I absolutely agree that "determinism" and "free will" and not opposites, as they are often portrayed. A coin is unpredictable but certainly does not have free will - who's to say humans aren't random but non-willed phenomena like that? However, points about free will also beg the question - exactly what do we define "free will" as? If one's discretion is determined entirely by the environment, but a person's discretion determines their actions, is that free will? What if one's discretion is determined by other parts of the brain that are just acting in accordance with physical laws? The question is just as much a scientific one as a philosophical one.

While the lack of technical content made sense for the audience, I would love to know more about the details. While weather predictions can take into account satellite imagery, real time weather data from on the ground locations, and a number of other always-recording sensors, data is much less plentiful for earthquake prediction. While you can set up seismic monitoring stations, I'd imagine these are expensive, and only give data very infrequently (i.e. when small earthquakes are felt).

1. Which part(s) of the discussion do you expect would be most illuminating for non-quantitatively-inclined readers? I really like how the article suggests we put uncertain events into four "buckets" - high-risk high-uncertainty (Apollo 11), high-risk low-uncertainty (Russian roulette), low-risk high-uncertainty (getting COVID-19), and low-risk low-uncertainty (driving). While this approach applies "stereotypes" to uncertain events, it's often much easier for humans to think about things when using categories as compared to the raw numbers. 2. Suggest another framing of the issues discussed that would be more effective. One method I love for evaluating risk of death is the micromort along with confidence intervals. In keeping with conventions for metric units, one micromort is a 1/1,000,000 (one in a million) chance of death. For example, skydiving comes with a risk of 8 micromorts per jump (8 in a million chance of death), while traveling 230 miles by car comes with a risk of 1 micromort. This unit lets us convert risk from a hard-to-understand fraction into a nice, low, round number, which is much easier to comprehend. Additionally, micromorts let us use error bars that make sense on our numbers. For example, skydiving has a risk of 8.4 micromorts per jump, with a 95% confidence interval between 7.7 micromorts and 9.3 micromorts - a low-risk low-uncertainty activity. But going to space, by contrast, we now know has risk of 32,000 micromorts (not taking into account modern advances in spaceflight technology), with a 95% confidence interval between 21,000 and 49,000 micromorts - far less certain than skydiving.

I largely agree with the government's lack of funding for SETI. Money is always tight in NASA's budget, and showing tangible results (like robots on Mars or probes outside the solar system) is the most important way they demonstrate to legislators they've "earned" their funding. As such, conducting "moonshot" projects like SETI (which, judging by all present evidence will likely find nothing) very unlikely to yield tangible payoffs might be seen by some as frivolous spending.

I totally agree! Prof. Laibson's discussion of present bias was really interesting, and is certainly a trap I see myself falling into. If I find myself with a few assignments due of varying lengths, I'll often choose to start with the shortest of these, unconsciously (I guess?) desiring the smaller satisfaction of finishing a smaller assignment then the larger but delayed payoff that would accompany a larger assignment.

Saying "AI can often be less accurate" than traditional methods, while true, obscures just how diverse types of AI and even machine learning can be. It might be better to frame this in the light of "different predictive methods work better and worse on certain problems, whether AI or non-AI". For example, while neural networks and search have come to dominate go and chess, they fail badly when put against partial-information games (for example, poker AIs usually use counterfactual regret minimization techniques).

I'm very much a Nick Bostrom fanboy - that would be so cool!

I agree that the way climate change threatens the deep ocean conveyer belt is extremely threatening. However, due to the nature of these models, it is extremely difficult (as Kammen mentions) to determine exactly how much warming would lead to this outcome. It's possible that climate change would not affect this pattern, while it's also possible that we could see dramatic temperature changes in Europe as a result in just a few decades. Quantifying the probabilities of each outcome is very difficult as well. Either way, the risk is so high that it demands immediate action to reduce emissions.