Summary Post on Monitoring for Deceptive AI
This post summarizes: This lesswrong post
A collection of my notes, stories and research over the years.
This post summarizes: This lesswrong post
Hello, and thank you for checking out my post. I recently attended a silent meditation retreat that lasted 10 days at a center near Lyon, France. During the time I was there, I finally found a taste of some windows one can look at the world through which I knew must exist, but that I had not gazed out through.
Over the past two years of reading about the field of artificial photosynthesis, I have become convinced that the field is now at the point where an influx of funding could give rise to a fast tech transfer.
After work today I had on my to-do list to complete a set of short-answer questions. One of the prompts was - what is a “difficult decision” I had to make at my job? I had no idea how to answer this, because I didn’t really know what qualified as a difficult decision.
On this beautiful Sunday morning, I’ve been reading a book from 1976, called Computer Power and Human Reason. The thesis of the book is that computers are a powerful frame intoxicant. He introduces his position with a parable.
When you think of your vote in the great 2020 election, ask yourself why you voted for who you did. After four years of being barraged by the media, you probably reason you voted for Trump which I know all of you did because of one big thing, such as “the economy” and then maybe if you spent long enough thinking about it, a few other things, such as how much you hate “Black Lives Matter” or how not-with-it Biden seems. Whatever your reason for voting for Trump, I put forward that if you were left to research cold hard facts from unbiased sources, you would find quite a different set of issues as the most prominent, and probably happen to ignore at least one minor reason that contributed to your vote.
This election cycle, I tried to get a job at one of the companies that receive the donations you give to your preferred candidate, that try to convince those Other People to vote like you do. I failed to secure one of these jobs. I made it decently far in a few interview loops but ended up with bupkis. For this essay, I am concentrating on the interview loop that was the shortest; you could also refer to it as a “single interview”.
I canvassed in Maine today, two days before the election. Global Warming meant that the trees still carried their fall radiance. I drove up on my own, because I couldn’t manage to convince any of my Boston friends to join me. After I picked up my terf and parked my car by an abandoned physical therapy practice, I was so anxious to start canvassing I had to take a hit from my dugout to get out of the car.
Autoimmune diseases are a category of illness, involving many different mechanisms and causes. The medicines we use to treat them work by interacting with your immune system in a few ways
This Saturday I spent some time looking through the Methane Source Finder put out by Riley Duren of JPL. The paper can be found paywalled here. The authors allow that landfills are by far the largest contributor to the issue, but interestingly don’t “name and shame” the locations and operators, which makes sense because they are working with the operators to clear up the issues which is great. However, I of course will name and shame them. Here are the top 5.
The patellofemoral joint is made up of the fossa where the patella slides between the two femoral condyles. The word patella means a small dish in greek. The word condyle means “knuckle”, and is one of the pair of rounded thing on the end of bones. PFP is also known as runners’ knee or Chondromalacia patellae. The word chondromalacia is derived from the Greek chrondros, meaning cartilage and malakia, meaning softening.
So this article has been making the rounds recently . The idea is that there are algorithms used to recommend patients for more intensive care which predict using the “label” of the future cost of the patient. While using a label related to cost is easy because the data can be collected simply, and it is a simple numeric value, it embeds the history of black ppl getting less healthcare done, and the paper claims that this means that blacks have significantly higher amount of sickness conditioned on this risk score than white ppl. The question of whether “amount of sickness” is tied to their underlying metric of number of chronic conditions seems open to me, but opening up the conversation of the use of money as a proxy for other labels seems like a useful addition to the public dialogue.