I was also surprised by this fact. I think there is a lack of awareness about earthquakes since they are infrequent. I would assume that someone further away from the epicenter is affected less by the earthquake than someone closer. Also, the 250 mile mark is wholly dependent on the magnitude of the earthquake. In addition to your questions, I would add whether it is easier to predict earthquakes that will be more severe.

I think the most illuminating part of the discussion for non-quantitatively-inclined readers is the part discussing the difference between low-uncertainty experiences and extremely high-uncertainty experiences. This part illustrates a good idea of what uncertainty is without using numerical data. Furthermore, the comparison between hunting and russian roulette prior gives a good explanation of the difference between risk and uncertainty.

Pedro, I think your question is very intriguing. Although transparency would be good in the scientific community, we also do not want the general public to decide that scientific studies are unreliable. Even though there are some cases in which companies alter data to be monetarily beneficial for them, I would like to say this is not the case with most scientific studies. I also agree with your statement in saying models are not 100 percent correct and there will be people who do not understand this concept and think that science is unreliable. It would have been interesting to see how Professor Loeb would respond to this question.

I think you bring up a really interesting topic of our knowledge of how artificial intelligence and machine learning works. Although it is true that we may not be able to see every step of the prediction making process based, I do think it is possible that we can program the machine to learn in a way that we can also understand. For example, looking for specific evidence or teaching the machine how to interpret certain data. Even though there are flaws in how the AI reaches a conclusion, I do think it is possible to improve it by improving the way we, as humans, implement AI.

I think this is really interesting because unlike regular tools, machine learning and AI can grow by itself. As artificial intelligence gains more information, it can be repurposed as a different tool. So, although this goes against Professor Shneiderman's views, it seems like AI should not be classified necessarily as a tool.