Luke, your comment reminds me of a fascinating opening line that has been mentioned in another PredictionX interview. Historian of science Steven Shapin wrote, “There was no such thing as the Scientific Revolution, and this is a book about it.“ You point out differences in ideas about when this impactful moment in human history actually began, suggesting that the “revolution” is actually fairly undefined and uncertain, (or, according to Shapin, non-existent). That debate aside, your comment also makes me reflect on present day, and wonder- how will the advancements of our time be viewed in the future? Are we in the midst, or rather on the cusp, of some AI Revolution, and will students in the future ponder over which discovery catalyzed it?

As a non-quantitatively inclined reader myself, there are a couple of aspects of this impactful article that most clearly break down the reality of risk and uncertainty as it relates to the coronavirus pandemic. First, the distinction between the four high/low-risk and high/low-uncertainty categories earlier on in the article is helpful. Even if the reader is fuzzier on the exact numbers, they can easily conceptualize the relative risk and uncertainty of the common sense examples for each category. Following this discussion with the implication that the pandemic lies closer to space missions on the uncertainty spectrum, but closer to lying on the couch on the risk spectrum, makes the argument easy to follow. An additional strength is framing data as "prior observations." Like the previous example, this part of the discussion relates the quantitative to something non-quantitative that is perhaps recognizable to a wider audience. Finally, the explanation of uncertainty as "how well we know the odds" is a concise but effective way to drive home a key point from this class, that just because there is uncertainty does not mean that scientists don't know what is going on.

Cici, these are really interesting questions. I also found interesting the differences between what is currently possible and impossible, currently known and unknown- if I hadn't watched the rest of the interview, the precision of measuring surface movements to the 10th of a millimeter would suggest to me that there would be fewer unknowns in predicting earthquakes. I also found it interesting that Professor Meade kept mentioning the many other unknowns in his work and how unlikely it is that we will ever know them, so I wonder if there was a time when researchers thought tracking surface movements that closely would be impossible before it actually ended up being possible.

Jaida, I'm glad to know that I was not alone in not knowing that there are whole careers and fields relating to the search for aliens! I found it interesting that Jill Tarter mentioned that even social scientists have a role, and the existence of fields like astroethics and astropsychology is, simply put, wild. I am also fascinated by the fact that all of these academic infrastructures exist, even though there is so much uncertainty about what (who?) is out there or where they are, and as you mention, if we are looking in the most efficient way. I guess it is better to be prepared than unprepared should one of Dr. Tarter's positives turn out to not be false...

Emily, your last question raises a really interesting point, and makes me wonder about the opposite- what happens when certain societal structures hinder a person's ability to make "better" decisions? Behavioral economics explores people not acting in their own self-interest, but I am curious if it takes into account people being unable to make choices in their own self-interest due to limits like financial burdens or structural prejudice (as we know, the playing field is not level for everyone). Additionally, we seem to be similarly interested in the individual aspect of prediction, and whereas previous interviews have focused on more large-scale prediction, like climate change or epidemics, it was interesting to hear this week in Dan Gilbert and David Laibson's interviews about personal level prediction. I only wonder how we can think about personal predictions and choices–and the question of free will–as they may relate to society's structural limitations.

Jaron, these are really interesting questions that I'd love to hear George Church's answers to. In light of the Ben Shneiderman interview, in which he encourages a more nuanced and realistic view of the future of AI, your post makes me wonder how someone like Professor Church thinks about the future of human genomics. Like AI may be overestimated in the popular imagination, are the possibilities of gene editing also overestimated? If so, why? Again drawing from a comment made by Ben Shneiderman, will we look back in a number of years and laugh at our concerns over the ethics and regulation of genomics editing if today's predictions don't come to pass?

Oswaldo, your comment caught my eye because Gina McCarthy's quote about people not accepting problems they don’t have solutions to really stood out to me as well. You also mention climate change denial persisting even in the face of advancing models, which makes me wonder: even if we somehow had a model for climate change with little to no uncertainty, would people take action? If model uncertainty isn't the only reason for climate change denial, what else is at play? This all makes me think about how some of the denial is not incidental or simply born of ignorance, but is actively being sown. In Merchants of Doubt: How a Handful of Scientists Obscured the Truth on Issues from Tobacco Smoke to Global Warming, Professor Naomi Oreskes of the Harvard history of science department writes about how a handful of contrarian scientists have worked to challenge the notion that there is consensus about the reality of climate change, even though there is. These "merchants of doubt," a powerful opponent even against increasingly advanced models, may play a part in people's unwillingness to accept climate change as a problem.