[imvetri]

Same here. When I studied genetic programming, I was hoping that's where problem solving evolve from as it was flawless. But recent events prove otherwise which made me believe we are using the wrong tool for the wrong problem. Here is why.

When AI gets to 100% accuracy, the equation to find the answer becomes 100% accurate. We no longer have to run the AI with heavy resources and equation can be converted to an executable program. This modal of AI will save computing power, and uses resources smartly.

Example.

AI tries to find right equation to add two numbers.

AI finds the equation to add two numbers.

AI outputs the equation as an executable program.

AI discards itself.

[jmmcd]

You might not be familiar with how neural networks work. When training they do use a lot of computing power. But when running they don't. Yes, they still require some external boilerplate code to multiply the matrices, but you already have it and it's not heavy. So yes there is some convenience in program synthesis in a human programming language, but it is a small convenience, not a game changer.

[falkaer]

If the solution program is sufficiently complex (as one would imagine it to be in non-trivial cases where we use AI, e.g. computer vision, speech synthesis, etc.), what makes you think the solution program is going to be more lightweight than running inference on an "AI model"? Futhermore, what guarantee do you have that the discovered solution is going to be efficient w.r.t. computation at all?