sweezyjeezy

Is it really not clear from context? "If introverts made an effort to be more extroverted, would it make them more successful?" Success I don't know what OP meant, but pay, seniority, I don't know that it changes the answer much.

Yeah but the statement in question is (quote) "you can be EQUALLY successful as an introvert" - and we're talking about whether people should, generally, strive to be more extroverted at work. That there exists a successful introvert is not in question and isn't a good criticism of the article to me.

Apart from the line "does it help in some ways, sure" the overall message of their post is that the author is wrong and that you can be equally successful without being sociable, which is a general statement.

It's not really supported by the anecdata that OP is a skilled, unsociable, successful person. That only tells us that this person, in their particular line of work, with their particular set of skills, can be successful. It also doesn't consider whether a version of them that was more sociable would be more successful.

Are you not generalising in the same way? Because you are introverted and successful, does that mean extroversion is bad advice for all?

This sounds naive. Most companies have some degree of 'politics' which is essentially people working in their own interests over the company's. This is somewhat unrelated to skills and value.

Also particularly in large organisations, work is not a perfect information game - there is no easy way to assign a fair / public a "value" to people to let you compare two employees. Most people will need to sell themselves or have other people sell them to get noticed, and some degree of social bonding can go a long way with this.

Can confirm this, LLMs write almost exclusively in broken polars. However I convert pandas files to polars semi-regularly, and it's still a hell of a lot faster to get an LLM to write the first draft and correct the 4 or 5 attribute errors it makes

FWIW Nate Silver also said don't trust any polls this year, and his gut was with Trump.

Exactly, unless you rerun with a "popular vote wins" election - it's not concrete at all. The campaigns would not have been run in the same way, and the people would not vote in the same way.

I say this every election when democrats play the "but we won the popular vote" card as well - that wasn't the game being played, so it doesn't really mean that much.

Yeah even the article says that X execs were the ones that pushed for this. Other than editorialised headlines this seems like a total win.

Yes I don't think they invent words, that really would be bad.

But what are you suggesting? It wouldn't be better if Venezuela had its language dictated by another country (e.g. Spain) - that would just be oppressive.

France has a mechanism to try to stop this: the Académie Française [1] that publishes the official French dictionary. Rather than simply recording language as its used, they do actively try to steer it. They're most well known for trying to suppress anglicisms, e.g. in the early 2000s they pushed 'courriel' instead of 'email'. That one did not work out - email is much more common, and finally entered the dictionary in 2009 (to this day labelled 'anglicism' and discouraged over 'mél').

FWIW the German equivalent is much less prescriptive, it only weighs in on grammar / punctuation.

[1] https://en.wikipedia.org/wiki/Acad%C3%A9mie_Fran%C3%A7aise

The (disproven) conjecture is for finite graphs.

I live near the 1.5m one, it's not really used by road vehicles (or at least I've never seen anyone try it), it's a path used by cyclists / pedestrians.

https://adamtownsend.com/heighthunt/height.php?height=1m5-5f...

Side note: as you might guess from the picture / measurement - this thing is extremely awkward to navigate, especially by bike.

Yeah I can relate! I've also heard Conan O'Brien say this before, he thinks that a big source of his comedy is just his brain outright not understanding things correctly.

I think "incongruity theory", that the article is alluding to, does actually apply to most of these. You're focusing on the context rather than the actual underlying mechanism driving the joke. e.g. the first one "bullying, where the joke is not particularly funny..." Consider that the incongruity of a comedian laying into someone verbally, compared to the way we're primed for them to talk in polite-society interactions, may be part of the reason why this works. Similarly example two - "Otis Elevators: They'll never let you down!" - there is an incongruity in the usual usage of the expression 'they'll never let you down' to here, that could be what makes this work as a joke.

I agree there are examples that incongruity doesn't cover, e.g. slapstick I personally believe is something a bit different, but generally I do think it's a pretty compelling explanation for a lot of modern comedy.

But what's the alternative here? "We spent longer than was minimally viable but we still don't have a good idea if it has market fit"-product? In my experience the code usually gets binned whether the idea gets traction or not. Some companies misjudge when to rewrite, but that doesn't make the MVP part of the process wrong.

The absolute greatest wastes of talent and humanity I've ever seen in tech didn't come from tech debt, those efforts were almost always at least working on a product that people were paying money for. The biggest wastes were from over-delivering products that hadn't and were never going to succeed.

