As well as performing arbitrary computations in one tick, we could also keep a CTC going as a "reset button": whenever we realise that an incorrect/sub-optimal decision has been made, we can go back to when we started.
This lets us 'brute force' our real-world interactions (ie. our IO), as well as "merely" gaining an infinite computing speed-up.
This could be exploited by something similar to the "Success Story Algorithm" http://www.idsia.ch/~juergen/directsearch/node13.html . The CTC-AI would some way of "scoring" the world (ie. a fitness function), some probability of modifying its decision-making policy, and some non-zero probability of resetting the world. If the reset probability is anti-correlated to the world's score, worlds which score higher over time are more likely, hence the machine's decisions are more likely to increase the score than decrease it, hence the policy is more likely to improve over time.
As well as performing arbitrary computations in one tick, we could also keep a CTC going as a "reset button": whenever we realise that an incorrect/sub-optimal decision has been made, we can go back to when we started.
This lets us 'brute force' our real-world interactions (ie. our IO), as well as "merely" gaining an infinite computing speed-up.
This could be exploited by something similar to the "Success Story Algorithm" http://www.idsia.ch/~juergen/directsearch/node13.html . The CTC-AI would some way of "scoring" the world (ie. a fitness function), some probability of modifying its decision-making policy, and some non-zero probability of resetting the world. If the reset probability is anti-correlated to the world's score, worlds which score higher over time are more likely, hence the machine's decisions are more likely to increase the score than decrease it, hence the policy is more likely to improve over time.