Forum Posts

Daniel Son
Harvard GenEd 2021
Harvard GenEd 2021
Apr 27, 2021
In Thoughts from Learners
Although it is highly unlikely that we will ever "know everything," is there even a niche subject where we can possibly eliminate uncertainty? Also, more specific to Professor Firestein's field of research, how can we ever be certain of a cause and effect relationship in something like the brain? It seems like there is an infinite set of external factors that we cannot control. How certain are we about the knowledge we have about the brain and is it possible that the information we have may be as inaccurate as a predictive system like phrenology?
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Daniel Son
Harvard GenEd 2021
Harvard GenEd 2021
Apr 27, 2021
In Thoughts from Learners
In the interview with Professor Firestein, I thought his explanation that since science is iterative, revision is a victory. When thinking about the great scientific discoveries, curiosity is what leads to new ideas and theories although these theories are often not fully proven. Uncertainty in science is inevitable because we do not have all the information necessary in order to always definitively prove an idea. Many times, we can only hypothesize and find supporting evidence and it is up to future scientists to revise these hypotheses with newfound knowledge.
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Daniel Son
Harvard GenEd 2021
Harvard GenEd 2021
Apr 22, 2021
In Health
In the discussion of uncertainty within earthquake predictions with Brendan Meade I was curious to know whether the uncertainty increases as time passes. Since one of the major uncertainties is the time frame that an earthquake will occur, as time passes, does the uncertainty decrease? Furthermore, what is needed in order to make accurate predictions on when earthquakes will happen?
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Daniel Son
Harvard GenEd 2021
Harvard GenEd 2021
Apr 22, 2021
In Health
I thought the discussion with Brendan Meade on his study of predicting earthquakes was really interesting. Although we are still unable to predict earthquakes, it will be interesting to see the prospects of predicting earthquakes. Furthermore, since there are no robust computational methods of creating simulations of earthquakes, there is increased uncertainty due to the lack of development in this field. It was also interesting to think about the fact that we may not know enough physics in order to be able to make predictions for earthquakes.
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Daniel Son
Harvard GenEd 2021
Harvard GenEd 2021
Apr 13, 2021
In Thoughts from Learners
In the discussion with Professor Loeb, the discussion of deeper understanding gave me some questions to think about. Although it is true that a deeper understanding of a topic will allow one to go in more directions, how do limitations apply to this idea? With the creation of supercomputers, humans are now able to create predictions for a multitude of things such as weather forecasts, quantum mechanics, climate research, etc. What would we do if humans reach the limits of our capabilities?
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Daniel Son
Harvard GenEd 2021
Harvard GenEd 2021
Apr 13, 2021
In Thoughts from Learners
I thought the conversation discussing the process to make a prediction was very interesting since I could compare it to Professor Laibson's point of view. Professor Loeb's view that although these machines can make predictions based on simulations and data, it does not give us an understanding of how the machine is making a prediction is fair. I would also be very curious to "look under the hood" of a computer to see how machine learning is enabling us to skip various steps in the process of making a prediction. Find more about the discussion here.
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Daniel Son
Harvard GenEd 2021
Harvard GenEd 2021
Apr 04, 2021
In Thoughts from Learners
In the interview with Professor Laibson, the topic of machine learning briefly came up and I would have loved to ask what he thinks the future of economics will be with enhanced machine learning and artificial intelligence. During the interview, Professor Laibson gave an example of repetition and rationality in which there is a difference between touching a hot pan and investing in the market. When touching a hot pan, after a few repetitions, it would be clear to not touch the pan anymore; however, when investing in the market, one may get lucky and create economical gain without the correct information. Even a person with many years of experience investing in the market may lose money, although making good investment decisions. For this reason, is it at all possible that machine learning and artificial intelligence will be implemented in a way that gives users of this technology an advantage over regular investors?
Behavioral Economics Question content media
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Daniel Son
Harvard GenEd 2021
Harvard GenEd 2021
Apr 04, 2021
In Thoughts from Learners
In the interview with Professor Laibson, I thought it was interesting to hear the discussion about potentially being able to create equations to model all types of economic activities similar to how we have specific formulas to evaluate nominal values. When talking about the economy, the subject of self-interest comes up a lot since we are trying to make predictions on growth of wealth. This makes creating equations to model behavioral economics very difficult since we have to assign a nominal value to something that is different for each person. For example, when dealing with the economy, it is inevitable that some may be more greedy than others. This behavior is varied among everyone and outliers can cause a model to be skewed heavily.
David Laibson - Thoughts content media
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Daniel Son
Harvard GenEd 2021
Harvard GenEd 2021
Apr 01, 2021
In Artificial Intelligence
If I had conducted the interview with Professor Ben Shneiderman I would have liked to talk more about the strengths and weaknesses of artificial intelligence and machine learning. In particular, I would have liked to hear more about the chess and music examples. Recently, people have been using artificial intelligence to compose music, and while I believe this may be possible for pop music due to its formulaic nature, people are trying to apply this to classical music too. Artificial intelligence and machine learning have been used to analyze hundreds of works from a specific composer with the goal to "finish" their unfinished works by replicating the composer's writing style, an example is Schubert's unfinished symphony. In fact, the work by the artificial intelligence is quite impressive in the way that it emulates specific writing styles and composes melodies referencing previous works. So, what are the limitations of artificial intelligence in its capabilities? Artificial intelligence is able to beat humans in a game of chess where the outcome is definitive, but is it possible that artificial intelligence can compose "better" music, which is judged subjectively?
Artificial Intelligence Question content media
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Daniel Son
Harvard GenEd 2021
Harvard GenEd 2021
Apr 01, 2021
In Thoughts from Learners
In the interview with Ben Shneiderman it was very interesting to hear how he distinguishes artificial intelligence from human capabilities. For instance, he clearly defines artificial intelligence as a tool (not a partner) to achieve certain goals. Furthermore, a major difference between artificial intelligence and the human brain is the method behind thought. The human brain does not think with the same logic that machines do, and for this reason machines and humans each have their own strengths respectively. However, the discussion on whether machines will continue to be just a tool can be further debated. If machine learning and artificial intelligence is capable of replicating human behavior, machines can potentially be more problematic than anticipated
Thoughts on Artificial Intelligence content media
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Daniel Son
Harvard GenEd 2021
Harvard GenEd 2021
Mar 30, 2021
In Thoughts from Learners
1) In the PredictionX Video with Rebecca Henderson, I thought it was really interesting to learn about the uncertainty behind quantitative models. Evidently there is uncertainty whether an investment will be profitable but this same principle can be applied to the shift to renewable energy. For companies, the transition to renewable energy is beneficial to everyone; however, it is necessary that the biggest companies collectively make the move together. In addition, companies need to reduce carbon emissions following a quantitative model precisely, otherwise there is increased uncertainty on the effectiveness. Additionally, I thought it was very coincidental that she mentioned accelerating probability of pandemics given our current situation. 2) In the PredictionX Corporations and Climate video with Rebecca Henderson, an engaging conversation could have come out of asking the extent of the harm that would come out of not reducing carbon emissions. Although there was a brief conversation about wanting to reduce carbon emissions by 2050, there is still a lack of awareness. Additionally, I think it would have been very interesting to delve deeper into the quantitative models that companies must follow in order to reduce carbon emissions.
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Daniel Son

Daniel Son

Harvard GenEd 2021
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