# Blogs by Category

## Film Study for Research

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Research ability, like most tasks, is a trainable skill. However, while PhD students and other researchers spend a lot of time doing research, we often don’t spend enough time training our research abilities in order to improve. For many researchers, aside from taking classes and reading papers, most of our training is implicit, through doing research and interacting with mentors (usually a single mentor–our PhD advisor or research manager). By analogy, we are like basketball players who somehow made it to the NBA, and are now hoping that simply playing basketball games will be enough to keep improving.

Drawing on this analogy, I want to talk about two habits that are ubiquitous among elite athletes, that have analogs in research that I feel are underutilized. Those who do pursue these habits as PhD students often improve quickly as researchers.

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I’ve spent much of the last few days reading various ICML papers and I find there’s a few pieces of feedback that I give consistently across several papers. I’ve collated some of these below. As a general note, many of these are about local style rather than global structure; I think that good local style probably contributes substantially more to readability than global structure and is in general under-rated. I’m in general pretty willing to break rules about global structure (such as even having a conclusion section in the first place! though this might cause reviewers to look at your paper funny), but not to break local stylistic rules without strong reasons.

• Be precise. This isn’t about being pedantic, but about maximizing information content. Choose your words carefully so that you say what you mean to say. For instance, replace “performance” with “accuracy” or “speed” depending on what you mean.
• Be concise. Most of us write in an overly wordy style, because it’s easy to and no one drilled it out of us. Not only does wordiness decrease readability, it wastes precious space if you have a page limit.
• Avoid complex sentence structure. Most research is already difficult to understand and digest; there’s no reason to make it harder by having complex run-on sentences.
• Use consistent phrasing. In general prose, we’re often told to refer to the same thing in different ways to avoid boring the reader, but in technical writing this will lead to confusion. Hopefully your actual results are interesting enough that the reader doesn’t need to be entertained by your large vocabulary.

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When I meet someone who works in a field outside of computer science, I usually ask them a lot of questions about their field that I’m curious about. (This is still relevant even if I’ve already met someone in that field before, because it gives me an idea of the range of expert consensus; for some questions this ends up being surprisingly variable.) I often find that, as an outsider, I can think of natural-seeming questions that experts in the field haven’t thought about, because their thinking is confined by their field’s paradigm while mine is not (pessimistically, it’s instead constrained by a different paradigm, i.e. computer science).

Usually my questions are pretty naive, and are basically what a computer scientist would think to ask based on their own biases. For instance:

• Neuroscience: How much computation would it take to simulate a brain? Do our current theories of how neurons work allow us to do that even in principle?
• Political science: How does the rise of powerful multinational corporations affect theories of international security (typical past theories assume that the only major powers are states)? How do we keep software companies (like Google, etc.) politically accountable? How will cyber attacks / cyber warfare affect international security?
• Materials science: How much of the materials design / discovery process can be automated? What are the bottlenecks to building whatever materials we would like to? How can different research groups effectively communicate and streamline their steps for synthesizing materials?

## Donations for 2019/2020

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Each year I aim to donate around 10% of my income. In 2019, I fell behind on this, probably due to the chaos of COVID-19 (but really this was just an embarassing logistical failure on my part). I’ve recently, finally, finished processing donations for 2019 and 2020. In this post I write about my decisions, in case they are useful to others; see also here for a past write-up from 2016.

## Individual Project Fund: Further Details

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In my post on where I plan to donate in 2016, I said that I would set aside \$2000 for funding promising projects that I come across in the next year: The idea behind the project fund is … [to] give in a low-friction way on scales that are too small for organizations like Open Phil to think about. Moreover, it is likely good for me to develop a habit of evaluating projects I come across and thinking about whether they could benefit from additional money (either because they are funding constrained, or to incentivize an individual who is on the fence about carrying the project out). Finally, if this effort is successful, it is possible that other EAs will start to do this as well, which could magnify the overall impact. I think there is some danger that I will not be able to allocate the \$2000 in the next year, in which case any leftover funds will go to next year’s donor lottery.

In this post I will give some further details about this fund. My primary goal is to give others an idea of what projects I am likely to consider funding, so that anyone who thinks they might be a good fit for this can get in contact with me. (I also expect many of the best opportunities to come from people that I meet in person but don’t necessarily read this blog, so I plan to actively look for projects throughout the year as well.)

I am looking to fund or incentivize projects that meet several of the criteria below: