12 ms·

How Google Is Remaking Itself for “Machine Learning First”

274 points by steven 10y ago

entee 10y ago
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/AI and making all those Google products I use better!
- wlamond 10y ago
  "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 would have been nice to see that pointed out in the article for folks who may have an interest in machine learning but don't know what's important to focus on.
  - tjl 10y ago
    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.
StevePerkins 10y ago
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.
- vimota 10y ago
  That first paragraph almost made me stop reading.
- lugg 10y ago
  > “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'?
  - StevePerkins 10y ago
    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.
    - aoki 10y ago
      if it makes you feel better, i don't think you were supposed to be inspired. "ML Ninja" is just the name of the rotation program. if your team sends you, it's because they need someone to get the training, not because the program name makes it sound cool. i doubt the PM thought it would be public when she named it.
    - nolepointer 10y ago
      Consider the turtle ...
    - srtjstjsj 10y ago
      What are the juvenile "testosterone" labels? The article leads with a low-testosterone star.
  - rhizome 10y ago
    The word "ninja" in recruiting was almost dead before today.
matt_wulfeck 10y ago
And my anecdotal experience is that it's working extremely well. Take the Google Photos app that does automatic image recognition and tagging. The other day I was looking for a picture we took of our cat the first night we brought him home. I remembered we left him with a blanket in the bathroom but couldn't remember much else. "kitten bathroom 2013" And there was a picture of the cat sitting in the tub on a blanket. Simply amazing.
- cxseven 10y ago
  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.
  - stcredzero 10y ago
    I guess someone at Google figured that imitating Apple is worth sacrificing what remained of their power user appeal. "Sacrificing what remained of their power user appeal" is imitating Apple!
  - dzhiurgis 10y ago
    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.
    - thedlade 10y ago
      I have also noticed that the results are becoming more spammy, I thought I was losing it.
    - erelde 10y ago
      With or without ajax? /jk. But 32Ko is already a lot if there's no images or js libraries.
DrNuke 10y ago
Not sure where it is going at all: evolutionary leaps often come from outliers and sometimes from serendipity. What about this reinforced confirmation bias?
- rhizome 10y ago
  On first blush, my sense is that a translation could go something like "we're prioritizing the analytics API over the results API." Not analytics in the webserver sense, but the OLAP/DW one. So, e.g. ad targeting fidelity over results presentation algorithms. Backend biz vs frontend.
xenihn 10y ago
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 past trig. I know that I'm looking at a good 6 months to a year just to get caught up on the math alone.
- TDL 10y ago
  I'm sure others in this thread will have some good advice on the math front. You will want to be comfortable with statistics (as it seems you already are aware), but you will also want to be comfortable with linear algebra as well. Andrew Ng's course has a quick tutorial on linear algebra, you might also want to check codingthematrix.com. Khand Academy is a decent place for stats, probability, linear algebra, & calculus. I know there has been some criticism of K.A. in the past, but I think it's a good resource to get an intro level understanding of those topics. As an intro to ML, I am a fan of Courseras ML specialization that is done by the University of Washington (https://www.coursera.org/specializations/machine-learning https://www.coursera.org/specializations/machine-learning). It's free, except for the capstone, and the instructors do a good job of giving both theoretical & practical grounding in various aspects of ML. I am sure others will have good suggestions as well. Good luck.
  - dzhiurgis 10y ago
    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 mathematical where Dato one seem too simple. Tensor Flow course seems humorously hard as 15 minutes in you get "Please implement Softmax using Python". Ok, maybe later.
    - 10y ago
z92 10y ago
That's a good change from "social first" from a few years back. Google was never a social company to start with. Remember Orkut? AI is google's leverage. It should explore on that path.
glx1441 10y ago
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).
- apsec112 10y ago
  Could you say more? What do you think are the technical inaccuracies?
  - argonaut 10y ago
    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).
    - a7x11 10y ago
      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.
xg15 10y ago
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 big transformation seems strange.
- peatfreak 10y ago
  > My impression so far was that Google more or less created the whole machine Learning movement How did you get this impression? It has little basis in reality.
  - ocdtrekkie 10y ago
    Good marketing, I presume.
- Houshalter 10y ago
  In 2008 Peter Norvig was quoted saying there was very little or any machine learning in Search. They found it unreliable.
  - a_imho 10y ago
    I thought 8 years is a lot and felt ML is just becoming mainstream. Interestingly trends shows me a steady incline for 'machine learning', while searches for 'neural networks' are dropping since 2004 https://www.google.com/trends/explore#q="machine%20learning"%2C"neural%20networks" https://www.google.com/trends/explore#q="machine%20learning"...
  - raverbashing 10y ago
    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 :)
arbre 10y ago
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 software engineers use the model. I do believe however that some experience is needed to understand what is possible and best benefit from existing tools or to be able to communicate with machine learning engineers about your needs.
- personjerry 10y ago
  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.
  - arbre 10y ago
    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!
    - personjerry 10y ago
      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.
- zmj 10y ago
  This is how software eats your job.
- giardini 10y ago
  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.
nborwankar 10y ago
Bit of a self-plug here - LearnDataScience http://learnds.com http://learnds.com has been well received as a starting point for newcomers. It's a set of Jupyter notebooks with a lot of hand holding. Git repo has data sets included so you can clone and go. All Python.
yomly 10y ago
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 around them so they can apply anything novel ideas developed internally to what they are currently working on. In those cases, hiring based on sound knowledge of CS fundamentals seems like a good bet... 60k engineers is a pretty terrifying number though.
- arcanus 10y ago
  I'm skeptical nevertheless. In my experience, most programming is very different than r+d, which often does require significant concentrated training or even the smartest will spin their wheels. It's hard to describe, but research (which the vast majority of ML remains) is something that even a sound knowledge of fundamentals might not remotely be enough.
- deleted 10y ago
  [deleted]
- TulliusCicero 10y ago
  60k is total number of full-time employees. It includes non-engineers, and does not include contractors.
- gonyea 10y ago
  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.
  - throwaway42069 10y ago
    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.
  - deleted 10y ago
jdeisenberg 10y ago
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.
- shoyer 10y ago
  He's still in charge of research -- he's just in charge of search now, too.
deleted 10y ago
[deleted]
hoodoof 10y ago
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.
- Bjorkbat 10y ago
  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.
  - Harimwakairi 10y ago
    "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?
- srtjstjsj 10y ago
  In between, they focused the entire company on switching from Desktop to Mobile.
  - paganel 10y ago
ycosynot 10y ago
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.
srtjstjsj 10y ago
When will they move past the "Slogan First" magpie direction-switching?
holografix 10y ago
Reading shit like this makes me wanna drop everything and start a Maths degree and get seriously into Machine Learning. Can you imagine being picked at work to study something AWESOME while being paid for it?!? She must be a genius.
tdkl 10y ago
I guess now we know who's responsible for asinine UI decisions lately (YouTube apps, Material wastespace design). /s
Dowwie 10y ago
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.

