Hacker News Board Hacker News, browsed like a classic message board
Live data from Hacker News

How Google Is Remaking Itself for “Machine Learning First”

backchannel.com

Post reply on HN
Print view View original

91–100 of 121 posts

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

#91

Post by emanuelsaringan »

raverbashing wrote: post #85
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…

> Articles like this for me tend to vindicate Google's notorious hiring processes. No, because they have rejected ML experts if they can't do their stupid dog & pony show. > hiring based on sound knowledge of CS fundamentals seems like a good bet... Too bad many of them can't get their heads around the ML math.

> No, because they have rejected ML experts if they can't do their stupid dog & pony show.

But that just strengthens the poster's point. When ML goes out of fad, what is the need for these ML "gurus" who only know ML but nothing else?

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

#92

Post by raverbashing »

Houshalter wrote: post #58
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.

To be fair Peter Norvig is much more "old AI" and shallow learning, which doesn't fit a lot of cases

Also 2008 in Deep Learning is 100 years ago :)

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

#93

Post by lunchTime42 »

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…

Sounds like a job machines could do..

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

#95

Post by tjl »

wlamond wrote: post #3
entee wrote: post #2

This is a really great idea, especially when done right. The difficulty with machine learning and AI is understanding the pitfalls inherent in selecting data and training systems. You can fool yourself pretty easily into thinking you've got something that works when you really don't. That said it sounds like they're doing things well, I have no doubt this will have a positive impact in demystifying the "magic" of ML/…

"And then (this is hard for coders) trusting the systems to do the work." Like you say, it can be easy to think that something works when it really doesn't. I hope that the above quote isn't meant to be interpreted as "believe the results are correct." Evaluation is paramount when working on these systems to avoid making such mistakes. I assume Google is including evaluation in their machine learning training, but it…

One big problem with ML is that it's highly based on your training set. There's been a few papers published in computational linguistics that discuss how poorly ML based sentiment analysis is if you try and apply the data to domains outside the training set. For instance, if you train the sentiment data on movie reviews (which is actually a data set commonly used for that purpose) and try and apply it to Twitter or the Web, the results are terrible. But, people keep on trying it.

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

#96

Post by ioeu »

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…

What fields would you guide them to instead?

I may be one of the developers you speak of (with academic aspirations), presently considering my path forward.

I'm sceptical if going down the ML swamp is the best way forward.

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

#97

Post by [deleted] »

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.

[deleted]

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

#98

Post by Dowwie »

I find this article alarming.

Jeff Dean said, "The more people who think about solving problems in this way, the better we'll be". I sincerely hope that Sundar emphasizes the thoughtful application of ML and not allow black box algorithms take too central a role.

This kind of hubris swept through wall street banks during the structured products boom, ultimately leading to products such as synthetic collateralized debt obligations. Taking Jeff Dean's opinion about whether machine learning would be a good thing is like taking the opinion of the creator of synthetic CDOs whether they were a good thing. The authors and evangelists are blinded by optimism and opportunity.

Is Sundar Pichai swept away by the opportunities of machine learning and too biased to be aware of risks ? Is Sundar acting like Stan O'Neil did as he pulled all the stops at Merrill Lynch and went all-in with CDOs? I hope he isn't. It does not seem to be the case as he mentions thoughtful use of ML.

Nonethless, caution should be taken.

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

#99

Post by tdullien »

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…

I am a mathematician by trade, and was doing development along with other stuff (reverse engineering and security work, first in my own company, then at Google). So ...

1) I think working knowledge of ML is extremely useful to many developers, and generally under-taught in universities. See the old Joel article which mentions "Google uses Bayesian filtering like MS uses the IF statement" http://www.joelonsoftware.com/items/2005/10/17.html). A well-rounded developer should know the basics (Logistic regression, SVMs, know some things about CDNNs etc.), it will make him much more adept at problem-solving. I suspect Google's internal push to get people up to speed is not to turn them all into ML researchers, but rather to make sure that everybody "knows the basics well enough".

So I think it is useful to teach developers about the things ML has to offer.

2) Mathematically, it seems that in ML the "engineering" side has run far ahead of the theory side. The sudden breakthrough in the mid-2000s is IMO still not fully understood - and parts of it may have been very accidental. Initially, it was thought that pre-training was the big breakthrough, but it is quite unclear what the big breakthrough was. It could be that simply the increase of data / compute sizes and the switch to minibatch-SGD explains why modern DNNs generalize well (interesting paper on the topic: https://arxiv.org/abs/1509.01240). There is a lot of good mathematics to be written, but I am not sure whether the folks at Google will write it - given the incentive structures (performance reviews, impact statements) it is unlikely that somebody gets promoted for "cleaning up the theory".

3) From a development perspective: There are a ton of interesting engineering problems underneath the progress in ML. If you look at Jeff Dean, he is a superstar engineer, not necessarily a mathematician, and a lot of the progress the Google Brain team made were engineering advances to scale / distribute etc. - so by training the engineers in ML, you also get to have better infrastructure over time.

So I don't think they are sending "developers into ML swamps"; I think they are trying to reach the point where "Google uses DNNs like MS uses IF".

Cheers, Thomas

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

#100

Post by cousin_it »

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.

Memorizing answers to algorithm questions is a poor time investment. I don't see a lot of people doing it. Most smart folks just learn how to design algorithms on the fly, that's much easier and more useful.
Post reply on HN