4 ms·

Isn't human reasoning mostly a bunch of statistical correlation? We see a ball drop, think "things fall when dropped", and that's our model, reinforced by thous

Isn't human reasoning mostly a bunch of statistical correlation? We see a ball drop, think "things fall when dropped", and that's our model, reinforced by thousands of everyday experiences. It's purely based on outcomes. We don't naturally reason through potential causes like "Mass exerts a gravitational force which attracts other mass."

deleted 12y ago
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- throwawaymsft 12y ago
  I think the insight with DeepMind is that it's built on layers upon layers. There might be a "manager" algorithm that is taking input from your memories, another from your mood, and a "manager of managers", and so on. A photo evokes a deeper meaning than is contained within the individual pixels. Similarly, I think the right algorithm may be able to extract more intelligent behavior from a larger set of myopic probability functions.
  - musername 12y ago
    The impression of a photo is taken from context, a photo isn't a closed system. That implies a larger set of probability functions, sure, some of it from genetic evolution, some from the evolution of thoughts. Reason draws from its intelligence from emotion, ie. emotional intelligence, though. There's convolution of primary senses, finely tuned, highly developed mechanisms and chemical reactions that motivate the strategic management you mentioned. In that sense, what we percieve as intelligent often enough is the abillity to do more with less, ie. compression of information. Therefore, one goal is to keep the probability functions few. What happens with ML is just that, it uses much less layers and neurons for context then the brain, but makes up for it in raw speed. To add to my wild musings from before, the tell in intel might be related to to tell which used to mean to count. Compare that with german erzaehlen and zaehlen. Same for to reckon, cognate with german rechnen (calculate).
musername 12y ago
we don't? probably because you got it backwards. The idea of attraction entails gravitation (and other forces that hold the ball up before the drop), which is the conclusion of a line of reasoning, that is a more detailed, generalized version of the observation "things fall when dropped". Sure, backtracking is a rather simple, if not the most simple approach, but for N=NP kind of problems, presumably, it is the only one and heuristics only change the order of the track, but never reduce complexity. That's my layman opinion, that seems to agree with the etymology of reason. Reason > ... > Ratio ... Reor. Reor is latin for to think, or calculate. Arithmetic in its simplest form, addition in the unary system ie. arranging pebbles (= lt. calculus), counting knots, simply counting. Now backtracking is just enumeration and elimination of possibilities. Ratio itself means measure, and a measurement always entails statistical error (does heisenbergs uncertainty principle prove that?).

deleted 12y ago

[deleted]

throwawaymsft 12y ago
I think the insight with DeepMind is that it's built on layers upon layers. There might be a "manager" algorithm that is taking input from your memories, another from your mood, and a "manager of managers", and so on. A photo evokes a deeper meaning than is contained within the individual pixels. Similarly, I think the right algorithm may be able to extract more intelligent behavior from a larger set of myopic probability functions.
- musername 12y ago
  The impression of a photo is taken from context, a photo isn't a closed system. That implies a larger set of probability functions, sure, some of it from genetic evolution, some from the evolution of thoughts. Reason draws from its intelligence from emotion, ie. emotional intelligence, though. There's convolution of primary senses, finely tuned, highly developed mechanisms and chemical reactions that motivate the strategic management you mentioned. In that sense, what we percieve as intelligent often enough is the abillity to do more with less, ie. compression of information. Therefore, one goal is to keep the probability functions few. What happens with ML is just that, it uses much less layers and neurons for context then the brain, but makes up for it in raw speed. To add to my wild musings from before, the tell in intel might be related to to tell which used to mean to count. Compare that with german erzaehlen and zaehlen. Same for to reckon, cognate with german rechnen (calculate).

musername 12y ago

we don't? probably because you got it backwards. The idea of attraction entails gravitation (and other forces that hold the ball up before the drop), which is the conclusion of a line of reasoning, that is a more detailed, generalized version of the observation "things fall when dropped". Sure, backtracking is a rather simple, if not the most simple approach, but for N=NP kind of problems, presumably, it is the only one and heuristics only change the order of the track, but never reduce complexity. That's my layman opinion, that seems to agree with the etymology of reason. Reason > ... > Ratio ... Reor. Reor is latin for to think, or calculate. Arithmetic in its simplest form, addition in the unary system ie. arranging pebbles (= lt. calculus), counting knots, simply counting. Now backtracking is just enumeration and elimination of possibilities. Ratio itself means measure, and a measurement always entails statistical error (does heisenbergs uncertainty principle prove that?).