[svnpenn]

What do people use ML for these days? I do computer programming, and I have done some work with video encoding, but this just seems like a huge investment money wise. So I am curious what use it is.

For my needs the most intensive thing I do is compile some large programs or encode some large video, which you can get a computer for that for like $800.

[proverbialbunny]

ML is typically used to find correlations in data. If something happens over and over again, there is a high chance it will happen again. Having such an algorithm that has identified this correlation allows it to identify when it will happen again. This allows for what is called predictive analytics.

This can be as simple as identifying when a customer will end their service with a business, as there might be a pattern before previous customers have left, predicting when new customers are going to leave, and giving them a coupon or similar right before they would otherwise leave. This problem is called customer churn.

It can be as complex as identifying when hardware will fail ahead of time, or even bio-ware. For example, I did a project that predicted when people were falling into depression before they could tell they were with a high accuracy rate. I also predicted other future medical issues ahead of time, like the probability an elderly person is going to fall over within the next handful of days.

On the business side there are a lot of use cases for ML, but it falls more into analytics than engineering, as it's about predictive insight.

[aunty_helen]

Here's an example for what I'm using it for: https://news.ycombinator.com/item?id=23608360

I explained the technical details in the sub comment.

I was looking at buying one of these Titan cards a few weeks back but then nvidia announced the next gen processors were coming out so have decided to wait until they refresh the 2 year old titan line instead of paying full prices for an almost out of date card.

When training models for the object detection, the current algo we're using isn't focused on memory efficiency. So the 8gb card we currently use to train models is unable to process images at the correct resolution. We have to down scale about half to get it to fit. With the Titan RTX you get 22gb which is enough.

On another note, the titan cards aren't the same as the normal geforce cards. Nvidia have gone to great lengths to ensure product differentiation so they can charge power users with business budgets more than people sitting at home playing games. One of the good things about the titan cards is they have a dual memory controller so you can write and read at the same time which improves your fill rate.

[p1esk]

People use ML for pretty much anything these days, including compiling programs [1] and encoding videos [2]

[1] https://arxiv.org/abs/1805.03441

[2] https://arxiv.org/abs/1904.12462