One of Firestein's most interesting points was his discussion of "mechanisms" across different fields. In fields like mechanical engineering, it's usually very easy to see why something works, while the "why" in chemistry and biology often takes years to discover. Firestein seems to imply around 29:20 that astronomy is one of the "easy" fields, which I don't quite understand - all the events we're discussing happened billions of years ago. I'd love to know more of his perspective about mechanisms in astronomy, and why it's comparatively easy or hard.

In the interview, Prof. Firestein says that "the future is very different in different sciences", and I totally agree - the different sciences happen on very different timescales. If I were a biologist (and disclaimer - I'm definitely not), almost every process I'd be working with would happen on timescales between seconds and years. At the very extremes, some processes (such as the onset of cancer) might take decades, but in just 100s of years most individual organisms will die. In 10,000s of years, you might begin to see species evolving. Beyond 4 billion years ago, there isn't any biology left to study at all. Other fields like chemistry, sociology, and psychology fare far worse. While this sort of thinking is useful within disciplines, I'd imagine it makes it very hard to think about events that take place on universe time scales. The supermassive black hole in the center of our galaxy will take 10^100 years to evaporate - there's just nothing any of these fields work with on a daily basis to compare this (though admittedly this is an extreme example).

I would have loved to hear the experts discuss more exactly how they compute uncertainties for their predictions of earthquakes, and how they take into account the possibility that their models may be wrong. They mentioned at 33:19 that meteorologists will often compute uncertainty by running many simulations with slightly different initial conditions, and computing in what fraction of those the event happens. A similar approach could probably be applied to earthquakes, and would similarly spit out an uncertainty value. However, this wouldn't be fully accurate, and would depend on a number of factors not taken into account - how certain we are about the correctness of the computer software (no bugs is HARD!), how certain we are about the laws that guide earthquakes, and how certain we are that the data we collect is accurate (accuracy I'd imagine is much more difficult to calculate than precision). I'd love to know how these researchers account for all these variables, and how/if they weight them in their uncertainty calculation.

One thing that shocked me was how accurately scientists were able to measure the movement of tectonic plates, down to distances on the order of tenths of a millimeter. The part that's even more impressive? It's done via GPS satellites, 20,000 kilometers above. I'm by no means an expert on GPS, but it seems like we'd have to know the speed and position of each GPS satellite down to absurd levels of precision, with respect to some non-moving origin (and considering tectonic plates are always moving, that's pretty hard). Even more crazy is the fact that the atmosphere between the GPS satellite and the Earth is always moving as well, distorting the apparent distance and requiring a detailed model of airflows, as well as clocks in both the satellite and ground station that are accurate down to the amount of time it takes light to travel 0.1 mm! I'm astonished it's possible to measure this accurately, and it's a testament to both how accurate our equipment is and how low we've gotten the uncertainty in these measurements to make that 0.1 mm meaningful.

One thing I'd love to have asked Jill Tarter is her opinions on the public hype that tends to surround any sort of announcement for weak evidence of extraterrestrial life. For example, she mentions the asteroid with the "huge aspect ratio" (Amoamua) and how she "half-jokingly said well, clearly that's alien technology" and how Avi Loeb went on to write a paper about it. But while trained scientists understand that such a comment is clearly not meant to be serious and that there is far too little evidence to label it alien technology, the public perceived it much differently, with some news outlets stating the asteroid was "evidence of extraterrestrial life". I'd love to have asked Dr. Tarter if she things the extreme public reaction to these discoveries is beneficial or harmful, and if the latter, what can be done to mitigate it. https://www.labxchange.org/library/pathway/lx-pathway:34dd3b2c-3aec-460a-817f-da4af2ed1577/items/lx-pb:34dd3b2c-3aec-460a-817f-da4af2ed1577:lx_simulation:e9099212?source=%2Flibrary%2Fclusters%2Flx-cluster%3AModernPrediction

