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How Google Is Remaking Itself for “Machine Learning First”

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

#71

Post by arbre »

personjerry wrote: post #51
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…

Hi arbre, would you mind explaining what is possible and what benefits from existing tools in machine learning at the moment? I am clueless and find ML rather frustrating to get into.

I meant that learning about ML and getting some field experience helps you figuring out when to use ML and how. For how to get into, there are a lot of resources and state of the art algorithms/papers/implementations are freely available. For me working on ML projects at my job and talking to some experts was ideal, but I am sure it is possible to learn on one's own with enough motivation. Good luck!

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

#72

Post by personjerry »

arbre wrote: post #71
personjerry wrote: post #51

Earlier quoted context omitted.

Hi arbre, would you mind explaining what is possible and what benefits from existing tools in machine learning at the moment? I am clueless and find ML rather frustrating to get into.

I meant that learning about ML and getting some field experience helps you figuring out when to use ML and how. For how to get into, there are a lot of resources and state of the art algorithms/papers/implementations are freely available. For me working on ML projects at my job and talking to some experts was ideal, but I am sure it is possible to learn on one's own with enough motivation. Good luck!

Ah, yes, I understood what you meant (and thank you for pointing to where I should look next!). I was hoping, too, that you might share your ML knowledge in layman's terms.

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

#73

Post by throwaway42069 »

gonyea wrote: post #62
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.

I know two people who've interviewed at Google in the past three months and have received a full slate of computer science homework problems.

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

#75

Post by srtjstjsj »

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…

> "everyone should work on machine learning"

> software engineers use the model.

You aren't disagreeing.

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

#76

Post by srtjstjsj »

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.

In between, they focused the entire company on switching from Desktop to Mobile.

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

#77

Post by srtjstjsj »

StevePerkins wrote: post #13
lugg wrote: post #12

Earlier quoted context omitted.

> “The tagline is, Do you want to be a machine learning ninja?” I don't really like the word, but I don't really give a flop either. I'm not sure how its better or worse than guru, rockstar, or any other lame word recruiters like to use to make us feel like the special snowflakes we are. Which word would you like to see in place of 'ninja'?

I'd rather see all of those juvenile testosterone labels discarded in general. Sheesh... " Do you want to make the world a better place? ", with a photo of Gavin Belson holding an animal, would make me more inspired.

What are the juvenile "testosterone" labels?

The article leads with a low-testosterone star.

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

#78

Post by visarga »

giardini wrote: post #61
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…

> And we won't understand how they work

Is this a critique of the human mind or a praise of AI?

> When all ML work is done we'll have great pattern recognizers but nothing remotely akin to thought

Maybe our brains too are nothing but pattern recognizers. Maybe they are nothing but chemical reactions, or energy fields. But being reductionist about AI won't help us understand it either.

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

#79

Post by visarga »

argonaut wrote: post #55
arcanus wrote: post #40

Earlier quoted context omitted.

The sad truth of the matter is that ML is more applied research at this point than a sensible set of programming problems. From that standpoint, graduate mathematics is more useful for a practitioner than any robust programming experience.

ML involves math. That does not mean it's "applied research," though. The math is mostly at the undergraduate-college-level, and is mostly applied math - except for very theoretical ML/statistics which a practitioner can easily avoid. The math involved straddles an awkward boundary where most undergrad math majors find the math quite simple, but most CS majors would think it's too much math.

There are two major cases: academic, related to algorithm design and industry - related to deployment of already existing algorithms on various data sets.

For a CS engineer who wants to be able to use the latest Inception neural net from Google in his pipeline, there is actually almost zero math need. It's like any other API. In goes the image, out comes the label.

What she would need to know, as a good utilizer of ML, is just a bunch of concepts, such as training/test/validation, bias/variation, how to extract features from data and how to select a good algorithm and framework. So it's mostly data cleaning and tuning hyperparameters, the latter of which can be learned by trial and error and by talking to experts. The direct applications of math for such an engineer would be pretty slim to nonexistent.

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