That’s only a mild annoyance. More serious thought viruses change your behavior by making you buy stuff, join a cult, vote a billionaire or whatever. Those viruses have real consequences.

Angle and atmospheric composition. Midday sun doesn’t always come at the same angle, because certain countries are closer to the equator. Also, pollution contributes to the color as well. Just look at some photos from China, and you’ll see what I mean. Natural phenomena like sand and ice can change the color too.

Then there’s also the fact that human eye+brain combo messes around with colors a whole lot. What you see (inside your head) is not exactly what’s on the outside of it. It’s reasonably close, but your brain does all sorts of tricks with the colors. That’s what some optical illusions are based on, and that’s why photography is so hard.

Hmmm… The description certainly fits. Just by eye-balling the graphs, they look very different from what I got, but I guess that’s just the expected result of running rbinom about a 6 million times. With a smaller simulation, it might not have been so apparent. Also, that’s what you get for skipping the maths and vibing the code without thinking too much about the details. Well, at least i got this far with absolutely minimal effort. :D

It appears that I need to switch to a better distribution. Thanks for looking into this mystery!

Wait, can chili do that? I don’t think I’ve experienced that even though my mouth has been on fire numerous times.

You’re right. I just ran rbinom 1E7 times and found that the probability of over drawing soup molecules is a bit too high for my taste.

When there’s only 1 left, you usually end up drawing 0 or 1 molecule. However, in rare cases, it can be higher, such as 2, 3, 4… molecules.

About 92% was 0, and 7.7% was 1, but the others were not negligible! There’s about 0.3% probability of over drawing, which is way too high for a simulation as serious as this one. In this quick test, there were 20 incidents where rbinom wanted to pull out 4 soup molecules when only 1 was available. We can’t have that, now can we!

Thanks!

If you want, I can increase the sample size. Just need to figure out how to add a timer to the code and set it to run for a few hours, maybe even overnight. A histogram with 10^6 bins should look pretty smooth.

Should also upgrade the visualization to ggplot2. Frivolous computations like this deserve no less.

I thought of making a vector with a length of about 1.671398e+25, but then I remembered what one time when when I tried to make a linear model with hundreds of dimensions. So yeah… We have gigabytes of RAM, and it’s still not enough. Not really a problem, as long as you don’t try to do anything completely ridiculous.

Instead, I just made a variable that simply contains the number of soup molecules and another one for the number of water molecules. Far simpler that way.

Here’s where the magic happens:

# Number of soup molecules drawn
soup_molecules_replaced <- rbinom(1, replacement_count, prob_soup)

The rbinom function is used to generate random numbers from a binomial distribution. It’s a discrete probability distribution that models the number of successes, i.e. scooping out a soup molecule. Rest of the codes is just basic infrastructure like variables, loops, etc.

BTW the variable names look ugly, because I couldn’t be bothered to tidy everything up. I really prefer camelCase, whereas Mistral seems to prefer underscores. That’s what you get for vibing.

Side note: If you do this kind of stuff for private purposes, you have to rely on your own hardware. If you plan to publish your discoveries, universities and publicly funded supercomputers might be an option. If there exists a Journal Of Recreational Mathematics And Useless Simulations (JORMAUS), I could totally publish this stuff and maybe even run my code on a supercomputer.

Ok, I couldn’t resist. Here are the results of running that simulation 6000 times.

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
   1085    1126    1140    1144    1157    1292       0 

This means that about half the time it’s somewhere between 1126 and 1157 spoons.

FYI: /u/protist@mander.xyz, /u/Sabin10@lemmy.world, /u/neo2478@sh.itjust.works

Now I’m really curious to find out how Kevduit, GreyStillPlays and The Spiffing Brit would play it.

I’m tempted to run a full simulation that really picks those molecules randomly.

Since excessive consumption of soup probably causes obesity, joint pain, cardiovascular disease, type 2 diabetes, depression and a long list of other conditions, we can unsafely say that the homeopathic soup should cure all of those.

Wow, someone actually bothered to do it properly! I just wrote some horrible R code and ended up with 1146 spoons to get to 50% probability of having either 1 or 0 soup molecules. So good to see that the answers were so close.

Ok, Now I’ve got some sort of estimate. Still didn’t do it “the proper way”, because writing a simulation was more fun than reading a few Wikipedia articles about mathematics, which would have taken… probably only a fraction of the time I spent on writing some horrible R code that produces suspicious results.

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 my while loop to 0.5 soup molecules.

Anyway, here are the results!

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.

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.

Edit: That first intuition was very wrong. Also, the LLM was wrong too. It was just counting milliliters, not molecules.

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.