fractionalhare

This is in support of the GP comment: while you can loosen your assumptions about what the underlying distribution is and don't literally need to know it, you can't get away from the fundamental limitations of statistics. Which is the original topic we're talking about.

Okay, so that's about the same as classical statistics. You're just waiving the requirement to know what the distribution is. You are still assuming there exists a distribution and that it holds in the future when you apply the model. Sure you may not be trying to estimate parameters of a distribution, but it is still there and all standard statistical caveats still apply.

> Indeed, with the use of autoencoders, we don't assume a single distribution, but rather a stochastic process.

Classical statistics frequently makes use of multiple distrutions and stochastic processes.

Based on what I see from quant prop trader friends of mine in the Chicago area, if Medallion was an order of magnitude smaller they could probably juice their returns up to 150-300% pretty reliably.

Both of them have been underperforming in the last decade or so. Medallion is still radically outperforming, however.

These figures are net. And for what it's worth, I am in the industry and I don't know any professional who thinks there's obvious fraud going on. It's possible, but you make it seem like there's a consensus that it's illegitimate when you say "most sane people." Frankly it's the other way around.

People who think the returns are fraudulent tend to be outside the industry and thoroughly unacquainted with what quantiles of returns are rare versus implausible. They usually hand wave a misinterpretation of Buffett's famous bet against hedge funds and Fama's (strong) Efficient Market Hypothesis.

For example: you can theoretically do better than a seasonal ARIMA model for time series analysis. But in practice it's very difficult, and you probably don't have the amount of data you need or even an economic justification. The improvements will usually be marginal, expensive and not worth it.

Most teams developing a new product or doing research - even within large and ostensibly sophisticated tech companies - won't usefully or economically outperform something like Box-Jenkins.

These things have been studied numerous times. Head over to r/AdvancedFitness or r/AdvancedRunning and you'll find people seriously disseminating acacemic, peer-reviewed research on precisely these topics.

It can take a while for certain things to go from practitioner consensus to academic consensus. But that doesn't mean academics aren't rigorously studying it. I also have doubts about your mask and parachute examples.

How is list comprehension a data science primitive? How did this get over 4,000 stars on GitHub with a glaring lack of basic data science functionality? Is this used by actual practitioners?

These don't seem related. I happily base my identity around my work. I don't care about my coworkers personalities or inclinations at all, as long as they're not assholes and they're competent.

Maybe I'm misunderstanding what you mean by identity? I don't hire people because I want them to be friends.

But I also don't think that commenter is disagreeing with you - when they say huge value, they're probably gesturing towards the fact that consumers will generally use whatever the most convenient and recognizable brand is. It will be hard for the coop to achieve a core customer base which allows them to survive if everyone gets off the plane and uses Uber instead of looking for the local coop.

Their actual point is 1) that it's a complex problem to make it a programmatic market which functions nationally (even internationally) and 2) that such a product will naturally have a feature and convenience advantage over regional-specific, capital-constrained alternatives.

What would you have Facebook do, fact check using Twitter because the CDC made this one bad call? Do you think the modal CDC consensus is worse than that of Twitter?

It might not look that way because there's a lot of (relatively) mainstream investment in quantum computing. However it's pretty common for speculative physics research to be pursued for years without ever coming to fruition. Especially when there are promising early results before it's shown that scaling the work reduces to an intractable problem.

They're losing to GitHub. While they advertise that they have 2/3 of the self-hosted git server market share, they just barely have a double digit share of the total VCS market, which is where the real enterprise money and value is. I don't have figures on hand, but I'd guess they have a single digit percentage of actual enterprise market share. GitHub has the same domination over its space that Google has over search, and Microsoft is excellent at competing on enterprise partnerships.

Tech companies usually scale out their products eventually anyway, but in GitLab's case it's probably existential if they want to maintain status as a high growth, VC-backed company. They need to find another of their competitors' moats which is easier to chip away at, or they need to change the game and introduce something original.

You can levy a dozen counterfactuals at the use of BLS data like this to support permanent WFH. The analysis needs to be way more holistic and BLS figures aren't nuanced enough.

I don't think that's the case. I think this has more to do with the fact that Google now deliberately surfaces as much information - including summaries, answers, tools and widgets - in the search results page itself, which incentivizes searching without a subsequent click. Google very much wants you to end your search journey on google.com, but I think that's because they believe it will keep people coming back. I do not think Google directly tries to keep people searching for the same thing.

This may still raise interesting antitrust concerns though.

Semantically, I generally understand a person who says they "liked" a thing to mean the social media verb. Context also makes it pretty clear.