5 ms·

My god if I hear this argument one more time I'm going to pop. What on earth gives you the idea that you are something more than statistical inference?

teej 6y ago
Yes, it’s widely believed that statistical inference is a part of how the brain operates. But we have barely scratched the surface in our understanding how the human brain works. Do you honestly believe statistical inference completely explains a human’s ability to learn?
- plutonorm 6y ago
  Of course.
randcraw 6y ago
Because it is possible to invent or imagine new things without resorting to endless random walks or blind trial and error. Induction isn’t probabilistic. It’s the origin of all discovery and based in sussing out what’s important in patterns, selectively proposing causal mechanisms and testing those hypotheses by following priors and implications — an essential basis for original and creative thought that no purely probabilistic engine can employ.
- FeepingCreature 6y ago
  You think a probabilistic engine can't model a human doing induction? At a certain level of capability, mere pattern imitation closes upwards.
ggrrhh_ta 6y ago
I think that the reason your mind pops is that you are not trying to understand what the person saying that is actually meaning. Stating that we are no more than statistical inference is no different to saying that we are no more math - really, you can build the deepest mathematical truths on statistical inference. The thing is that the math/s.i. that ML models do is too trivial in comparison to the math that would be required to describe a human identity. It can detect faces very well, it can calculate fifths roots very quickly, it can solve discrete problems quickly, yes, but its achievements are, in comparison to actual human understanding (and purpose), more closer to those of another tool, like a hammer, than those of humans. edit: typos/grammar
- plutonorm 6y ago
  I hear what they are saying and I reject it as trivially incorrect. Of course all we are is math. What else do you think there is? "The thing is that the math/s.i. that ML models do is too trivial in comparison to the math that would be required to describe a human identity" why do you think this? How do you know the expressive power of neural networks? How have you guaged the expressive power of biological neural networks. How have you made any of these conclusions? I am exasperated.
  - ggrrhh_ta 6y ago
    You are exasperated because when I wrote "that ML do is too trivial..." you did not realize that for ML I meant "the current state of the art of ML"... I never assumed we are more than math. Just that there is a lot to still learn about the structure in which those buildings blocks are organized in order to fully tame what is understood as human intelligence. If we could simulate all the neurons of a real adult living brain accurately enough it would be intelligent from a human perspective, and still, both the model and us would be equally far, very far, from understanding how to arrive to that point of neural organization to achieve that kind of intelligence.
- 20after4 6y ago
  For a moment I had myself convinced that your response was written by GPT-3. Part of me hopes that is the actual fact.
bsenftner 6y ago
Because statistical inference does not create complex operating models designed to fit a subset of reality from a larger encompassing model of reality which humans base our daily decisions and life philosophies. Just because we do not deep dive into the complexities of existential questions every waking moment does not say we're not operating scaffolds which depend upon prior existential examinations. We're complex to say the least, and we require reasons - and that very idea: reason is what is not present in statistical inference. Without a prior body of human conversation, statements, and larger expressions of philosophy based upon human reason, statistical inference goes nowhere.
- hackinthebochs 6y ago
  >reason is what is not present in statistical inference. But why think reason isn't a kind of statistical inference? GPT-3 demonstrates a rudimentary capacity for reasoning and its "merely" a statistical inference engine.

teej 6y ago

Yes, it’s widely believed that statistical inference is a part of how the brain operates. But we have barely scratched the surface in our understanding how the human brain works. Do you honestly believe statistical inference completely explains a human’s ability to learn?

plutonorm 6y ago
Of course.

randcraw 6y ago

Because it is possible to invent or imagine new things without resorting to endless random walks or blind trial and error. Induction isn’t probabilistic. It’s the origin of all discovery and based in sussing out what’s important in patterns, selectively proposing causal mechanisms and testing those hypotheses by following priors and implications — an essential basis for original and creative thought that no purely probabilistic engine can employ.

FeepingCreature 6y ago
You think a probabilistic engine can't model a human doing induction? At a certain level of capability, mere pattern imitation closes upwards.

ggrrhh_ta 6y ago

I think that the reason your mind pops is that you are not trying to understand what the person saying that is actually meaning. Stating that we are no more than statistical inference is no different to saying that we are no more math - really, you can build the deepest mathematical truths on statistical inference. The thing is that the math/s.i. that ML models do is too trivial in comparison to the math that would be required to describe a human identity. It can detect faces very well, it can calculate fifths roots very quickly, it can solve discrete problems quickly, yes, but its achievements are, in comparison to actual human understanding (and purpose), more closer to those of another tool, like a hammer, than those of humans. edit: typos/grammar

plutonorm 6y ago
I hear what they are saying and I reject it as trivially incorrect. Of course all we are is math. What else do you think there is? "The thing is that the math/s.i. that ML models do is too trivial in comparison to the math that would be required to describe a human identity" why do you think this? How do you know the expressive power of neural networks? How have you guaged the expressive power of biological neural networks. How have you made any of these conclusions? I am exasperated.
- ggrrhh_ta 6y ago
  You are exasperated because when I wrote "that ML do is too trivial..." you did not realize that for ML I meant "the current state of the art of ML"... I never assumed we are more than math. Just that there is a lot to still learn about the structure in which those buildings blocks are organized in order to fully tame what is understood as human intelligence. If we could simulate all the neurons of a real adult living brain accurately enough it would be intelligent from a human perspective, and still, both the model and us would be equally far, very far, from understanding how to arrive to that point of neural organization to achieve that kind of intelligence.
20after4 6y ago
For a moment I had myself convinced that your response was written by GPT-3. Part of me hopes that is the actual fact.

bsenftner 6y ago

Because statistical inference does not create complex operating models designed to fit a subset of reality from a larger encompassing model of reality which humans base our daily decisions and life philosophies. Just because we do not deep dive into the complexities of existential questions every waking moment does not say we're not operating scaffolds which depend upon prior existential examinations. We're complex to say the least, and we require reasons - and that very idea: reason is what is not present in statistical inference. Without a prior body of human conversation, statements, and larger expressions of philosophy based upon human reason, statistical inference goes nowhere.

hackinthebochs 6y ago
>reason is what is not present in statistical inference. But why think reason isn't a kind of statistical inference? GPT-3 demonstrates a rudimentary capacity for reasoning and its "merely" a statistical inference engine.