51–60 of 78 posts
So you're getting into a "We need to grow really fast to get more data than our competitors so we can add ML and create more value to the product so we can grow faster and get ahead of competitors".
I.e. ML is a competitive advantage which is often hard to come by for startups.
So, of the 4 outcomes:
(1) buy in now + worthless
(2) buy in now + 100x
(3) don't buy in + worthless
(4) don't buy in + 100x
(4) is a terrible position to be in, (2) is a good position to be in, and (1) only costs a little, and (3) is even. So when you are deciding to buy in or don't, you are deciding between good + little loss or terrible + even.
I agree with other points here, but I also think nobody wants to wake up 2 years from now and be the only company that was not investing in machine learning. It could turn into nothing, or it could be 100x the time and money put in now. So, of the 4 outcomes: (1) buy in now + worthless (2) buy in now + 100x (3) don't buy in + worthless (4) don't buy in + 100x (4) is a terrible position to be in, (2) is a good positio…
I agree with other points here, but I also think nobody wants to wake up 2 years from now and be the only company that was not investing in machine learning. It could turn into nothing, or it could be 100x the time and money put in now. So, of the 4 outcomes: (1) buy in now + worthless (2) buy in now + 100x (3) don't buy in + worthless (4) don't buy in + 100x (4) is a terrible position to be in, (2) is a good positio…
Because there is a real moat with data ownership and pipelines. If you want to do any analysis you quickly find that learning to properly use scikit-learn and tensorflow (or your machine learning algorithm of your choice) is atleast an order of magnitude lower of work than getting the data. For instance, I wanted to build a machine learning algorithm which took simple data from the SEC filed 10-Q and 10-K, which are…
One thing I’ll mention is that this is true both at the very early stages of a ML project, and even when an ML project is scaled up and in production. Oftentimes, the data pipeline is the true way in which a model will improve versus anything else, so it’s pretty critical that these data pipelines are setup to get an initial dataset but also to scale properly.
It’s one reason I started Scale (scale.com). It was viscerally clear that the real bottleneck to ML was getting the needed data, and in our case, annotating that data appropriately. It is very heartening to hear it echoed in this whole thread that data is very clearly what “matters” for ML.
Google and Facebook use algorithms to try and minimize dollars spent on moderating their sites, with limited success. If they can avoid paying human beings to make judgement calls, they save billions of dollars a year.
It’s reasonable to be skeptical of startups who claim that they’ll use ML someday. Ask them how they’re using human labor today to perform the tasks that they’d like ML to do, and what their runway is for that work at their current burn rate. If they don’t deliver a viable ML labor reduction by that time, they will either collapse or worsen their service.
Earlier quoted context omitted.
So much this!!! In all fairness, it doesn't matter what you pick up, you'll spend north of 80% of your time preparing and pre-processing data, which is almost always catastrophically tedious and boring. Annoyingly that also applies to publicly available datasets - pre-processing is still most of the work. For instance, the tensorflow team invested a lot of time and effort into tf.data for that reason but imo it doesn…
I agree with you, though I actually personally don't find pre-processing data tedious and boring. I kind of like knitting it all together. On a side note... data pre-processing is often viewed a side job that needs to get done before the real work can begin. I don't think I've ever been able to prepare a data pipeline without making decisions about the data that will impact the outcome. For example How do you deal wi…
I'd much rather hire a good programmer with some statistical knowledge than the other way around.