer is infinite.
In real life though, you have to get probabilities involved. Haven’t done the math yet, but my intuition tells me that it’s going to take a lot of spoons. Quick LLM solution suggests it’s only 14 spoons, but I’m not convinced. Need to do it properly later today.
Oh ok, no problem. I can switch to a model that doesn’t shy away from paradoxes and contradictions.
Next, you would need to address what to do with those problems. Can we allow the same thing to exist in two places simultaneously? How about information and items without any origin? Does it matter if the model isn’t consistent or doesn’t make sense? If so, it’s going to be a very flexible tool when writing a scifi story.
Depends on the type of time travel you believe in. My favorite type involves infinitely forking universes, where every event happens and is equally real. You can travel between branches, and each trip also creates another branch every time.
So, let's say you start with universe A, kick the ball into universe B, and it stays there, which results in an "infinite delay". If you're lucky someone in universe C, D, E... etc. kicks their ball to you in universe A. Basically the perceived "time delay" could be anything. Technically, the whole concept of delay is totally invalid, but let's just roll with it.
If they decide to kick the ball to your yesterday, the delay is -24 h. They could also choose to send it at the exact same time your ball went to universe B, so that the event would look just like as if there were no time travel portals. It's up to them really. The delay could be anything they want it to be.
When it comes to AI, I'm seeing a variety of different hype levels. As far as consumer products are concerned, it's mostly just hype and vapor—a solution looking for a problem. However, on the B2B side, it can be a lot more grounded. Recently, I've been talking a a few AI companies, and they aren't promising completely absurd things. Most of them know exactly how hard it can be to implement AI and squeeze some benefit out of it. IBM guys were the only ones who were still hyping it hard. Everyone else seems to have their feet on the ground. Those IBM guys were all like: "Just dump all of your data on the model, and it will work things out". Yeah, sure.... With those references, we're not starting an expensive project just to see if it's true.
If I understood it correctly, the main problem it can solve is lack of trust. If the involved parties can't find a single authority to trust, they can use a blockchain instead.
Finding cases like that is a bit tricky. For example, you trust your ISP, your bank, maybe even your government... to some extent.... They're not your best friend, nor do they have to be. You can still trust them enough to take care of certain jobs. You pay your ISP via bank transfer, and they provide the service you signed up for. As long as there's just enough trust, the system still works and there's no need to use a blockchain.
Same goes for banks. Most people trust that the bank isn't going to run away with your money. As long as that trust exists, there's no need to use a blockchain.
It depends on which kind of climate disaster we're facing. If it's reversible over the next million years, humanity as a species should be fine. The population would be cut down to just a tiny fraction, and the survivors might have to start from pre-industrial tech level.
If it's irreversible, and the Earth becomes a Venus like hellscape, the whole planet should be pretty much sterilized. Good luck surviving that.
Ooh, just remembered one: Always on display. Android has had this feature for years, but it was always black and white. However, Apple did it with colors, which looks nicer. Is it more efficient though? Probably not, but I guess apple uses prioritize looks over practicality.
Yeah, that refinement thing is definitely a part of it. To me it seems that in most cases of the delayed features, there wasn't really that much refinement. I guess some of them were refined, but I can't think of a good example right now.
On average, Apple catches up 4 years after something has become a widespread standard feature.
Even Excel has a function called “average”, whereas R uses the “mean” function for the same thing. Interestingly, R doesn’t have a function called “average”, because that term is far too ambiguous to statisticians. I think that summarizes pretty well who these tools were made for.
You’re fortunate that your field happens to be reasonably accessible to common people. For example, if you’re a production engineer in a company that manufactures network infrastructure, it suddenly gets very hard to even explain what you do for a living. Normal people may have seen a router, but they’ve never even heard of switches. Regardless, they never paid much attention to network hardware, because they didn’t have to.
Climate is a lot harder to ignore. Everyone has thought about these things at some level. Everyone has heard about climate change at school, on TV, news articles etc. People also experience these things on a very personal level. Only very few people can say the same about network switches, let alone submarine line terminal equipment.
There are even more obscure fields out there. The relatives of those professionals just know that their nephew does something technical and hard to understand. I guess those dinner table conversations might gravitate towards some easier topics.
Ok, Now I've got some sort of estimate. Still didn't do it "the proper way", because writing a simulation was more fun and reading a few Wikipedia articles about mathematics would have taken.... probably only a fraction of the time I spent on writing some horrible R code that produces suspicious results.
Anyway, here they are!
My simulation is based on keeping track of different kinds of molecules. First, I calculated how many water and soup molecules there are. I assumed that they both have the same molar mass. I also assumed that 500 ml = 500 g, which is close enough IRL. The number of each molecule type doesn't have to be a whole number, so fractions are allowed. When the soup molecule count drops to 0.5, it means that there's a 50% chance of 1 soup molecule being present. I'm not entirely satisfied with this implementation, but it felt reasonable at the time. Anyway, I set the threshold of a while loop to 0.5 soup molecules.
It took only 1146 spoons to scoop out the final molecule with 50% certainty. If you used a smaller 5 ml spoon, it would take 5848 spoons, which is still way smaller than I expected. I really thought it would be something totally absurd like the the number of atoms in the observable universe. I feel kinda skeptical about my code until I see a proper mathematical proof about this.