How Google Is Remaking Itself for “Machine Learning First” • Hacker News Board

Re: How Google Is Remaking Itself for “Machine Learning First”

#61

Post by giardini » Thu, Jun 23, 2016, 4:17 AM UTC

arbre wrote: post #33

I don't believe in "everyone should work on machine learning". I worked on several deep learning models but I don't really like it. It is a very different job than software engineering in my opinion. ML is more about gathering data and tuning the models as opposed to building stuff. I have spent months working on models and barely wrote any code. It is more efficient to have ML experts focus on the modeling and softw…

I concur. ML isn't programming per se; it is experimental problem-solving with a particular dataset and algorithm. Your result may/not work well, may/not generalise, and will almost undoubtedly not contribute anything new to any discipline, even to ML. When all ML work is done we'll have great pattern recognizers but nothing remotely akin to thought. And we won't understand how they work or the best way to build the next one. It isn't AI, although it is a part of AI, just as the visual system is part of AI.

I was reading Domingos' "The Master Algorithm" several days ago and a mathematician inquired about the book. He knew a group of ML developers. His opinion was that "ML doesn't look very interesting: all you do is play with the parameters, turn the knobs, and/or change the model until something works. There's no real progress there; nothing substantial."

Rather than sending a batallion of bright developers into the ML swamp where they will largely be frustrated, learn little and contribute less, I'd be tempted to guide them into other fields.

Re: How Google Is Remaking Itself for “Machine Learning First”

#62

Post by gonyea » Thu, Jun 23, 2016, 4:39 AM UTC

yomly wrote: post #37

Articles like this for me tend to vindicate Google's notorious hiring processes. While it is true that for most people will not need to be able to whiteboard a binary tree inversion in their day to day, it seems like they expect their engineers to be able to throw themselves at any problem they're given and require them to be able to pivot in skillset quickly, and have an appreciation of all the developments going on…

Google's largely moved away from those BS questions. They just bias towards people who memorize answers on Leetcode, but aren't actually capable of producing anything.

Re: How Google Is Remaking Itself for “Machine Learning First”

#63

Post by dgacmu » Thu, Jun 23, 2016, 4:43 AM UTC

arbre wrote: post #33

I don't believe in "everyone should work on machine learning". I worked on several deep learning models but I don't really like it. It is a very different job than software engineering in my opinion. ML is more about gathering data and tuning the models as opposed to building stuff. I have spent months working on models and barely wrote any code. It is more efficient to have ML experts focus on the modeling and softw…

Absolutely. In general, ML needs a collaboration between ML expertise and application domain expertise. It's very helpful if there's someone who can help bridge those two - enough app experience to understand the domain deeply, and enough ML experience to know what questions to ask of the ML gurus and what pitfalls to expect. As I see it, that's one of the goals of the ML ninja program.

Re: How Google Is Remaking Itself for “Machine Learning First”

#64

Post by etangent » Thu, Jun 23, 2016, 4:47 AM UTC

arbre wrote: post #33

I don't believe in "everyone should work on machine learning". I worked on several deep learning models but I don't really like it. It is a very different job than software engineering in my opinion. ML is more about gathering data and tuning the models as opposed to building stuff. I have spent months working on models and barely wrote any code. It is more efficient to have ML experts focus on the modeling and softw…

I don't know. I know some engineers who have spent months going back-and-forth over communication protocols while barely writing any code, yet somehow their job is considered to be quite core to software engineering. I don't really see how fine-tuning communication protocols is fundamentally different from fine-tuning machine learning models. But overall, I agree with your sentiment: different things are different and appropriate for different people.

Re: How Google Is Remaking Itself for “Machine Learning First”

#65

Post by Harimwakairi » Thu, Jun 23, 2016, 4:56 AM UTC

Bjorkbat wrote: post #56

hoodoof wrote: post #50

I seem to recall Google focusing the entire company on social/GooglePlus. Is this now saying the company is now being focused on machine learning in the same way? Reminds me of the Ballmer/Gates strategy of everything must be Windows, which seemed flawed to me.

