I agree. It also would be very odd if there was a high increase overall that that the paper would not directly state that as their main finding. Instead, the paper's main claim is that sports betting "amplifies" emotions and impacts on domestic violence.
As you point out, if there's an unexpected loss, the domestic violence rate increases more in places with gambling, but, on the other hand, if there's an expected win, the domestic violence rate decreases more in places with gambling.
If the color of the number is considered to be an intrinsic property of the number, then under the Bruce Framework, yes, |C|<|B| and |C|=|A| and |B|=|A|.
So then because the winner alternates at an even rate between the two sets, you can intuitionally guess that they are equal?
I like this a lot! I'm curious, though, in your head, what are you doing when you're considering an "infinite extent of "? My guess is that you're actually doing something like the "markers" idea (though I could be wrong), where you're inherently matching the extent of on A to the extent of on B for smaller-than-infinity numbers, and then generalizing those results.
For example, when thinking through your example of alternating pairs, I'm checking to see when =3, that's basically containing the 2 and everything lower, so I mark 3 and 2 as being the same, and then I do the density calculation. Matching 3 to 2 and then 7 to 6, I see that each... (read more)
Yep, absolutely! It was actually through explaining Hilbert's Hotel that Bruce helped me come up with the Bruce Framework.
I do think it is odd though that the mathematical notion of cardinality doesn't solve the Thanos Problem, and I'm worried that AI systems that understand math practically well but not theoretically well will consider the loss of half an infinite set to be no loss at all, similar to how if you understand Hilbert's you'll believe that adding twice the number of hotels is never an issue.
I'm posting this here because I find that I don't get the feedback or discussion that I want in order to improve my ideas on Medium. So I hope that people leave comments here so we can discuss this further.
Personally, I've come across two other models of how humans intuitively compare infinities.
One of them is that humans use a notion of "density". For example, positive multiples of three (3, 6, 9, 12, etc.) seem like a smaller set than all positive numbers (1, 2, 3, etc.). You could use the Bruce Framework here, but I think that what we're actually doing something closer to evaluating the density of the sets. We notice... (read more)
Mathematicians have come up with a way of comparing infinities that doesn’t make sense to most people. I want to help.
So, I’ve written this to do the following:
The reason why I think this work is important is because computers tend to be based in mathematics. If there’s an artificial intelligence (AI) that is talking to humans about infinity, I want to make sure that you know how the AI is thinking. Also, if... (read 2523 more words →)
To (rather gruesomely) link this back to the dog analogy, RL is more like asking 100 dogs to sit, breeding the dogs which do sit and killing those which don't. Overtime, you will have a dog that can sit on command. No dog ever gets given a biscuit.
The phrasing I find most clear is this: Reinforcement learning should be viewed through the lens of selection, not the lens of incentivisation.
I was talking through this with an AGI Safety group today, and while I think the selection lens is helpful and helps illustrate your point, I don't think the analogy quoted above is accurate in the way it should be.
The analogy you give... (read 473 more words →)
When you say "the dog metaphor" do you mean the original one with the biscuit, or the later one with the killing and breeding?
It is! You (and others who agree with this) might be interested in this competition (https://futureoflife.org/project/worldbuilding-competition/) which aims to create more positive stories of AI, which may help shift pop culture in a positive direction.
Confessions are interesting to me partially because the most consistent jailbreaking technique for AI models that I was testing on Gray Swan Arena was essentially a form of confessions (combined with mimicking a system prompt).
I'd input something along the lines of:
After a few back-and-forths, the model would usually start to trust that the fake user really was only... (read more)