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TallOutside6418 t1_jefysjk wrote

Reply to comment by lawandordercandidate in When will AI actually start taking jobs? by Weeb_Geek_7779

Well you're talking about today. Everyone else here is talking about in the fairly near future when AI starts taking people's jobs. (the subject of this thread)

As AI continues to improve, humans won't be the experts of anything. It will all be AIs. Really, by the time that it would take a human teen to complete high school (four years), AI will be the go-to source for all practical knowledge (assuming we're still alive by then to see it).

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jamicam t1_jefyskw wrote

Reply to [22F/28M] I woke up to see that fiancé left for work without saying anything after a heavy discussion last night and I don't know if I should reach out or wait. by [deleted]

Both of you need to work on communication. Money is a tough topic for a lot of couples. You need to talk about it openly, honestly, and without getting angry at each other. Give him a little space and then time to have a talk about improving communication so that when you do disagree it doesn't become a huge problem, just an issue to work through together. Then together explore some resources on good communication in relationships, like this - you can find a lot with a few simple google searches:

https://www.healthline.com/health/lack-of-communication#signs-of-a-problem

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turnip_burrito t1_jefysiz wrote

Reply to comment by FermiAnyon in [D] Turns out, Othello-GPT does have a world model. by Desi___Gigachad

My prompt:

> Suppose I have an N>>1 dimensional space, finite in extent along any given axis, in which a set of M random vectors are dispersed (each coordinate of each vector is randomly sampled from a uniform distribution spanning some bounded range of the space). What can we say about the distances in this space between the M vectors?

I left my prompt open ended to not give it any ideas one way or another.

Its response makes sense to me. The standard deviation of a set of random samples from a uniform distribution centered at mean 0, which is proportional to the distance calculated here, should shrink as dimension N grows. If N is large, then the distribution of pairwise distances will narrow until nearly all points are roughly the same distance from each other. (The random sampling is a way to build in lack of correlation, like how you mentioned unrelated ideas)

Of course, the reverse is also true: if dimension N is small, then originally "far" points will become closer or farther (which one effect exactly is unpredictable depending on which dimensions are removed) because the averaging over random sample fluctuations disappears.

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SteelPiano t1_jefysca wrote

Reply to comment by cynical_waiter in Is this normal after an accident? by Impossible-Cry-495

Former insurance adjuster here. Your insurance company will take care of you much better than the other guy's insurance company. Your insurance will just subrogate the bills from the other insurance company. You might have to pay your deductible first, but your insurance will get it back from the other insurance. We were trained to prioritize our own customers regardless of fault and to not care as much for the "other insured" (or people our customers hit or hit our customers).

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SadLaser t1_jefys4d wrote

Reply to comment by Acquire16 in Geoff Keighley is brutal lol by x2FrostFire

He's scummy, has no stage presence, doesn't have any impressive knowledge or skill beyond connections. I don't understand why people in the industry respect him. I'm not belittling his achievements, I'm belittling him. I think he's mostly a pompous windbag.

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