Daniel Lemire's blog

, 3 min read

Why I think a lot of Machine Learning research is misguided

5 thoughts on “Why I think a lot of Machine Learning research is misguided”

  1. I edited the post. Sorry.

  2. Suresh says:

    I think you misquoted me slightly. My beef is with neural nets and genetic algorithms, rather than with “machine learning algorithms” that a quick read of your post might imply

  3. Yuhong YAN says:

    Well, I have the same feeling of ML methodolgy, not only limit to NN and GA. ML has developed a set of testing data and testing context, so that people can compare their algorithms objectively. So what? You can’t compare your algorithm with all the existing improvements on the existing algorithms. So to declare your algorithm is better has no meaning. You have to tell me “interesting insights into the structure of a problem”. I like Daniel’s “solving problem is engineering work”. Exactly what subtle in my mind.

  4. Robin says:

    Daniel, are you familiar with Douglas Hosftadter and friends’ work on microdomains ? Tabletop, letter spirit, copycat, metacat, etc. ?

    I’ve read all his books, exchanged a few times with him over email, but he’s almost never quoted (nor does he quote much of the usual scientific papers, which might explain why…)

    Anyhow, I love his work.

  5. Robin: No, I wasn’t familiar with Douglas Hosftadter but through his entry in wikipedia, I learned a lot about him just now.