For me I could only do it because I had "f*ck you" money gained through investments, other people are able to do it because of welfare systems, or even through friends and family.

When does Elon work?

Someone suggested that people with 10k karma and/or 10 years subscription to this site should be able to do things (such as auto-ban) to those accounts.

The account that misrepresented your comment and thus acted in bad faith is one of those 10k+ accounts.

To me, this is a data point showing the fallacy of long term subscription and/or karma accrual as evidence of their quality/good faith abilities

A recent example, Mitchell Hashimoto was pointing out that he wasn't "first to market" with his product(s), he was (at least) SEVENTH

If this were seven government funded teams solving the same problem, people would lose their minds over the 'waste' But when private companies do it, we call it efficient market competition. The duplication is the same - we just frame it differently.

Edit: fixed some typos caused by fat fingers on a phone keyboard

>If this were seven government funded teams solving the same problem

The problem here is "government funded" - the trials are not rationalized by free-market economics. That is, a 5% better product in the end would not be worth seven competing developments initially.

This assumes that the duplicated effort arrives at a solution that is better than if it were done by a single team.

> >If this were seven government funded teams solving the same problem

> The problem here is "government funded" - the trials are not rationalized by free-market economics. That is, a 5% better product in the end would not be worth seven competing developments initially.

I think you're saying that 5% is worth it when the free market does it, but 5% gain isn't when the government does it?

I'm hoping you're not because that's impossible - the end result is precisely the same

It is not. Seven teams all working under one leadership is quite different to seven leaderships each working with one team.

When different governments (e.g. USA and USSR), and thus different leaderships, are both trying to solve the same problem (e.g. travel to the moon), that too is considered efficient competition.

If a government did this (e.g., seven independent agencies competing for a moon landing), people would call it "fragmented," "uncoordinated," and "bureaucratic infighting."

When complete organizational separation is introduced, the concerns you speak of go away. In the USA, the ARPA (you might recognize that name from the thing you're using right now) program regularly enables "seven" independent leaders to tackle a problem and this is widely considered a resounding success.

- If they do a good job with leadership, only one team will be necessary. Anything else is truly a waste.

- If they do a poor job with leadership, every team will fail. Any more than one is also truly a waste[1].

The latter is the most likely outcome, of course. Now, when you absolve yourself from the process then those points still apply, but now you have several leaders duking it out to see which one doesn't fail. But, for the same reasons, those leaders each only benefit from having one team.

[1] You could argue that all teams are truly a waste, but one team is necessary to show that leadership failed. That brings abstract value, even if it fails to deliver the intended value. You don't know until you try.

The repeated movement of the goalposts here is only evidence of the no real scotsmen strategy being employed.

Not necessarily. Unless you think a global democratic government formed overnight?

> The repeated movement of the goalposts here

Whatever it is you are reading in other threads has no relevance to this one.

This is a distraction. Whether it's 300 million voters or 300 million iPhone users, both groups act as the ultimate arbiter of value. If a customer stops paying, the "leadership" of a company fails. If a voter stops voting for one party, or the other, the "leadership" of a state fails. The mechanical result on the "seven teams" is identical: the unsuccessful ones are defunded.

Further, this proves the detachment from reality you are bringing to the conversation - everybody in the private sector knows the golden rule - your customers ARE your employers

THEY dictate what they will pay for, and therefore what can be sold (unless you are a fan of monopolies forcing people to buy things they do not want to)

> Whatever it is you are reading in other threads has no relevance to this one.

Your dishonesty only highlights your bad faith, and as such we are done here.

Political parties in democracy are quite literally labor unions. The people in them do not independently lead the state, they are merely employees, hired by the leadership. You know, that's what you host elections for — to choose which employee you want to hire from the set of candidates who want the job. They may act as sub-leaders within the capacity of their job, but they are not the top leaders we are talking about. "Leadership" here was never intended to be about "middle managers".

That seems pretty obvious, but perhaps this confusion is the source of your misunderstandings?

> and as such we are done here.

Done with what? Thinking other threads are related this one? That is a good idea.

That's not quite my read - the original says per branch there was a 1/3 reduction, but your comment appears to say 1/3 total redundancy.

