There's a section of the interview with Stuart Firestein that discusses the issues with how people learn about science and science history throughout their primary and secondary education. While I think Firestein makes clear how he'd want people to learn about/understand science during these early years, I wanted to ask why he thinks science is taught in this problematic way? Especially with other scientists (i.e. Professor Goodman) seemingly agreeing on the issue, and that many science teachers have knowledge beyond the standard that they teach, it's odd that there hasn't been considerable changes made to this method of teaching.

While it's not technically something I "learned", an idea presented in the interview with Stuart Firestein that I liked was the notion that science created ignorance. I like this idea because it's something I wouldn't really think about. No matter how far back you define the "start" of science, there's always the phenomenon of a discovery that is only known to a small subset of people. Even the simplest concepts of today were at one point seen as hidden knowledge, and even when they became known by a majority of the public, there's always that small percentage that has no idea of the concept. Even today, uncolonized tribes throughout the world have little to no knowledge of most technology and scientific discoveries. While I personally feel odd describing these groups as "ignorant", they do lack awareness to much of modern society. Although there are plenty of other things that could have caused the origin of ignorance, the idea that science is the culprit is one I enjoy! https://www.labxchange.org/library/pathway/lx-pathway:53ffe9d1-bc3b-4730-abb3-d95f5ab5f954/items/lx-pb:53ffe9d1-bc3b-4730-abb3-d95f5ab5f954:lx_simulation:9041b2ca?source=%2Flibrary%2Fclusters%2Flx-cluster%3AModernPrediction

Something I found fascinating from Susan Murphy’s half of the interview is her work on helping people overcome addiction and avoid stress. In particular, she mentions developmental technology that can warn an individual if they will be entering a period of high stress that may lead to relapse. Her example involves said technology warning the individual about a stress period a few hours away. However, I was wondering how far in the future this technology would be able to detect a stress period? I will give the benefit of the doubt, seeing as she describes the technology as in the early stages of research and trial. But in conjunction to earthquake prediction -- which can only foretell a coming earthquake a few days or hours before it occurs -- it seems worth knowing if this stress detector has a greater range, or if it too has ‘nearsighted’ vision. https://www.labxchange.org/library/pathway/lx-pathway:825945a0-367c-45dc-82b7-3d160c6e6f7a/items/lx-pb:825945a0-367c-45dc-82b7-3d160c6e6f7a:lx_simulation:5ad35586?source=%2Flibrary%2Fclusters%2Flx-cluster%3AModernPrediction

Because I’m doing my final project on earthquake prediction, there wasn’t much from the Brendan Meade half of the video that really felt like ‘new’ information. However, there was an analogy he made that I found quite interesting, that being between buildings and bike helmets. For one, I did not know you’re supposed to replace your bike helmet after getting in an accident; it seems obvious, but as someone who’s not an avid bike rider, it never crossed my mind. But more importantly, Meade points out that while many buildings made in areas with frequent earthquakes try to accommodate for these disasters, the buildings are like bike helmets and are not meant to withstand multiple earthquakes. Similarly with the bike helmets, this is something that sounds obvious when you hear it but still likely would not have crossed your mind beforehand. While it’s one of many reasons, I think buildings not getting proper care is why so much devastation can happen after an earthquake. https://www.labxchange.org/library/pathway/lx-pathway:825945a0-367c-45dc-82b7-3d160c6e6f7a/items/lx-pb:825945a0-367c-45dc-82b7-3d160c6e6f7a:lx_simulation:5ad35586?source=%2Flibrary%2Fclusters%2Flx-cluster%3AModernPrediction

For one, I had no idea that Jill’s life and work helped inspire the movie “Contact”! How iconic. When detailing the interesting ways the creative arts have been used to aid her work, she states that science fiction has helped people to conceive a scientific future that could be. To further this conversation, I'd simply ask in what other ways have creative art mediums aided in understanding or developing science. I’m sure there are thousands of examples, but I’d just want to hear what she’d have to say :) 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 think the most interesting thing I took away from the interview with Jill Tarter was the ways we look for aliens. For one, I didn’t think we WERE looking for aliens (it makes sense in hindsight, but the thought never crossed my mind). Throughout the entire video, Tarter elaborates on how science has currently been using electromagnetic signals to search for extraterrestrial life. However, prompted by something mentioned by Professor Goodman, she states that electromagnetic signals may be the worst thing to use/look for. Due to this, those that work with her are turning their attention towards other ways to find extraterrestrials. While it’s not the most fascinating thing, knowing that not only are we actively looking for aliens, but that we’re altering how we look for new life forms, is just super interesting to me. 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

In the interview with David Laibson, he states that he does not think humanity will be able to predict every single thing about the future, or at least that said ability would be generations down the line. However, does Laibson want us to have that ability? Does he think that a perfect knowledge of the future is the goal for predictive technology? While the question may not pertain to the main subject of the interview, I think it would just be an interesting perspective to hear, especially seeing as his statement in the video was so brief. https://www.labxchange.org/library/pathway/lx-pathway:b5121779-9f49-49db-93d9-80d5d67dadb3/items/lx-pb:b5121779-9f49-49db-93d9-80d5d67dadb3:lx_simulation:f10b9110?source=%2Flibrary%2Fclusters%2Flx-cluster%3AModernPrediction

