Day 1 Discussion
On day 1, we learned about the Space Weather prediction problem, became more familiar with the data, trained machine learning models for the problem, and performed some initial evaluations, and started investigating XAI.
Please have one of your team members reply in the comments to to each of these questions after discussing them with the team.
Here are the Space Weather Jupyter notebooks:
- More beginner oriented: https://github.com/ai2es/tai4es-trustathon-2022/blob/main/space/magnet_lstm_tutorial.ipynb
- More advanced user oriented: https://github.com/ai2es/tai4es-trustathon-2022/blob/main/space/magnet_cnn_tutorial.ipynb
The TAI4ES Space GitHub Readme page is here.
- The lectures today and introduction to the Trust-a-thon both emphasized the importance of thinking about trust and the end user while developing AI. In this reflection, describe how your group thinks about both the trustworthiness of AI and how the end user fits into the AI development process. Then discuss how this thinking came into play for your first day of work on the Trust-a-thon: How did you integrate your user and/or their needs today? How would the day’s work have gone if you didn’t have an end user or had been assigned a different one?
- Finally, the lectures also covered interdisciplinary collaboration, with this in mind take some time to think about how you worked together as a team: What went well and what could have been better? Did you integrate all team members? How will you keep doing things well the rest of the week and what is your plan for improving the areas that could have gone better?
- Can you describe the physical process between solar wind and ground geomagnetic disturbances? What is the Dst index primarily used for? [Hint: Large changes in solar wind velocity and density combined with the magnetic field oriented southward typically results in significant changes in the geomagnetic field near the Earth’s surface about an hour later.]
- Roughly 85% of the time, near Earth is geomagnetically quiet. How might these infrequent solar wind events make modeling their predicted effects challenging? How might you make an accurate model with very few extreme events/samples?
- How are the input data differently distributed? How are the input data correlated or uncorrelated with each other? How are they correlated with Dst?
- Based on what you’ve learned so far, what do you think your user will care about most? How do you think you can help your user view this model as trustworthy?