We’re excited to see what new creative applications come in the next year.Ah, the great chatbot explosion of 2016, for better or worse—we liken it to the mobile app explosion we saw with the launch of i OS and Android.
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The danger here, unlike the mobile app explosion (where we lacked expectations for what these widgets could actually do), is that we assume anything with a conversation interface will converse with us at near-human level. This is going to lead to disillusionment over the course of the next year but it will clean itself up fairly quickly thereafter.
When our fund looks at this emerging field, we divide each technology into two components: the conversational interface itself and the “agent” behind the scenes that’s learning from data and transacting on a user’s behalf.
(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.
Then came a healthy mix of investors, both private and public.
We’ve written a bit about this, and the framework we use to think about how agents will evolve is a CEO and her support staff.
Many Fortune 500 CEOs employ a scheduler, handler, a research team, a copy editor, a speechwriter, a personal shopper, a driver, and a professional coach.
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Or what else in the world can we reconfigure to make it look more like a game? Developers are dodging meter maids (brilliant—a modern day Paper Boy), categorizing cucumbers, sorting trash, and recreating the memories of loved ones as conversational bots.