feral
In the EMH people do still trade, it's just that only those with new information trade profitably.
In the joke scenario, as a parallel, you just need the economist to model that there's a chance they are the first person to see the bill.
Then you have a game theory game, where your chance of being the first person to see the bill depends on what everyone else thinks their chance was to be the first person, and hence did or didn't pick up the bill.
Then there is some optimal mixed strategy where people try pick up the bill with a certain probability, and it all works out.
There appear to be stable configurations for the 4 body problem too, even if the 4 masses are similar.
Its not fair to say there's no periodic pattern possible with 4 suns, then, even though most initial configurations might lead to chaotic behavior.
So this article is talking about "to what extent did Irish culture/language/poetry influence early English poetry.
Why might we think there could be an influence there? I'm Irish, but no expert of any sort on history, so going to speculate here, but there's a common story in Ireland that Christianity came to Ireland late, just before the 'dark ages' in Europe, and that Christian culture and monasteries thrived in Ireland, and then Irish monks later carried this learning back into Europe. You can think of it a bit like the Foundation scifi stories!
So maybe that's a factor here.
I'm not a big believer in making detailed notes during a 1:1
When necessary, I capture key points and action items in my phone while walking.
Typically, both people walk, using good Bluetooth headphones.
I was finding the combination of staring at the screen while coding, and then also during meetings, to be very straining.
Something about having to focus intently on the screen during a 1:1 was particularly tiring. Maybe because it's socially rude to look away while the other person is talking, so you end up really staring at the screen?
Now, instead, during a walking meeting, I exercise and get to look into the distance.
The most surprising thing for me was the ability to have technical conversations like this. I expected we'd really suffer without a whiteboard. Sometimes this is true, but I find we can have a lot of very technical conversations with voice only, and actually it's often easier than with video chat - because with voice only, I can focus more on the technical issues being discussed.
Finally, there are some days where I have a couple of hours walking meetings scattered throughout the day, and this is a lot of extra movement, which has to be good.
I wish there were more employers experimenting with different communication modalities like this; I feel the pandemic for all it's costs, has shown there are some great opportunities for other ways of working which have been underexplored.
Just to zoom right in on neural networks:
People often say this, and I never see a solid argument.
I know very little about biological neural networks.
Clearly they are very different in some respects, for example, meat vs silicon.
But I never see a good argument that there's no perspective from which the computational structure is similar.
Yes, the low level structure, and the optimization is different, but so? You can run quicksort on a computer made of water and wood, or vaccum tubes, or transistors, and it's still quicksort.
Are we sure there aren't similarities in terms of how the various neural networks process information? I would be interested in argument for this claim.
After all, the artificial neural networks are achieving useful high level functionality, like recognizing shapes.
We're not making absolute statements about either new or old things.
We're not saying any one new thing definitely won't last another 10 years, the same way we're not saying any one old thing will definitely last another 10 years.
The whole discussion is about what things are likely to do.
There's a place for formal logic in discussions of probabilistic models, but it can also confuse people.
> Believing that any rocker that is new won't make it is wrong, in fact you can be certain that some will.
If you were made to bet money repeatedly on whether a new rocker or an old rocker would still be a rocker in 5 years, you would end up with more money if you always picked the old rocker. This means that older rockers last relatively longer and new rockers last relatively shorter ("on average" / "by expectation" / "probabilistically"); that's literally the same statement.
That the underlying mechanism might be gradual removal doesn't change this, and doesn't matter.
It would, if the only thing that could make the streets wet is rain; ie if we were in a limited model where that was true.
We're talking in the context of a limited model in this thread. My point was that the statements are equivalent in this model.
Alternatively:
Imagine we were talking about the size of a pizza and how big a dinner it will lead to.
A blog says "wider diameter pizzas generally lead to bigger meals. Narrower diameter pizzas generally lead to smaller meals".
The first comment here says "just because wider pizzas implies bigger means doesn't mean narrower pizzas implies smaller meals, the blog has made a logical error!".
I reply "in the context of roughly round pizzas the two statements are the same".
You then give the example about the rain and streets. Hopefully it's clear that your general logical point isn't relevant to the pizza discussion, where there is a relationship between width and area.
