But if they did there wouldn't have been this conversation, which may have resulted in a few people clicking on the article to see what actually happened that others would but have read the article. And this is happening in many places, not everyone looks at reddit's comments.

That moment the reflective wrapping explodes off looks like a video-game boss going into phase two, even the soundtrack in my mind suddenly got an electric guitar riff and the rhythm got way more serious.

> Knowing that neural networks are theoretically Turing complete does not imply that the networks we train (ie the sets of weights we in fact encounter) have created Turing complete solutions.

• A computer algorithm is anything that runs over an automaton and taking some input encoding a question, gives us the answer.
• ML are systems where we create a model and adjust it through some configuration, until it will, given some input encoding a question, give us the answer.
• ML can only solve the problems its own system can solve. A turing complete ML system can solve anything a turing machine can.
• It stands to reason that some problems can only be truly solved through an algorithm (e.j. if the possible inputs are uncountable infinite).
• If we assume that an ML model can solve these problems, we have to assume that it can encode in its configuration algorithms, including some that we know. Otherwise we assume there's a limit.

Now I wouldn't take this to say that it would learn to be optimal. Say we trained an AI to sort lists, I could see it encoding a sorting algorithm within its network eventually, but I can't say if it'd ever discover an O(NlogN) algorithm, even if pressure was put to optimize the solution as well as being correct. But something that we can say is that neural networks may be able to do Markov Chain models internally, as its own sub-algorithm, if that's the way to solve the problem. But the assumption of this is why we think so much about neural networks nowadays.

That said the problem of sufficiently good learning is not trivial at all. And we certainly could discover its impossible to do. But at the moment, AFAIK, there's no reason not to think it can't happen.

The fact that we observed this happening is good, it basically validates the assumptions and models that we've had up to know, and implies that "sufficiently good learning" is attainable. There may still be limits (like finding the optimal algorithm, vs just an algorithm). So there's a lot of value in seeing it.

But to day-to-day applied ML research I am not sure if it really has that much of an impact, this lays ground work though.

The really interesting discovery here. More than the conclusion the interesting thing is how they reach it, the ability to reach it. As ML starts being used in more areas, we'd want to be able to audit an ML model and verify that it effectively has found a useful solution, and isn't just over-fitted beyond what we understand. Being able to identify algorithms within the system, and be able to split the AI model into simpler "steps" that do all the things, we'd be able to validate that it has found a good solution.

Again not something we need to solve now, but being able to know how to do it is a good thing to start doing already.

And on a more complex theme. This sets a better understanding of how ML models work, and in the process they can give us a hint of how intelligent systems in general work themselves, and we could then revisit that. This is like a longer-vision here. Being able to deconstruct models we may start seeing patterns and start forming more interesting math to describe intelligent systems in general. Which is where mapping it to organic models could allow proving strong AI, for example.

This isn't that surprising though.. it's already been proven that neural networks are turing complete, and therefore any arbitrary program can be described with a "static" (that is weights/parameters are not changed) neural network of sufficient complexity.

So it isn't so much a "new discovery" as much as "validation of something that we new was going to be observed".

Don't get me wrong, this is going to be interesting. It gives us insight into how things work. That is, actually understand what is the solution a neural network built. Also it'd be interesting to work backwards and see if certain algorithms tend to happen naturally on sufficiently complex systems. Optimization sounds natural. Then the next step would be to analyze and see if they happen on organic beings that have intelligent systems (animal neural systems may be too complex, IMHO, to observe cleanly at first but we may have something interesting on simpler systems for plants, fungi or such, with better understanding we may look for this in more complex systems, such as animals).

This would start giving us an insight into how intelligence works. If strong human-like AI is the philosopher's stone to turn lead into gold (now possible with a particle accelerator and sufficient resources), this may be the equivalent of understanding the difference between elements and molecules: a solid first step to start forming a model that we can test and refine. That said we're still a bit far from that.

I think though interesting things will happen from us understanding AI better, and having a better understanding of how they actually work (as in what is the system that the neural network hit on), rather than a handwavy "statistical magic" that we have nowadays.

It is and it isn't, since it wasn't the new hires that got fired exclusively.

