What a neural network is and how it’s used to learn cars to drive themselves
In: Technology
Someone gives you a task to complete, like going from a to b. You have no idea how to achieve this. Imagine a list of all things you can do right now. All options are equally good or bad for you. So you just start trying random things. Flailing your arms. You see and realize, you are not moving forward at all and your arms start hurting. So you try something else, shaking your legs. Oh, you started moving a bit. You dropped down to the floor immediately, but at least there is some progress. So you know this is the direction to go. The next time, you try shaking your legs back and forth and accidently make a step before you fall. This reinforces your idea that reaching b has something to do with moving your legs a certain way.
Basically, it goes step by step. You define the goal, give sensors to allow interpretation of the world around, define a set of possible actions, and then let it go. Each generation knows the success or failure of the ones before, together with the memory of what was done and how it worked. From there it is trial and error, many many many generations later sensory input (cameras etc) is interpreted properly and the action taken is correct.
The neutral network are the reinforced “action”-paths taken based on the sensory inputs and achievement of goals.
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