The most interesting thing I took away from the interview with Dan Gilbert is the knowledge that humans are seemingly not the only creatures on Earth that think about their future. While Gilbert states that it’s unlikely that other species fantasize about the future, he explains that there are some studies on behavioral patterns of animals that allow them to “anticipate” future events. Specifically (and the detail I found most interesting), he brings up an experiment showing how, due to certain behavior patterns displayed, corvids of all species can potentially acknowledge a time in the future that has not yet occurred. I find this information to be surprising but also not?? Common folk superstitions of birds and other animals being able to foresee events in nature makes this scientific revelation believable, but the information is just as unforeseen nonetheless. https://www.labxchange.org/library/pathway/lx-pathway:53ffe9d1-bc3b-4730-abb3-d95f5ab5f954/items/lx-pb:53ffe9d1-bc3b-4730-abb3-d95f5ab5f954:lx_simulation:5e3f229f?source=%2Flibrary%2Fclusters%2Flx-cluster%3AModernPrediction

In the interview with Ben Shneiderman, a student asks how we’d be able to distinguish a human from a highly capable machine if said machines would one day be able to near perfectly mimic human behavior. In response, Shneiderman brings up how predictive machines are simply tools to enhance how humans live, and that the goal should not be for them to mimic our behavior. However, going off of this statement, I’d ask what predictive A.I. and algorithms are doing when they make predictions. From my perspective (and to be fair, I know practically nothing about machine learning), these devices are using input data to attempt to mimic how humans make predictions or reason how nature may behave. But by Shneiderman’s terms, they are doing something else. Perhaps I missed it, but what it is that these machines ARE doing, I’d simply like to know :) https://www.labxchange.org/library/pathway/lx-pathway:53ffe9d1-bc3b-4730-abb3-d95f5ab5f954/items/lx-pb:53ffe9d1-bc3b-4730-abb3-d95f5ab5f954:lx_simulation:997b23d6?source=%2Flibrary%2Fclusters%2Flx-cluster%3AModernPrediction

I think one of the key things I took from the Ben Shneiderman video is that not everything is A.I. Although this seems obvious, as Shneiderman points out, there’s often a linguistic issue when it comes to “A.I.” and what the general public thinks A.I. is. To use other things we’ve touched on in class, people tend to think that devices that use statistical or simulative algorithms for prediction must be some sort of A.I. This synthesization of all algorithmic machines as being A.I. adds to our dystopian fears of machine learning, and although this may not hinder the advancement of machine learning, a perpetual cycle of distrust of A.I. amongst the general public is created. Again, this idea is not inherently “new” information, but I think it’s something that could be talked about more even in spheres outside of technology. Doing so would both break this perpetual cycle and help people rethink the roles of machine learning in our daily lives. https://www.labxchange.org/library/pathway/lx-pathway:53ffe9d1-bc3b-4730-abb3-d95f5ab5f954/items/lx-pb:53ffe9d1-bc3b-4730-abb3-d95f5ab5f954:lx_simulation:997b23d6?source=%2Flibrary%2Fclusters%2Flx-cluster%3AModernPrediction

While I’d crafted this question while watching the McCarthy video, I realize it could be a valuable question in any of the interviews. When it comes to making actions about Climate Change, who should science be trying to convince to act?? Consumers/Constituents (i.e. the everyday citizen)? Corporations? Politicians? Which group should take priority, if any, and how has the formulation/presentation of climate models adjusted to appeal to that group? In each of the interviews, this is answered in little bits, with the individual often focusing on how scientists have approached one of the listed groups. However, I think it would be interesting to see which group others think should be the focus on Climate Change education. https://www.labxchange.org/library/items/lb:HarvardX:b0b1d3dd:lx_simulation:1

In the interview with Rebecca Henderson, she mentions a phenomenon known as “Feedback Effects”. Upon hearing it, I thought it would have something to do with climate modeling; that models are perhaps intentionally altered in response to negative feedback. However, feedback effects are actually more similar to the concept of “unknown unknowns” discussed in Nate Silver’s book. Henderson explains that feedback effects are unforeseen outcomes of an event, and while they could be perceived as possible, they are difficult to predict. She gives an example of greenhouse gases that are currently trapped under ice sheets. If these sheets melt due to climate change, they will release an explosion of additional greenhouse gases into the atmosphere. This will in turn increase the rate of global heating, which will cause ice melting and rising sea levels to occur even faster than currently predicted. However, if we act before then, it is unlikely that the release of these gases will occur. Henderson describes these kinds of instances as “discontinuity”. While we can make predictions in a way that can be described as linear, our response to that prediction can go in a variety of non-linear directions, and feedback effects exist too far down these non-linear paths to foresee. https://www.labxchange.org/library/items/lb:HarvardX:99683ddd:lx_simulation:1