Link to the Stuart Firestein conversation. I found the discussion on how science is taught in schools very interesting. Specifically, that science classes before grad school is essentially learning what science already knows and memorizing it. Mr. Firestein expressed his worry that people that don’t go into science have this as their last formal interaction with science. I would love here if he has any ideas of how to change this fact. I think this is an interesting question because I can see both sides. The idea that there is so much more to science than just textbook knowledge is very interesting and I think more thoughtful classes early in education would be fascinating. However, it does seem important to know where science has been in order to successfully have a class like he is talking about. So would the answer be to encourage people to take more classes after these introductory classes? Or, maybe to inject some less textbook/historical classes between them?

Link to the Stuart Firestein conversation. I enjoyed the conversation of smelling the past and future very much. I know I smell things everyday that remind me of some kind of experience but don’t often consciously think about this phenomenon. It is also thought provoking that these memories really only are associated with emotional experiences. I would be curious if other senses, like sight, work in this same way. I certainly am reminded of things that have emotional connotations to me due to something I see fairly often. However, I think it is possible that seeing something is more likely than smelling something to also remind me of nonemotional experiences. In particular, seeing something on the street could remind me of something I learned in a class. Maybe seeing someone drop their keys reminds of learning about the Theory of Gravity, for example. To me, this seems more plausible than a smell triggering a similar type of nonemotional memory. But, I may be overestimating how often sight has triggered this kind of memory for me, as it is not something I consciously think about. In either case, it would be interesting to explore if different senses are associated with memories in different ways.

This is my interesting thought based on the Susan Murphy and Brendan Meade interview. I am very interested in Susan Murphy's thoughts on mobile health, specifically the idea that there could be medical or physiological precursors to stress. I think that getting some kind of notification that a person is building up stress could be very helpful. Particularly if this notification comes with some ways to mitigate this. I also think this could be helpful for people that deal with anxiety. I am in no way an expert on the subject but for me stress and anxiety typically go hand in hand. If there was some kind of model that can predict when a person was going to have stint of anxiety it might be very helpful for that person to be able to mitigate that. Whether it is meditation or some other method, it could be helpful to be able to try to combat the anxiety before it really comes on. However, there is a possibility that, for some people, knowing anxiety is on the way might only amplify said anxiety. It would be interesting to see research about how people would react differently to knowing that they are might encounter stress or anxiety in the future and whether it could possibly be counterproductive in some cases.

This is my question based on the Susan Murphy and Brendan Meade interview. I would like to dig deeper into the modeling work that Brendan Meade was discussing during the section from 2:47-7:33. This modeling is obviously being done with the end goal of reliable earthquake prediction in mind. But, he says that the model is essentially linear and that there are no precursors to an earthquake. So, are these models in the hopes that they eventually shed some light on a precursor? He also mentions that these models aid in us learning what the earth is doing at a steady state. I generally get what he means by this but it would be interesting to hear him dig deeper.

This post is about Jill Tarter's Video. The Drake Equation is something that has fascinated me ever since I learned about it in another Gen Ed at Harvard. So, I would love to ask some questions about it. I know she said it is not useful for prediction because the uncertainties for some of the terms are too large. So, I guess my question would be surrounding which of these terms she feels the error is too large for prediction and if she has any hope that we may be able to decrease any of these uncertainties. I would assume it is the term that represents the length of an average civilization, which is a huge driving factor in the output of the equation, but I would be curious to hear her thoughts.

This post is about Jill Tarter's Video. One thing I found very surprising and interesting is Jill Tarter’s thoughts on the what if first contact happens. She mentions that the first thing she would ask an extraterrestrial civilization is about how they made it through the “technological adolescence” with destroying themselves. This opened up an interesting line of thinking to me because that question assumes, at least partially, that the extraterrestrial civilization had similar interpersonal issues as we have had. Isn’t it certainly possible that they didn’t even have the same timeline as us. Or maybe they got stalled at a different point in the technological advancement. I do think she hints at this by saying that we will have to find our own way but that she would like to know how they got through in order to glean some kind of insight. However, it seems to me that it would be very unlikely that a civilization took very similar path to the one we are on.