274 points by steven 10y ago

entee 10y ago

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/AI and making all those Google products I use better!

wlamond 10y ago
"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 would have been nice to see that pointed out in the article for folks who may have an interest in machine learning but don't know what's important to focus on.
- tjl 10y ago
  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.

StevePerkins 10y ago

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.

vimota 10y ago
That first paragraph almost made me stop reading.
lugg 10y ago
> “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'?
- StevePerkins 10y ago
  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.
  - aoki 10y ago
    if it makes you feel better, i don't think you were supposed to be inspired. "ML Ninja" is just the name of the rotation program. if your team sends you, it's because they need someone to get the training, not because the program name makes it sound cool. i doubt the PM thought it would be public when she named it.
  - nolepointer 10y ago
    Consider the turtle ...
  - srtjstjsj 10y ago
    What are the juvenile "testosterone" labels? The article leads with a low-testosterone star.
- rhizome 10y ago
  The word "ninja" in recruiting was almost dead before today.

matt_wulfeck 10y ago

And my anecdotal experience is that it's working extremely well. Take the Google Photos app that does automatic image recognition and tagging. The other day I was looking for a picture we took of our cat the first night we brought him home. I remembered we left him with a blanket in the bathroom but couldn't remember much else. "kitten bathroom 2013" And there was a picture of the cat sitting in the tub on a blanket. Simply amazing.

cxseven 10y ago
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.
- stcredzero 10y ago
  I guess someone at Google figured that imitating Apple is worth sacrificing what remained of their power user appeal. "Sacrificing what remained of their power user appeal" is imitating Apple!
- dzhiurgis 10y ago
  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.
  - thedlade 10y ago
    I have also noticed that the results are becoming more spammy, I thought I was losing it.
  - erelde 10y ago
    With or without ajax? /jk. But 32Ko is already a lot if there's no images or js libraries.

DrNuke 10y ago

Not sure where it is going at all: evolutionary leaps often come from outliers and sometimes from serendipity. What about this reinforced confirmation bias?

rhizome 10y ago
On first blush, my sense is that a translation could go something like "we're prioritizing the analytics API over the results API." Not analytics in the webserver sense, but the OLAP/DW one. So, e.g. ad targeting fidelity over results presentation algorithms. Backend biz vs frontend.

xenihn 10y ago

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 past trig. I know that I'm looking at a good 6 months to a year just to get caught up on the math alone.

