Deep Machine Learning… Wow!

Key Point: Those of you that regularly read my blog know that I write a lot about “repotting ourselves” through personal disruption and reinvention. I also point out how digital disruption is impacting literally all organizations surrounding our personal eco-systems. However, sometimes I read or watch something that just makes stop in my tracks and think, “Holy S#!&, Lorne do you REALLY know what’s going on out there? In his recent article on, Steven Levy writes about Google remaking itself as a “Machine Learning First” company. It made me pause. 25,000 top-notch Google engineers worldwide are dedicated to this. Facebook is doing the same. What does this mean to you and me? Please read a few quotes from the article to get the drift:

  1. “Though machine learning has long been part of Google’s technology — and Google has been a leader in hiring experts in the field — the company circa 2016 is obsessedwith it. In an earnings call late last year, CEO Sundar Pichai, laid out the corporate mindset: ‘Machine learning is a core, transformative way by which we’re rethinking how we’re doing everything. We are thoughtfully applying it across all our products, be it search, ads, YouTube, or Play. And we’re in early days, but you will see us — in a systematic way — apply machine learning in all these areas.’”
  2. “Obviously, if Google is to build machine learning in all its products, it needs engineers who have mastery of those techniques, which represents a sharp fork from traditional coding. As Peter Domingos, author of the popular ML manifesto The Master Algorithm, writes, ‘Machine learning is something new under the sun: a technology that builds itself.’ Writing such systems involves identifying the right data, choosing the right algorithmic approach, and making sure you build the right conditions for success. And then (this is hard for coders) trusting the systems to do the work.”
  3. “’The more people who think about solving problems in this way, the better we’ll be,’ says a leader in the firm’s ML effort, Jeff Dean, who is to software at Google as Tom Brady is to quarterbacking in the NFL. Today, he estimates that of Google’s 25,000 engineers, only a “few thousand” are proficient in machine learning. Maybe 10 percent. He’d like that to be closer to a hundred percent. ‘It would be great to have every engineer have at least some amount of knowledge of machine learning,’ he says.”
  4. “Google’s bear-hug-level embrace of machine learning does not simply represent a shift in programming technique. It’s a serious commitment to techniques that will bestow hitherto unattainable powers to computers. The leading edge of this are ‘deep learning’ algorithms built around sophisticated neural nets inspired by brain architecture. The Google Brain is a deep learning effort, and DeepMind, the AI company Google bought for a reported $500 million in January 2014, also concentrates on that end of the spectrum. It was DeepMind that created the AlphaGo system that beat a champion of Go, shattering expectations of intelligent machine performance and sending ripples of concern among those fearful of smart machines and killer robots.”
  5. “The competition to hire recent graduates in the field is fierce, and Google tries hard to maintain its early lead; for years, the joke in academia was that Google hires top students even when it doesn’t need them, just to deny them to the competition. (The joke misses the point that Google does need them.) ‘My students, no matter who, always get an offer from Google.’ says Domingos. And things are getting tougher: Just last week, Google announced it will open a brand new machine-learning research lab in Zurich, with a whole lot of workstations to fill.”

Character Moves:

  1. Look… You and I may never be ML (machine learning) engineers, but we better know what the implications are for our organizations and ourselves. It’s like, geez, “I’m just learning about the impact of all things digital, the Internet of everything, full mobility, new Gen Data science, block chain and now this advanced ML?” Yup! (for ATBers who read this blog and want to be part of Pinnacle 3, you will aspire to learn copious amounts about block chain, AI and cognitive ML).
  1. Within five years, there are credible predictions that 45 percent of what we do will be replaced by Artificial Intelligence/Machine Learning. How will it impact you? How are you going to participate? Know this: When companies like Google apply 25,000 engineers at 100 percent focus, hang onto your hat. What hat is that going to be?

Deep ML in The Triangle,


One Millennial View: Man, sometimes I wish I paid better attention in math class. I waved bye-bye to engineering skills circa 7th grade, but all joking aside, it certainly makes you think about your relevance. We’ve all likely seen Tesla cars driving themselves. There will always be a role for us humans, and I don’t think we’re facing a Terminator situation anytime soon, but staying relevant is obviously something we should be frequently asking ourselves. Basic understanding of concepts like ML keeps us ahead of the game, because rest assured, a machine can likely learn something a lot quicker and cheaper than you can. Even Millennials have to be wary about getting left behind by rapid technological advancements like ML. 

– Garrett

Edited and published by Garrett Rubis

One Comment
  1. Steve Nguyen says:

    I appreciate Garrett’s contribution to Lorne’s post because he calls out the “learning” component of “machine learning”. Yes, machine learning and those that build its algorithms will continue to be in high demand. With as much information as there is pushed to us these days, we’ll need to rely on machines to continue to help us filter better. Make no mistake, however, that the machines still must learn from human behavior and human contributions. Google, Facebook, et. al. exist because of the vast numbers of people using their platforms. They’ve built networks of people from which they can learn from those peoples’ attitudes, content, and behaviors. I think it’s useful to consider that machine learning doesn’t just happen. It requires the contribution and input from us humans to truly be effective and for machine learning to be as intelligent as we need it to be.

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