I was especially interested in the intersection of philosophy and prediction because much of what we’ve learned about modern prediction falls within a very practical context. Prediction is often used to help in very practical ways in our everyday lives: informing clothing choices, our driving routes, etc. I was especially interested in Hall’s commentary on philosophy within theoretical prediction, as used by Newton and Mendel. A theoretical approach to predictions rather than empirical allows scientists to think beyond the data and on a larger scale and leads to the discovery of often accurate theoretical predictions. One question I would have asked is how can modern philosophers use philosophical principles to create predictions in a time in which accurate data is widely available for virtually any question. Many of the ancient philosophers we learned about earlier in the semester used theory to make predictions that often ended up being incorrect, so I’m interested in how philosophers can use theory to make predictions without falling into the traps of previous philosophers.

I agree, the potential to 3D print vaccines is very exciting! I wonder how effective 3D printing vaccines is when compared to more traditional methods. I was also interested in Mr. Rees' beliefs on the intersection of climate change and politics, especially lobbying for an industry such as the meat industry.

Most of my experience with AI happened today. Yesterday, I had taken an econ exam (multiple choice) on which the class average was a 66%. I had access to the test and was able to see what I had gotten wrong, and I wanted to see how well chat GPT would have done on the test. When asked the questions that did not include images, it answered about 60% of the questions correctly. Many of its answers provided accurate explanations of why it selected that option. However, it made many errors, in fact, it tended to err on the same questions I did. It had detailed explanations for its incorrect answers, demonstrating the "hallucinations" mentioned in the NYT series. When prompted again with the same question, it would sometimes spit out different results or completely contradict itself. Its explanations were very helpful when they were accurate, but required me to have a general understanding of the topic to know whether it was spitting out nonsense or not. To me, this demonstrated the strengths and weaknesses of AI. Very smart but easy to confuse and then ultimately to be confused by. I then read this article, which was equally fascinating as it was disturbing.

I was also struck by the medical comparison McCarthy used to illustrate the role uncertainty should play in climate predictions. I agree that people tend to dismiss unpleasant information and try to convince themselves the information isn't accurate, however, I wonder what the difference between a patient's response to a cancer diagnosis and society's response to climate change. Both are terrible pieces of information that would be devastating to the person receiving the information, but while most patients accept a diagnosis and start treatment, society has resisted accepting climate change predictions. It's hard to imagine a piece of news being more devastating than a cancer diagnosis, but I'm guessing the scale of the destruction that climate change will likely cause is just too vast, even when compared to a cancer diagnosis. This difference could also be caused by the fact that there are interested parties in denying climate change, while there are few interested parties in denying a diagnosis.