Shri
67
3
Shri has not written any posts yet.
You may be interested in this white paper by a Google enginer using a NN to predict power consumption for their data centers with 99.6% accuracy.
http://googleblog.blogspot.com/2014/05/better-data-centers-through-machine.html
Looking at the interals of the model he was able to determine how sensitive the power consumption was to various factors. 3 examples were given for how the new model let them optimize power consumption. I'm a total newbie to ML but this is one of the only examples I've seen where: predictive model -> optimization.
Here's another example you might like from Kaggle cause-effect pairs challenge. The winning model was able to accurately classify whether A->B, or B->A with and AUC of >.8 , which is better than some medical tests. A writeup and code were provided by the top three kagglers.
I used ideas I learned here to resolve a problem that I've failed at for over 10 years.
I was in an volatile arguement. My base rate of regreting arguements with this person is >90% over my entire adult life. I was really confident, perhaps even arrogant in hindsight. Then I remembered to think of our disagreement as travelers comparing independatly composed maps against a common territory. I proceded to draw a causailty DAG representing my own thinking. He added some nodes and edges I hadn't considered, but made sense after listening to him.
I felt the confidence of my position slipping away in my mind as the murkiness of uncertainty appeared. We... (read more)
I'll post the obvious resources:
80k's US AI Policy article
Future of Life Institute's summaries of AI policy resources
AI Governance: A Research Agenda (Allan Dafoe, FHI)
Allen Dafoe's research compilation: Probably just the AI section is relevant, some overlap with FLI's list.
The Malicious Use of Artificial Intelligence: Forecasting, Prevention, and Mitigation (2018). Brundage and Avin et al.: One of the earlier "large collaboration" papers I can recall, probably only the AI Politics and AI Ideal Governance sections are relevant for you.
Policy Desiderata for Superintelligent AI: A Vector Field Approach: Far from object-level, in Bostrom's style, but tries to be thorough in what AI policy should try to accomplish at a high level.
CSET's Reports: Very... (read more)