Or... both can be true.
1,162 karma · joined June 17, 2017
I think learning is really hard work, and so most people's first reaction to hard work is to say No, and then go and construct an a posteriori rationale for why actually they shouldn't do that hard work (it's not that useful, you're never gonna use it, you're an expert at something else, etc).
Similar story for why asking data structures in job interviews is a bad idea when you're an applicant (but the people who have been hired and are hiring, do think it's good to ask)
That was my point.
Mention some mathematically advanced idea: out come the pitchforks about how you don't need that, all you need is code/market size/scalability/product fit/investment/execution.
Mention a banality that anyone who studied algebra knows: frontpage.
Lets take this hour to be an average hour. Over 12 hours in a day that the mall is presumably open, that's 22*12 = 264 leads.
> the mall isn’t behind these giveaways; they merely rent out the advertising space (for around $1.5k per day).
This would imply that these leads need to be worth at least $1500/264 = $5.68 per lead. Does this look like too much to anyone else? I would have guessed that independently of what they're selling, the conversion rate has to be attrocious, as people signed-up not because of their interest for the thing being sold but because they wanted a free car.
Maybe the reason why this works is that people who are stupid enough to give out their names on a mall promotion, are probably also easy to push shit on.
EDIT: I forgot to add at least two other costs: they need to lease the car, and they need to pay employees to keep an eye on everything. So that figure is more of a lower bound.
Regarding what I'm doing now, let me know if you want to talk in private. Nothing too exciting though.
I don't pride myself of being a highly moral person, but even to me this seems pretty weak. I think I'm less shocked by "I don't care if I get people dead as long as I'm getting paid", than by "ordinarily I'm really concerned about the consequences of my actions, but since I got that extra mortgage I will get people killed if I have to". Somehow it seems the former is just imoral, whereas the latter is imoral and stupid.
In any case, OP wasn't claiming that "everyone has a price", he was asking for examples where someone did have a price, so your comment doesn't even apply.
Ex-physicst here. JumpCrisscross is absolutely right. It's a hard sell and it should be. Saying "I want a better machine but I don't know what I'm looking for" is only worse than "I want to put objects together but I don't know what machine I'm building."
The space of what's concievably possible is too large to explore if you're not guided by an hypothesis, even if you didn't have money constraints. It's not a human problem, reality is just complex.
EDIT: It is suspected that Michael Cohen, being a long time Trump friend and personal lawyer, is familiar with Trump's money laundering. If Michael Cohen believes he's at risk of being sent to jail for many years, he might collaborate with the FBI.
> Nowadays advertising mainly tries to sell things that people don't need. (...)
Free market fundamentalists (I'm not one of them) would tell you that no one can judge better than the individual themselves what they need or don't need, and that if an individual says they're willing to part with their money to buy X, then that's them saying they actually need it.
Even though I don't buy this view because the world is more complicated than the binary cause-attribution that it implies, I do think it brings up the question of who decides who needs what. If I believe Axe deodorant will get me "the hottest babes" and I decide really need it, would you have the government override my decision?
This is an excellent comment because it explains Stallman's accuracy (vs his opponents) not in terms of the fundamentals of their reasoning but in terms of their heuristics for judging motivations.
That's not a context. That's the statistic they're calculating.
> To me 60% sounds good, but apparently not to people in this thread :)
What does "good" mean, and compared to what? If you can't put a number on "good" you're flying blind.
But hey, I'm just trying to warn people that the maths are garbage here; but if that looks good enough for you go ahead and trade it.
- during earnings provide extremely positive guidance for next quarter. this will bump stock up
- as time goes by, slowly update your guidance downwards. if this process is slow, hopefully stock won't be very affected. repeat this until you're convinced that your public updated guidance is below your private earnings predictions.
- when earnings come, you'll over-shoot your depressed guidance. This will bump stock up. Again, provide extremely optimistic guidance for next quarter.
All methods that you'll ever see have >50% accuracy, because if you find a signal with <50% accuracy you'll just flip the sign in the sginal and call it >50% accuracy.
Here's a bit relevant to this conversation:
> Previous work on predicting the directionality of Bitcoin prices has shown that significant signal exists in the price of the cryptocurrency. Hegazy and Mumford (2016) compute an exponentially-smoothed Bitcoin price every eight minutes; using the first five left derivatives of this price as features in a decision-tree based algorithm, they predict thedirection of the next change in Bitcoin price with 57.11% accuracy.
> Their results substantiate earlier research done by Madan, Saluja, and Zhao (2014), who found that by using the Bitcoin price sampled every 10 minutes as the primary feature for a random-forest model, they could predict the direction of the next change in Bitcoin price with 57.4% accuracy.
> An alternative model was used by Sebastian, Katabarwa, and Li (2014), who use the Bitcoin price sampled every minute as the primary feature for a forward-feed neural network. Their results suggest that this system predicts future Bitcoin price directionality with 60% accuracy.
The most glaring evidence that this entire paper is garbage is the fact that zero time is spent on putting these numbers (57.11%, 57.4%, 60%) in context. What do I mean by context? For example, observations like the fact that for the same dataset if you use a daily resolution and your prediction is always "up", you'll beat those accuracies. Obviously, the reason why this discussion is absent is because it's a lot harder than just dumping a dataset into sklearn.
So the cycle is:
- problem exists
- quickly centralized solution to problem appears
- new problems appears
- slowly decentralized alternative solution to old problem appears
- repeat
I would add that it seems that the big winners are the ones coming up with centralized solutions. The inventors of decentralized solutions don't have nearly as great rewards.
I have no strong opinion either way. But I was wondering if your belief isn't motivated more by your familiarity (with those scenarios) than anything else.
Personally I would note that in those examples you mentioned you have freedom of movement, which to me seems isn't a politically unacceptable solution in the UK.
Post history is relevant to assess if the person you're talking to has a very strongly set view on an extremely complex matter. If they do, that suggests that the reasons for the person spending time defending that view might not be motivated by exploration of ideas, but by evangelization of their view.
I think having an estimation of the person's motivation is important because conversations with people motivated by exploration of ideas can make for interesting, insightful and educational conversation, whereas the ones movitated by evangelization tend to be reduced to the same catchy but superficial arguments that they've learned over the years.
That was a common sentiment amongst Americans.
As a European, you don't really think "yeah, screw those American monopolies... but I like the European ones"
Why would he be damned if he does? Because he knows things don't look good. In that case, it makes perfect sense that the stock drops like a rock. Am I missing something?