It means an incident in a person's past. It's most often used in law enforcement as a short term for prior conviction or arrest

If they are talking about "evidence", usually it's in regards to a prior conviction or a prior charge that a person faced. Like, "the judge went easy on the sentencing because the defendant had no priors" - they had a clean criminal record.

It probably is using the term in context of Bayesian statistics, where it is a measure of how likely you estimate changes of something happening (the probability distribution), before you have any evidence on the phenomenon.

noun. plural priors. : the superior ranking next to the abbot of a monastery. : the superior of a house or group of houses of any of various religious communities. US law enforcement, informal : a previous instance of arrest or conviction for a crime. Thanks to Google

Hello, AI researcher here!

We borrow the term "prior" or "prior probability" from Bayesian statistics, and in simple language you can think of a "prior" as being "what we believed before we saw the evidence". Prior just means "former" or "before" in Latin. After we see some evidence we update our beliefs, and that becomes our new "prior" for any further reasoning. We use a calculation called Bayes Rule to find our current belief (posterior probability) from our previous belief (prior probability) and some evidence.

The least informative prior, and also the default if we have no information at all, is "I know nothing". If you know nothing about the probability of some event occurring then you regard all outcomes as equally likely. For example, when I roll a fair die I am completely ignorant about the outcome, so my prior is that any outcome has a probability of 1/6.

I can say I have a more informative prior when I know something about the probability of an outcome. If I want the probability of it raining tomorrow, I could say "I know nothing", or I could think about it and actually, the weather today is probably similar to the weather tomorrow, and it was sunny today, so actually my prior is that it's a bit more likely than not to be sunny tomorrow - let's say 65% or so.

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Your first example is saying that it's silly to spend effort finding a good prior and then forgetting to take into account the actual evidence. For example, if i want to know if it will rain today, it is silly to think about yesterday's weather and forget to just look out the window.

Your second example is talking about how "extreme" a prior is. In Bayesian calculations, if you believe a probability is at 100% or 0% then there is no mathematical way to change it. This is an extreme prior. The more confident you are something will or won't happen, the harder it is to change your mind. The example, then, is saying that it is strange to talk about a "high-uncertainty" prior (one which is nearer to the "I know nothing" default position) as being extreme - without context I cannot tell you why they are saying that.

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If you read the examples given, you can see this is the wrong definition for this context.

Thanks! That helps.

I don't know if links are allowed, but the post is "Why I think strong general AI is coming soon" by porby, on LessWrong.

Thats a massive post and I don't have time to parse it out for you, sorry. If you have specific questions about priors I'm happy to try help.

Oh yeah I wasn't expecting you to read it, just answering the question of where it came from, don't worry haha. I think I understood most everything, anyways.

Very welcome!

Also, in medicine it's used to mean "a previous study". If a radiologist needs to see a prior, then they're looking at a previous exam in order to compare it to a current exam.

The term is also used in medical imaging to refer to an older study you use as a reference. Like, say, women past a certain age are offered a mammography screening every year or two, and here, the "prior" is last year's screening images. Radiologists will have the current (the primary) and several old (the priors) images on screen side by side so they can compare and spot any changes.

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