Brassiere's Question

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« on: April 24, 2013, 06:05:21 pm »
Quote from: sciborg2
So, what was this anyway?

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« Reply #1 on: April 24, 2013, 06:05:27 pm »
Quote from: Jorge
Well, from Bakker's original post it is this:

“The problem consists in articulating the relation between the dialectical structure of the conceptual and the non-dialectical structure of the real in such a way as to explain how real negativity fuels dialectics even as it prevents dialectics from incorporating its own negativity.”

According to Scott, if you translate it into slightly more comprehensible Philoso-speak, it's something like:
"any causal explanation of intentionality should also explain why intentionality seems causally inexplicable."

In Jorge-speak:
"Neuroscience should explain why 'meaning' seems to be inexplicable by neuroscience."


I think there was something more. A challenge that Mr. Brassier poses: can we embrace nihilism (truth) despite its ugliness? Can we dare to leave intentional concepts behind?

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« Reply #2 on: April 24, 2013, 06:05:33 pm »
Quote from: sciborg2
Interesting. [Thanks!] I take it this is where the BBT comes in?

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« Reply #3 on: April 24, 2013, 06:05:41 pm »
Quote from: Callan S.
When I was young, I was thinking how to make an AI (as you do). I was thinking it'd need possitive reinforcement and negative reinforcement. But how do you make 'positive'? What is 'positive'? What material.

Then I realised it'd just be a signal. That's it. From the outside it's just a signal. From within it's something else...but really, even then I'm thinking, what's inside a wire?

What is 'embracing nihilism', anyway? If you look at nihilism like a tool, one way to apply it is against itself - a nihilistic organism does not stick around for long. The intentional is like an economy backed up by the darwinistic need (kind of like money backed up by gold). As much as the nihilistic blade severs, it can just as much sever it's own absolute/embraced use.

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« Reply #4 on: April 24, 2013, 06:05:47 pm »
Quote from: Curethan
Have a look at neural networks, Callan.  An oldish AI concept, they are circuits built via a reinforcement algorithm - interesting but somewhat limited.

Here's a link;
http://www.doc.ic.ac.uk/~nd/surprise_96/journal/vol4/cs11/report.html