I’m specifically interested in finding people who are well-incentivized to gather, make, and evaluate arguments about the nearness of AGI. This task should be their primary professional focus.
I see this activity as different from, or a specialized subset of, measurements of AI progress. AI can progress in capabilities without progressing toward AGI, or progressing in a way that is likely to succeed in producing AGI. For example, new releases of an expert system for making medical diagnoses might show constant progress in capabilities, without showing any progress toward AGI.
Likewise, I see it as distinct from making claims about the risk of AGI doom. The risk that an AGI would be dangerous seems, to me, mostly orthogonal to whether or not it is close at hand. This follows naturally with Eliezer Yudkowsky’s point that we have to get AGI right on the “first critical try.”
Finally, I also see this activity as being distinct from the activity of accepting and repeating arguments or claims about AGI nearness. As you point out, AI safety researchers who work on more prosaic forms of harm seem biased or incentivized to downplay AI risk, and perhaps also of AGI nearness. I see this as a tendency to accept and repeat such claims, rather than a tendency to “gather, make, and evaluate arguments,” which is what I’m interested in.
It seems to me that one of the challenges here is the “no true Scotsman” fallacy, a tendency to move goalposts, or to be disappointed in realizing that a task thought to be hard for AI and achievable only with AGI turns out to be easy for AI, yet achievable by a non-general system.
Scott wrote a post that seems quite relevant to this question just today. It seems to me that his argument is “AI is advancing in capabilities faster than you think.” However, as I’m speculating here, we can accept that claim, while still thinking “AI is moving toward AGI slower than it seems.” Or not! It just seems to me that making lists of what AI can or cannot do, and then tracking its success rate with successive program releases, is not clearly a way to track AGI progress. I’d like to see somebody who knows what they’re about examining that question, or perhaps synthesizing multiple perspectives on the way AI becomes AGI and showing how a given unit of narrow capabilities progress might fit into a narrative of AGI progress from each of those perspectives.
That’s a good point.
I’m specifically interested in finding people who are well-incentivized to gather, make, and evaluate arguments about the nearness of AGI. This task should be their primary professional focus.
I see this activity as different from, or a specialized subset of, measurements of AI progress. AI can progress in capabilities without progressing toward AGI, or progressing in a way that is likely to succeed in producing AGI. For example, new releases of an expert system for making medical diagnoses might show constant progress in capabilities, without showing any progress toward AGI.
Likewise, I see it as distinct from making claims about the risk of AGI doom. The risk that an AGI would be dangerous seems, to me, mostly orthogonal to whether or not it is close at hand. This follows naturally with Eliezer Yudkowsky’s point that we have to get AGI right on the “first critical try.”
Finally, I also see this activity as being distinct from the activity of accepting and repeating arguments or claims about AGI nearness. As you point out, AI safety researchers who work on more prosaic forms of harm seem biased or incentivized to downplay AI risk, and perhaps also of AGI nearness. I see this as a tendency to accept and repeat such claims, rather than a tendency to “gather, make, and evaluate arguments,” which is what I’m interested in.
It seems to me that one of the challenges here is the “no true Scotsman” fallacy, a tendency to move goalposts, or to be disappointed in realizing that a task thought to be hard for AI and achievable only with AGI turns out to be easy for AI, yet achievable by a non-general system.
Scott wrote a post that seems quite relevant to this question just today. It seems to me that his argument is “AI is advancing in capabilities faster than you think.” However, as I’m speculating here, we can accept that claim, while still thinking “AI is moving toward AGI slower than it seems.” Or not! It just seems to me that making lists of what AI can or cannot do, and then tracking its success rate with successive program releases, is not clearly a way to track AGI progress. I’d like to see somebody who knows what they’re about examining that question, or perhaps synthesizing multiple perspectives on the way AI becomes AGI and showing how a given unit of narrow capabilities progress might fit into a narrative of AGI progress from each of those perspectives.