> as with javascript, Brendan Eich appears to be channeling technology up from the depths of hell. I wonder if he created the prototype in a 10-day fit of a fever dream as well.

This made me laugh! But hyperbole aside, this premise would seem to (by nature of efficient laziness) eventually result in people spam watching ads with bots - despite any ways of preventing this, people will find a way.

All systems with value at stake attract fraud, including paywalls (credit card fraud, obviously; circumvention too). This does not mean we give up and go home. On the contrary, Google and FB take all your data and do strong antifraud work on their native ad-inventory properties. Brave aims to do the same on your device without all that tracking, along with other projects such as https://hcaptcha.com and its protocol foundation, https://www.hmt.ai/.

The protocol foundation whitepaper looks like an interesting read - how far along are the open source implementations?

I'm particularly interested in applications of dynamic open-sourced metrics (ranging from corporate carbon footprinting to labour tokenization) - is this the vision for hmt? Only things I've found online so far are Grafana/Prometheus (etc) and Uber's m3, which is built on top of the former. Anything else you know of tackle that topic?

For me, it comes down to what kinds of these risky models are most interesting. Some can be interesting because of potential profit or minimizing loss (financial or actuarial) and others are inherently (theoretical physics).

To add to your tail risk point - I wonder how many people foresaw the Venezuelan oil crisis way back when, or even less likely, the Saudi Arabian oil complex attack in 2019. And of course, the current situation we're in with CoVID that an entire university of forward thoughtful looking people didn't call until it was a week away. As an aside, do insurance companies significantly alter their policies when such a cat-5 hurricane is imminent? What preparations would they make in the face of that sort of event?

Are you talking about chaos theory in the last paragraph? I'll read that article you linked in a bit and see what more I have to say, from skimming through it looks as though my question from the previous paragraph may be answered.

>as an aside, do insurance companies significantly alter their policies when such a cat-5 hurricane is imminent? What preparations would they make in the face of that sort of event?

You cannot retroactively alter a policy, for obviously good reasons. The main preparation policy-wise is that insurance companies do not knowingly write new policies in the affected area when disaster is ongoing or imminent. Reinsurers will also avoid writing new treaties (which is what a standard reinsurance policy is called) -- for these reasons the Florida cat reinsurance market is typically dominated by policies that incept on June 1st and run to May 31st of the next year.

Internally, the companies will start modeling what they think their potential losses will be almost immediately, as investors expect a fairly quick turnaround on getting initial loss estimates out the door.

There's a fairly new paper here, albeit not yet peer reviewed, on the promise of maximum entropy models in an actuarial setting. The appendix has references to the earlier papers: https://www.casact.org/pubs/forum/20wforum/07_Evans.pdf

Thanks for the link, I'll check it out.

unrelated, but - why are some names green?

The comments I've read have been really strongly worded either for or against the motif of this article. All in all I thought this essay was a nice albeit not too deep train of thought through kinds of thinking - not anything worth getting into fisticuffs over.

Yet some comments make me feel like they expect, with the threat of harsh criticism, uber-deep and profoundly insightful content from PG on a highly consistent basis. Maybe it's the phenomenon where wider audiences give rise to (or amplify) polarizing views.

What kinds of techniques are used in discourse analysis? How do you approach investigating problems in pragmatics (like how can you really establish a context for asking questions about contexts?) and can you give me some examples of interesting cases? I've wanted to do deeper reading on this topic for a while, which has intensified since I've been getting deeper into pure math. Can you point me in some directions to look for book recommendations? I have this as a jumping off point: https://www.nature.com/articles/s41562-020-0924-8 but anything else is much appreciated.

I'm only a lowly master's student so I don't have a good hold of all literature, and pragmatics & discourse aren't really my forte, so I think I'd rather refer you to a great resource: the Handbook of Linguistics, editors Aronoff and Rees-Miller. If you don't have hold of basic linguistics theory and concepts, you may give An Introduction to Language by Fromkin, Rodman, Hyams a go. For deepening on Chomskyan syntax, which has roots in mathematical / logical approaches, I suggest Syntactic Structures Revisited by Howard Lasnik, which is a truly wonderful pedagogical achievement, given the excess complexity of Chomskyan theory. In any case tho, if you are familiar with basics of linguistics, the Handbook is _the_ resource to familiarise yourself with latest research and history of ideas of any subdiscipline of linguistics.

Trying to talk about it a bit myself, I think I should start with saying that I don't really know methodology in pragmatics, but it and discourse analysis (DA) are pretty close to each other. Discourse analysis is an umbrella term for many research methodologies, and it's a hugely multidisciplinary field, so it's hard to pin it down. Tho suffice to say the concerns of DA inside of linguistics is separate from that in literary studies and the Foucauldian tradition, which tend more towards philosophical approaches. DA in Linguistics is more exact in general and focused on extents of written or aural or signed text and conversations. One of the most common tools is transcriptions peculiar to DA. There on we investigate different properties like structure, pauses or intonation in how they relate to different pragmatic goals, like turn taking in speech and signaling coherence, deixis, etc., in more "purer" DA research, and other strands of research like Critical Discourse Analysis or Feminist Discourse Analysis may then extrapolate how these reflect power relations or social preconceptions. This probably overlaps a lot with pragmatics---and a lot of theoretical and analytical tooling like speech act theory or Gricean maxims are shared---but AFAIU DA is more interested in textual (i.e. speech, not necessarily written) context here, whereas pragmatics in more mechanical and semiotic workings---tho I doubt subfields of linguistics are as distinct as some of the literature makes them seem to be. In any case, a more concrete method that's used a lot in DA (or other subfields) is corpus analysis, where large, often annotated corpora is used in order to test what constituents are found together.

