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Comment by minimaxir | Hacker News Reader

In this case, a tutorial from Facebook carries more informative weight than a Medium thought piece (and Facebook legitimately is a leader in ML).

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> and Facebook legitimately is a leader in ML

I am curious how you came to that conclusion. I would think that Google is a leader in ML - unless I am missing something.

> "a leader in ML"

not /the/ (only) leader in ML.

Google, FB and several of the chinese tech conglomerates are leaders within ML.

I am not trying to be a fan boy here. I am genuinely curious. What makes someone a leader in ML applications.

Google has more than studies and blog post about machine learning, apart from research and opensource ML applications, they are heavily dependent on machine learning in almost all their major product that I know of. For the most part they extremely good at it.

What and how does FB use machine learning in their product?

One example would be the automatic face detection of people that were not originally tagged in uploaded photos.

FB uses ML to do image recognition, face recognition, spam filtering and news ranking. They probably have many many more applications.

PyTorch by itself would be enough to make FAIR a leader. PyTorch is being used in many research papers and was/is a leader in "eager execution" mode for neural nets. But there are many other things, such as fastText (see more projects here https://github.com/facebookresearch).

Picking which items to show you in your newsfeed; making recommendations for places based on your friends; automatically generating blind-friendly captions for uploaded photos (read the hover text for some of your friends’ photos); suggesting events you might be interested in; friend suggestions; ads.

Machine learning is basically the product that FaceBook actually sells. At the end of the day, they are an advertisement delivery service. They get paid based on user interaction with ads and sponsored content. The way they determine what ads to show to whom and when is all ML.

On the user side of things, they use it to suggest friends, detect/recognize faces in pictures, etc.

- Facebook employs some of the most accomplished and widely respected researchers in artificial intelligence, such as Yann LeCunn [1]

- Every year, researchers from the Facebook Artificial Intelligence Research Lab (FAIR) present their research at top conferences such as NIPS [2]

- FAIR also produces some of the most capable and widely used open source software in machine learning, such as Torch and Caffe2 [3]

If you'd like to see how Facebook uses machine learning, take a look through its blog posts related to recruiting [4].

_______________________

1. https://research.fb.com/people/

2. https://research.fb.com/facebook-research-at-nips-2017/

3. https://research.fb.com/downloads/

4. https://www.facebook.com/careers/life/machine-learning-at-fa...

Thanks for the info, I didn't know about FAIR or that pytorch came from facebook research.

Also note that FBs open source ML projects are generally easier to use than Google's.

Things like FastText are completely standalone which makes them really easy to integrate into other things.

I've been working with FastText almost daily and I completely agree. The code rocks and it is really easy to quickly do your own modifications i.e. the source is clean and well-structured.

The newsfeed is one of the most powerful AIs in the world. It is constantly learning, from engagement and ad spend, when to serve various types of content to various types of people. A glorious paperclip optimizer that 2 billion people feed data to daily.

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