The Firestein interview addressed science education and made me wonder: why do most instructors place so much emphasis on what people know than what questions they have? Is science education really training to become a perfectionist? Also I believe science classes in high school should give attention to open questions and fields. For example, in most high school physics classes all the material corresponds to Newtonian mechanics but modern astrophysics, quantum mechanics, or relativity are not discussed. It would be nice if biology classes give a preview of computational neuroscience. I believe doing this would enhance the notion of 'the more I learn, the more I realize how much I don't know' which can spark curiosity instead of carrying a boring predisposition of claiming that science has all the answers.

Firestein had some interesting points with regards to how far off in the future some sciences await to observe certain phenomena. As an example, chemists usually don't have to wait long to see a type of decay compared to astrophysicists who in principle have to wait millions of years to see galaxy collisions (but computer simulations come in handy). I liked the idea of science allowing for 'childish curiosity and adult skepticism', I think this comes from discoveries being mostly unexpected and people wanting to learn more but still want to be careful with experimental results as these can be flawed, which makes them subject rigorous evaluation.

Brendan Meade mentioned the following phrase: 'anywhere a physicist goes, a mathematician was there 200 years ago'. But the round table thinks that current mathematics is a bit stagnant in its applications to other sciences that have a more predictive essence/goal. ML now appears to be taking over the role of math in supporting predictions and having practical applications, I wonder if the round table thinks math and theoretical physics have gone too abstract and why?

I did not know predicting earthquakes is one of the hardest problems in science and specifically where, how large, and when they are expected to happen. I thought a lot of resources where devoted to this study but Brendan Meade clarified that money is put into investigating the aftermath of earthquakes, which benefits seismologists. I also found enlightening the methods used to anticipate these disasters. I was amazed to learn that there is now the possibility to measure the Earth's surface movements with high precision, which is a proxy to how big an earthquake might be. I correctly anticipated the importance of numerical simulations to Dr. Meade's efforts, but it is sad to know that there is still not enough computational resources and that the physical models currently used are oversimplified.

Avi Loeb discussed conservatism in the scientific community with regards to being afraid to present ideas that are not "sufficiently polished" or have the potential of being wrong. He claims that this behavior is perhaps due to a desire to maximize recognition in the form of prizes, newspaper articles, etc. If some of the major purposes of science are understanding our reality at the deepest level and enhancement of curiosity, then what good are prizes for? I know it is important to give credit, but to what extend is this detrimental to the advancement of the field?

I am in total agreement with Avi Loeb's thoughts on how predictions could be made in the hypothetical 21st century rainbow diagram. Skipping 'Rule', 'Theory', and 'Explanation' via machine learning can have practical benefits such as fast creation of drugs as Prof. Goodman pointed out, yet this process can be a bit rubbish if trying to ultimately understand the fundamentals of the underlying problem/phenomena. Loeb gives a fantastic example of the value of understanding the problem at large by mentioning the general theory of relativity and its technological implications. I believe we shall not settle to using ML to blindly crunch data and solve an exclusive kind of problem, having breadth in the underlying mechanisms can have equal and probably more benefits.

Dan Gilbert mentions that humans use data from the past to make prediction and/or a simulation about the future (in particular, he mentions what would it be like to have a vacation at a beach as an example), but there are times in which the information from the past is misrepresented and that leads to flawed forecasts about the future. Gilbert argues the root of this is in irrationality, my question is what criteria is needed to avoid falling into this trap and thus reduce the uncertainty in these type of predictions?

An interesting piece of information I learned from Dan Gilbert's interview was the concept of error correction, that is adjusting a prediction (likely mental) model as one puts it to the test. An example of this was given by Prof. Goodman's experience when tripping on sidewalks, the prediction could be arriving to a destination safely and without undergoing an embarrassing moment (such as tripping). The method of transportation is part of the model, in Prof. Goodman's case it would be walking. However, as she trips there is nothing that can stop gravity from her making contact with the ground but during such instance she can readjust the model and conclude that the sidewalk is not a good idea. There have been plenty of moments in my life when I did not obtained a desired outcome I immediately said to myself "perhaps doing this was not for the best" or "next time I will follow this method" and I now realize this is error correction in a prediction model. I am starting to agree more with the claim that humans are prediction and error correction machines.

Dan Kammen raised the importance of social media for communicating aspects of energy and climate. His view of how to effectively do this is in correspondence with Gina McCarthy's ideas. However, social media has the dangers of trolls, fake news, and childish fights. My question to Kammen is how can scientists and good citizens combat the dark side of social media while still being able to effectively bring awareness os issues involving climate, the Earth, and energy? I think this is a crucial discussion to have since the spread of misinformation can decrease the credibility of scientists and have negative consequences socially and environmentally sooner than later. Will collaboration between social media companies and climate scientists be necessary to establish stronger accountability and filter out misinformation? Watch Dan Kammen's interview here.

I was intrigued by the discussion of psychology and climate change. In particular, it never occurred to me to think of the challenge it is to make climate change relevant to the public in the same way a, for example, medical diagnosis is. As mentioned in the video, a reason why this is the case is because climate comes across as something foreign and thus unworthy of vast attention. The following quote from Gina McCarthy caught my attention: "People don't accept problems there is not solution to" (9:12). I now understand how this idea applied to the time when scientists first learned climate change was a threat and therefore needed to be communicated but there was not a set of solutions, which led to denial by some folks that has been exacerbated to this date despite the advancement in models and proposals to address the problem. McCarthy's insights made me more aware of why it is important to communicate climate change as a threat to not only the environment but to public health, in addition to making the science more digestible by getting rid of the gobbledygook. Watch Gina McCarthy's interview here.