Designing Autonomous AI

Chapter 4. Building Blocks for Machine Teaching

My eight-year-old son, Christien, loves to play with LEGO blocks. He can play for hours building cars, jets, and landscapes. I enjoy building things together with him. Sometimes, when we are building, we need just the right piece to complete a section. So, we search through large bins, trolling for just the right piece for the job. When we find a block that performs the right function, the whole structure comes together nicely.

The same is true for AI brains. Autonomous decision-making that works in real life doesn’t magically emerge from a monolithic algorithm: it is built from building blocks of machine learning, AI, optimization, control theory, and expert systems.

Here’s an example. A group of researchers at UC Berkeley, under Pieter Abbeel, taught a robot how to walk. This robot, Cassie, looks a little like a bird with no torso (just legs). The AI brain that they built to control the robot snaps together decision-making modules of multiple different types and orchestrates them in a way that makes sense with what we know about how walking works. It combines math (control theory), manuals (expert systems), and machine-learning AI modules to enable faster learning of more competent walking than any of those decision-making techniques could on their own.

You can see from Figure 4-1 that this brain uses different modules to perform different functions. It uses PD controllers to control the joints. As you learned in Chapter 2, PD ...