As a factual question, Iām not sure if peopleās opinions on the shape of AI risk can be divided into two distinct clusters, or even distributed along a spectrum (that is, that factor analysis on the points of opinion-space would find a good general factor), though I suspect it may quite weakly be the case. For instance, I found myself agreeing with six of the statements on one side of Scottās dichotomy and two on the other.
As a public epistemic health question, I think issuing binary labels is harmful for further progress in the field, especially if they borrow terminology from religious groups and the author identifies with one of the proposed camps in the same post he raises the distinction. See the comment by xarkn on LW.
Even if the range of current opinions could be well-described by a single general factor, we should certainly use less divisive terminology for such a spectrum and be mindful that truth may well lie orthogonal to it.
I agree with most of thisāclusters probably not very accurate, divisive religious terminology, him identifying with one of the camps while describing them.
Can you elaborate a bit more on why you think binary labels are harmful for further progress? Would you say they always are? How much of your objection here is these particular labels and how Scott defines them, and how much of it is that you donāt think the shape can be usefully divided into two clusters?
I find that, on topics that I understand well, I often object intuitively to labels on the grounds that they arenāt very accurate, or donāt describe enough nuance, but for topics I am not expert on, I sometimes find it useful to be able to gesture at the general shape of things.
I guess Iām still interested in possible paths to understanding AI risk that donāt require accepting some of the āweirderā arguments up front, but might eventually get you there.
Having thought more about this, I suppose you can divide opinions into two clusters and be pointing at something real. Thatās because peopleās views on different aspects of the issue correlate, often in ways that make sense. For instance, people who think AGI will be achieved by scaling up current (or very similar to current) neural net architectures are more excited about practical alignment research on existing models.
However, such clusters would be quite broad. My main worry is that identifying two particular points as prototypical of them would narrow their range. People would tend to let their opinions drift closer to the point closest to them. This need not be caused by tribal dynamics. It could be something as simple as availability bias. This narrowing of the clusters would likely be harmful, because the AI safety field is quite new and weāve still got exploring to do. Another risk is that we may become too focused on the line between the two points, neglecting other potentially more worthwhile axes of variation.
If I were to divide current opinions into two clusters, I think that Scottās two points would in fact fall in different clusters. They would probably even be not too far off their centers of mass. However, I strongly object to pretending the clusters are points, and then getting tribal about it. I think labeling clusters could be useful, if we made it clear that they are still clusters.
On the paths to understanding AI risk without accepting weird arguments, maybe getting people worried about ML unexplainability may be worthwhile to explore, though I suspect most people would think you were pointing to algorithmic bias and the like.
As a factual question, Iām not sure if peopleās opinions on the shape of AI risk can be divided into two distinct clusters, or even distributed along a spectrum (that is, that factor analysis on the points of opinion-space would find a good general factor), though I suspect it may quite weakly be the case. For instance, I found myself agreeing with six of the statements on one side of Scottās dichotomy and two on the other.
As a public epistemic health question, I think issuing binary labels is harmful for further progress in the field, especially if they borrow terminology from religious groups and the author identifies with one of the proposed camps in the same post he raises the distinction. See the comment by xarkn on LW.
Even if the range of current opinions could be well-described by a single general factor, we should certainly use less divisive terminology for such a spectrum and be mindful that truth may well lie orthogonal to it.
I agree with most of thisāclusters probably not very accurate, divisive religious terminology, him identifying with one of the camps while describing them.
Can you elaborate a bit more on why you think binary labels are harmful for further progress? Would you say they always are? How much of your objection here is these particular labels and how Scott defines them, and how much of it is that you donāt think the shape can be usefully divided into two clusters?
I find that, on topics that I understand well, I often object intuitively to labels on the grounds that they arenāt very accurate, or donāt describe enough nuance, but for topics I am not expert on, I sometimes find it useful to be able to gesture at the general shape of things.
I guess Iām still interested in possible paths to understanding AI risk that donāt require accepting some of the āweirderā arguments up front, but might eventually get you there.
Having thought more about this, I suppose you can divide opinions into two clusters and be pointing at something real. Thatās because peopleās views on different aspects of the issue correlate, often in ways that make sense. For instance, people who think AGI will be achieved by scaling up current (or very similar to current) neural net architectures are more excited about practical alignment research on existing models.
However, such clusters would be quite broad. My main worry is that identifying two particular points as prototypical of them would narrow their range. People would tend to let their opinions drift closer to the point closest to them. This need not be caused by tribal dynamics. It could be something as simple as availability bias. This narrowing of the clusters would likely be harmful, because the AI safety field is quite new and weāve still got exploring to do. Another risk is that we may become too focused on the line between the two points, neglecting other potentially more worthwhile axes of variation.
If I were to divide current opinions into two clusters, I think that Scottās two points would in fact fall in different clusters. They would probably even be not too far off their centers of mass. However, I strongly object to pretending the clusters are points, and then getting tribal about it. I think labeling clusters could be useful, if we made it clear that they are still clusters.
On the paths to understanding AI risk without accepting weird arguments, maybe getting people worried about ML unexplainability may be worthwhile to explore, though I suspect most people would think you were pointing to algorithmic bias and the like.
Thank you!