I think that this could be a really interesting way of debugging vision systems around edge cases. Mask out everything but the attribution area and run separate image-to-text models to see what they identify and then check for consensus.

This also makes me think of what it might mean for human-monitored AI systems. People will need to be trained to quickly identify if the model is behaving poorly and flag it, as opposed to if the decision is correct. It’s more like driving a race car where you have to monitor the performance of the vehicle and adapt to that. You can’t stop racing if the tires are wearing out or you’re running out of fuel. You have to adjust your behavior to optimize the behavior of the system as it is. Which implies that we need simulators of AI systems that break a lot.

Third World War: System, Process and Conflict Dynamics

How digital media drive affective polarization through partisan sorting

• Politics has in recent decades entered an era of intense polarization. Explanations have implicated digital media, with the so-called echo chamber remaining a dominant causal hypothesis despite growing challenge by empirical evidence. This paper suggests that this mounting evidence provides not only reason to reject the echo chamber hypothesis but also the foundation for an alternative causal mechanism. To propose such a mechanism, the paper draws on the literatures on affective polarization, digital media, and opinion dynamics. From the affective polarization literature, we follow the move from seeing polarization as diverging issue positions to rooted in sorting: an alignment of differences which is effectively dividing the electorate into two increasingly homogeneous megaparties. To explain the rise in sorting, the paper draws on opinion dynamics and digital media research to present a model which essentially turns the echo chamber on its head: it is not isolation from opposing views that drives polarization but precisely the fact that digital media bring us to interact outside our local bubble. When individuals interact locally, the outcome is a stable plural patchwork of cross-cutting conflicts. By encouraging nonlocal interaction, digital media drive an alignment of conflicts along partisan lines, thus effacing the counterbalancing effects of local heterogeneity. The result is polarization, even if individual interaction leads to convergence. The model thus suggests that digital media polarize through partisan sorting, creating a maelstrom in which more and more identities, beliefs, and cultural preferences become drawn into an all-encompassing societal division.

SBIRs

• 9:00 kernel methods discussion with Rukan. Need to look at a way of using SD to look at “outlier-ness” maybe SD of all points – SD of all other points
• 10:00 experiment logger review
• Got GeoPandas installed!
• An example for a Windows Python 3.7 install in the directory where the whl files are located would be (in order):
  • pip install .\GDAL-3.4.2-cp37-cp37m-win_amd64.whl
  • pip install .\Fiona-1.8.21-cp37-cp37m-win_amd64.whl
  • pip install geopandas
• Integrate GeoPandas and start on a textured-polygon map import that is lat/long accurate. Start with Mercator?
• Start on one of the many papers that are due over the next few weeks

GPT Agents

• Got the EmbeddingExplorer App mostly done yesterday. Need to get author information for the other keywords, generate some corpora, and train a model!
• 4:00 Meeting

Book

• Roll in Brenda’s edits