It's totally statistics, but that second paragraph really isn't how it works at all. You don't "code" neural networks the way you code up website or game. There's no "if (userAskedForThis) {DoThis()}". All the coding you do in neutral networks is to define a model and training process, but that's it; Before training that behavior is completely random.
The neural network engineer isn't directly coding up behavior. They're architecting the model (random weights by default), setting up an environment (training and evaluation datasets, tweaking some training parameters), and letting the models weights be trained or "fit" to the data. It's behavior isn't designed, the virtual environment that it evolved in was. Bigger, cleaner datasets, model architectures suited for the data, and an appropriate number of training iterations (epochs) can improve results, but they'll never be perfect, just an approximation.
But the actions taken by the model in the virtual environments can always be described as discrete steps. Each modification to the weights done by each agent in each generation can be described as discrete steps. Even if I'm fucking up some of the terminology, basic computer architecture enforces that there are discrete steps.
We could literally trace each command that runs on the hardware that runs these things individually if we wanted full auditability, to eat all the storage space ever made, and to drive someone insane. Have none of you AI devs ever taken an embedded programming/machine language course? Never looked into reverse engineering of compiled executables?
I understand that these things work by doing these steps millions upon millions of times, but there has to be a better middle ground for tracing these things than "lol i dunno, computer brute forced it". It is a mixture of laziness, and unwillingness to allow responsibility to negatively impact profits that result in so many in the field to summarize it as literally impossible.
But the actions taken by the model in the virtual environments can always be described as discrete steps.
That's technically correct, but practically useless information. Neural networks are stochastic by design, and while Turing machines are technically deterministic, most operating systems' random number generators will try to introduce noise from the environment (current time, input devices data, temperature readings, etc). So because of that randomness, those discrete steps you'd have to walk through would require knowing intimate details of the environment that the PC was in at precisely the time it ran, which isn't stored. And even if it was or you used a deterministic psuedo-random number generator, you'd still essentially be stuck reverse engineering the world's worse spaghetti code written entirely in huge matrix multiplications, code that we already know can't possibly be optimal anyway.
If a software needs guaranteed optimality, then a neural network (or any stochastic algorithm) is simply the wrong tool for the job. No need to shove a square peg in a round hole.
Also I can't speak for AI devs, in fact I've only taken an applied neural networks course myself, but I can tell you that computer architecture was like a prerequisite of a prerequisite of a prerequisite of that course.
It's totally statistics, but that second paragraph really isn't how it works at all. You don't "code" neural networks the way you code up website or game. There's no "if (userAskedForThis) {DoThis()}". All the coding you do in neutral networks is to define a model and training process, but that's it; Before training that behavior is completely random.
The neural network engineer isn't directly coding up behavior. They're architecting the model (random weights by default), setting up an environment (training and evaluation datasets, tweaking some training parameters), and letting the models weights be trained or "fit" to the data. It's behavior isn't designed, the virtual environment that it evolved in was. Bigger, cleaner datasets, model architectures suited for the data, and an appropriate number of training iterations (epochs) can improve results, but they'll never be perfect, just an approximation.
But the actions taken by the model in the virtual environments can always be described as discrete steps. Each modification to the weights done by each agent in each generation can be described as discrete steps. Even if I'm fucking up some of the terminology, basic computer architecture enforces that there are discrete steps.
We could literally trace each command that runs on the hardware that runs these things individually if we wanted full auditability, to eat all the storage space ever made, and to drive someone insane. Have none of you AI devs ever taken an embedded programming/machine language course? Never looked into reverse engineering of compiled executables?
I understand that these things work by doing these steps millions upon millions of times, but there has to be a better middle ground for tracing these things than "lol i dunno, computer brute forced it". It is a mixture of laziness, and unwillingness to allow responsibility to negatively impact profits that result in so many in the field to summarize it as literally impossible.
That's technically correct, but practically useless information. Neural networks are stochastic by design, and while Turing machines are technically deterministic, most operating systems' random number generators will try to introduce noise from the environment (current time, input devices data, temperature readings, etc). So because of that randomness, those discrete steps you'd have to walk through would require knowing intimate details of the environment that the PC was in at precisely the time it ran, which isn't stored. And even if it was or you used a deterministic psuedo-random number generator, you'd still essentially be stuck reverse engineering the world's worse spaghetti code written entirely in huge matrix multiplications, code that we already know can't possibly be optimal anyway.
If a software needs guaranteed optimality, then a neural network (or any stochastic algorithm) is simply the wrong tool for the job. No need to shove a square peg in a round hole.
Also I can't speak for AI devs, in fact I've only taken an applied neural networks course myself, but I can tell you that computer architecture was like a prerequisite of a prerequisite of a prerequisite of that course.