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I didn't understand the superiorty theory explanation and the other two seemed to both be forms of nervous laughter, subversion of expectation.

I study game design and a popular book on this subject, 'A Theory of Fun', poses that laughter is a communicative behaviour to attract the attention of those around us.

This ties into my understanding laughter (somewhat built on the youtube channel 'Charisma On Command which did a video on how to be funny easily); we laugh to draw other people's attention to what we are witnessing. We cheer / applaud for the same reason.

It seems to often be the case that people laugh at something when it presents itself, is unusual / contradicts their expectation of normal behaviour, and then is not responded to in a negative way by the observing community.

We are then updating our knowledge of what is socially acceptable within that community, and broadly bt more vaguely within our society. This usually relates to slapstick and behavioural comedy.

Or maybe someone tells a bizarre story, or acts out an absurd character. We then might be laughing as we update our understanding of human capacity for creativity and exploration of ideas. And we laugh to draw others nearby towards that too.

Maybe something terrible happens to someone, but a) their misfortune as stopped and they are stable and b) we are not at risk of the same misfortune. Very often people find these situations hilarious. Crying and screaming are reserved for different, more urgent or severe messages, but laughter is the 'look at me' reaction. it signals that a stable, safe and observable article of misfortune is present.

All of this could be considred 'learning directive humour / laughter'. Nervous laughter seems to be something else, where we laugh to communicate to others that we've decided to approach a seemingly threatening (physically, socially, whatever) situation by assuming good intent on those in our company. Maybe someone makes an odd comment about knives whilst waving one around. As I recall, nervous laughter only usually comes from those 'in the line of fire'. No nervous laughter = "I'm not putting up with any nonsense". Nervous laughter = "It's ok everyone, I'm going to act as a guinea pig and leave myself vulnerable so we can all see how this plays out".

That's probably not the best explanation of how I view nervous laughter, but I think it's signalling intend / consent to those around us to steer their expectations of an awkward situation.

I talk a lot about a lot of stuff. If you wanna read it, look here; https://docs.google.com/document/d/1LBTqx4krO2hDrM8yuAeIcHBrg9Owoc9OcHhewbfKb2c/edit?usp=sharing

They left out Miphat Theory…

You realize what you missed and it becomes funny. You're essentially learning someone else's humor.

I always said the funniest jokes are the ones that need explaining. It means you missed something.

https://www.reddit.com/r/Music/comments/4z3u55/wikipedias_explanation_of_warren_gs_regulate/

Then I don't think you understand how it works. The Bias will train itself out within a few cycles. Because that's how it works. The AI will start using that "flawed" data and then, as it progresses, will slowly integrate it's new findings into the pool. It may take a few years, but, if policing was misweighted, the AI would allocate the resources where they were needed. If you train an AI to do basic addition, and to know numbers, once it knows enough numbers you can't tell it 1+1=6. If I ask the AI for the number between 7 and 9, it will list off 6+2, 5+3, 4+4 etc. I can tell it 2+3 is the answer, but it will search and say I'm incorrect Because based purely on the data, I cannot be correct. We can compare that to the earlier arguments. The AI can see crime at points X, Y and Z in neighborhood B but crime in Q in neighborhood A.

I am lol. "Yes sir, no sir, here's my license sir, have a nice night."

And I'm saying that letting "academic parties" get their hands on it is going to simply nudge bias the opposite way. Positive bias. That will get us nowhere until the AI fixes itself at which point people will screech that somehow the AI went racist again lol. Academics has a serious issue with bias but that's an entirely different argument.

I understand it may be so now, but if they use historical data to train the ai, then any racial bias from previous decades, will show.

What if you were targeted not by your actions but by the looks of your car?

All I'm saying is that the training data needs to be vetted by several academic parties, to eliminate as much bias as possible.

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What if we used victim surveys as training data instead wherein victims of crime can specify the place that the crime occurred.

Found the professor

To respond to the problem of laughter in response to already known jokes, I think the guiding principle behind such reaction is in contrast of seriousness. Incongruency itself isn't funny on it's own, but when you add a grounded or serious context to it, it starts to gain shape.

In Monty Python (which this article refers to in notes), extreme care was put into making it look authentic at a glance, but they carefully break that serious setting to introduce humour. If you take a look at sitcoms, they do the same thing in principle, just focus it on different elements.

Expectation is a part of humour but not the whole story. Comedy is about contrast. Contrast between the elements and the personal experience of the individual is what allows this funny business to exist.

If anybody knows humor, it’s Kramer

Why does that make it funny again? Can you explain?