Talking about your interest in math here, you may or may not find what you want in this. On the one hand, language is an incredibly flexible mechanism and almost everything, from words to farts to where one looks at has semiotic and discourse-relevant content, and this is something that conflicts a lot with more logically / mathematically motivated approaches like Chomskyan grammar. Simply put, language is hard to pin down, because unlike say in physics, chemistry, or astronomy, your subject matter is an extremely diverse, constantly changing beast that's produced with animals with extreme agency. But OTOH universe, biology, or materials are messy in their own ways and mathematical approaches have been useful in these pursuits. So it depends on where you come to it from, really.

As to the article you linked I'd say it falls under the umbrella of computational semantics which is an area I'm totally alien to (FWIW I'm more partial to usage based grammar and sociolinguistics). It does use corpora but that's not exclusive to discourse analysis (in fact its a methodology that began around the 90s and is used across all subdisciplines of linguistics today, maybe bar phonetics). There's an over reliance on the concept of the "word" as some basic elementary unit here, which is not really the case. There being a word to express something in a language or not is not really a barrier or obstacle to expressing it in any language. The study feels like it could really make use of another linguist among its authors. There are disciplines that explore language contact and variation, second language education, and translation in itself, which could have a word or two here.

Let me Cormac that for you (for a perfect Hemingway score)...

  I got some thoughts on that.  I'm a student, see.

  They call me master, but it don't mean much.  Learned a bit from Aronoff down around San Pedro.  Rees-Miller too.  Real men of discipline.

  'bout myself, I got thoughts on discourse, but that's pragmatism talkin'

  I hear you like math though.  Chomsky's a son of a bitch, and he do know grammar.  But he can't put you in a box even if you talk animals, or rocks - 'cause he don't know where you come from.

  And that thing you asked me to read?  

  I read it.

  Keeps the rain off, but ain't that what semantics is all about?  Keeping concepts from seeping into your cloths to dampen your soul?  

  Your brain?

  I said too much already, but discipline's a creature of words.  A silver toung'd preachers words if you ask me.

Thanks for the great resources. Are you aware of any online communities that an amateur/hobbyist-linguist may benefit from?

There's r/linguistics, tho quality content and well-intentioned participants are limited. Y'know, general reddit things. There are some other subdiscipline specific subreddits too, tho all fairly small. This is a multireddit I made that contains the ones I know of: https://www.reddit.com/user/gkayaalp/m/linguistics/

The best community I know of is the LinguistList. I follow their RSS feeds to keep on top of what's happening in the field, but they also maintain many mailing lists, which you might wanna give a look.

There's also linguistics.stackexchange which does have some good content and answers.

There's some small linguist presence on Humanities Commons (https://hcommons.org).

LingBuzz is like an arXiV--discussion board hybrid thing.

If you're into conlanging, I know that some communities/subreddits exist but IDK the names or URLs, unfortunately.

Other than that sadly we don't have anything to the extent that CS or physics enjoy.

Edit: You're welcome, BTW. If you want to ask about anything more specific, resources or otherwise, I'd love to help.

Can you elaborate more on (or give an example of) "mechanical honesty"? From the outside looking in, it doesn't seem unfeasible to implement biases in mechanical systems (especially government departments!), but I haven't given it a lot of thought.

The phone book.

It's a public mapping of people and businesses with how to contact them. Any dishonesty would be noticed, because everyone has the same phone book.

Contrast with getting a phone number from $large-company, be it Yelp, Google or otherwise. The only thing stopping them from selling this privilege to an intermediary is their reputation, and clearly where Yelp is concerned, this isn't enough.

One could argue that Google has been exploiting this since the creation of AdWords: they will happily sell the right to advertise on a search for your exact company name to a competitor, a nice income stream which verges on extortion.

It's even possible to deliver personalized dishonest results; applications including search engines know who you are, and can lie to you and only you.

It's certainly possible to be 'mechanically dishonest', but no question that contemporary technology makes it easier and more lucrative.

Another example is targeting job ads to particular demographics. You can put out an ad that only white men between the ages of 22 and 30 who come from an upper class background will see. And women, minorities and people from poor backgrounds, not only won't see them, they won't know they exist.

I don't think Yelp has done anything other than "lie in the phone book" so far, so it's a bad example.

A better example might be where Uber made their app turn off a bunch of shady privacy a violating settings, but only if the phone was geographically located at Apple HQ.