I'm pretty sure Sam Walton didn't establish Walmart with the hope of being acquired.
It's bizarre that we live in a time where we can't even fathom a business that is fundamentally very profitable, we just envision growing the company until it's attractive enough for someone else to take on the unsustainable cost of running the business: either get acquired by a large company or hoist your debt onto the public market.
Investment really did used to be about more than a complex "greater fool" game.
As an ex-smoker, I also used to just toss cigarette butts anywhere without thought. Decades after quitting it's still surprising to me that I ever thought this was normal (I've never, otherwise, been a big litterer).
At least when I began smoking in my early teens this was just what smokers did. When you were done smoking you just tossed the butt on the ground and stomped it out. I remember being annoyed when someone scolded me for littering, at the time it somehow felt different but I admitted the logic didn't quite work out.
I suspect a lot of this behavior originated from behaviors established before filtered cigarettes were the norm. Just tobacco rolled in thin paper, especially when burnt to the end before it burns your fingers, will likely break down after the first heavy rain. Filtered butts however, hang around for a long time.
> I can see how it feels like cheating to coax the model to produce the answer you want. But... it's not!
If it's for a single example, it is absolutely cheating. As an AI engineer this is a particular point of frustration where people complain because a large system can't return the result they want, when they were able to get the answer they wanted on their own with a lot of prompt hacking.
Each prompt is basically a point in latent space, and if you're "tweaking" the prompt what you're really doing is just re-rolling the dice until you land in a neighborhood closer the answer you want. You're not better at prompting, you just got lucky and are confusing that for insight.
Now if you're specific prompting trick works across a suite of evaluations, then you are probably on to something. But what people are doing in most cases is equivalent to performing some ritual before pulling the handle on a slot machine and then, when they finally win, claiming that they finally stumbled upon the correct ritual.
A good QA person is to a software developer as a good editor is to a writer. Both take a look at your hard work and critique it ruthlessly. Annoying as hell when it's happening, but in my experience well worth it because the end result is much higher quality.
I might just be too old, but I remember when QA people didn't typically write tests, they manually tested your code and did all those weird things you were really hoping users wouldn't do. They found issues and bugs that would be hard to universally catch with tests.
Now we hoist QA on the user.
Working with younger devs I find that the very concept of QA is something that is increasingly foreign to them. It's astounding how often I've seen bugs get to prod and ask "how did it work when you play around with it locally?" only to get strange looks: it passed the type checker, why not ship it?
Programmer efficiency these days is measured in PRs/minute, so introducing bugs is not only not a problem, but great because it means you have another PR you can push in a few days once someone else notices it in prod! QA would have ruined this.
This drives me crazy. It's a cheap way of saying we're ok shipping crap. In the past, I've been part of some QA audits where the developers claimed their customer support log sufficed as their test plan. This wasn't safety-critical software, but it did involve what I would consider medium risk (e.g., regulatory compliance). The fact that they openly admit they are okay shipping bad products in that environment just doesn't make sense to me.
> don’t waste your time writing your own neural net and backprop.
I don't think you should be combining writing a neural network with doing backprop since I don't know anyone working with serious ML who is not using some sort of automatic differentiation library to handling the backprop part for them. I'm not entirely sure people even know what they're saying when they talk about backprop these days, and I suspect they're confusing it with gradient optimization.
But anyone seriously interested in ML absolutely should be building their own models from scratch and training them with gradient descent, ideally start with building out your own optimization routine rather than using a prepackaged one.
This is hugely important since the optimization part of the learning is really the heart of modern machine learning. If you really want to understand ML you should have a strong intuition about various methods of optimizing a given model. Additionally there are lots of details and tricks behind these models that are ignored if you're only calling an api around these models.
There's a world of difference between implementing an LSTM and calling one. You learn significantly more about what's actually happening by doing the former.
> the optimization part of the learning is really the heart of modern machine learning
It’s an important component but I wouldn’t say it’s the main factor. ML is ultimately about your data, so understanding it is critical. Feature selection and engineering, sampling, subspace optimization (e.g. ESMMs) and interpreting the results correctly are really the main places you can squeeze the most juice out. Optimizing the function is the very last step.
Basically, you can go ahead and optimize down to the very bottom of the global min but a model with better features and better feature interactions is going to win.
Further, there are a ton of different optimizers available. SGD, Adam, Adagrad, RMSProp, FTRL, etc. With just one hour a day, you could spend six months simply writing and understanding the most popular ones.
I have a few friends who had major liquidity events happen to them and they each own 3-5 homes as investment homes. I believe these properties were paid full in full and in cash, so no mortgage.
This small proportion of my friends is mortgage free, but they own multiple houses so a fairly large proportion of my friends houses are mortgage free.
As this other comment pointed out [0] 40% of homes are mortgage free, but that says nothing about home owners.
Anecdotally I know that none of my neighbors are mortgage free. However I also have a few friends that had major cash windfalls things like startup exits or time working in hedge funds, each of them owns 3-5 houses outright as investments.
So, with this small sample I can easily see that of the total number of homes owned by friends/neighbors, 40% being mortgage free sounds about right, but far less of my friends/neighbors are mortgage free.
I agree overall, but the census should be measuring the primary residence of the respondent and exclude second houses or renters. Basically it's measuring households and not houses.
Of all the ex-FAANG people I've worked with, I've found the ex-Amazon ones to be, by a wide margin, the least impressive group.
I'm not huge fan of Google, but will readily admit that, on average, the ex-Googlers I've worked with have tended to be great engineers and very sharp people.
So, at least from that anecdote, there does seem to be a difference in each of these hiring filters.
I feel the same way and would also never work at a FAANG.
That said, I suspect this is would be viewed as a benefit of the process since I'm fairly confident Amazon and the like don't want people who feel this way working for them regardless of the talent of those individual people.
I feel a better analogy would involve (A) a demand that is outside the official job-description or duties and (B) isn't a fixed-bar but instead makes multiple employees fight each other in an indefinite race to the bottom.
Ex: "Are you willing to spend your free time becoming the General's #1 personal servant? No? Sorry, there's no place for you in the infantry."
I don't find better but more vague perhaps in attempt to sound nuanced.
If one goes by job description Amazon is acting strictly within those parameters. And that race to the bottom is not really hunger games.
On the other hand if one goes by unsaid rules of games, everyone including management, HR, employee know FAANG salary is multiple times of equivalent IT workers. And perhaps 95% of FAANG employees are not really developing new algorithms, hauling terabytes of data on their shoulders or being the force of good whatever that means. Just keep doing made-up work and wait until reach x million dollars goal. After that leave quietly or loudly by writing a nasty blog post lambasting management.
So mostly people understand they have to keep up the facade because talking about what's really happening will make it rather awkward for everyone.
I think they're saying the interaction effect of doing drugs, drinking and considering oneself a victim is negligible on overall success.
It doesn't seem like a bold claim to me. I've known plenty of poor, struggling working class people. The only impact drinking or claiming they're a victim have is influencing how much fun they have at parties.
I've happen to know plenty of wildly successful people who also drink and/or think they're victims. Again, primary effect I've noticed seems to be how much fun they are at parties.
It's bizarre that we live in a time where we can't even fathom a business that is fundamentally very profitable, we just envision growing the company until it's attractive enough for someone else to take on the unsustainable cost of running the business: either get acquired by a large company or hoist your debt onto the public market.
Investment really did used to be about more than a complex "greater fool" game.