This isn't a problem at all for modern type systems. OCaml, Haskell, and even C++ have Turing complete type systems and this is one of the last concerns for developers.
I think it's because everything is far more spread out in the US.
The reinforcement learning framework is perfect for representing cause and effect. An agent could learn that in a state of no fire, taking an action of rubbing sticks together would transition into a state of having…
Neural networks do exactly what you are describing as "symbolic reasoning". It seems to be a common thing recently to dismiss modern ML techniques as curve fitting, but these fundamental models are extremely powerful.…
The "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.
This isn't a problem at all for modern type systems. OCaml, Haskell, and even C++ have Turing complete type systems and this is one of the last concerns for developers.
I think it's because everything is far more spread out in the US.
The reinforcement learning framework is perfect for representing cause and effect. An agent could learn that in a state of no fire, taking an action of rubbing sticks together would transition into a state of having…
Neural networks do exactly what you are describing as "symbolic reasoning". It seems to be a common thing recently to dismiss modern ML techniques as curve fitting, but these fundamental models are extremely powerful.…
The "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.