Similarly, in the Lindy model, which we are actually discussing, there is a relationship between age and expected lifetime. That relationship is more complex than the pizza one, which confuses things, but hopefully the pizza example makes it clear why there isn't a general logical error here.
Sure, you can have a much richer model of the world than the Lindy model provides.
>fact that it is young doesn't mean it is doomed, or even that it is not good. The Lindy effect says nothing in that direction.
No; it says that young things are less likely to last than old things. If the average expectancy is small, sure it doesn't guarantee any one thing is doomed (some aren't) but it absolutely does tell you that most of them aren't going to last long.
Imagine a friend tells you that they know someone who is planning to become a professional rock star when they leave school.
They are probably not going to make it, because most people who try don't. You can't be sure, because some people do become rockstars. But it's not an error to be sceptical of their chances.
If you hear they are still gigging after 3 years, even if they haven't made it yet, you are probably a tiny bit less sceptical.
That all makes sense, right?
No; not sure where you got that from.
>For a product at T = 0, why would it be optimal to assume its lifespan would be T = 0 as well, rather than the average age of all products?
I'm not saying it would be. (The original blog might, but that's irrelevant to my posts here.)
If you knew the average lifespan of all products, that would be your best estimate of the lifespan of a new product. (You can't use average age naively without thinking about right censoring.)
If the average lifespan of a product was short, then the average lifespan of a new product would be short. As the product aged, it's expected lifespan would increase.
I.e. the longer something has been around the longer it will be around, or, equivalently, the shorter it has been around the shorter it will be around (both obviously taking about expected times.) Make sense now?
So if we assume that's implicit in my example, how does the Wikipedia page on the contrapositive help now? (It doesn't, as my argument isn't relying on any property of it.)
But I think you are outside the Lindy effect model at that point.
To put this in the example of the original post: The author says visual studio code is expected to last less time than VIM.
You could counter by saying: "hey, maybe, uh, the rise of Product Management as a discipline has meant that modern software overall will have longer lifetimes, and hence it's not fair to guess that VIM will outlive VSCode".
And that'd be a fine position. But imo the right way to frame that isn't "the author did an incorrect logical inversion of the Lindy effect model"; rather it would be "I don't think the Lindy effect model applies to this domain".
(No one is saying the Lindy model is universal.)
I guess you could say you want to apply it only within a given year of software; so, we're happy to look backwards and apply the Lindy model to software written in 2011, but we've no idea how to think about the lifespan of software written in 2022, and aren't allowed make any inferences from software written before 2022.
That's fine, but that's an additional constraint we've added, is outside the Lindy model, and, really, we're in "all models are wrong, some are useful" territory here, where I'd ask "is it really useful to throw away all that previous data? Wouldn't it be a better starting point to use the lifetimes of previous years as at least a prior?" And if you grant that, then I think theres no logical error here.
Why can't we 'invert' it, just because it's statistical effect? Yes, in your formulation, some of the new things in the pool will go on to live a long time, while others will be selected out.
But so what? We are talking about the expected lifetime of an item in the pool, conditioned only on it's age. There's no fundamental problem making a statement that this expected lifetime is short for new things, even if some fraction of those new things will last a long time, right?
After all, we don't know any one individual item that's been around a long time will last a lot longer. We only know we expect it to. Because even long lived items have finite lifetime, hence they'll eventually die (and when they do it'll be really surprising, because they've been around so long; but it will happen eventually.)
And so the statement is always talking about expected lifetime, whether for items that have already lasted a long or short time.
(Hence I still don't think there's really any logical 'inversion' here.)
Your statement A:"The longer something has been around, the longer it will be around" is not the opposite of B:"the shorter something has been around, the shorter it will be around".
Rather they mean the same thing. 'longer' and 'shorter' here are just English language ways of referring to the same time t that an object has been around.
If someone tells you "the longer a distance is, the more time it takes to walk it", that is exactly the same as "the shorter a distance is, the less time it takes to walk it"; there's no logical leap there.
I could conceive a rule that says "archeological artefacts are likely to be around for a long time" and it'd be a mistake to conclude that this means that non-archelogical artefacts will only be around for a short time.