About 10th in the UK with that search, so probably this isn't across every geography.

I did say generally, but fair point :b

I think it's rarely valid to compare countries' economics to your own personal finance, macroeconomics are a lot more complicated. Example: for employed people, going into (or avoiding) debt generally won't affect their yearly wages, but analogously it can for countries (e.g. well spent debt can stimulate economic growth).

Not particularly, only thing I can think of is if we analysed it and saw there was some bias in the digits, but no one expects that (pi should be a 'normal number' [1]). I think they did it as a flex of their hardware.

[1] https://en.wikipedia.org/wiki/Normal_number

I mean - it's more than a sellable product, the reason we're doing this is to be able to advance medicine. A good understanding of the "why" - would be great, but if we can advance medicine quicker in the here and now without it, I think that's worth doing?

> When we go to use that as knowledge we have no idea what unintended side effects may occur and no real information telling us where to look or how to identify side effects should they occur.

Alright and what if this is also a lot quicker to solve with AI?

You're using "AI" quite broadly here. Here's a perspective from computer vision (my field).

For decades, CV was focused on trying to 'understand' how to do the task. This meant a lot of hand crafting of low level features that are common in images, finding clever ways to make them invariant to typical 3D transformations. This works well for some tasks, and is still used today in things like robotics, SLAM etc. However - when we then want to add an extra level of complexity - e.g. to try and model an abstract concept like "cat", we hit a bit of a brick wall. This happens to be a task where feeding a large dataset into an (mostly) unconstrained machine learning model does very well.

> The classical approach was to understand how genes transcribe to mRNA, and how mRNA translates to polypeptides; how those are cleaved by the cell, and fold in 3D space; and how those 3D shapes results in actual biological function.

I don't have the expertise to critique this, but it does sound like we're in the extreme 'high complexity' zone to me. Some questions for you:

- how accurate does each stage of this need to get to useful performance? Are you sure there are no brick walls here? How long do you think this approach will take to deliver results?

- do you not have to validate a surprising classical finding in the same way that you would an AI model - i.e. how much does the "why" matter? "the AI can never provide a guarantee of correctness" - is true, but what it was merely extremely accurate, in the same way that many computer vision models are?

The paper I linked should hopefully mark me out as far from an AI apologist, it's actually really bad news for GenAI if correct. All I mean to say is the clickbait conclusion and the evidence do not match up.

Entertaining, but I think the conclusion is way off.

> their vision is, at best, like that of a person with myopia seeing fine details as blurry

is a crazy thing to write in an abstract. Did they try to probe that hypothesis at all? I could (well actually I can't) share some examples from my job of GPT-4v doing some pretty difficult fine-grained visual tasks that invalidate this.

Personally, I rate this paper [1], which makes the argument that these huge GenAI models are pretty good at things - assuming that it has seen a LOT of that type of data during training (which is true of a great many things). If you make up tasks like this, then yes can be REALLY bad at them, and initial impressions of AGI get harder to justify. But in practice, we aren't just making up tasks to trip up these models. They can be very performant on some tasks and the authors have not presented any real evidence about these two modes.

[1] https://arxiv.org/abs/2404.04125

Edit: previous title was "Leetcode for ML" or somesuch...

I like the idea and might try some! But as a warning: leetcode is specifically aimed at prepping for interviews, and I've never seen questions like these in an interview (I'm somewhere between an MLE and ML researcher FWIW). The most common kinds of ML-specific things in my experience are:

- ML system design (basically everyone does this)

- ML knowledge questions ("explain ADAM etc.")

- probability + statistics knowledge

- ML problem solving in a notebook (quite rare, but some do it)

Not sure I understand your argument - from a customer perspective why is the service worse, and why do you believe that it has BECOME worse?

Some research to the contrary [1] - tldr is that they didn't find evidence that generative models really do zero shot well at all yet, if you show it something it literally hasn't seen before, it isn't "generally intelligent" enough to do it well. This isn't an issue for a lot of use-cases, but does seem to add some weight to the "giga-scale memorization" hypothesis.

[1] https://arxiv.org/html/2404.04125v2

> doesn't follow from any law of nature

Natural selection gave us hearing.

If all humans could echo-locate except a subset who couldn't, I would say that group is at a small disadvantage, yes, because the set of things the main group can do is 'objectively' greater. I don't know how relevant it would be for us especially in the daytime, but hey.