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.<p>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.
Articles like this for me tend to vindicate Google's notorious hiring processes.<p>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.<p>In those cases, hiring based on sound knowledge of CS fundamentals seems like a good bet...<p>60k engineers is a pretty terrifying number though.
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.
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.<p>"kitten bathroom 2013"<p>And there was a picture of the cat sitting in the tub on a blanket. Simply amazing.
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?<p>Reminds me of the Ballmer/Gates strategy of everything must be Windows, which seemed flawed to me.
I was kind of surprised this article hooks with that relatively small "Ninja" workshop. My impression so far was that Google more or less <i>created</i> 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.<p>After Google Now, DeepDream and all the self driving car hype, reading about that workshop being the start of the big transformation seems strange.
Peter Domingos? Really? Did they mean Pedro?<p>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).
That's a good change from "social first" from a few years back. Google was never a social company to start with. Remember Orkut?<p>AI is google's leverage. It should explore on that path.
I find this article alarming.<p>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.<p>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.<p>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.<p>Nonethless, caution should be taken.
Bit of a self-plug here - LearnDataScience <a href="http://learnds.com" rel="nofollow">http://learnds.com</a> 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.
Not sure where it is going at all: evolutionary leaps often come from outliers and sometimes from serendipity. What about this reinforced confirmation bias?
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!
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.
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.<p>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.
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.
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.