T
Touchstone
Guest
Andy_T81:
As to the bootstrapping of symbols, I don’t think that’s a problem for machines any more than humans. Humans arrive at birth “designed” (by nature impersonally, or God) to manipulate symbols, to associate referents with mental symbols that provide the grist of meaning and semantics. And they do so as a matter of their nature – they are “goal-seeking” programs, and the symbolic construction process coalesces around these goal-states – acquiring food, comfort, emotional connection/satisfaction, control over local objects, navigation, etc.
This is not a problem for a machine, to be “born” with a goal-seeking design. In fact this is how programs that learn and adapt do get designed – they are equipped with generic facilities for symbolic construction based on incoming information, and they are given goal-states that that the program is biased toward. This is the bootstrapping of semantic contexts for a machine. The evolutionary algorithms I’ve worked on for network anomaly detection had goals it was “born” with. But as it went, it made up its contextualized semantics on its own, based on the (name removed by moderator)ut from the environment it was sampling. The same code learned and adapted in different ways to different network environments, even as it pursued the same goal-states.
As it turns out, the more successful instances and runs were the ones where we provided the minimal “starting semantics”, and made it learn as much “from scratch” as possible. The goals remain the same, but where you have a) goal-states, b) adaptive networks that can learn through model refinement through constant feedback loops and c) algorithms that cull and refining learning toward the goal-states in a), you have “automatic meaning construction”. It’s software, and thus overtly designed as far as it’s starting point, but the machine in those applications learns, adapts, and “self-considers” as it reviews what it has learned and is learning and how that tracks against its goal-states. The human mind is a formidably exotic machine, on any understanding of CTM, and the cost and complexity of even first order approximations of the human brain in computing hardware is staggering.
But the practical pieces that are already out there are not just practically useful, but instructive as working mechanisms that perform key functions we’ve long intuited to be the magic powers of our inner, supernatural homunculus.
-TS
Right, but you can’t have it both ways. If thoughts are composed of symbols, the homunculus problem is dismissed, because all meaning and semantics are necessarily algorithmic and reducible to automata. If you think that thought cannot be symbolic “all the way down”, you are positing the homunculus. Pick one.It’s as simple as this: Symbols require somebody to link the symbol with the thing symbolized, otherwise it is not a symbol, just meaningless matter. Therefore symbols, by their very definition, require somebody who “really adopts intentions”. This is a problem for the proponent of CTM who wants to call thoughts symbols. You are somehow projecting this onto my position, yet never have I promoted the idea that thoughts are composed of symbols. I’m arguing against that position, remember?
As to the bootstrapping of symbols, I don’t think that’s a problem for machines any more than humans. Humans arrive at birth “designed” (by nature impersonally, or God) to manipulate symbols, to associate referents with mental symbols that provide the grist of meaning and semantics. And they do so as a matter of their nature – they are “goal-seeking” programs, and the symbolic construction process coalesces around these goal-states – acquiring food, comfort, emotional connection/satisfaction, control over local objects, navigation, etc.
This is not a problem for a machine, to be “born” with a goal-seeking design. In fact this is how programs that learn and adapt do get designed – they are equipped with generic facilities for symbolic construction based on incoming information, and they are given goal-states that that the program is biased toward. This is the bootstrapping of semantic contexts for a machine. The evolutionary algorithms I’ve worked on for network anomaly detection had goals it was “born” with. But as it went, it made up its contextualized semantics on its own, based on the (name removed by moderator)ut from the environment it was sampling. The same code learned and adapted in different ways to different network environments, even as it pursued the same goal-states.
As it turns out, the more successful instances and runs were the ones where we provided the minimal “starting semantics”, and made it learn as much “from scratch” as possible. The goals remain the same, but where you have a) goal-states, b) adaptive networks that can learn through model refinement through constant feedback loops and c) algorithms that cull and refining learning toward the goal-states in a), you have “automatic meaning construction”. It’s software, and thus overtly designed as far as it’s starting point, but the machine in those applications learns, adapts, and “self-considers” as it reviews what it has learned and is learning and how that tracks against its goal-states. The human mind is a formidably exotic machine, on any understanding of CTM, and the cost and complexity of even first order approximations of the human brain in computing hardware is staggering.
But the practical pieces that are already out there are not just practically useful, but instructive as working mechanisms that perform key functions we’ve long intuited to be the magic powers of our inner, supernatural homunculus.
-TS