• What do you, personally, think you will remember most about this interview a year from now? The most salient piece of Susan Murphy's discussion was how the usage of mobile health research helps us identify and become self aware of our surroundings and the people who surround us. That is, mobile health collects so much data about us so that we can become more aware of how we feel. Mobile health can efficiently comb through huge datasets and identify trends that we may find it hard it to notice, especially because we as humans may be subject to many biases and sometimes be blinded to conclusions. By noticing the influences in our lives (whether positive or negative), we can accordingly plan for the things we want to do. • How do you think any aspect of the interview will affect your own future, or society's future? This interview highlighted mobile health's room for good, as it makes control of one's health much more accessible and personalizable. At the same time though, the interview made me feel that mobile health still lacks some of what makes us trust healthcare (and especially doctors). For example, I trust the weather forecast with fair amount of accuracy even though the prediction is made for everyone who lives in the area. However, I don't know if I'd trust a predictive machine to inform me about my health and make recommendations. I'd still rather visit a human doctor and speak to them to give me an informed recommendation. Of course, it's very possible that the human doctor performs worse than a predictive machine (either by giving a bad diagnosis or poor recommendations). Still, from an emotional perspective, I'd feel better with a doctor who could speak to me and give their opinion. Societally, it could make access to healthcare much easier and cheaper, and especially in lower income areas with less healthcare, could make drastic improvements. Thus, mobile health is a field that should be continually improved upon to improve trust and accuracy and perhaps one day shape healthcare. 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&fullscreen=true

I enjoyed the discussion of the public's perception of science and the concept of unknown unknowns. Jill Tarter's work exemplifies the delicate balance between garnering public interest and maintaining scientific/academic integrity. Public interest is vital for funding projects like hers, yet scientific rigor must not be compromised. Therefore, it's crucial that her work remains popular enough to sustain public interest while also contributing significantly to scientific advancement. I also think that publicly available courses like PredictionX play a crucial role in drawing interest to such topics, serving as a bridge between scientific exploration and public understanding and continuing to promote these topics for future advancement. Jill Tarter's field of astronomy also probably helps her with public perception; I think astronomy is a field that most people have a bit of fascination with (e.g. the eclipse as well as the science fiction she later talks about), making it more publicly appealing and easier to draw an audience to. https://www.labxchange.org/library/items/lb:HarvardX:68789c56:lx_simulation:1?fullscreen=true

What's it like balancing the things that others want you to explore (whether public opinion or sources of funding) with the things that you yourself are curious about? Furthermore, with extraterrestrial life becoming increasingly politicized, how do you ensure you remain scientific and objective? I'm curious about hearing how competing interests are actually managed. At the end of the day, funding and interest is (mostly) zero sum, so improving one objective probably results in the detriment of another, and I want to hear about how this is managed. https://www.labxchange.org/library/items/lb:HarvardX:68789c56:lx_simulation:1?fullscreen=true

I thought that Prof. Gilbert's part about error correction and how our brains do error correction was really interesting. Of course, we form models and understandings of the world around us, but these models are very oftentimes wrong. Thus, we must reformulate our models to account for the things that we previously did not. However, as demonstrated by Prof. Goodman in the walking down sidewalk example, even correcting our models is not enough to stop us from failing sometimes. As she said, if we're already falling down, then we have made a correction that the sidewalk isn't very smooth, but it's too late now for the correction to the model to be made, and instead we have to be panic mode and hope that we catch ourselves from falling. Thus, our cognitive model of the world is constantly changing and being updated based on new information; however, this updating must be highly rapid in order for mishaps to be avoided (e.g., Prof. Goodman would've avoided falling if she had updated her model of the sidewalk soon enough to realize that it wasn't smooth). 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&fullscreen=true

Prof. Laibson mentioned that supposing "big data" was "big" enough, an ML model could predict the stock market 20 years from now. How "big" would this data need to be? Would it be on the scale of Laplace's demon, where every factor in the world would need to be determined, or would a smaller subset of information be enough for this prediction? I'm curious about this question because everything is thereotically predictable by Laplace's demon, so when can we draw the line between predicting things that need the information held by Laplace's demon and predicting things that only need a piece of that information? 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&fullscreen=true

https://www.cnn.com/2024/03/18/health/measles-vaccine-cdc-alert/index.html Health officials are worried about rising measles rates, and they are advising doctors to be cautious of this spread and encourage vaccination among patients (especially young ones). Many popular tourist destinations have been hit by measles outbreaks, and the CDC is also slightly concerned about vaccination rates falling. Health officials are appealing to the public to encourage vaccination, highlighting their effectiveness (2 doses of measles vaccination is 97% effective at preventing infection), and encouraging immunization before international travel. The article also estimates the infection rates after exposure, and symptoms are also highlighted so that readers can identify if they might have measles. High risk individuals are also identified, as are the changes in trends of both measles and measles vaccinations.