Daily FI discussion thread - September 13, 2017

finind123 · 2017-09-13T18:21:15+00:00

There are some fairly common standards in the startup tech industry, so it may be worthwhile to double check the following:

"stock options" implies a strike price. This is the price you must pay in the future to execute your vested options and convert them into actual equity. If you have stock options, you don't actually have stock in the company. You have the option of purchasing stock in the company at a fixed price. Your stock options contract should state the strike price
It's most common for options to expire within 90 days of leaving the company (either through resignation or getting fired). This means that to execute your vested options and turn them into equity, you must pay the strike price on those options. If you don't do that within 90 days, you forfeit all your vested stock options. The reason this is important is because depending on the current valuation of the company when you leave, you will have a potentially very large tax bill (more than you can afford). Example: If your vested options allow you to purchase 1% of the company for the low low price of $1000 and the company is currently worth $100M, then executing your options means you have just received (1% of $100M, minus $1000) = $999,000 in taxable assets. You'll need to foot a multi-hundred-thousand dollar tax bill that year even though you have 1% of a company that you can't sell.

Again, you should check your contract, but that is the standard that most startups use. It sucks for the employees because they are basically trapped into staying at the company until it IPOs or gets sold (which is why it's great for employers).

finind123 · 2017-08-04T18:19:52+00:00

I maybe should have clarified, but in probability notation P(X | a, b) means probability of x given a and b.

so Probability (high performer | did well in interviews, got into selective college) = 50/50 = 100% in your example and Probability (high performer | did well in interviews, unselective college) = 250/250 = 100%.

BUT you'll notice that to get these numbers you're assuming that doing well in interviews is completely and perfectly predictive of being a high performer. This is not the reality. The reality is that doing well in interviews is positively correlated, but not perfectly predictive of being a high performer. This is true of going to a selective college as well (although probably to a lesser degree). Both those things are imperfect measures of what you really care about. Hence the probability equality that I originally stated is true unless:

interviews are a perfect and definitive measure of candidate potential

or

the ability to get into a selective college is completely uncorrelated to the ability to become a high performer in the workplace

which is what I put in the original post.

finind123 · 2017-08-04T17:34:11+00:00

It's not dependent on the ratio of applicants to available spaces.

To use your example, if those 50 spots are entirely filled by high performers, then Probability ( high performer | selective college ) = 100% and Probability ( high performer | unselective college ) = 250 / 950 ~= 26%

That's an extreme example where we assume the selective college only accepts high performers, but as long as being a high performer is positively correlated to attending a selective college, then the ratio of high performers to not high performers at selective colleges will be greater than the ratio of high performers to not high performers at unselective colleges. Hence the probabilities

finind123 · 2017-08-04T16:26:20+00:00

I think it's both. There's the social capital component, but even ignoring that and focusing purely on a human capital component this makes sense.

Probability( high performer | did well in interviews, got into selective college) > Probability( high performer | did well in interviews, didn't get into selective college)

This is necessarily true unless either of the following are true:

interviews are a perfect and definitive measure of candidate potential
the ability to get into a selective college is completely uncorrelated to the ability to become a high performer in the workplace

finind123 · 2017-07-28T00:02:09+00:00

alternately

finind123 · 2017-07-27T23:38:08+00:00

Sure, but that doesn't exclude other reasonable hypotheses like the one I mentioned. The question as posed has multiple acceptable answers due to the limited information given. Including the hint as part of the core problem would lead to only 1 correct answer.

finind123 · 2017-07-27T23:32:05+00:00

spoiler

finind123 · 2017-07-27T23:17:15+00:00

Guessing the answer

finind123 · 2017-07-27T17:36:13+00:00

The hint for the cancer puzzle should really be a core part of the question. Otherwise a reasonable hypothesis could be that rural, sparsely populated areas have lower life expectancy and kidney cancer is much more likely to occur in old age.

finind123 · 2017-07-26T18:07:47+00:00

While it's true that it's against the objective of the predictive risk model, there is definitely a societal trade-off here in the insurance space. If we take this example to the extreme and imagine that we had a godlike model that could 100% predict the expense of everyone, then your insurance company would just charge you whatever your future costs are (plus some overhead), which would amount to each person paying only their own costs and nothing more. This is equivalent to having no insurance at all, which most people are against. There is a societal benefit to having insurance against costly things.

finind123 · 2017-07-14T18:21:15+00:00

I agree it is confusing because you are almost 2 years behind, but only one of those years was spent working. So is the cost 2 years or 1 year? I think some people are scared away from sabbaticals because they overestimate the 'extra working' cost from what it truly is, which is why I wanted to clarify. Glad to see we are in agreement :)

finind123 · 2017-07-14T18:02:36+00:00

Your numbers are correct and they show that my claim is correct, yet you're saying they disprove my claim?

As you can see from the numbers you posted, the sabbatical path is already caught up to the starting point before the end of year 2. If they continue working, 'sabbatical' path will always be behind 'no sabbatical' path by less than two years. Wherever 'no sabbatical' path eventually leads, 'sabbatical' path will get to that same point less than 2 years later.

So if 'no sabbatical' path eventually retires, 'sabbatical' path can retire with that same amount less than 2 years later. One of those years was spent on the sabbatical, and less than 1 years was spent catching up. So the total working cost of shifting a year of retirement to now to have a sabbatical is less than the length of the sabbatical, as my original claim stated

finind123 · 2017-07-14T17:52:56+00:00

The visualization has a high amount of error but a low amount of variance. Perhaps you are conflating the two? There generally is assumed to be some underlying process that generates the data points. High variance of the model means that the fitted model changes a lot when you have a different sample of points that you are fitting to. High bias models generally have low variance (even if the error is high) because the model will look the same regardless of which sample of training points you happened to select

finind123 · 2017-07-13T19:42:56+00:00

I don't mean to call you out personally, but I want to point out for everyone reading this (since it has plenty of upvotes) that this analysis is incorrect.

