Suppose you want to build a robot to achieve some real-world goal for you—a goal that requires the robot to learn for itself and figure out a lot of things that you don’t already know. There’s a complicated engineering problem here. But there’s also a problem of figuring out what it even means to build a learning agent like that. What is it to optimize realistic goals in physical environments? In broad terms, how does it work? In this series of posts, I’ll point to four ways we don’t currently know how it works, and four areas of active research aimed at figuring it out. This is Alexei, and Alexei is playing a video game. Like most games, this game has clear input and output channels. Alexei only observes the game through the computer screen, and only manipulates the game through the controller. The game can be thought of as a function which takes in a sequence of button presses and outputs a sequence of pixels on the screen. Alexei is also very smart, and capable of holding the entire video game inside his mind.
Original text:
https://intelligence.org/2018/10/29/embedded-agents/
Narrated for AI Safety Fundamentals by Perrin Walker of TYPE III AUDIO.
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