Deep neural networks are usually trained on GPUs which can provide a huge speed up, and is much cheaper. However they can easily be run on CPUs after being trained. I don't see why they wouldn't have access to GPUs in production though.
Well, it depends, you can definitely have servers with NVidia hardware, and you don't need to have all of them with it. (Amazon offers GPGPU servers on EC2 for example)
Amazon only offers very costly Tesla and Quadro cards on their GPGPUs. Super expensive. For scale, if you do not need the extra memory on the Tesla cards, what you want is commodity NVIDIA GeForce cards that are a fraction of the cost.
"Basically everything in FB has one main goal: Ads"
Is this actually true, or a glib answer? Ads certainly don't feel like much of the FB experience, nowhere near the level of Google, or even Twitter. They're negligible, really.
Whereas shaping the feed and identifying people in pics, etc, is central to the FB experience.
> Ads certainly don't feel like much of the FB experience, nowhere near the level of Google, or even Twitter.
Facebook creates an artificial distinction between labeled ads (which are, of course, obvious ads) and posts with paid reach (which are also ads, but presented as normal content), exactly for the purpose of maximizing the quantity of ads while minimizing the impression of content being dominated by ads.
Right, so ads don't feel like much of the FB experience.
My point is that FB seems much, much more focused on creating an engaging user experience than it does on ads, so it seems wrong to say that their deep learning efforts have ads as their one main goal.
> Right, so ads don't feel like much of the FB experience.
Right, but the issue was the claim that with FB everything is about ads, not the UX feels like it is about ads. Making ads not feel like as much of the FB experience as they are is, itself, about ads -- and, particularly, is about maximizing the degree to which the FB experience is actually about ads.
"Making ads not feel like as much of the FB experience as they are is, itself, about ads..."
Well, we just disagree here. I see it from exactly the other end. That the main FB goal is to engage users, and minimizing the (necessary, but grudgingly so) ads is about that.
FB's bait is user engagement. So, applying machine learning to the goal of engaging users makes sense.
But, to monetize that user-engagement they, of course, use ads. Applying machine learning directly there makes sense too.
Beyond that, it's just semantics. Yes, one could accurately say that everything FB does is about ads because, ultimately, they are a for-profit company which derives its revenue from advertising. In that way, the ultimate goal of all of its activity is aimed at generating ad revenue.
But, that doesn't mean that every single activity or bit of technology that supports that activity is directly tied to driving ad revenue. That is to say, that if FB featured no advertising but, instead, charged users, they would still likely deploy machine-learning to user-engagement and, thus, customer-retention.
What does matter is what their primary goal is- Is it to increase user engagement for its own sake (and ad revenue is an incidental necessity), or is it to increase user engagement to maximize their ad revenue?
That is, is the main goal of Facebook to engage and connect people via its platform, or is it to make money from selling ads?
From everything I've seen from them thus far, I just don't believe ad revenue is their main goal. They do seem incredibly obsessive about engagement, but ads seem like a neglected afterthought.
What it feels like to the user is vital. The AI is probably to find the most engaging way to display ads. More engagement with ads == high ad valuation.
Not necessarily Ads in the traditional sense, but FB also sells content reach (Promoted Content) and targeting
Shaping the feed is certainly one part of it. But I guess that part of the success of FB is that they don't make it, as you said, not make the ads feel as much part of the experience.
Categorization is critical to boosting precision in information retrieval tasks. Deep networks for cropped object recognition are an important source of (inferred) content categories (especially when other context, e.g. photo captions, are not useful). While a good image categorizer would be very useful for ad targeting and auctions, the technology can also boost precision for news feed, graph search, etc., which improves user experience.
Secondary goals might be: Spam detection and extracting info from the graph/pictures, etc
FB identify people from your Social Graph in Pictures, for example