Great project. I’d love to hear more details. Somehow I missed this post when it was released but it was pointed out to me yesterday.
I’ve been developing a project for the past couple of months that lines up quite closely (specifically, the goal of exploring additional scenarios as you highlighted in the takeaways). I have a very short time horizon for the completion of this particular part of the project (which has interfered with refining the survey much as I’d have liked) but I’d be happy to share any results.
The project I’ve been working on cobbling together is broadly similar. I compiled a list of AI scenario “dimensions,” key aspects of different scenarios, with three to four conditions for each dimension. Conditions are basically the direction each could go in a scenario, e.g. “takeoff” would be a dimension, and “fast, slow, moderate (controlled), and (uncontrolled) would be example conditions, or “AI paradigm” would be a dimension, with deep learning, hybrid, new paradigm, embodiment, deep learning plus something else would be the conditions).
The plan so far is to try and get judgments on the individual components or plausible components of each scenario and then use a scenario mapping tool (based on GMA, with some variations) to cluster all possible combinations.
I have a longer version of the survey for both impact and likelihood, and a short version for just likelihood that’s easier to complete. GMA doesn’t usually use elicitation, so this could be interesting, but thus far the questions have been a challenge.
This should provide a large grouping of possible combinations to explore. I’m requesting likelihood (and impact) rankings on each, which should refine the number of options, and then we can parse different clusters to explore unique potential futures (Without rankings, the output is in the tens of millions of options). A more detailed overview is here if you’re curious, or shoot me a direct message. I hope to try and put together a more comprehensive version later in the year with other data sources as well.
Great project. I’d love to hear more details. Somehow I missed this post when it was released but it was pointed out to me yesterday.
I’ve been developing a project for the past couple of months that lines up quite closely (specifically, the goal of exploring additional scenarios as you highlighted in the takeaways). I have a very short time horizon for the completion of this particular part of the project (which has interfered with refining the survey much as I’d have liked) but I’d be happy to share any results.
The project I’ve been working on cobbling together is broadly similar. I compiled a list of AI scenario “dimensions,” key aspects of different scenarios, with three to four conditions for each dimension. Conditions are basically the direction each could go in a scenario, e.g. “takeoff” would be a dimension, and “fast, slow, moderate (controlled), and (uncontrolled) would be example conditions, or “AI paradigm” would be a dimension, with deep learning, hybrid, new paradigm, embodiment, deep learning plus something else would be the conditions).
The plan so far is to try and get judgments on the individual components or plausible components of each scenario and then use a scenario mapping tool (based on GMA, with some variations) to cluster all possible combinations.
I have a longer version of the survey for both impact and likelihood, and a short version for just likelihood that’s easier to complete. GMA doesn’t usually use elicitation, so this could be interesting, but thus far the questions have been a challenge.
This should provide a large grouping of possible combinations to explore. I’m requesting likelihood (and impact) rankings on each, which should refine the number of options, and then we can parse different clusters to explore unique potential futures (Without rankings, the output is in the tens of millions of options). A more detailed overview is here if you’re curious, or shoot me a direct message. I hope to try and put together a more comprehensive version later in the year with other data sources as well.