The correction is happening in the market. During the Trump era companies converted tax money into buy-backs, and did other things that inflated the price with no real growth to back it. But everyone did it, and the interest rate was low, so investors felt ballsy and went with it. In 2020 it got even worse, and a recession hit everything, except the market, because everyone was doing it, and the interest was low so investors felt ballsy and went with it.

Then in 2021 the economy lurched again, and things started moving. The interest rates were low and the investors felt ballsy, so the market stayed waaaay overinflated. But investors refused to believe it was their fault. The market reacted by, rather than lowering the market, making everything more expensive until the market was effectively cheaper. But it couldn't be the investors fault, so people assumed that supply was limited due to issues with the distribution. But as distribution and supply improved, the inflation kept, but it couldn't be the investors. But something had to be done, and so interest rates went up. When interests rates went up, investors weren't able to leverage their investment against loans as effectively, so they started feeling less ballsy, their coke waning, and so they started selling, which lead to a snowball effect. The investors couldn't have done anything wrong, so it couldn't be a market only correction "the recession is coming" said Chicken Investor and everyone said the same. The prices kept falling, but the rest of the economy was doing just fine.

Now investors don't use economic models, that is they are not based on theory and science to be proven to actually describe the way things work. Instead they use financial models, which play it a bit more loose and are more of general guides on how the economy works. So we have a model where companies decide how much of a company's valuation is real and how much speculative. That is if you buy a $100 stock, it may be that if the company folded right there and then you'd get, at most, $80, and the other $20 were bets on the future (that didn't pay off). They use a model that goes backwards, they look at the revenue and profits the company makes, they look at the capital they have, the number of employees, etc. and from it create a base valuation. When the stock price is higher than valuation, that means there's speculation, how much investors are willing to do depends on how ballsy they feel. When the stock price is at-price it's considered "a safe bet" basically a good place to put your money that will have some returns and is good as a backup. When the stock is worth less than the minimum valuation, people assume that the company is imploding, losing value and worth getting pennies on the dollar, and therefore will be worth even less in the future. When the economy is doing well this model is very good, it basically is about company size vs how people feel about betting on it, a clash between reality and observation. But it doesn't capture all cases. Like using a barometer to find out how high above sea-level a boat is and realizing that if it lowers that means the boat is sinking, unless it's just the tide going down. Same here, the model doesn't capture cases where there's a large market correction, and it really doesn't work well in these cases. But that would be admitting that the investors got greedy, and the investors did not do a mistake, it must be the employees that got it wrong!

And so companies do layoffs. There will be those that try to make excuses, to say this is a solution to a real problem, which is absurd because there isn't. They will say that the companies over-hired and are losing money for it. But the profits and information doesn't show that at all. The companies did over-hire, and therefore should set a period of internal-optimization with low-hiring rates, and a focus on performance and project analysis. So by the time the market stabilizes and the correction is over, the companies would find themselves roughly where they should be. The need for mass-layoffs is only to keep investors happy with their arcane (at least to them, many don't seem to understand the logic behind them) models. But of course, this would mean that it's the investors who are being too greedy, and they can't be wrong. The thing is this won't prevent the hit to investors for the next quarters. It doesn't mean that investors won't get their money, we were just 4 months ahead of schedule. This is like getting your May paycheck deposited on February by accident and then withdrawn. It just isn't their money yet but it will get to them. But the investors can't be wrong or impatient in their greed. People will argue "lowering the amount of employees will result in lower prices", except, of course, we already had very low levels of unemployment way before COVID, and inflation wasn't hitting hard. Sure there was overhiring in 2020, but this itself was a symptom of over-valued companies, no the source of the problem. And even then inflation was driven a lot by companies demand, based on their high prices. As long as companies are not reducing their demand on products and services, we won't see this go down. The other thing is that employee expenses are pretty small compared to other day-to-day sources of costs, yet we are not seeing a rush to cut on those down (because those actually harm the company's profit in the short-term). That is, once you look at the bottom-lines, the same problem of limited profit growth (while revenue has been booming) is still there. The reality is simple: you hold the boat, keep it slow, and rake in the huge increases in profit from just internal optimization.