One of the things I liked most about Jill Tarter's interview was her willingness to answer questions with "I don't know" when it applied. That's not to say Dr. Tarter isn't an expert - her work in her field has been very influential, and I learned a lot about SETI from the interview. Instead, by the nature of her field, there are a tremendous number of both known-unknowns and unknown-unknowns, and she acknowledges this gracefully. For example, when discussing the values for the terms in the Drake equation, she says "I don't know" and states that the error bars are just too large right now, and that more research is needed. Similarly, when discussing things like what alien signals might look like and how life might originate on other planets, Tarter similarly states that we just don't know enough. But that's exactly the reason we do science - to find out the answers to these questions. https://www.labxchange.org/library/pathway/lx-pathway:34dd3b2c-3aec-460a-817f-da4af2ed1577/items/lx-pb:34dd3b2c-3aec-460a-817f-da4af2ed1577:lx_simulation:e9099212?source=%2Flibrary%2Fclusters%2Flx-cluster%3AModernPrediction

I found Gilbert's discussion of Laplace's demon applied to neuroscience to be very interesting. As he says, it would be silly to believe that everything in the universe was predictable, EXCEPT for the brains of a few apes on one planet. If physics can in fact be calculated from a starting state, the universe has to be pre-determined as Prof. Goodman and Prof. Gilbert discuss (at 38:35). However, Gilbert goes on to explain that uncertainty saves the day and "preserves free will", as it's not actually possible to "predict [the state of the universe] at moment X+1 because there's inherent uncertainty". While this is true, these uncertainties occur at the quantum scale, and it has not yet been shown that they meaningfully influence the behavior of nerve cells, which are 10^10 times larger. But even if this was the case, the brain is (probably) a chaotic system, meaning it might still be possible to argue for free will even if uncertainty had no meaningful impact. I'd love to have asked Gilbert whether he thought quantum uncertainty played a major role in brain functions, and whether his answer to that question dictated his answer to the larger question of free will.

One of the most interesting things Dan Gilbert discussed was how we mis-remember the past to generally be more positive. I certainly agree with the premise - I believe it has been reproduced in the lab by psychologists, and is definitely something I can see myself doing. As Gilbert says, this memory is "lightly constrained by facts", but the brain seems to have evolved to not mind rewriting some details with ones it considers more preferable. The part where I disagree, however, is when Gilbert says this "mis-rememberance" will be impossible because of all the extra data and photographs we have access to now. While it's true I have access to photos from ten years ago, I look at then very, very rarely, and I'm sure this will only decrease with time. Thus, a person who is willing to turn a blind eye to the less-ideal parts of the past (i.e. most of us) could simply "not check" what the truth really was, and continue believing their slightly re-written personal history.

If I had conducted the interview with Prof. Shneiderman, I would have spent much more time discussing the technical details of what is involved in modern AI systems. Throughout the interview, he discussed AI in the very abstract sense, generalizing dozens of different approaches together. For the points he was trying to make, this was a good thing to do - if you're mainly interested in how humans will interact with AI, it might not matter whether ReLU neurons are used or not. However, when discussing questions like "what is good data" or "when will the predictions of AI be accurate", the details of the implementation suddenly become very relevant. While some architectures (such as support vector machines) are very sensitive to bad data, others (such as the GPT-3 network) are built so it has almost no impact upon them. A similar story applies when discussing machine learning as a "black box" - while certain types of ML have this problem, others such as decision trees are very transparent. All in all, if I were conducting the interview I would have asked Prof. Shneiderman to discuss the specific architectures and approaches he was referring to throughout the interview, though understand why he chose not to mention these details.