But that doesn't seem to be how the Lindy effect is formulated, either on Wikipedia or on your post, so there doesn't seem to be an error in applying it to new things.
It seems we agree with Wikipedia that ML contains RL?
It's true RL is also studied in other fields.
I struggle to see how that means that RL is a good answer to "What are some AI algorithms that are still in use, that is not Machine Learning".
In the spirit of the cutting edge, any chance you could give me a chain-of-reasoning on that inference?
Eg Wikipedia: "Reinforcement learning (RL) is an area of machine learning " https://en.m.wikipedia.org/wiki/Reinforcement_learning
To sketch here:
Consider R the reproductive number of a disease (or R_e, the effective reproductive number, to be specific). This depends on both the inherent contagiousness of the disease, but also on the behavior of the population. If people choose to have fewer contacts (or bars are closed) then R decreases.
Let's say covid is spreading. We ask people to limit their contacts, and see if this stops spread.
We can think of this as trying to remove edges from the contact graph that the disease spreads on.
The contact graph becoming connected or unconnected as we remove edges is clearly percolation.
(Subject to some modeling assumptions about edges being removed at random, but these assumptions are common to both Erdos renyi graph models and SIR style compartmental models).
Make sense?
The size of the total epidemic varies sharply as R is changed in this way.
(Although sit on top kayaks are usually called as such, confusingly).
I understand why he did it, but wish he didn't. If I was a leader at YouTube I'd be pretty concerned about this - long term, it drives your platform in the direction of clickbait. Yeah there's benefits to that, but could also turn into a competitive weakness for YouTube. I think they should improve the recommender to make this unnecessary.
This isn't a good model. Boosting your company's profit by 100k doesn't mean they can boost any company's profit by 100k. If they could, they'd capture a lot more of the 100k.
His basic argument is that the contract is you paid $10 for each book you got, so the author doesn't owe you the next book.
I disagree with this. First off, the fact that the fans are so upset is evidence that this wasn't _their_ understanding of the contract.
The thing is, they didn't just spend $10. They spent time getting invested in the story. This emotional investment is left unfulfilled if they don't get the implicitly promised ending.
Now, let's turn this around. Let's say fans do decide that their $10 strictly comes with no expectation of the next book coming.
Well then many fans wouldn't have bought the first books until the whole series is finished. It would likely become uneconomical to write long fantasy series. Writers like Martin would suffer.
It's easy for Martin or whoever to drop the ball on the implicit contract now, after becoming rich; but if this contract hadn't existed, authors like Martin would probably have desired to create it explicitly. If you believe that, then Gaiman's argument doesn't hold up.
And so not finishing a promised series is taking from that trust commons.
Of course, sometimes humans _can't_ finish something, and readers should accept that risk. The authors shouldn't be harassed.
But wanting them to follow through is not the same as thinking "they're your bitch".
- The author has not deeply understood the subjective interpretation of probability, as widely used by bayesians, and applicable to this sort of reasoning
- the author, and the sources the author quotes based on a quick read, seem unfamiliar with quanifying uncertainties not just as point estimates, but instead by using a probability distribution, which you can use to easily tell the difference between a mean probability of .5 based on say 5 heads and 5 tails, vs one based on 500 heads and 500 tails.
The point that it's often difficult to tell whether a particular probability should be 0.0001 or 0.00001 is a fair one.
But overall I don't take much reassurance from this article.
Consider getting a good pair of sound isolating earphones, or even ear defenders!
Not to neglect your child; but to reduce stress levels so you can problem solve effectively.
The cry of your baby is one of the most emotionally powerful sounds there is. You'll experience extreme stress if you can't calm them down quickly. Probably for good evolutionary reasons, so the parent doesn't ignore their child.
But sometimes there's a problem you just can't instantly solve.
If you find yourself getting really stressed, and losing your ability to act effectively, being able to put something on your ears and reduce the volume while you, e.g. change the nappy/diaper, can reduce stress and help you be a better parent.
I only had to do this a handful of times for each of our kids, but when it was helpful, it was really helpful.