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Simulating a mind is not the same as simulating mind processes. I doubt that you can create a mind that's similar to a human mind without the relevant elements
by narag 7y ago
Simulating a mind is not the same as simulating mind processes.
I doubt that you can create a mind that's similar to a human mind without the relevant elements that are took for granted when we think of a human being: senses, perception, pain, pleasure, fear, volition... a body! and the real-time feedback loop that connects us to our environment and our peers.
The same could be said about animals' minds. That's why it's still impossible to make even a mosquito brain. It's a question of texture. Making a decision for a human involves a complex cloud of subsystems working in unstable equilibrium, more of a boiling cauldron than an algorithmic checklist. When you're scared, you're not just thinking that somethind is dangerous and rather avoid it, you are feeling something very uncomfortable and you want to stop it.
What if you want to advance in creating some kind of simpler mind now when you still haven't the means to build a complete organism? That's an interesting problem. Would immersing programs in a virtual world be useful? Or would it be better to make robots face the real world directly? I believe that you need, as a minimum, a system that integrates sight with hearing and touching sensors, and some kind of incentive system.
After some results, maybe using machine learning, the emergent organization could be applied as a building block to more complex robots. Meanwhile, trying to teach machines some human capabilities will not lead to generalized IA, but to more of the same we have now, that it's very useful, just not quite qualifies for the label.
- 7373737373 7y ago>senses, perception, pain, pleasure, fear, volition... a body! Yes! Almost all neural networks have no self-model and thus no self-awareness because they cannot perceive themselves. They only see the inputs. They do not see the result of their actions. This makes developing a self-model impossible. They cannot develop an internal model of internal vs external causes. What their "boundary of influence" is. Differentiation between internal and external causes. They are trained and then used, immutable, unlearning after training. Even if they could perceive their outputs during training and/or evaluation, they cannot perceive themselves otherwise, making it practically impossible to deduce by themselves what they even are. They can't inspect themselves. The causal loop needs to be closed for all of this to happen.
- viuphiet 7y agoThe "self-model" you're talking about is the agent in the Reinforcement Learning framework. It moves between states in an environment and learns from reward it earns from each action.
- 7373737373 7y agoYes, although I wonder if, or how well these self-models develop in practice compared to the world-models. Say, if a 2D-agent has a rectangular body shape, it probably won't develop a high-level representation of that fact unless its actions allow it to perceive it accurately. Purely figuring that out from collisions produced by basic actions (rotate left, rotate right, move forward etc.) seems to be practically infeasible. It has neither sight (observing self-movements) nor self-touch (which would allow it to observe its boundaries and relate it to what it has seen).