It’s been almost two years since this blog began. In that time, I’ve learned enough to shift my expectations in several ways.

  1. Initially, the idea was for a general purpose ML blog where different people could contribute posts. What has actually happened is most posts come from me, with a few guest posts that I greatly value. There are a few reasons I see for this.
    1. Overload. A couple years ago, I had not fully appreciated just how busy life gets for a researcher. Making a post is not simply a matter of getting to it, but rather of prioritizing between {writing a grant, finishing an overdue review, writing a paper, teaching a class, writing a program, etc…}. This is a substantial transition away from what life as a graduate student is like. At some point the question is not “when will I get to it?” but rather “will I get to it?” and the answer starts to become “no” most of the time.
    2. Feedback failure. This blog currently receives about 3K unique visitors per day from about 13K unique sites per month. This number of visitors is large enough that it scares me somewhat—having several thousand people read a post is more attention than almost all papers published in academia get. But the nature of things is that only a small fraction of people leave comments, and the rest are essentially invisible. Adding a few counters to the site may help with this.
    3. Content Control. The internet has a huge untapped capacity to support content, so one of the traditional reasons for editorial control (limited space) simply no longer exists. Nevertheless, the time of readers is important and there is a focus-of-attention issue since one blog with all posts on all topics would be virtually useless. In an ideal world, the need for explicit content control would disappear and be replaced by a massive cooperative collaborative filtering process. This shift is already well underway since anyone can start their own blog and read anything they choose. Tighter integration of collaborative filtering into the overall process will surely be useful. I’ve reorganized my links to other blogs to make this a little bit easier. In the last couple years, many new machine learning related blogs have started (just recently: Yee Whye’s), which is great in several ways.
    4. Difficulty. Talking clearly about things you barely understand (and no one else does) is simply very difficult. Expending the effort to write clearly about them in a post is not too difficult from expending the effort to write clearly about them in a paper, which is the traditional mechanism of publishing. There is no simply way around this problem, although changing people’s expectations may be helpful. Right now, the expectation in academia is (partially) set by the academic paper. A different expectation, more akin to the way we discuss problems with each other in person, may be helpful.

    For the record, I’m always happy to consider posts by others. If you are considering your own blog, trying a guest post or two is a great way to experiment. Many people don’t have the time or inclination to run their own blog, so guest posts are essential.

  2. What is a good post? A good post is fundamentally an interesting post, but “interesting” can be broken down further.
    1. Speak plainly. The review process in academia can sometimes favor the convoluted over the plain. A blog strongly encourages otherwise since the backgrounds of readers are very diverse. If you aren’t self-editing for simplicity, you aren’t being simple enough.
    2. Believe in it.
    3. Lack of comments is not always lack of interest. As an example the posts of the form “interesting papers at <conference>” tend to get very few comments, but they are some of the most viewed.
    4. Avoid duplication. The most obvious way to use a blog is as a mechanism for posting finished research. It’s ok for this, but the most interesting way of using the blog are for topics which could not be stated as a research paper.

5 Replies to “Retrospective”

  1. Hi,
    I think another important reason why persons don’t write guest posts on blogs is because blogs are percieved as being rather personal views. And it is more or less true. Each blogger has a certain way of writing and a personal vision of a given domain and even if it is a competent and interesting blog it’s hard for a guest to write something in the same way. A solution to that could be a more impersonal portal on the topic (machine learning in this case) with some reviewers that filter the posts. I think it is easier to form a community with this approach and get more feedback.
    Nevertheless it’s important for blogs like this one to exist (it is one of the blogs that convinced me that blogging is really useful) precisely because they give an uncensored and different-than-accademic view on a domain that is highly esoteric.
    Best regards,

  2. I know of some active group blogs ( is the most well-known; is another example by some folks I know). The posters are like-minded and have common interests, but they don’t all post in the same way. I’ve never participated in one, but my guess is that it depends on having the right set of people with sufficient motivation and interest in the project, as well as “buy-in” to the idea of a group blog. e.g., if you had five people each of whom wanted to start a blog on the same topics anyway (and they knew each other well), they may do well with a group blog.

  3. I really like this blog. Indeed, more interaction would be better.

  4. Pingback: Blogging « ylloh

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