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There's no fundamental law that requires neural networks be hard to understand. In fact debugging and interpreting neural networks is a very active area of rese

by breck 8y ago

There's no fundamental law that requires neural networks be hard to understand. In fact debugging and interpreting neural networks is a very active area of research and getting easier everyday. In twenty years I would not be surprised if tools are so good, due to economic forces, that it's easier to understand why a neural network made a decision than it is to understand why some complex hand written conditionals program reached a certain result.

salawat 8y ago
That still doesn't solve the problem. Nodes X, Y, and Z reached action potential tells us nada useful about useful about the NN. It's not a question of not being able to run the code in debug; we can absolutely do that. It's a question of the outcome only being dependent on a seemingly random set of numbers which no one can really reason from. That's the issue. With teenagers, we at least have the ability to structure incentives such that they continually improve their driving behavior, and most importantly, they are actually capable of learning after you cut them loose with the car. No hardware upgrades required. The car on the other hand? Not so much. W.r.t another poster's suggestion of the employment of actuarial models: I consider the employment of insurance to be a less than satisfying marshaling of our economic time, and a backdoor social control mechanism that still just makes me fidget. But that's just me.
- breck 8y ago
  > It's not a question of not being able to run the code in debug; we can absolutely do that. Don't think of debugging/stepping through neural network code using the same tools as stepping through procedural code. In the future you'll have better visual tools that show you a lot of information about the state of the network at once, rather than just the contents of a few registers that you see with a modern debugger.

by breck 8y ago

salawat 8y ago

That still doesn't solve the problem. Nodes X, Y, and Z reached action potential tells us nada useful about useful about the NN. It's not a question of not being able to run the code in debug; we can absolutely do that. It's a question of the outcome only being dependent on a seemingly random set of numbers which no one can really reason from. That's the issue. With teenagers, we at least have the ability to structure incentives such that they continually improve their driving behavior, and most importantly, they are actually capable of learning after you cut them loose with the car. No hardware upgrades required. The car on the other hand? Not so much. W.r.t another poster's suggestion of the employment of actuarial models: I consider the employment of insurance to be a less than satisfying marshaling of our economic time, and a backdoor social control mechanism that still just makes me fidget. But that's just me.

breck 8y ago
> It's not a question of not being able to run the code in debug; we can absolutely do that. Don't think of debugging/stepping through neural network code using the same tools as stepping through procedural code. In the future you'll have better visual tools that show you a lot of information about the state of the network at once, rather than just the contents of a few registers that you see with a modern debugger.