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The maturing of that stack might explain why more established companies are more focused on building legitimate machine intelligence capabilities.

Anyone who has their wits about them is still going to be making initial build-and-buy decisions, so we figured an early attempt at laying out these technologies is better than no attempt.

The dominant platforms (in the machine intelligence case, Facebook, Slack, Kik) race to get developers to build on their platforms.

That means we’ll get some excellent bots but also many terrible ones—the joys of public experimentation.

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id=yv5EBg AAQBAJ&pg=PA203&lpg=PA203&dq=chat israeli women camera&source=bl&ots=VZz6CCURzh&sig=Ag6Qog1pyh Rg Wu NDVS61Lb Xn3GE&hl=en&sa=X&ved=0ah UKEwjc76zn-63YAh VJJs AKHa09DQUQ6AEIQTAJAsk me • Male • 35 years old. (originally published by O'Reilly here, this year in collaboration with my amazing partner James Cham!If you're interested in enterprise implications of this chart please refer to Harvard Business Review's The Competitive Landscape for Machine Intelligence)Almost a year ago, we published our now-annual landscape of machine intelligence companies, and goodness have we seen a lot of activity since then.A newsworthy example of game-tested technology entering the real world was when Deep Mind used neural networks to make Google’s data centers more efficient.This begs questions: What else in the world looks like a game?(Our fund focuses on the future of work, so there are some machine intelligence domains where we invest more than others.)At the same time, the hype around machine intelligence methods continues to grow: the words “deep learning” now equally represent a series of meaningful breakthroughs (wonderful) but also a hyped phrase like “big data” (not so good! We care about whether a founder uses the right method to solve a problem, not the fanciest one. For v1.0, we heard almost exclusively from founders and academics.

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