This is a question about the David Laibson Video. The question I would ask was sparked by a quick diversion that the conversation took. He mentioned that the stock market is a combination of many peoples forecasts and predictions. But, he mentions that there isn’t enough data to incorporate machine learning in order to make a forecast of the stock market. I am curious what he means by this and would ask him to elaborate. Is it that there aren’t enough data points or that there is too much unknown. I assume it is the latter, but I would be really interested to hear what kind of unknowns would be necessary to make this kind of prediction.

This is a post for the David Laibson Video. The thing that stood out to me as very interesting is his thoughts about hybridization of the simple Occam’s razor type models with the more complicated machine learning predictive models. Earlier in the interview, he mentioned that it is likely that sometimes these will be working in tandem and sometimes in parallel. This is echoed in the conversation about the combination of the rational-based economics theories being combined with behavioral theories of economics to create a model that we can actually use for predictions. It seems that the best way forward might always be some kind of confluence between these two methods, especially when dealing with something as complicated as economics. This also made me wonder about where the line is between something that is simple enough to rely just on machine learning and something that requires this merging. Early in the interview, Professor Goodman mentions that she is fine with google maps using only machine learning and not using the theory based Padua Rainbow, so that got me thinking about what the factors are that would make us want to incorporate more than just ML.

I am somewhat confused about his Ben Schneiderman’s distinction between a tool and something that is mimicking the form of a human or animal. He says that tools don’t mimic a human or animal but instead enhance them but this just doesn’t fit so perfectly as I am thinking about it. He says a plane is not mimicking a bird, and physically he is right. But looking at it in a more broad sense, a plane is mimicking a bird from the point of view of the flightless human being. So, I think it is reasonable to say that birds were likely part of what sparked the quest for human flight. Based on these musings, I think I would like to probe further about the relationship between something that mimics and bird or animal and something that is considered a tool.

The most surprising thing to me in the Ben Shneiderman interview is his insistence that Artificial Intelligence is just a tool to enhance already existing capabilities. This idea that AI and Machine Learning could just be a tool for human beings, like a hammer or an automobile, is very interesting to me. I know that some of the apprehension about AI is that it is going to replace human beings. This idea sparked a thought while I was watching this interview because AI isn’t really analogous to a hammer as people were likely not apprehensive to use a hammer or saw when it was invented. So, it does surprise me that he doesn’t go a bit further into the differentiation between the power of AI and other tools. Maybe it could be compared to things like automobiles and airplanes, which may have seen the same kind of apprehension as AI is experiencing.

Link to Gina McCarthy interview. How do you get these translations of climate science to people that aren’t seeking it out? - This seems like a good question to ask because most of the population just sees headlines and does not dig into the science behind the headlines. How do you make it relevant to people? - It is hard to make climate science relevant to the general public, so I would have asked her what her strategies are to explain it to people with no background. Economic reasons seam to be a big reason that people want to resist climate change measures. She mentioned that a low carbon future would be better economically, so I would ask her to expound on that. - As the interview goes on she explains this a bit more, but I think this is a crucial point and is an opinion that isn't shared by every everyone, so I would be interested to here more about it. If businesses have known for a long time that they need to shift towards environmentally products and practices, have these been implemented slowly because of the general public’s lack of belief? - This is something I have always wondered personally. I specifically have always been fascinated in how long it will take for the general public to completely accept electric cars, if it is going to happen at all.

I am writing on the Gina McCarthy interview on Climate Change. The most surprising part of this video for me is the idea that it could be more effective to convince people of climate change via doctors or clergy instead of the scientists that are actually conducting the research on this subject. Gina McCarthy says that she has done this because people don’t tend to question their doctors or leaders in their faith community. While this is not necessarily surprising, hearing it said out loud by someone of her stature was very striking to me because it doesn’t make much sense. Doctors are basing their diagnoses on science and past knowledge, which is very similar to what climate change experts are using. Faith leaders are largely not using any science in their explanations of the world. This sets up an interesting contrast that I am struggling to reason with and is leaving me with more questions than answers. If people believe doctors, then why wouldn’t they believe similarly qualified climate experts. Is it because they know a doctor’s diagnosis will almost certainly effect them in their lifetime while climate change has a much larger uncertainty?