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Yes, and I am pushing back about the spectrum of utility vs transparency that you are suggesting. I think that the usefulness of having a transparent process, especially when it comes to policing, vastly outweighs the usefulness of any opaque process with more predictive power. I think you need to update your definition of usefulness to account for how useful it is to have processes that people can completely understand and therefor trust.

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I'm not saying its evil to create beings that are capable of suffering. I would say that giving a machine, that has no other choice than to follow the instructions given to it, the capability to suffer would be evil.

And again, this machine would have to be specifically designed to be able to suffer. There is no emergent suffering that results from mathematical equations. Don't develop warm feelings for your laptop, I guarantee you they are not returned.

I'm not talking about sentience, sapience, or consciousness, or anything like that, I'm talking about experience. All computers are self-aware, their code includes references to self. I would say machine learning constitutes a basic level of intelligence. What they cannot do, is experience.

It's actually very interesting that you say we don't have a good enough understanding of consciousness yet. The thing about consciousness is that it's not a concrete term. It's not a defined logical principle. In considering what consciousness is, we cannot just do empirical research (it's very likely consciousness cannot be empirically proven), we have to make our own definition, we have to make a choice. A computer would be entirely incapable of doing so. The best it would be able to do is measure how the term is used and derive something based off that. And those calculations could get extremely complicated and produce results we wouldn't have come up with. But it wouldn't be able to form a genuine understanding of what "consciousness" entails.

This goes for art too, computers might be able to spit out images and measure which ones humans think is beautiful and use that data to create a "beautiful" image, but there would be nothing in that computer experiencing the image. It's just following instructions.

There's a thought problem called the Chinese Room. In it, you have a man, placed in a room, that does not speak a word of Chinese. When you want your English letter translated to Chinese, you slide it through a slit in the wall. The man then goes to work and looks up all possible information related to your letter in a bunch of dictionaries and grammar guides. He's extremely fast and accurate. Within a minute you get a perfect translation of your letter spit out the slit in the wall. The question is: does the man in the room know Chinese?

For a more accurate comparison: the man does not know English either, he looks that up in a dictionary as well. It's also not a man, it's a piece of machinery, that finds the instructions on how to look at your letter and how to hand it back to you in another dictionary. Every time you hand him a letter, the computer has to look in the dictionary to find out what a "letter" is and what you should do with it.

As for the problems with using AI or other computer-based solutions in government, yeah, pretty much. The real risk is that most police personnel isn't technically or mathematically inclined, and humans have shown a tendency to blindly trust what the computer or the model tells them. But also, if there was a flaw in one of the dictionaries, it would be flawlessly copied over into every letter. And we're using AI to solve difficult problems that we might not be able to doublecheck.

I see your point that multiple AI combined could compliment each other's radicalization of the distribution of resources in a community. But considering the sole question of predictive policing, by what method could it generate crime? This whole system works much differently than the YouTube algorithm. The YouTube algorithm is designed to monitor you individually for all of your interactions on the site in order to better retain you. Predictive policing, as far as I can tell, does not have the mechanics of engaging with the public, only with the police and the statistics that are made available to the city.

I just fail to see how it could increase crime without a way to access the interactions of citizens or criminals.

>And you're missing the point of the field if you're making the trivial observation that working out an explanation decreases the usefulness.

That's not what I said! I'm saying that limiting AI to only the explainable may decrease usefulness.

This is trivially true. Imagine that you have many AI programs, some of which you can interrogate and some that you can't. You need to pick the one to use. If you throw out the explainable ones, you have fewer tools. It's not a more useful situation.

>That is the point. We want to decrease it's usefulness and increase its accuracy in fields where the accuracy is paramount.

But accuracy isn't the same as explainability! A more accurate AI might be a less explainable one. Like a star poker player with good hunches vs a mediocre one with good reasoning.

We might decide that policing is too important to be unexplainable so we decide to limit ourselves to explainable AI and we put up with decreased utility of the AI in exchange. That's a totally reasonable choice. But don't tell me that it'll necessarily be more accurate.

> Saying "math reduces the usefulness by requiring an explanation for seemingly okay steps" is to miss the point of what mathematics is trying to do.

To continue the analogy, there are things in math that are always observed to be true yet we cannot prove it. And we might never be able to prove them. Yet we proceed as if they are true. We utilize that for which we have no explanation because utilizing it makes our lives better than waiting around for the proof that might never come.

Already math utilizes the unexplainable. Why not AI?

My philosophy of humor class in college was a treasured memory. The professor would make us watch the weirdest YouTube videos and if anyone laughed he would pounce on them and demand us explain ourselves. Truely the peak of academics. Explaining why a joke is funny kills it, but having to do so academically makes it horseshoe back around to being funny again.