Google, Deep Learning and the Neural Net


Reposted from Medium Backchannel:

“I need to know a bit about your background,” says Geoffrey Hinton. “Did you get a science degree?” Hinton, a sinewy, dry-witted Englishman by way of Canada, is standing at a white board in Mountain View, California, on the campus of Google, the company he joined in 2013 as a Distinguished Researcher. Hinton is perhaps the world’s premier expert on neural network systems, an artificial intelligence technique that he helped pioneer in the mid 1980s. (He once remarked he’s been thinking about neural nets since he was sixteen.) For much of the period since then, neural nets — which roughly simulate the way the human brain does its learning— have been described as a promising means for computers to master difficult things like vision and natural language. After years of waiting for this revolution to arrive, people began to wonder whether the promises would ever be kept.

But about ten years ago, in Hinton’s lab at the University of Toronto, he and some other researchers made a breakthrough that suddenly made neural nets the hottest thing in AI. Not only Google but other companies such as Facebook, Microsoft and IBM began frantically pursuing the relatively minuscule number of computer scientists versed in the black art of organizing several layers of artificial neurons so that the entire system could be trained, or even train itself, to divine coherence from random inputs, much in a way that a newborn learns to organize the data pouring into his or her virgin senses. With this newly effective process, dubbed Deep Learning, some of the long-standing logjams of computation (like being able to see, hear, and be unbeatable at Breakout) would finally be untangled. The age of intelligent computers systems — long awaited and long feared — would suddenly be breathing down our necks. And Google search would work a whole lot better.

This breakthrough will be crucial in Google Search’s next big step: understanding the real world to make a huge leap in accurately giving users the answers to their questions as well as spontaneously surfacing information to satisfy their needs. To keep search vital, Google must get even smarter.

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Google’s Insightful Approach To Talent

google talent

Reposted from Forbes:

A recent tweet from the UK-based @hrmagazine caught my eye: “When Google hires, it deliberately looks for learners – favour the ability to learn new things over past experience.”

It also reminded me that earlier this year, one of my favorite journalists,Thomas Friedman, had written a fascinating piece for the New York Times interviewing Laszlo Bock, Google’s SVP of People Operations, about their talent philosophy. “The No. 1 thing we look for is general cognitive ability,” Bock had said in that interview, “and it’s not I.Q. It’s learning ability. It’s the ability to process on the fly. It’s the ability to pull together disparate bits of information.”

It’s such a simple but powerful insight, as @hrmagazine too seems to appreciate.  If I had to pick one quality to build a strong team around, I don’t believe I could come up with another single attribute that would be more helpful.  And the real power behind it is that any company can benefit from it.   It’s universally applicable – whether for Google, Alibaba, the solid 160 year old life insurance insurance company I used to work for, or Joe’s Car Repair down the street.  Every kind of business can benefit from employees who simply want to learn new skills and better ways of doing things.  Let’s consider some reasons why…

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Data Scientist: The Sexiest Job of the 21st Century [INFOGRAPHIC]

Data Scientist: The Sexiest Job of the 21st Century [INFOGRAPHIC]

In Google Analytics’ “Sharpening Your Skills for the Data-Driven Age” infographic, the case is made for data fluency and its associated skills and the data to back it up. Links to vital resources to dig deeper are included and worth perusing.