--- Jiri Jelinek <[EMAIL PROTECTED]> wrote: > Eric, > > I'm not 100% sure if someone/something else than me feels pain, but > considerable similarities between my and other humans > > - architecture > - [triggers of] internal and external pain related responses > - independent descriptions of subjective pain perceptions which > correspond in certain ways with the internal body responses > > make me think it's more likely than not that other humans feel pain > the way I do.
There is a simple proof for the existence of pain. Define pain as a signal that an intelligent system has the goal of avoiding. By the equivalence: (P => Q) = (not Q => not P) if you didn't believe the pain was real, you would not try to avoid it. (OK, that is "proof by belief". I omitted the step (you believe X => X is true). If you believe it is true, that is good enough). > The further you move from human like architecture the less you see the > signs of pain related behavior (e.g. the avoidance behavior). Insect > keeps trying to use badly injured body parts the same way as if they > weren't injured and (unlike in mammals) its internal responses to the > injury don't suggest that anything crazy is going on with them. And > when I look at software, I cannot find a good reason for believing it > can be in pain. The fact that we can use pain killers (and other > techniques) to get rid of pain and still remain complex systems > capable of general problem solving suggests that the pain quale takes > more than complex problem solving algorithms we are writing for our > AGI. Pain is clearly measurable. It obeys a strict ordering. If you prefer penalty A to B and B to C, then you will prefer A to C. You can estimate, e.g. that B is twice as painful as A and choose A twice vs. B once. In AIXI, the reinforcement signal is a numeric quantity. But how should pain be measured? Pain results in a change in the behavior of an intelligent system. If a system responds Y = f(X) to input X, followed by negative reinforcement, then the function f is changed to output Y with lower probability given input X. The magnitude of this change is measurable in bits. Let f be the function prior to negative reinforcement and f' be the function afterwards. Then define dK(f) = K(f'|f) = K(f, f') - K(f) where K() is algorithmic complexity. Then dK(f) is the number of bits needed to describe the change from f to f'. Arguments for: - Greater pain results in a greater change in behavior (consistent with animal experiments). - Greater intelligence implies greater possible pain (consistent with the belief that people feel more pain than insects or machines). Argument against: - dK makes no distinction between negative and positive reinforcement, or neutral methods such as supervised learning or classical conditioning. I don't know how to address this argument. Earlier I posted a program that simulates a programmable logic gate that you train using reinforcement learning. Note that you can achieve the same state using either positive or negative reinforcement, or by a neutral method such as setting the weights directly. -- Matt Mahoney -- Matt Mahoney, [EMAIL PROTECTED] ----- This list is sponsored by AGIRI: http://www.agiri.org/email To unsubscribe or change your options, please go to: http://v2.listbox.com/member/?member_id=231415&user_secret=e9e40a7e
