You claimed that a general learning algorithm without decision-theory or utility function is possible. I pointed out that all (harmless) practical learning algorithms we know of do in fact have decision theories and utility functions.
/facepalm
There is in fact such a thing as a null output. There is in fact such a thing as a learner with a sub-Turing hypothesis class. Such a learner with such a primitive output as “in the class” or “not in the class” does not engage in world optimization, that is: its actions do not, to its own knowledge, skew any probability distribution over future states of any portion of the world outside itself.
It does not narrow the future.
Now, what we’ve been proposing as an Oracle is even less capable. It would truly have no outputs whatsoever, only input and a debug view. It would, by definition, be incapable of narrowing the future of anything, even its own internal states.
Perhaps I have misused terminology, but that is what I was referring to: inability to narrow the outer world’s future.
This thing you are proposing, an “oracle” that is incapable of modeling itself and incapable of modeling its environment (either would require turing-complete hypotheses), what could it possibly be useful for? What could it do that today’s narrow AI can’t?
You seem to have lost the thread of the conversation. The proposal was to build a learner that can model the environment using Turing-complete models, but which has no power to make decisions or take actions. This would be a Solomonoff Inducer approximation, not an AIXI approximation.
There is in fact such a thing as a learner with a sub-Turing hypothesis class. Such a learner
with such a primitive output as “in the class” or “not in the class” does not engage in
world optimization, that is: its actions do not, to its own knowledge,
skew any probability distribution over future states of any portion of the world outside itself.
…
Now, what we’ve been proposing as an Oracle is even less capable.
which led me to think you were talking about an oracle even less capable than a learner with a sub-Turing hypothesis class.
It would truly have no outputs whatsoever, only input and a debug view. It would, by definition, be
incapable of narrowing the future of anything, even its own internal states.
If the hypotheses it considers are turing-complete, then, given enough information (and someone would give it enough information, otherwise they couldn’t do anything useful with it), it could model itself, its environment, the relation between its internal states and what shows up on the debug view, and the reactions of its operators on the information they learn from that debug view. Its (internal) actions very much would, to its own knowledge, skew the probability distribution over future states of the outer world.
/facepalm
There is in fact such a thing as a null output. There is in fact such a thing as a learner with a sub-Turing hypothesis class. Such a learner with such a primitive output as “in the class” or “not in the class” does not engage in world optimization, that is: its actions do not, to its own knowledge, skew any probability distribution over future states of any portion of the world outside itself.
It does not narrow the future.
Now, what we’ve been proposing as an Oracle is even less capable. It would truly have no outputs whatsoever, only input and a debug view. It would, by definition, be incapable of narrowing the future of anything, even its own internal states.
Perhaps I have misused terminology, but that is what I was referring to: inability to narrow the outer world’s future.
This thing you are proposing, an “oracle” that is incapable of modeling itself and incapable of modeling its environment (either would require turing-complete hypotheses), what could it possibly be useful for? What could it do that today’s narrow AI can’t?
A) It wasn’t my proposal.
B) The proposed software could model the outer environment, but not act on it.
Physics is turing-complete, so no, a learner that did not consider turing complete hypotheses could not model the outer environment.
You seem to have lost the thread of the conversation. The proposal was to build a learner that can model the environment using Turing-complete models, but which has no power to make decisions or take actions. This would be a Solomonoff Inducer approximation, not an AIXI approximation.
You said
which led me to think you were talking about an oracle even less capable than a learner with a sub-Turing hypothesis class.
If the hypotheses it considers are turing-complete, then, given enough information (and someone would give it enough information, otherwise they couldn’t do anything useful with it), it could model itself, its environment, the relation between its internal states and what shows up on the debug view, and the reactions of its operators on the information they learn from that debug view. Its (internal) actions very much would, to its own knowledge, skew the probability distribution over future states of the outer world.