There was, according to the original, a 40% increase in number of branches, meaning a net increase in tellers (my math might be off though)

edit:

100 branches → 140 branches = +40%

100 tellers/branch → 67 tellers/branch = -33%

140 × 67 = 9,380

100 × 100 = 10,000

net difference -620 or just over 6% (loss)

And, to be equally as fair, the only genuinely good guys are the ones that are too small to enforce their will upon others directly - small countries without arms who are forced to find other ways to engage with others in order to achieve whatever goals they have (resource acquisition)

The Americans have been extremely adept at dominating the discourse via non-government pathways (Hollywood)

But beyond the pedanticness and authority appeals, I think keeping the term AI distinct from AGI is just useful so it can be an umbrella term for all the human-like smart-ish things computers do. And so its Wikipedia page doesn't have to be re-written.

Look at my comment history. I emdash. But I adapted by removing the spaces around them—AI hasn’t similarly adapted.

Most comments on HN with emdashes aren’t slop. But if it starts getting into Wernicke word-salad territory and there are emdashes? With spaces? At that point, it’s fair to flag.

I'm laughing not at you but the ludicrousness of the times - I use endash heavily, have done for a minute, but now I see endash used by LLMs with no surrounding spaces.

I think that the "identify AI by some artefact" is just another game of whack-a-mole, and the better approach is to look at the quality of what's being presented.

I have argued before, and still feel strongly, that LLM/AI generated images/audio/text is causing a stronger inspection of what's being presented as fact, which is a healthy thing (how far that will go is yet to be determined, as per when the availability of Photoshop generated content exploded)

Goddamit. (Flippity floppity floop.)

> LLM/AI generated images/audio/text is causing a stronger inspection of what's being presented as fact, which is a healthy thing

If it is, I agree. What I think is actually happening is folks are skimming and then concluding on vibes. Unfortunately, that means “I don’t agree” gets lumped in with “this is slop.”

Machine Learning papers, for example, used to have a terrible reputation for being inconsistent and impossible to replicate.

That didn't make them (all) fraudulent, because that requires intent to deceive.

So the answer is that we still want to see a lot of the papers we currently see because knowing the technique helps a lot. So it’s fine to lose replicability here for us. I’d rather have that paper than replicability through dataset openness.

This doesn't mean the model only works on that specific dataset - it means ML training is inherently stochastic. The question isn't 'can you get identical results' but 'can you get comparable performance on similar data distributions.

By definition, they involve variance that cannot be explained or eliminated through simple repetition. Demanding a 'deterministic' explanation for stochastic noise is a category error; it's like asking a meteorologist to explain why a specific raindrop fell an inch to the left during a storm replication.

Working on the next paper is seem as the better choice.

Moreover if your code is easy for others to run then you're likely to be hit with people wanting support, or even open yourself to the risk of someone finding errors in your code (the survey's result, not my own beliefs).

There are other issues, of course. Just running the code doesn't mean something is replicable. Science is replicated when studies are repeated independently by many teams.

There are many other failure modes SOTA-hacking, benchmarking, and lack of rigorous analysis of results, for example. And that's ignoring data leakage or other more silly mistakes (that still happen in published work! In work published in very good venues even)

Authors don't do much of anything to disabuse readers that they didn't simply get really look with their pseudorandom number generators during initialization, shuffling, etc. As long as it beats SOTA who cares if it is actually a meaningful improvement? Of course doing multiple runs with a decent bootstrap to get some estimation of the average behavior os often really expensive and really slow, and deadlines are always so tight. There is also the matter that the field converged on a experimentation methodology that isn't actually correct. Once you start reusing test sets your experiments stop being approximations of a random sampling process and you quickly find yourself outside of the grantees provided by statistical theory (this is a similar sort of mistake as the one scientists in other fields do when interpreting p-values). There be dragons out there and statistical demons might come to eat your heart or your network could converge to an implementation of nethack.

Scale also plays into that, of course, and use of private data as the other comment mentioned.

Ultimately Machine Learning research is just too competitive and moves too fast. There are tens of thousands (hundreds maybe?) of people all working on closely related problems, all rushing to publish their results before someone else published something that overlaps too much with their own work. Nobody is going to be as careful as they should, because they can't afford to. It's more profitable to carefully find the minimal publishable amount of work and do that, splitting a result into several small papers you can pump every few months. The first thing that tends to get sacrificed during that process is reliability.