Using various metaphors carefully and fluidly is key. No one is sufficient. Not this one, nor any other.

I say: go back to basics. One good foundational point is dispelling confusion and conflation around "intelligence". So many people have woefully narrow and unexamined notions of "intelligence". It wouldn't be unfair to say many people have broken definitions. Broken because they just aren't good enough to make meaningful progress in a modern world where many kinds of agents display many different kinds of intelligence. Such broken definitions are often too specific; too arbitrary; too rooted in binary thinking.

Many of our current language patterns are liabilities. Not to mention corporate and organizational cultures where hazy definitions slide around and few people will admit that they don't really know what others mean by the term. Sometimes it feels like a big charade where no one wants to hurt anyone's feelings nor appear uninformed. And so it goes, some kind of elaborate mystical ritual where the confused participants lead each other further into madness.

With this in mind, I find tremendous value in Stuart Russell's definition of intelligence: the ability of an agent to solve some task. An agent is anything that makes a decision: a human, an animal, a system of any kind. This definition intentionally leaves out any notion of (a) humans; (b) consciousness; (c) some arbitrary quality line. This usage cuts through so much bullsh*t. I highly recommend finding a way to shift conversations towards it wherever possible. This isn't easy in my experience. We have so much baggage and crufty thinking, even we're able to put aside our baser instincts.

One might say that Russell's definition just "kicks the can down the road". I don't think so. It encourages people to define their metrics a bit more clearly -- hopefully out loud or on paper -- for a particular context. It is one step closer to clarifying things. One step in the right direction -- to stop pretending like we all know each other means -- and instead actually pose an answerable question.

Now, what about "general" intelligence you say? Well, one step at a time. Wait until a group of people have demonstrated some ability to find some kind of consensus on particular tasks. It is hard work to socialize these ideas. Defining general intelligence in meaningful ways is really hard and contentious. It often becomes a lightning rod for all number of other disagreements.

As one example, look at the shitstorm around various sociological attempts to measure the general aspects of intelligence in humans. Without attempting to summarize it in any detail, there has been a huge dumpster fire involving: poor statistical understanding, shoddy research, tone-deaf communication, willful misinterpretation, accusations of racism, and so on. There are pockets of truth in there, but even trying finding the core nuggets of useful truth something makes everything radioactive, depending on the context. A typical person in modern culture is usually unable to calmly make sense of these issues, and who can blame them? Statistical understanding doesn't grow on trees. The same goes for understanding machine learning theory.