Companies are not really making an effort to reduce the impact on the long-term, and this solution won't quite make it work. Markets reach equilibrium really well, but they can do this in all sorts of fashion, and will destroy everything in their path. But hey, the investors do not get greedy, and this isn't their mistake. Somehow it's all for us right? Either way, at least I can say this (being one of the many people laid off) this too shall pass. It's just the flow of things.

You're looking at this backwards.

The only employees who make it to middle management are employees who authentically already care...

...about the company's bottom line. It doesn't matter that 90% of people realize it is a bad idea and wouldn't do it, only that 10% gets to go beyond team manager. And when you look at the distribution of employees this makes sense. As to why? Well that's an individual thing. Maybe naivety and really believing trickle down and they'll be protected for helping the company. Maybe greed and ambition and they realize this is what they have to do to move upward. Maybe some level of sociopathy or narcissism where the desire for power alone is enough to get them going.

And they really get nothing. During layoffs most people don't realize, but middle managers are on the chopping block by default. Less slaves means you need less whip masters. There's no loyalty, benefit or protection as a middle management, all their job is to be disposable villains who take them blame so execs can keep collecting their bonuses with employee moral untouched.

So yeah, makes perfect sense. If there's enough evidence that employees could sue the company, all they have to do is act "appalled" at the actions of "a rogue manager" and then "promptly discipline" them by firing them. While they get other mid-mgmt to keep trying to bust the union through other methods until something sticks.

These are the same people who argue that almost everything is discovered but "accident". 90% of "accidental" discoveries I hear about is some scientist testing the assumed "obvious" case, and discovering it was actually wrong.

Some of the most revolutionary science discovery comes from research validating (and failing to) the "obvious" assumption.

And what does it add to say it's obvious? That we get to post ourselves in the back and say "I'm not a researcher doubt the actual work, but at least I knew that already"?

Or it could be both, there's a lot of feedback loops on the digestive system. Or it could be that both things are mostly unrelated, but there's a common issue which causes both unrelated (e.j. high processed sugar which make consumers crave more, separately also promotes monoculture on the guy biome).

Thing is there's very little papers identifying casual effects, only correlations. That's why there isn't an official medical advise, but just recommendations from people on the field (as in going this can't hurt). If the scientific community actually believed there was an issue proven to be caused by gut biome, we'd read papers on how to tweak probiotic cocktails and what not to prevent this.

This was, to the company, a reasonable risk. They already had a loss, large enough that it was worth it to get lawyers and try to argue as hard as they could. Sometimes you do the math and spending thousands for a 1% shot at recovering millions of worth it.

Negligence is the correct assessment, but more often than not it's lack of understanding, but penny pinching. At least you wouldn't see them spending this much on lawyers. People get experts to make certain things are safe, but many don't understand that IT person is not a cyber security expert. For all we know this court theater was to argue they were legitimately lead to believe they were covered for this situation and therefore can point at someone else.

That said this is based on generalisms, and I've seen a lot of companies with serious mismanagement. This specific case could certainly be the case of someone who would not listen to reason or advise. All I'm stating here is, as far as I can tell, there's not enough info to be reasonably certain.

In theory it's around the area that the generation side spreads to larger than the population, in practice this won't matter much since the size of disconnected populations that su but reach a branch is tiny compared to the greater world, unless you happen to be a member of that specific branch. The other factor is that incest probably covers a good chunk of that tree, which child reduce how quickly your tree widens (that is there could be multiple people in your tree that only have two grandparents) so technically our generation exponentiation is not 2, but some slightly smaller number (the average). It probably is very close to 2 though, so it should be a good enough estimate. Another factor that we're not considering is that not everyone reproduced, and that we really want the population that reproduced, but that's hard to measure and the number is probably pretty close to the total population.

So you find the average gap between generations, lets say 17 years (consider that for the greatest amount of time people got married at 13 and it was common to have had most of your children before 20). So it's mapping the population in year *x* vs *((YOB-x)/17)^2* where *YOB* is the year you where born. The x` is almost certainly with an error of 10 years, though it could easily be a century before you even hit the millennia. I am sure you won't go to BCE, and I'd be surprised if the year was any time before 500CE.

