> Stats books are so dry, they don’t have the inclination to share these kinds of stories.
It doesn't have to be that way. My pandemic lockdown read was a 10 dollar Stats textbook[1], that comes with tons of classic examples: the Salk polio vaccine, a prosecutor misusing the multiplication rule using purely circumstantial evidence ("what are the odds that police pulled over the wrong couple matching 10 different pieces of description by the victim?"), the classic Gallup poll showing FDR would defeat Landon (versus incumbent _Literary Digest_ showing a Landon win), Gosset's history with Guiness, the early history of probability as gambling strategy, a controversy over Mendel's data on pea plant heredity being _too_ clean, and so on.
Sadly, while this book left me well prepared to apply statistical reasoning in my day job, it's departure from typical pedagogy left me feeling unprepared for further reading based on perusal of Stats Wikipedia -- what's a kernel? what's a moment? etc.
In a former life I taught some intro stat courses from this book. It’s a good book for an intro course for people who aren’t going on to further stat classes, although I think for a current class I’d want something that acknowledges how statistics and computers have gotten all tied up with each other. (I don’t have any recommendations - it’s not my job to know this any more.)
> I don't know what to think about humanity sometimes.
If it's any solace, selection bias is a thing. Those applicants that can't Fizz Buzz? They rarely get hired, and are rarely kept on for long when they do get an offer. Spoolsky made the argument like 20 years ago that as a result they flood the zone, and produce a massively biased applicant stream.
It's absolutely treated as insider info by policy where I work. It might not rise to SEC enforcement action, but if caught you probably won't have a job anymore...
Why would your employer care what you invested in? How would they be impacted if you bought or sold public stock of someone they interact with? Would they care if you traded Google, Apple, Microsoft or Oracle?
One possible explanation is that we require this of our vendors and suppliers, and the easiest way to achieve that is by making it reciprocal.
The actual policy wording:
> No Third-Party Trading or Tipping. Do not trade in the securities of another company when aware of material nonpublic information about that company in connection with your work at ******. This includes trading in the stock of ****** suppliers, manufacturers, vendors, or customers, such as cellular network carriers or other channel partners. You must also not tip material nonpublic information about another company.
But that's been known for months now. At one point a bloomberg podcast mentioned in passing that nVidia inventory now has a lead time measured in months. Which kinda sounds like the makings of a bullwhip effect induced bubble[0]. Everybody wants inventory now, so they put in massive orders, hopefully get some fraction of what they asked for, and if they happily get too much they can, in theory, easily sell their surplus into the market. Think about how many "AI platforms" boil down to "we have GPUs!"
_If_ this is the bullwhip scenario, then it's basically a gamble about how many million cards nVidia can ship before their own short supply bubble bursts. Or the AI bubble more generally.
Exactly. Very few people have actual, factual knowledge about whether Wall Street traders analysts are right or wrong about some company performance metric. They say "they know" but it's just hunches and gut feelings. The people who actually know for a fact are insiders and cannot legally trade on the knowledge. Everyone else are just gamblers who think they "have a system" that works.
Working at a FAANG, I've definitely seen founders and startup employees chafe at decision making that they felt powerless over. Stuff like legal refusing contracts with GCP due to reasons and then doing it anyways, or coping with staff engineers dictating decisions they'd have decided differently by quitting.
When RSU vests, it is no longer an RSU. Not sure why you would call it “dividend-equivalent” as they are regular dividends at that point (unless your broker lends them out in which case you might get substitute payments in lieu.)
I'm just using the terms that show up on my etrade plan confirmations. In lieu of actual dividends, the terms of the grant say the company will issue RSU dividend equivalents, commensurate with what the dividend would have been had it been purchased on the grant date (or something similar, I don't have that paperwork at hand).
I suppose that might be some special amendment in your specific RSU plan electing to pay some cash on top, not inherent to the nature of an RSU, which is certainly not a stock (i.e. on the corporate financial reports that payment would not be showing up as a dividend).
Once upon a time it was said by Google leadership the biggest cost Google pays is opportunity costs, chiefly in the form of projects and products they don't pursue.
My expectation is that whatever logic held up last quarter held up this quarter. It's possible the optimal allocation is some of both, but what's the tradeoff that would drive that instead of one being mathematically superior to the other?
Dearth relative to Apple's size and relative to the amount of research that competitors have been doing.
But I think part of the problem is that Apple simply hasn't focused on the tasks and the methods and the people that have now turned out to be so impactful.
They have clearly been course correcting for a while now as some of the more recent papers show, and they have done successful research in areas such as health AI.
Curious then, why they keep recruiting search engineers[1]. And why they run a web crawler[2]. And why typing "Taylor Swift" into safari offers a Siri Suggested website before Google.
I guess what people mean by search engine is "show ads alongside web search to as many people as possible"?
It doesn't have to be that way. My pandemic lockdown read was a 10 dollar Stats textbook[1], that comes with tons of classic examples: the Salk polio vaccine, a prosecutor misusing the multiplication rule using purely circumstantial evidence ("what are the odds that police pulled over the wrong couple matching 10 different pieces of description by the victim?"), the classic Gallup poll showing FDR would defeat Landon (versus incumbent _Literary Digest_ showing a Landon win), Gosset's history with Guiness, the early history of probability as gambling strategy, a controversy over Mendel's data on pea plant heredity being _too_ clean, and so on.
Sadly, while this book left me well prepared to apply statistical reasoning in my day job, it's departure from typical pedagogy left me feeling unprepared for further reading based on perusal of Stats Wikipedia -- what's a kernel? what's a moment? etc.
[1]: https://www.amazon.com/Statistics-Fourth-David-Freedman-eboo...
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