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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”

#51

Post by personjerry »

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.

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

#52

Post by superuser2 »

dzhiurgis wrote: post #32
cxseven wrote: post #8

Earlier quoted context omitted.

Strangely, half the time I try to use Google Now on my phone, it doesn't seem to understand basic queries that worked two years ago. And in the meanwhile, features and APIs that used to allow more reliable and explicit control (e.g. like in Picasa) are being shut down. I guess someone at Google figured that imitating Apple is worth sacrificing what remained of their power user appeal.

Just yesterday I was amazed I was unable to Google 'what is the smallest website possible' or 'what can you fit on 32kb website' or whether html demoscene exists at all. Or sometimes the results seem like complete spam, instead of showing me answers on xcode, it was showing some heavily seo-ized apple blogs.

Pretty sure "HTTP/1.0 200 OK" is the smallest possible website.

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

#53

Post by dorian-graph »

xenihn wrote: post #9

Anyone happen to have a suggested self-teaching path for Machine Learning? I.e. books and courses. I know that Andrew Ng's course is a great resource, but I know that I'm not ready to start it yet. I'm actually way behind on the mathematical pre-requisites, so recommendations for that would be greatly appreciated as well. I've never taken a statistics course, and never received any formal education for mathematics pa…

I'm a big fan of this website in general, and they have a specific guide for 'everyone interested in machine learning'.

https://www.metacademy.org/roadmaps/cjrd/level-up-your-ml

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

#54

Post by argonaut »

apsec112 wrote: post #26
glx1441 wrote: post #20

Peter Domingos? Really? Did they mean Pedro? Sigh. Another instance of pop science getting most everything wrong (and I haven't even bothered to write anything about the technical content in the article).

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).

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

#55

Post by argonaut »

arcanus wrote: post #40
dzhiurgis wrote: post #31

Earlier quoted context omitted.

This Coursera specialization is almost polar opposite of Andrew Ng's one. It gives a very rudimentary explanation of a concept and then gets you to do a very basic practical exercise using their framework. The tests are simple enough that you can just replace $variable and pass it, but you'd hardly find it applicable with real world problem. I've started with Andrew Ng course and found it way too dry and too much mat…

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.

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

#56

Post by Bjorkbat »

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 machine learning and apply it everywhere will probably work out pretty badly. As fascinating as ML is, I just haven't bothered to learn it yet because 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.

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

#57

Post by zmj »

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…

This is how software eats your job.

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

#58

Post by Houshalter »

xg15 wrote: post #21

I was kind of surprised this article hooks with that relatively small "Ninja" workshop. My impression so far was that Google more or less created the whole machine Learning movement (out of necessity from their two core field, search and ads/analytics) and is employing several authorities of the field. After Google Now, DeepDream and all the self driving car hype, reading about that workshop being the start of the bi…

In 2008 Peter Norvig was quoted saying there was very little or any machine learning in Search. They found it unreliable.

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

#59

Post by eplanit »

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...

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

#60

Post by vecter »

xenihn wrote: post #9

Anyone happen to have a suggested self-teaching path for Machine Learning? I.e. books and courses. I know that Andrew Ng's course is a great resource, but I know that I'm not ready to start it yet. I'm actually way behind on the mathematical pre-requisites, so recommendations for that would be greatly appreciated as well. I've never taken a statistics course, and never received any formal education for mathematics pa…

If the only math you know is up to trig, you're probably multiple years away from getting caught up on the math.

You need to first learn calculus and linear algebra, and learn them very well. I would also recommend having a good understanding of probability. Learning all of these well will take at least a year, if not longer. For instance, I took one year of calculus in high school and then one semester each of linear algebra and probability, which that adds up to two years.

You'll need calculus so you can do optimization (i.e. at the simplest level, take a derivative, set it to 0, and solve. Of course there's more you can do with calculus in Machine Learning). You'll need linear algebra for almost everything in Machine Learning. Lastly, probability will be useful for understanding very basic methods like Naive Bayes[0]. There are other methods built on probability also[1].

If you skimp on learning any of these, you will never be able to understand Machine Learning at a deep level, much less even a shallow level.

[0] https://en.wikipedia.org/wiki/Naive_Bayes_classifier

[1] https://en.wikipedia.org/wiki/Graphical_model

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