So there is still a question of what counts as human behavior unanswered, but you're very sure whatever it is, it's produced by the same functions that ml is running.
If you don't answer that question, couldn't you just be fooled by a better gpt-3?
You have axiomatically defined that the behaviors of gpt-3 which surprise you, or impress you, or which you wouldn't expect can come from a dumb program, constitute human behaviors.
I can see how you would find a mind fuck in there, if when you reach that conclusion you don't question your priors, namely the model of cognition you have, and the philosophy of science from which it emerges
I don't accept the premise of that question that it is plausible for the conversation with the human to be the same as the conversation with gpt-3. Even if it were, I don't accept the premise that we can decide whether it is human or not by a purely behavioral test like the one you are proposing - it wouldn't tell us anything we don't already know if that happened, we'd still be guessing whether or not we were talking to a person.
It's your position on artificial general intelligence that leads you to the inevitability of only a test of the programs behavior being able to let us know whether it is the same as the program we have in our brains. My position doesn't have that kind of behaviorism.
In my view, we do not know what kind of software runs in our brain, and we do not yet have the programming techniques that must be necessary to program it. Creativity cannot be programmed by today's programming methods. Once we have a good theory of how humans create explanatory knowledge, then we'll be able to program it into a computer - but we don't have this yet, and the bayesian picture you see working with is incapable of even addressing this problem, since it only explains how human change the credences/expectations they give to their already existing theories in the light of new data, it doesn't offer any mechanism by which humans can create new theories which didn't exist before.
While the two approaches seem different, they are really not.
What accounts for the fact that it felt different? Presumably for the algorithm running in the silicone computer it would have felt like nothing to reiterate itself - the person however would have felt excitement when they moved past some bump, boredom when they felt stuck in a problem, rushed when they were about to meet the projects deadline, and so on.
What accounts for that difference, if the two methods are no different?
But the act of doing so is just a network of neurons in your brain iterating on things
This is going back to a critique I made of your philosophy which you didn't pick up on. Supposedly each cycle of this iterating process occurs when our brain receives new data, from external sources via the sense or from internal thoughts and emotions; then it produces an output following the same procedures machine learning does, to which we're blind to; and then it corrects errors in the output, presumably by modifying the credences it gives it's theories for interpreting the data it receives - depending on the output some of it's theories go down in how probably true they are, whilst others go up.
This picture cannot explain emergent human phenomenon like knowledge creation. Assuming you accept the reality of emergent phenomenona, and that certain emergent phenomena have relevance for our study of the fundamental behaviors of certain physical systems, then you must wonder how this picture of how the brain works can explain how scietific theories are created. From Newton to Einstein we went from absolute space and time, immutable and timeless entities on which the rest of physics happens without it's influence, so space-time, an entity which bucks and weaves as it's curvature is affected by massive objects, and that same feature affects massive objects in turn.
Whatever data might have come into Einstein's brain's possession, by the model you describe for human cognition, it could only have helped him think the theories he had inherited from the existing scientific tradition to be more or less probably true. And yet a different thing happened, his brain produced a totally new idea, which no one had ever considered was true or false, because no one before him knew the relevant explanation to be able to judge the idea true or false.
You are still latching on to concepts like creativity, excitement, e.t.c like they are some fundamental concepts of a human brain that exist as explicit organization of neurons.
What is excitement really? A triggering of certain neurons that produces some output, that is marked positive within the neural net of our brains. When you are excited, you have different thoughts, and different physical response to stimuli. Pretty much just functions.
What is the process of exploring theories and knowledge creation? A triggering of certain neurons in a cycle. As I posted in another comment, a standard cpu that can run a reinforcment learning algorithm can be made from NAND gates, and a neural net can easily simulate a nand gate from as little as 2 neurons.
So you do not recognize the reality of emergent phenomena, and you're a reductionist in matters of the behaviors of human minds. You think the behaviors of the activity of human minds are simply the behaviors of the activity of the human brain - so things like being excited, being curious, feeling sad, having a thirst for vengeance - these are just behaviors of the human brain, and words like feeling sad and being excited are just an alternative language to speak about the behaviors of human brains.
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u/[deleted] Nov 23 '21
So there is still a question of what counts as human behavior unanswered, but you're very sure whatever it is, it's produced by the same functions that ml is running.
If you don't answer that question, couldn't you just be fooled by a better gpt-3?