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The current state of machine intelligence 3.0

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. This year’s landscape has a third more companies than our first one did two years ago, and it feels even more futile to try to be comprehensive, since this just scratches the surface of all of the activity out there.

As has been the case for the last couple of years, our fund still obsesses over “problem first” machine intelligence—we’ve invested in 35 machine intelligence companies solving 35 meaningful problems in areas from security to recruiting to software development. (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 “” now equally represent a series of meaningful breakthroughs (wonderful) but also a hyped phrase like “big dataBig Data describes data collections so big that humans are not capable of sifting through all of it in a timely manner. However, with the help of algorithms it is usually possible to find patterns within the data so far hidden to human analyzers. ” (not so good!). We care about whether a founder uses the right method to solve a problem, not the fanciest one. We favor those who apply technology thoughtfully.

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  1. Sandeep Raut

    RT @SwissCognitive: The current state of machine intelligence 3.0 https://t.co/yWg23yWQsE @jamescham, @shivon https://t.co/bOhjkG36Cj

  2. Antonio Grasso

    RT @SwissCognitive: The current state of machine intelligence 3.0 https://t.co/yWg23yWQsE @jamescham, @shivon https://t.co/bOhjkG36Cj

  3. Andrae Christopher

    RT @SwissCognitive: The current state of machine intelligence 3.0 https://t.co/yWg23yWQsE @jamescham, @shivon https://t.co/bOhjkG36Cj

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