I would compare it to theoretical physics,
This is actually a really interesting and potentially apt comparison. FAI may end up being something like String theory: a region in math space that has zero practical applications. (but given the published work in FAI to date, String Theorists may take offense at such a comparison)
Earlier I said:
If FAI is or can be made tractable, it will be a technological system: some combination of hardware and software, an actual practical invention.
SI's conception of 'FAI' as math (whatever that means) is competing with the growing number of pragmatic mainstream approaches, most of which are loosely brain inspired. Humans have internal mechanisms for empathy and altruism which could be reverse engineered and magnified in machines.
But it all depends on what one means by "math". If you count algorithms as new math, then the vast numbers of computer scientists and programmers, and most of the folks working on AGI designs, are thus mathematicians. If by "math", you mean the stuff that academic mathematicians typically work on, then one is hard pressed to find any connection to AGI (friendly or not).
Series: How to Purchase AI Risk Reduction
A key part of SI's strategy for AI risk reduction is to build toward hosting a Friendly AI development team at the Singularity Institute.
I don't take it to be obvious that an SI-hosted FAI team is the correct path toward the endgame of humanity "winning." That is a matter for much strategic research and debate.
Either way, I think that building toward an FAI team is good for AI risk reduction, even if we decide (later) that an SI-hosted FAI team is not the best thing to do. Why is this so?
Building toward an SI-hosted FAI team means:
Both (1) and (2) are useful for AI risk reduction even if an SI-hosted FAI team turns out not to be the best strategy.
This is because: Achieving part (1) would make SI more effective at whatever it is doing to reduce AI risk, and achieving part (2) would bring great human resources to the cause of AI risk reduction, which will be useful to a wide range of purposes (FAI team or otherwise).
So, how do we accomplish both these things?
Growing SI into a better organization
Like many (most?) non-profits with less than $1m/yr in funding, SI has had difficulty attracting the top-level executive talent often required to build a highly efficient and effective organization. Luckily, we have made rapid progress on this front in the past 9 months. For example we now have (1) a comprehensive donor database, (2) a strategic plan, (3) a team of remote contractors used to more efficiently complete large and varied projects requiring many different skillsets, (4) an increasingly "best practices" implementation of central management, (5) an office we actually use to work together on projects, and many other improvements.
What else can SI do to become a tighter, larger, and more effective organization?
They key point, of course, is that all these things cost money. They may be "boring," but they are incredibly important.
Attracting and creating superhero mathematicians
The kind of people we'd need for an FAI team are:
There are other criteria, too, but those are some of the biggest.
We can attract some of the people meeting these criteria by using the methods described in Reaching young math/compsci talent. The trouble is that the number of people on Earth who qualify may be very close to 0 (especially given the "committed to AI risk reduction" criterion).
Thus, we'll need to create some superhero mathematicians.
Math ability seems to be even more "fixed" than the other criteria, so a (very rough) strategy for creating superhero mathematicians might look like this:
All these steps, too, cost money.