Comment by rodeo_flagellum on New York City, NY – ACX Meetups Everywhere 2021 · 2021-08-31T16:04:11.291Z · LW · GW

This looks excellent! I will certainly be attending, and will likely bring several friends with me (even though they have only interacted with ACX dialogue through me). 

I am excited to meet new people! 

Comment by rodeo_flagellum on Can I teach myself scientific creativity? · 2021-07-26T05:50:28.911Z · LW · GW

Your description of the processes you employ to enhance creativity in your students might be better described as behavioral algorithms. I would describe a behavioral algorithm as a sequence of behaviors or stimuli that increases the likelihood that some behavior, thought, or sentiment occurs or changes in a directed manner.  While I have not found many instances of this phrase being used in this way (a quick Google Scholar search doesn't return much), I would argue that this definition is still valuable. A hypothetical example (little bearing on reality) of a behavioral algorithm of the form ([behavior/stimuli sequence] → [outcome]) could be [10 minutes meditation → 3 minutes mild-intensity exercise → 10 minutes meditation → 10 minutes of any music → green tea] → [reduction in temporally local depressive feelings]. 

I have done some observational experiments (informal) to gauge behavioral algorithms that enhance creativity and that reduce depressive feelings. I will briefly describe the latter, as it pertains to the topic of this post. 

During my subway commutes several summers ago, I forced myself to generate 5 features of society or life that I thought could be better, and then I forced myself to come up with a solution to each of these. Before generating the problems, I would sit still for 15 minutes and try to avoid thinking about anything. I did this exercise (the still-mindedness and problem/solution generation) each day for one month. At the end of the exercise I found that it became much easier to generate problems and solutions, that my descriptions of the solutions became more detailed and practical, and that the solutions themselves seemed to be slightly more creative (this is subjective; I would say that the solutions became somewhat more clever). It could be the case that my creativity was not actually increasing, and rather that I was simply getting more efficient at generating ideas of the same degree of creativity (I don't know how creativity is measured) as I had going into the informal experiment. Different approaches might be needed for improving the 'cleverness' or depth of a creative idea versus improving the rate of creative idea generation, where the level of creativity in this case is equal to the person's baseline creativity. 

I have not devoted the necessary time to generate robust experimental designs to test different behavioral algorithms for improving various dimensions of my health, creativity, or productivity, but I think it'd be interesting to scope out this topic more. It would be awesome if you could test out several variations of the current behavioral algorithms you use with your students, and then report how the outcomes differ between variations.

Thank you for this post as well!

Comment by rodeo_flagellum on Open and Welcome Thread – July 2021 · 2021-07-23T21:41:44.538Z · LW · GW

I have being reading content from LW sporadically for the last several years; only recently, though, did I find myself visiting here several times per day, and have made an account given my heightened presence. 

From what I can tell, I am in a fairly similar position to Jozdien, and am also looking for some advice.

I am graduating with a B.A. in Neuroscience and Mathematics this January. My current desire is to find remote work (this is important to me) that involves one or more of: [machine learning, mathematics, statistics, global priorities research]. 

In spirit of the post The topic is not the content, I would like to spend my time (the order is arbitrary) doing at least some of the following: discussing research with highly motivated individuals, conducting research on machine learning theory, specifically relating to NN efficiency and learnability, writing literature reviews on cause areas, developing computational models and creating web-scraped datasets to measure the extent of a problem or the efficacy of potential solution, and recommending courses-of-action (based on my assessments generated from the previous listed entity). 

Generally, my skill set and current desires lead me to believe that I will find advancing the capabilities of machine learning systems, quantifying and defining problem afflicting humans, and synthesizing research literature to inform action, all fulfilling, and that I will be effective in working on these things done as well. My first question: How should I proceed with satisfying my desires, i.e. what steps should I take to determine whether I enjoy machine learning research more than global priorities research, or vice versa? 

It is my plan to attend graduate school for one of [machine learning, optimization, computer science] at some point in life (my estimate is around the age of 27-30), but I would first like to experiment with working at an EA affiliated organization (global priorities research) or in industry doing machine learning research. I am aware that it is difficult to get a decent research position without a Master's or PhD, but I believe it is still worth trying for. I have worked on research projects in computational neuroscience/chemistry for one company and three different professors at my school, but none of these projects turned into publications. This summer, I am at a research internship and am about to submit my research on ensemble learning for splice site prediction for review in the journal Bioinformatics - I am 70% confident that this work will get published, with me as the first author. Additionally, my advisor said he'd be willing to work with me to publish a dataset of 5000 fossils image I've taken of various fossils from my collection. While this work is not in machine learning theory, it increases my capacity for being hired and is helping me refine my competence as a researcher / scientist.  

Several weeks ago, I applied to Open Philanthropy's Research Fellow position, which is a line of work I would love doing and would likely be effective at. They will contact me with updates on or before August 4th, and I anticipate that I will not be given the several follow-up test assignments OpenPhil uses to evaluate its candidates, provided that their current Research Fellows have more advanced degrees and more experience with the social sciences than I do. I have not yet applied to any organizations whose focus is machine learning, but will likely begin doing so during this coming November. This brings me to my final questions: What can I do to increase my capacity for being hired by an organization whose focus is global priorities research? Also, which organizations or institutions might be a good fit for both my skills in computational modeling and machine learning and my desire to conduct global priorities research?

Any other advice is welcome, especially advice of the form "You can better prioritize / evaluate your desires by doing [x]", "You seem to have [x] problem in your style of thought / reasoning, which may be assuaged by reading [y] and then thinking about [z]", or "You should look into work on [x], you might like it given your desire to optimize/measure/model things". Thank you, live well.