A more interesting question is: assuming modern family conventions, and generally full population mix, how long would it take until someone is probably a descendants of everyone that reproduced?

The data is pretty damming, even casual observations imply the situation is much worse. Given how consistent it is with cold actors, you'd expect to also have some friends who are in a similar place, with intimacy replacing popularity as the reason you get to follow the fall from grace.

Certainly a lot of kids go through it, but Hollywood has a way of making it worse. When you think about what the parents are ok with, and must constantly allow on their kids, you can see why they are natural targets to abusers. Also the popularity is equally traumatizing: it's like when you screw up so badly you need to move to another town to get a chance, except every town knows and is laughing at you together.

Lazy parents have always been a thing for all generations, they all show it. If you see it more in Gen X and millennial, it's more because baby boomers in their 50s forward and late Milleanials/Early Zoomers in their very early 20s tend to have less children due to age.

And it's not about lazy parenting. Certainly there's better parents and worse parents out there. It's tired parenting. We live in a society that is not friendly to having families and children. I'm glad you have made the decision to control and regulate. I hope that, when you get home after an all-hands that kept you at work until 8pm, and have the 4 year olds fighting and having a tantrum, while Mom herself is just exhausted after trying to mediate a conflict that must simply be let to happen, that you'll find the strength to not put a pause on the conflict and give them media devices, but will instead, somehow, find the strength to be a good parent at that moment too. And if you don't, please don't feel bad. It's a marathon, it's 20 years, humans make mistakes, it's about doing the best you can. And maybe it'll get hard with guests, I've certainly seen it where the guests themselves get uncomfortable (but guess what, kids are rude, inconsiderate, and waaaay to energetic, it's easy to judge hard to actually do).

Most rockets make it to LEO, no need for more. We'll probably still have Leo rockets and then use skyhooks to move into upper orbit where moving by rockets. The thing is we rarely need to do this right now. Maybe once Artemis puts a lunar station, and there's incentive to mine or something in the moon, it might make sense to bake a skyhook bridge from LEO into Lunar orbit. But that is going to take a while, we might see it in our lifetime, but not anytime soon.

Yup, the thing is, even with preserved you still have to contend with the natural decay of DNA. It's a half life of 521 years. This doesn't mean half the DNA is gone, but half the bonds. So after a couple centuries all you'd see are a bunch of pieces of DNA that you have to, somehow, assemble in the right order, that's a puzzle on a different level. The only way to slow down there process is to freeze which, Uber right conditions, can extend the half life by thousands of years.

Also wooly mammoths are not that old. The pyramids where built when there where still mammoths. But that depends what mammoth we are talking about.

And that's the other reason. When we talk about mammoths, we are talking about many species, that lived across all the north of America and Eurasia for around 5 million years. When we talk about dodos, it's a species that only lived in an island (not Madagascar but way smaller Mauritius) over who knows how long (their branch splits 25 million years ago, but they are so different from their closest relative species that it's hard to define when the actual dodo, vs predecessor species, evolves) but it may not be that much. So the chances that you have pieces of dodos that happened to be in a place that preserve them well enough is much lower than that of any mammoths'.

It's more than that. If something has a beginning, then you can deduce there's a before. If there's a before this means that things can change.

The party is trying to erase its beginning to make itself appear eternal, as the only ways things could ever be. It wasn't deliberately vague to let people imagine. It's part of the political agenda of the party to rewrite human nature to ensure it can't be removed.

Separate issues.

The Uni thinks its worth a certain amount and can justify it because people pay it. People will normally get grants and fellowships to achieve this, and the name of the school helps a lot with the grants.

But these rarely give you a stipend, and some scholarships given by the school require you work at the school as well. People find themselves as employees and students. But just like being an employee won't give you any benefits in class, there's no reason than being a student means you can demand less of your job. Two separate roles and separate issues.

Perfectly fair that if the college is going to offer jobs "to help people self-sustain" they should be capable of self-sustenance with that job. A Union is a good way for students to define what are their needs.