That's an interesting way to look at it. I would argue that Google+ didn't work out because Google was trying to play catch-up in a field that it just lacked knowledge in (social networks). Whereas with machine learning, they're not playing catch-up, everyone else is. Of all the other tech titans out there, they're the ones really leading the pack. That remark aside though, I agree with you. An attempt to go hard on…

"An attempt to go hard on machine learning and apply it everywhere will probably work out pretty badly. I haven't the slightest idea what new and novel problem I'd solve with it that doesn't have a better solution through a more straight-forward approach."

Assuming they have the money, isn't this exactly the kind of reason Google should train up a wide spectrum of engineers from different teams and then see how they apply machine learning to their respective domains? It would be foolish for Google's management to think they can divine a priori all the best possible uses of ML in their various lines of business. Why not tool up a bunch of smart people, set them loose, and see what works?

Re: How Google Is Remaking Itself for “Machine Learning First”

#66

Post by ycosynot » Thu, Jun 23, 2016, 5:02 AM UTC

Maybe I talk nonsense, but the term "machine learning" could be detrimental to learning it, because it feels so machinesque ... It's a cool term, but also very vague and mystical, and from the antropomorphism it kinda implies the engineer is a teacher, or a translator. You're not even started, and you're already confused.

Surely it is better to talk of learning deep neural nets, and such things. Or maybe "machine training" would be less intimidating. But I guess we're stuck with it, and it's not so bad.

Re: How Google Is Remaking Itself for “Machine Learning First”

#67

Post by a7x11 » Thu, Jun 23, 2016, 5:06 AM UTC

argonaut wrote: post #54

apsec112 wrote: post #26

Earlier quoted context omitted.

Could you say more? What do you think are the technical inaccuracies?

A few I noted: Neural nets don't emulate the brain. NIPS is not an obscure conference, it's been the top ML conference for decades (sure, it's an obscure conference to laymen, but so is pretty much every science publication conference).

Agreed with this guy. Back when I started grad school (2012), NIPS was already so big they moved it to Vegas, but the casino venue didn't fly so well, so it moved to Montreal. NIPS was obscure maybe in the early 2000s, but definitely NOT since the last 5 6 years.

Re: How Google Is Remaking Itself for “Machine Learning First”

#68

Post by tjl » Thu, Jun 23, 2016, 5:13 AM UTC

eplanit wrote: post #59

StevePerkins wrote: post #4

Great article, but I can't help but CRINGE at the "ninja" references. I think that's already played out within the industry... and although pop-tech writers tend to lag a few years behind, it will sound extremely dated in the mainstream within a few years.

Agreed, and I've been waiting over a decade now for the demise of tiresome qualifiers like "ninja" and "on steroids" (we could all add a few, I'm sure). I was really, really tired of "uber", too, but now that one seems here to stay for quite a while longer. Oh, well...

I agree that these are all problems, but the one that bothers me most is the use of "x master race" such as PC master race. Considering the association with the Nazis and genocide, I keep hoping that it will finally die.

Re: How Google Is Remaking Itself for “Machine Learning First”

#69

Post by dj-wonk » Thu, Jun 23, 2016, 6:03 AM UTC

yomly wrote: post #37

Articles like this for me tend to vindicate Google's notorious hiring processes. While it is true that for most people will not need to be able to whiteboard a binary tree inversion in their day to day, it seems like they expect their engineers to be able to throw themselves at any problem they're given and require them to be able to pivot in skillset quickly, and have an appreciation of all the developments going on…

"probably almost half of its 60,000 headcount are engineers"

Re: How Google Is Remaking Itself for “Machine Learning First”

#70

Post by shoyer » Thu, Jun 23, 2016, 6:09 AM UTC

jdeisenberg wrote: post #44

The article says that Mr. Giannandrea is no longer head of the machine learning division; out of curiosity, who has taken that position? It's not clear from the article.

He's still in charge of research -- he's just in charge of search now, too.