I always assumed AI at Facebook was a vanity division like self-driving at Uber.
And Does it make sense to separate Waymo and DeepMind out from Google since that's the owner? Idk. Just a thought.
But even if they weren't exactly #2, they're probably inarguably top 5 right? That's still pretty amazing. And that's without really considering international companies. I have no idea what the AI capabilities of Chinese tech companies are, for example.
With Google and ml I connect so much positive and life-changing like: health care, weather, language model, deepmind ... There research blog and papers are great.
And then Facebook.
Yeah what is Facebook doing with ml? Optimizing for the next bullshit to make our society worse?
Non if there things are popping up anywhere.
Do they actually do anything which helps people instead of making them more victims of fb?
Who else has multiple products scaling to billions of users daily that are built around features that use AI?
They also work on AI research that only they can really work on due to their scale.
FB is planning on spending 20b this year alone on AI and AI related infrastructure.
I suggest finding more polite ways to critique a post. Otherwise discussions tend to get boring quickly.
Also, the GP was explicitly describing their perception, not trying to promote it as truth. One interesting response would be "I really think that's false. I wonder how we reached such different conclusions."
2021 $46.75 B
2020 $32.67 B
2019 $24.83 B
2018 $25.37 B
2017 $20.59 B
2016 $12.52 B
2015 $6.21 B
2014 $4.93 B
2013 $2.81 B
2012 $0.54 B
2011 $1.73 B
Which is exactly why some consider it to be a vanity division.
All that brain power devoted to a company nobody (genuinely) respects, and whose sense of vision -- in the current year, 2022 -- teeters between stale and outright delusional.
But hey, if it gets you $300k a year for easy work and resume cred for the kind of job you really want -- go for it.
Agreed. It's all about the application and purpose (and the hubris of some of its practitioners), not the theory (or even the demonstrated effectiveness in certain areas).
When we see these disconnects at "work", on the ground -- in terms of lack of relevance / applicability to what the business actually needs -- they are sometimes quite startling, in fact.
Edit: never mind I found it https://ai.facebook.com/tools/
t. Almost 20 years of leadership positions in adtech.
They are all powered by neural networks
I have a lot of bad things to say about the company and their products, but they and google are almost single (double?) handedly responsible for the advances in ML/AI that we've seen over the last years. (Not to say they are at all the only ones who have contributed, but if you subtracted their contributions we would have a very different world, certainly an average schmoe like me wouldn't be able to fire up world class models with millions or billions of dollars of R&D in them in a free hosted notebook connected to GPU, and actually solve difficult ML problems)
Thanks
Are the people who depend on it seriously incapable of taking up the load?
Probably. First of all most are domain experts not at all systems programmers. That’s the main value of such libraries.
And even for those who work close to the iron: plenty depend on, say, the Linux kernel without being able to write patches or even diagnose problems. And few, if any kernel developers have submitted patches to, say, GCC.
If these suggestions are credible and don't come from the Facebook team, that might sidestep your concerns.
Is there reason to expect this to change? Maintaining something like Torch and keeping it competitive in terms of speed is a HUGE amount of work for systems development, writing insane numbers of GPU kernels, etc. After having read it, I wouldn't quite yet call the tone of the discussion you linked "serious talk"...
[1]: https://discourse.julialang.org/t/state-of-machine-learning-...
BTW I was speaking generally about open source libraries, not just PyTorch, though indeed a Julia port sounds interesting.
You could argue that at it's peak Bell labs was a vanity division. That research may have changed the world, but very little of it likely ended up benefiting AT&T in any major way financially. It's telling that once AT&T was broken up Bell labs, while existing in some form for years after, was never reestablished.
But the days of telecom research making rapid and monumental advances peaked decades ago. Nokia or AT&T or Huawei or Ericsson could quadruple their R&D spending and it wouldn’t reestablish the impact of Bell Labs of last century, because it’s simply a much more mature field.
Whether it was a vanity division or not, it did serve one practical purpose for AT&T: it presented the company as a benevolent monopoly, spending its profits on developing technologies that benefitted the nation. This helped stave off anti-trust action for a long time.
I work for a big AI consultancy, but we haven't seen relevant work from Meta for a while. Their non-bayesian stuff is "meh" and often seriously misguided in their business approach. (which makes sense, they're not accustomed to work as consultants on setups with limited data)
What the hell is a big AI consultancy? As far as I can tell, these do not exist. Why would you expect to see meta doing consulting work at all?
It seems snake oil adjacent to me, at least at this point.
Maybe you meant something else, but that’s how your statement reads.
I see it as this, you can throw money at a project to build a new OS but you still need the right people to have an actually good OS.
There's a ton of companies that would be happy to pay them to keep working on it.