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?

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.

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?

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.

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?

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.

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.