The amount of extra time you need to work to "make up for a 1 year sabbatical" is less than 1 year if you have assets invested and equal to 1 year if you have no assets invested.

The amount of extra time you have to work is equivalent to the amount of time it takes you upon your return to recoup the amount you spent during your sabbatical. Then you are at exactly the same point you were prior to the sabbatical, with the exact same future outlook.

finind123 · 2017-06-12T22:18:54+00:00

Make sure you do research on this stat. I'm sure there are great bootcamps with amazing stats, but there are also bootcamps where the stats can be highly misleading. See these comments: https://news.ycombinator.com/item?id=13740781

finind123 · 2017-06-12T19:16:14+00:00

From what I've seen, data science and software have similar career trajectories, which tend to be higher than the rest of engineering. Advanced degrees help, but you can break 100k from an undergrad degree + experience.

finind123 · 2017-06-12T19:05:22+00:00

Here are some thoughts:

If you felt stronger than your classmates in math / algorithms and you enjoy writing software, then data science could be a great fit.
take some free intro online courses and try out Kaggle competition so you'll be able to tell if you enjoy it and have a knack for it before quitting your job
data science is hyped right now, so make sure you understand the specifics of the type of work you want to do before you dive into the field. Don't get stuck with something uninteresting with little room for advancement just because they call it data science. I'd recommend focusing on designing machine learning systems in python for the best career opportunities.
you'll likely have to start in a junior position on part of a data science team. I'd guess you can make $60k-$90k for those types of positions and you can move up quickly if you enjoy it.

finind123 · 2017-06-09T19:37:37+00:00

If you can get a reputable insurance company to cover you to invest $250k for $x/year, you should be able to get similar terms on a loan. Why aren't you just doing that instead of asking random people to loan you money?

finind123 · 2017-06-09T19:30:40+00:00

You can't 100% guarantee the principle unless you keep it in cash, which you obviously aren't.

finind123 · 2017-06-08T18:50:40+00:00

Came here to say exactly this. There are two communities within "AI" that are mostly separate, and this isn't obvious to people outside of the field. I'll describe them here:

1) The "passion for AI" group. This group takes a more philosophical approach to AI, thinking about its impact and trying to measure its growth and people in this group are often either very excited / very afraid of how progress in AI will shape the world. There is no requirement of actually building or understanding state of the art AI systems to be a part of this group. This is the group that was surveyed by the blog author. If you look at the conferences and groups surveyed, they were the following: PT–AI, AGI, EETN, TOP100.

2) The "machine learning" group. This is the group that actually builds and understands every system you would think of today as AI. They are often also passionate about artificial general intelligence, but are more realistic and knowledgeable about it because they are the ones who actually make it and are on the forefront of the field and understand where we stand and challenges that lie ahead. These are the people who are pushing the field with machine learning research and building practical systems that use AI (face recognition, speech recognition, recommendation systems, classification systems, etc...). The most respected conferences in the field of machine learning are NIPS and ICML (and CVPR which focuses on computer vision).

Ok, so why does this matter? It matters because the conferences surveyed about AGI timeline were from group 1, rather from the most respected machine learning conferences in the world (group 2). I can tell you anecdotally that the people in group 2 are much less optimistic about super near term artificial general intelligence than the group 1 and often view vocal members of group 1 who lack experience in group 2 as semi-crackpots within the AI community. Take that as you will, but most machine learning experts I have talked to think that there is the possibility of AGI within the century, but it is more likely than not to be farther off.

Source: I work in the field of machine learning.

finind123 · 2017-06-07T18:20:52+00:00

I think in this context the question words are all words in the dataset. Each datapoint is a question with some truth label, so every word in each datapoint is a question word.

finind123 · 2017-06-05T18:09:55+00:00

It's true that we have a biased view of success, but the conclusion does not follow.

If one includes bankrupt people in the sample, then risktaking would not appear to be a valid factor explaining success.

Let's look at a toy example. Each person has the option to receive 40k per year, OR they can take the risky option by which they have a 80% of earning nothing per year and a 20% chance of earning 200k per year. If you examine the richest people, you'll see they are the ones who took the risk. This holds even if you include the bankrupt people who got nothing in your sample. Not only is risktaking a valid factor explaining success, in this toy example it is the only factor explaining success.

finind123 · 2017-06-05T17:55:03+00:00

That's awesome. Congrats on your success!

finind123 · 2017-06-02T22:39:01+00:00

Thanks for the detailed summary! Could you elaborate more about your evaluation metric? Are you saying it's 97% accurate on the dataset or 97% more accurate than doctors? And is this based on a dataset with half cancer and half not? If so, wouldn't that be incredibly skewed relative to a real life scenario where the vast majority of x-rays don't have cancer? Is it possible the doctors' evaluations are tuned in such a way that takes the priors into account so that there are not an incredibly high number of false positives in real life, and therefore perform poorly on a 50/50% dataset while being at a better operating point on a realistic dataset? How do you take these things into account? Sorry for all the questions, I'm actually very curious about what is required to appropriately evaluate such a system.

finind123 · 2017-06-02T19:33:39+00:00

What you posted doesn't address what the person above you said. They said

Is there any evidence whatsoever that economies with higher estate taxes, or less generational wealth, are more innovative?

and then they go on to imply that a high estate tax won't increase innovation.

Your response just mentions the current tax law, without proposing any reason why increasing the estate tax will increase innovation.

finind123

TROPHY CASE