https://youtu.be/vodNabH5qoM
But some important context:

Earlier this month I saw a post regarding a Misinformation Susceptibility Test and was curious how 20 binary questions could be an indicator of someones media biases.

I started digging into the related paper and while the methods and analysis was interesting, there was still a lot of questions. So I reached out to Dr Rakoen Maertens who headed the study and we agreed to a discussion on the assessment and his experiences in social psychology.

The video above is an unlisted, unedited cut of the interview and I'd love to get some feedback:

Firstly: I have offered the Dr a tildes invite and he may engage with any questions or discussion. Time was limited and there were a lot of topics that was only briefly touched on or overlooked. Here is the original paper and supplementary resources if you want to see some of the language model work and bigger 100 question tests.

Secondly: I am going to do a more through edit and posting this on a dedicated channel. Since cutting off reddit, twitter and tiktoc; I've sort of rediscovered a love learning and investigations. I'd like to know if people like this form of engagement and discussions. No fancy production, just simply engaging with the research and academics behind topical and interesting ideas.

I'm already reading into fandom psychology, UV reflective paint, children's TV and CO2 scrubbing technology.

https://themarkup.org/hello-world/2023/07/08/ai-environmental-equity-its-not-easy-being-green

A few months ago, I spoke with Shaolei Ren, as associate professor of computer science at University of California, Riverside, and his team about their research into the secret water footprint of AI. Recently, Ren and his team studied how AI’s environmental costs are often disproportionately higher in some regions than others, so I spoke with him again to dig into those findings.

His team, which includes UC Riverside Ph.D. candidates Pengfei Li and Jianyi Yang, and Adam Wierman, a professor in the Department of Computing and Mathematical Sciences (CMS) at the California Institute of Technology, looked into a path toward more equitable AI through what they call “geographical load balancing.” Specifically, this approach attempts to “explicitly address AI’s environmental impacts on the most disadvantaged regions.”

Ren and I talked about why it’s not easy being green and what tangible steps cloud service providers and app developers could take to reduce their environmental footprint.