Thanks for sharing the original definition! I didnāt realise Turing had defined the parameters so precisely, and that they werenāt actually that strict! I
I probably need to stop saying that AI hasnāt passed the Turing test yet then. I guess it has! Youāre right that this ends up being an argument over semantics, but seems fair to let Alan Turing define what the term āTuring Testā should mean.
But I do think that the stricter form of the Turing test defined in that metaculus forecast is still a really useful metric for deciding when AGI has been achieved, whereas this much weaker Turing test probably isnāt.
(Also, for what itās worth, the business tasks I have in mind here arenāt really ācomplexā, they are the kind of tasks that an average human could quite easily do well on within a 5-minute window, possibly as part of a Turing-test style setup, but LLMs struggle with)
This is a fantastic , clearly written, post. Thank you for writing up and sharing!
In the 3 models, why is outcome_2 not included as a predictor?
Iām just trying to wrap my head around how the 3-wave separation works, but canāt quite follow how the confounders will be controlled for if the treatment is the only variable included from wave 2.
For example, in the first model:
Suppose āactivismā was a confounder for the effect of āveganuaryā on āoutcomeā (so āactivismā caused increased āveganuaryā exposure, as well as increased āoutcomeā).
Suppose we have 2 participants with identical Wave 1 responses.
Between wave 1 and wave 2, the first participant is exposed to āactivismā, which increases both their āveganuaryā and āoutcomeā values, and this change persists all the way through to Wave 3.
The first participant now has higher outcome_3 and veganuary_2 than the second participant, with all other predictors in the model equal, so this will lead to a positive coefficient for veganuary_2, even though the relationship between veganuary and outcome is not causal.
I can see how this problem is avoided if outcome_2 is included as a predictor instead (or maybe as well as..?) outcome_1. So maybe this is just a typo..? If so I would be interested in the explanation for whether you need outcome_1 and outcome_2, or if just outcome_2 is enough. Iām finding that quite confusing to think about!