civilized

Forgive my ignorance - is that true even for non-synonymous mutations? They usually have a hard time disrupting the big structure?

If the mutations were non-synonymous, resulting in different amino acids, the fact that they keep the natural function is still kinda cool. Very much a pure research result AFAICT, but worth a little something.

Yes, I see. There's a desire to map intervals pointwise through functions, but also a desire to produce intervals by all-pairs calculations, and the impossibility of representing both interpretations in one notation leads to some inconsistencies.

Why x^2 isn't always x * x

It turns out he's claiming they're different if x^2 is interpreted as squaring each element in the interval x, while x * x is interpreted as a cross product: the interval obtained by multiplying all pairs of elements in the interval. But I haven't ever seen anyone use x^2 to mean pointwise squaring on an interval x. Is that some kind of standard notation?

Not your mistake, he does his best to imply that the hats are dropping on heads.

He's got a future in marketing.

But is that "willingness to do for a low price" some kind of inherent property of artists as a group of people, or does it come from somewhere else? (The "or" here is not necessarily XOR.)

What if it all comes down to supply and demand? Maybe the supply of artists is much greater than the demand for art, while for tech products it is reversed?

In the past I have thought about it as you do. But it occurs to me now that this is also a property of tech products, isn't it? These products can be built by a small team and serve millions or billions of people. The most successful ones usually expand the team (Google Maps now employs over 7,000 people!) but it remains true that only a small team is really needed to keep the thing going.

So why are tech employees thought of differently than entertainers? Why is the math so different, such that tech employees have much more predictable and favorable employment prospects?

This is the real story. Mouse Jiggles a Key Performance Indicator at Major US Bank.

While the law allows it, in my opinion there is no real moral basis for a company to lay claim to all of an employee's time. If the employee is sufficiently responsive and productive, there is no issue. If not, the company can dismiss the employee. There is no reason for the company to have more surveillance and control power over the employee than this.

Providing objective, accurate, relevant contextual information that reasonably makes people outraged is not in itself an appeal to emotion and outrage.

The information is relevant to how we view Wells Fargo as an ethical entity. Bad food in the cafeteria would not be relevant.

Why not go even further? Why not say that the whistleblower was wrong and Microsoft business leadership was right? Maybe their profits from ignoring this issue have been fantastic, and the externalities from e.g. mass theft of national security secrets are not Microsoft's problem.

I think that context about the company in the article is context about the article, but I understand your difference of opinion.

To see my point, consider this pull quote from Wells Fargo which is contained in the article:

“Wells Fargo holds employees to the highest standards and does not tolerate unethical behavior,” a company spokesperson said in a statement.

It is important to understand that this is coming from a company whose recent unethical behavior went far beyond what anyone really thought plausible.

Context: in the 2010s, Wells Fargo management looked the other way while its sales force scammed customers, creating millions of fraudulent accounts (with associated fees) to meet performance targets and quotas [1]. The Fed imposed an asset cap as punishment in 2018, and as of today, the asset cap remains in place.

[1] https://en.wikipedia.org/wiki/Wells_Fargo_cross-selling_scan...

Many of you will be familiar with this story: military pilot gear was once designed for the average person, but then they realized that actually, most people deviate significantly from the average in at least one way. So they made the gear adjustable, and that greatly improved performance and reduced mistakes.

Why is it that in tech we are often told a seemingly contrary narrative -- that everything is better, or at least more profitable, when targeted to some hypothetical average person, and who cares about the diversity of individuals?

Leetcode-style tech interviews fall far short of standardized tests. In real standardized tests, the same questions are given to everyone, and effort is made to develop new questions not known to the test takers.

They're more like a standardized test than a pure shoot-the-shit interview is, but still pretty far away.

the fact that there is a standardized test

...a standardized test? No. There are tests. They sure as heck aren't standardized.

Maybe they should be, since everyone seems to be doing the same thing.

Hopefully it'll all be available in a fire sale when these companies finally have to be stripped for parts.

Believe it or not, we talk, and their input influences my actions.

