4 ms·

Well, here is what I believe brains do: https://news.ycombinator.com/item?id=9022206 https://news.ycombinator.com/item?id=9022206 It makes total sense for the

Well, here is what I believe brains do: https://news.ycombinator.com/item?id=9022206 https://news.ycombinator.com/item?id=9022206 It makes total sense for the brain's job to be minimizing surprise, because minimizing surprise is the best and most basic strategy for survival.

toxik 8y ago
With all due respect, one sentence explaining how you think the mind works isn't really worth much. It doesn't amount to much more than "the brain tries to explain reality." Yes, ok, but how do you translate that into some algorithm? How does it relate to gradient descent methods on neural networks?
- rusticpenn 8y ago
  Surprise can be defined as an unexpected difference or anomaly. One Class Classification or anomaly detection can be a very good trainer for a generic AI.
- Rainymood 8y ago
  >With all due respect, one sentence explaining how you think the mind works isn't really worth much. With all due respect, one sentence can be worth a lot. Some examples: > F = MA Another > E = MC^2 And another > G_{\mu, \nu} = 8 \pi G (T_{P\mu, \nu} _ \rho_{\Lambda} g_{\mu, \mu}) Another example >To be, or not to be; that is the question; Et cetera, et cetera. The length of something does not necessarily imply that an idea is weak, maybe the idea is really deep? Dismissing an idea based on length is idiotic. Sorry for the rant.
  - toxik 8y ago
    None of these mean anything without their respective context. In fact, they're all pretty pedestrian taken as a single sentence.
gnode 8y ago
This is incomplete, but it sounds similar to the memory-prediction framework theory (https://en.wikipedia.org/wiki/Memory-prediction_framework https://en.wikipedia.org/wiki/Memory-prediction_framework) and similar theories. Although I might be biased / primed to interpret it this way. Basically, according to such theories, we don't really "decide" anything; we carry out what we predict we're going to do, by hierarchically modelling patterns of input and output together in a hierarchy. E.g. "I am eating an apple" -> "I see an apple" and "I'm bringing the apple closer to my mouth" -> "I see lines and colour" and "I tense my hand and move my arm" Adding a biological perspective, my opinion is that motivation arises from attention modulation by neurotransmitters like dopamine and noradrenaline, and feeding this back into the abstract theory, the hierarchy of recognition favours converging on models with high stimulation weight ("I am eating tasty food" or "I am avoiding a car crash" rather than "I am completing boring paperwork")

toxik 8y ago

With all due respect, one sentence explaining how you think the mind works isn't really worth much. It doesn't amount to much more than "the brain tries to explain reality." Yes, ok, but how do you translate that into some algorithm? How does it relate to gradient descent methods on neural networks?

rusticpenn 8y ago
Surprise can be defined as an unexpected difference or anomaly. One Class Classification or anomaly detection can be a very good trainer for a generic AI.
Rainymood 8y ago
>With all due respect, one sentence explaining how you think the mind works isn't really worth much. With all due respect, one sentence can be worth a lot. Some examples: > F = MA Another > E = MC^2 And another > G_{\mu, \nu} = 8 \pi G (T_{P\mu, \nu} _ \rho_{\Lambda} g_{\mu, \mu}) Another example >To be, or not to be; that is the question; Et cetera, et cetera. The length of something does not necessarily imply that an idea is weak, maybe the idea is really deep? Dismissing an idea based on length is idiotic. Sorry for the rant.
- toxik 8y ago
  None of these mean anything without their respective context. In fact, they're all pretty pedestrian taken as a single sentence.

gnode 8y ago

This is incomplete, but it sounds similar to the memory-prediction framework theory (https://en.wikipedia.org/wiki/Memory-prediction_framework https://en.wikipedia.org/wiki/Memory-prediction_framework) and similar theories. Although I might be biased / primed to interpret it this way. Basically, according to such theories, we don't really "decide" anything; we carry out what we predict we're going to do, by hierarchically modelling patterns of input and output together in a hierarchy. E.g. "I am eating an apple" -> "I see an apple" and "I'm bringing the apple closer to my mouth" -> "I see lines and colour" and "I tense my hand and move my arm" Adding a biological perspective, my opinion is that motivation arises from attention modulation by neurotransmitters like dopamine and noradrenaline, and feeding this back into the abstract theory, the hierarchy of recognition favours converging on models with high stimulation weight ("I am eating tasty food" or "I am avoiding a car crash" rather than "I am completing boring paperwork")