AlexFinks

I would think that Linux financially scales better than Windows, which is why Windows Server + SQL is shrinking while Linux server is growing, and now Linux is the world's most popular system for servers, especially for highest-traffic sites.

To me, the mark of an artificial intelligence is general analogizing ability, and that's not what we have here.

A system capable of general analogy could potentially write its own drivers with some light scaffolding or guidance. A system capable of general analogy would be able to form causal models of our world, and to be sensitive to the differences between mere correlation and correlation with causal potential, just as rats and ravens do.

This system has not yet even tackled the intelligence of rats and ravens.

I think we're still on the same page for consciousness; we're just using different words, except for the fact that you add a reflexive (or feedback) component to consciousness, which I think is an appropriate addition.

I still maintain that there is an objective study to subjectivity, and that the few examples I talked about are of good relevance to our understanding of consciousness.

There is indeed a science of consciousness, such as with anesthesiology or pharmacology. These sciences are concerned with the space of awareness of a subject.

I'd also say that there is an objectivity to subjective perception; for example, if two travelers in space find that the distance between themselves is shrinking, while either party may justly argue that it is the opposing side that is moving, and that we are standing still (or some other possible combination), we can still both make objective agreements on what the other party may subjectively see.

We can objectively know that the other party is equally clueless as us in determining our velocities.

I personally think there is an enormous benefit to science to understanding subjective perception. I imagine the problem of distributed AI's having different perceptions, and having to make sense of their different perceptions in order to solve problems.

I think it's better to privately consult an attorney on anything you think is substantially suspicious to your superiors than to go through the chain of the command.

Exposing your dangerous intentions to report potentially illegal behavior too early could mean inadequate time to prepare for repercussions.

I think that transparency and opaqueness provide different kinds of security, but they are related to security. It would be wrong to say that they are independent simply because an opaque system can still have the ability to be extremely secure.

But why is it dangerous, on balance, to make assumptions of causality from data and statistics alone?

Animals, such as rats and ravens, face this problem all the time, and yet they can meaningfully effect the world in such manner that would imply causal understanding, and a sensitivity towards the difference between mere correlation or a correlation with causal potential.

Humans do the same as well, naive people who have never learned about experimental design, or have never learned the concept of correlation, also make useful judgments on the causal model behind ordinary problems and events.

How did these machines make actionable judgments on causality with nothing more than noisy inputs to their sensory systems? Through what technique did they discern the difference between mere correlation, and a correlation with exploitable causality?