Simple sex chatbot
Simple sex chatbot
(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 get a lot of questions about whether there will be “one bot to rule them all.” To be honest, as with many areas at our fund, we disagree on this.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. We are getting inbound inquiries from a different mix of people.(Note that some CEO’s have a chief of staff who coordinates among all these functions, so perhaps we will see examples of “one interface to rule them all.”)You can also see, in our landscape, some of the corporate functions machine intelligence will re-invent (most often in interfaces other than conversational bots).Successful use of machine intelligence at a large organization is surprisingly binary, like flipping a stubborn light switch.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.
Otto’s self-driving trucks delivering beer on their first commercial ride even seems like a bonus level from Grand Theft Auto.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.It’s hard to do, but once machine intelligence is enabled, an organization sees everything through the lens of its potential.Organizations like Google, Facebook, Apple, Microsoft, Amazon, Uber, and Bloomberg (our sole investor) bet heavily on machine intelligence and have its capabilities pervasive throughout all of their products.(originally published by O'Reilly here, this year in collaboration with my amazing partner James Cham!