TDL 10y ago
I'm sure others in this thread will have some good advice on the math front. You will want to be comfortable with statistics (as it seems you already are aware), but you will also want to be comfortable with linear algebra as well. Andrew Ng's course has a quick tutorial on linear algebra, you might also want to check codingthematrix.com. Khand Academy is a decent place for stats, probability, linear algebra, & calculus. I know there has been some criticism of K.A. in the past, but I think it's a good resource to get an intro level understanding of those topics. As an intro to ML, I am a fan of Courseras ML specialization that is done by the University of Washington (https://www.coursera.org/specializations/machine-learning https://www.coursera.org/specializations/machine-learning). It's free, except for the capstone, and the instructors do a good job of giving both theoretical & practical grounding in various aspects of ML. I am sure others will have good suggestions as well. Good luck.
- dzhiurgis 10y ago
  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 mathematical where Dato one seem too simple. Tensor Flow course seems humorously hard as 15 minutes in you get "Please implement Softmax using Python". Ok, maybe later.
  - 10y ago

z92 10y ago

That's a good change from "social first" from a few years back. Google was never a social company to start with. Remember Orkut? AI is google's leverage. It should explore on that path.

glx1441 10y ago

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).

apsec112 10y ago
Could you say more? What do you think are the technical inaccuracies?
- argonaut 10y ago
  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).
  - a7x11 10y ago
    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.

xg15 10y ago

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 big transformation seems strange.

peatfreak 10y ago
> My impression so far was that Google more or less created the whole machine Learning movement How did you get this impression? It has little basis in reality.
- ocdtrekkie 10y ago
  Good marketing, I presume.
Houshalter 10y ago
In 2008 Peter Norvig was quoted saying there was very little or any machine learning in Search. They found it unreliable.
- a_imho 10y ago
  I thought 8 years is a lot and felt ML is just becoming mainstream. Interestingly trends shows me a steady incline for 'machine learning', while searches for 'neural networks' are dropping since 2004 https://www.google.com/trends/explore#q="machine%20learning"%2C"neural%20networks" https://www.google.com/trends/explore#q="machine%20learning"...
- raverbashing 10y ago
  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 :)

arbre 10y ago

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 software engineers use the model. I do believe however that some experience is needed to understand what is possible and best benefit from existing tools or to be able to communicate with machine learning engineers about your needs.

personjerry 10y ago
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.
- arbre 10y ago
  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!
  - personjerry 10y ago
    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.
zmj 10y ago
This is how software eats your job.
giardini 10y ago
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.

nborwankar 10y ago

Bit of a self-plug here - LearnDataScience http://learnds.com http://learnds.com has been well received as a starting point for newcomers. It's a set of Jupyter notebooks with a lot of hand holding. Git repo has data sets included so you can clone and go. All Python.

yomly 10y ago

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 around them so they can apply anything novel ideas developed internally to what they are currently working on. In those cases, hiring based on sound knowledge of CS fundamentals seems like a good bet... 60k engineers is a pretty terrifying number though.

arcanus 10y ago
I'm skeptical nevertheless. In my experience, most programming is very different than r+d, which often does require significant concentrated training or even the smartest will spin their wheels. It's hard to describe, but research (which the vast majority of ML remains) is something that even a sound knowledge of fundamentals might not remotely be enough.
deleted 10y ago
[deleted]
TulliusCicero 10y ago
60k is total number of full-time employees. It includes non-engineers, and does not include contractors.
gonyea 10y ago
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.
- throwaway42069 10y ago
  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.
- deleted 10y ago

jdeisenberg 10y ago

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.

shoyer 10y ago
He's still in charge of research -- he's just in charge of search now, too.

deleted 10y ago

[deleted]

hoodoof 10y ago

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.

Bjorkbat 10y ago
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.
- Harimwakairi 10y ago
  "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?
srtjstjsj 10y ago
In between, they focused the entire company on switching from Desktop to Mobile.
- paganel 10y ago

ycosynot 10y ago

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.

srtjstjsj 10y ago

When will they move past the "Slogan First" magpie direction-switching?

holografix 10y ago

Reading shit like this makes me wanna drop everything and start a Maths degree and get seriously into Machine Learning. Can you imagine being picked at work to study something AWESOME while being paid for it?!? She must be a genius.

tdkl 10y ago

I guess now we know who's responsible for asinine UI decisions lately (YouTube apps, Material wastespace design). /s

Dowwie 10y ago

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.