The lack of communication of potentially valid reasons such as these might contribute to why people view RTO mandates with cynicism.

If we knew why we were being asked to RTO, we could solve that problem in the way we thought best, combining in-person and remote time. And those of us with junior team members we care about are already solving for this in the way we think best.

Apologies, my bad, but I'm a bit too late to edit. The experts say that R qualifies as a lazy language [1, 2].

My impression was that R was mostly an eager language that somehow allowed for laziness. I will research this further and hopefully suss out why I got confused.

[1] https://dl.acm.org/doi/10.1145/3360579

[2] https://www.r-bloggers.com/2018/07/about-lazy-evaluation/

R is not lazy. It has non-standard evaluation mechanisms (formulas, promises, quosures...) that enable to you to write domain-specific languages that "do what the user meant".

If your code (or the code of the libraries you're using) doesn't use any non-standard evaluation tools, evaluation will be eager and work like any other ALGOL language.

It is possible to make some objects behave in a lazy way, but this is also true of many other languages.

I think there is a law of convenience, and it also explains why many technologies improve at a consistent exponential rate. People are very good at finding convenient ways to make something a little better each year, but every idea takes some minimal time to execute.

Abstract - admirably concise and accessible:

How will the emergence of ChatGPT and other forms of artificial intelligence (AI) affect the skill premium? To address this question, we propose a nested constant elasticity of substitution production function that distinguishes among three types of capital: traditional physical capital (machines, assembly lines), industrial robots, and AI. Following the literature, we assume that industrial robots predominantly substitute for low-skill workers, whereas AI mainly helps to perform the tasks of high-skill workers. We show that AI reduces the skill premium as long as it is more substitutable for high-skill workers than low-skill workers are for high-skill workers.

I don't know if I agree with the "literature" as represented here. It seems to thoughtlessly conflate low-skill with blue collar and high-skill with white collar. I would expect the impacts of AI on wages to vary across skill levels of white collar workers.

Weren't non-competes just banned by the FTC? Could make a fine mess of this.

And I suppose the individual owners were paying rent in some form to Red Lobster Inc, since it owned the land? It might be that all that changes for them is who they write the check out to. And possibly the amount, if Red Lobster Inc was in the habit of subsidizing its locations.

It sounds like a legal pump-and-dump scheme with sophisticated counterparties. You would think that the counterparties would wise up over time?

It often is, but this isn't some kind of free money tree that only rich people can access. Loading up a company with debt requires a creditor. Selling underlying assets requires a buyer. If these counterparties don't offer enough money to offset what PE spent to buy the company, PE loses. And this often happens, including, apparently, in this case!

Good point. I think this changes the story a bit. It's not exactly that PE is predatory. If PE were unlocking the value of assets held by an underperforming company, the transaction could be explained as the creative destruction of capitalism making room for something better to hold those assets.

In this case, it's more like private equity wasn't as smart as Red Lobster's owners, so now Red Lobster's owners have extra capital to allocate in the economy. Which is also arguably good.

If you liked the restaurant, of course, none of this is much comfort. But if nobody with a ton of money thinks Red Lobster is a good use of capital, from either a financial or sentimental perspective, it may go the way of the dodo.

I have the same question every time I see one of these articles. I think I've even posted the question in previous HN threads on private equity shenanigans. The question is:

Why is this profitable?

If the land is worth $1.5 billion, it should have cost PE more than $1.5 billion to buy the company. Then there would be no way to make a profit by selling the land, paying yourself, and letting the company go belly-up.

Why does PE keep doing this? Presumably because it works? But why does it work? Are the sellers less sophisticated at asset valuation than the buyers, and frequently lowball themselves? Or maybe owners/stockholders are sometimes just tired of holding this asset, want cash to reinvest somewhere else, and are willing to cash out at a discount?

Very good to hear this. As a data analyst, I have tended to dismiss LLMs as irrelevant because of the black-box mentality. In some industries, this even holds back adoption of technology that is now considered mature and boring, like tree ensembles in machine learning.