Because that's a conceptually straightforward assumption that we can safely make in a philosophical argument.

But this is not a philosophical argument.

To recap:

• I suggested that an AI which is a precursor to the FAI should come to understand human values by interacting (over an extended 'training' period) with actual humans - asking them questions about their values and perhaps performing some experiments as in a psych or game theory laboratory.
• You responded by linking to this, which as I read it suggests that the most accurate and efficient way to extract the values of a human test subject would be by carrying out a non-destructive brain scan. Quoting the posting:

So when we try to make an AI whose physical consequence is the implementation of what is right, we make that AI's causal chain start with the state of human brains - perhaps non-destructively scanned on the neural level by nanotechnology, or perhaps merely inferred with superhuman precision from external behavior - but not passed through the noisy, blurry, destructive filter of human beings trying to guess their own morals.

• I asked how we could possibly come to know by testing that the scanning and brain modeling was working properly. I could have asked instead how we could test the hypothesis that the inference from behavior was working properly.

These are questions about engineering and neuroscience, not questions of philosophy. The question of what is right/wrong is a philosophical question. The question of what do humans believe about right and wrong is a psychology question. The question of how those beliefs are represented in the brain is a neuroscience question. The question of how an AI can come to learn these things is GOFAI. The question of how we will know we have done it right is a QC question. Software test. That was the subject of my comment. It had nothing at all to do with philosophy.

You make AI correct on strong theoretical grounds, and only test things to check that theoretical assumptions hold in ways where you expect it to be possible to check things, not in every situation.

Ok, in this context, I interpret this to mean that we will not program in the neuroscience information that it will use to interpret the brain scans. Instead we will simply program the AI to be a good scientist. A provably good scientist. Provable because it is a simple program and we understand epistemology well enough to write a correct behavioral specification of a scientist and then verify that the program meets the specification. So we can let the AI design the brain scanner and perform the human behavioral experiments to calibrate its brain models. We only need to spot-check the science it generates, because we already know that it is a good scientist.

Hmmm. That is actually a pretty good argument, if that is what you are suggesting. I'll have to give that one some thought.

I think this reflects the practical problem with Friendly AI - it is an ideal of perfection taken to an extreme that expands the problem scope far beyond what is likely to be near term realizable.

I expect that most of the world, research teams, companies, the VC community and so on will be largely happy with an AGI that just implements an improved version of the human mind.

For example, humans have an ability to model other agents and their goals, and through love/empathy value the well-being of others as part of our own individual internal goal systems.

I don't see yet why that particular system is difficult or more complex than the rest of AGI.

It seems likely that once we can build an AGI as good as the brain we can build one that is human-like but only has the love/empathy circuitry in it's goal system with the rest of the crud stripped out.

In other words if we can build AGI's modeled after the best components of the best examples of altruistic humans, this should be quite sufficient.

So this problem - that human language concepts are far too complex and unwieldy for formal verification - is not a problem with human language itself that can be fixed by using other language choices. It reflects a problem with the inherit massive complexity of the world itself, complexity that human language and brain-like systems are evolved to handle.

These folks seem to agree with you about the massive complexity of the world, but seem to disagree with you that natural language is adequate for reliable machine-based reasoning about that world.

As for the rest of it, we seem to be coming from two different eras of AI research as well as different application areas. My AI training took place back around 1980 and my research involved automated proofs of program correctness. I was already out of the field and working on totally different stuff when neural nets became 'hot'. I know next to nothing about modern machine learning.

Not quite.

As for the start with the right meaning part, I think it is extremely hard to 'solve' morality in the way typically meant here with CEV or what not.

I don't think that we need (or will) wait to solve that problem before we build AGI, any more or less than we need to solve it for having children and creating a new generation of humans.

If we can build AGI somewhat better than us according to our current moral criteria, they can build an even better successive generation, and so on - a benevolence explosion.

As for the second part about preserving it accurately, I think that ethics/morality is complex enough that it can only be succinctly expressed in symbolic associative human languages. An AGI could learn how to model (and value) the preferences of others in much the same way humans do.