I was shocked by the very one-sided view Prof. Shneiderman presents towards his view that AI is only a "tool" for humans, like a bulldozer or airplane. It's true he makes some good points, and the position he seems to hold is a reasonable one, but presenting it as the only reasonable view seems like an extreme position. In my opinion, every technological improvement is a tool as Shneiderman suggests, until suddenly it is something much more. Take for example the job of human computer - a common profession in the early-mid 20th century that helped with everything from the war efforts to sending man to the moon. When the slide rule was first invented, this new tool did much of the calculating of a human computer's job, but certainly did not render them obsolete. Neither did the invention of the electric calculator. However, once personal computers became mainstream, they did almost perfectly replace human computers. Now, the job just does not exist anymore - electronic computers are not mere tools for these human computers, but rather eliminated them entirely. But don't take my word for it - listen to people who actually know what they're talking about! It is well known that Elon Musk and Stephen Hawking believe AI will result in unemployment unlike anything seen before in human history, and consulting firms like McKinsey believe that almost 25% of human work could be fully automated using existing technologies. Or listen to true AI experts - researchers such as Martin Ford and Erik Brynjolfsson believe "this time could be different" - that instead of simply making humans more efficient at their work as previous industrial revolutions have done, advances in AI will eliminate large swaths of the job market altogether. Neither one of these possible futures is certain, but presenting only one - that AI is nothing more than a "tool" - seems irresponsible.

Throughout his interview, Kammen touches on sustainability in a few places, discussing plastic vs metal straws and incentivizing developers to prioritize product life cycles through performance contracts. However, he does not bring up one of the more controversial ways to go green - recycling. While recycling metals is well understood and economically viable, recycling paper and plastics is much, much harder. These products cannot be re-made into their original forms, and must instead be "down-cycled" into lower grade products. Second, we do not have the capacity to do much of this recycling (in particular plastics recycling) natively - instead, plastic waste must be shipped abroad where the externalities associated with the industry can be offloaded to poorer countries. But most importantly, at present recycling plastics is not economically viable - it takes more money, and releases more greenhouse gases to recycle a pound of PET than to make one from scratch. I'd love to get Daniel Kammen's opinion about whether the push towards recycling makes sense to help combat climate change. While the idea is sound in theory, the fact that recycling only saves energy for metals makes me wonder if the effort to get people to change their ways would be better spent elsewhere. Link to Kammen's interview: https://www.labxchange.org/library/pathway/lx-pathway:825945a0-367c-45dc-82b7-3d160c6e6f7a/items/lx-pb:825945a0-367c-45dc-82b7-3d160c6e6f7a:lx_simulation:fa741ca2?source=%2Flibrary%2Fclusters%2Flx-cluster%3AModernPrediction

For me, the most surprising thing from the three videos was Daniel Kammen explaining why we should "go green" in ways that don't make sense on paper. He gives the example of metal straws - in theory, the single-use plastics model used by these straws is wasteful, and creating one metal straw instead and using that repeatedly would be better for the planet. Indeed, I've been to many restaurants that tried to go green by reducing straw usage. There are a few problems with this in practice, however. First, the up-front cost is way higher to make a metal straw - mining the metal, smelting it, and extruding it all take a ton of energy. In fact, this cost is so high that if the metal straw is only used for 1-2 years, it would be worse for the environment than if a person had just used plastic straws. Furthermore, Kammen says that we must recognize people are "only gonna change a certain number of things per year" (49:16). When viewed from this lens, straws become even worse as the amount of pollution they contribute to the environment is miniscule. Compared to things like sourcing clean energy or not eating meat one day of the week, choosing not to use straws barely changes one's carbon output. On this topic, Kammen claims that we should put "maximum effort" into changing things that matter, like buying clean energy, reducing driving, and changing one's diet. But that doesn't mean the idea to eliminate straws, despite its ineffectiveness, is bad - Kammen hopes that this will encourage people to start caring about climate change and other environmental issues more, which will then act as a gateway to them making more meaningful lifestyle changes. Link to Kammen's interview: https://www.labxchange.org/library/pathway/lx-pathway:825945a0-367c-45dc-82b7-3d160c6e6f7a/items/lx-pb:825945a0-367c-45dc-82b7-3d160c6e6f7a:lx_simulation:fa741ca2?source=%2Flibrary%2Fclusters%2Flx-cluster%3AModernPrediction