# elbow921

Karma: −21
• What if most people would develop superhuman intelligences in their brains without school but, because they have to write essays in school, these superhuman intelligences become aligned with writing essays fast? And no doomsday scenario has happened because they mostly cancel out each others’ attempted manipulations and they couldn’t program nanobots with their complicated utility functions. ChatGPT writes faster than us and has 20B parameters where humans have 100T parameters, but our neural activations are more noisy than floating-point arithmetic.

• This is what I am wondering: Does this algorithm, when run, instantiate a subjective experience with the same moral relevance as the subjective experience that happens when mu opioids are released in biological brains?

• ‘By ‘obvious to the algorithm’ I mean that, to the algorithm, A is referenced with no intermediate computation. This is how pleasure and pain feel to me. I do not believe all reinforcement learning algorithms feel pleasure/​pain. A simple example that does not suffer is the Simpleton iterated prisoner’s dilemma strategy. I believe pain and pleasure are effective ways to implement reinforcement learning. In animals, reinforcement learning is called operant conditioning. See Reinforcement learning on a chicken for a chicken that has experienced it. I do not know any algorithms to determine whether there is anything to be like a given program. I suspected this program experienced pleasure/​pain because of its paralells to the neuroscience of pleasure and pain.

• As this algorithm executes, the last and 2last variables become the program’s last 2 outputs. L1′s even indexes become the average input(reward?) given the number of ones the program outputted the last 2 times. I called L1′s odd indexes ‘confidence’ because, as they get higher, the corresponding average reward changes less based on evidence. When L1 becomes entangled with the input generation process, the algorithm chooses which outputs make the inputs higher on average. That is why I called the input ‘reward’. L2 reads off the average reward given the last 2 outputs. The algorithm chooses outputs that make the number of ones outputted closer to the number that has yielded the highest inputs in the past. This makes L2 analogous to ‘wanting’.

# [Question] Does this al­gorithm ex­pe­rience plea­sure and suffer­ing when run?

17 Apr 2023 2:29 UTC
−8 points