Oh, how the machine intelligence landscape has grown. What’s next?
Today, Shivon & co. released their 2016 market map, and there are many more logos this year. Some quotes:
“Think of these models as narrowly focused employees with great memories and not-so-great social skills — idiot savants.”
“The gaming world offers a perfect place to start machine intelligence work (e.g., constrained environments, explicit rewards, easy-to-compare results, looks impressive)—especially for reinforcement learning. And it is much easier to have a self-driving car agent go a trillion miles in a simulated environment than on actual roads. Now we’re seeing the techniques used to conquer the gaming world moving to the real world. A newsworthy example of game-tested technology entering the real world was when DeepMind used neural networks to make Google’s data centers more efficient. This begs questions: What else in the world looks like a game? Or what else in the world can we reconfigure to make it look more like a game?”
“Today machine intelligence can use data and new algorithms to generate a model too complex for any human programmer to write.”
“Machine intelligence’s first useful applications in an industry tend to use data that previously had lain dormant. Health care is a prime example: We’re seeing predictive models that run on patient data and computer vision that diagnoses disease from medical images and gleans lifesaving insights from genomic data. Next up will be finance, transportation, and agriculture because of the volume of data available and their sheer economic value.”
From https://www.oreilly.com/ideas/the-current-state-of-machine-intelligence-3-0
and https://hbr.org/2016/11/the-competitive-landscape-for-machine-intelligence
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