machine learning i - by vincent huang - a slice of my mind

applying to phd programs is funny because you need to write about what you think the most important research directions in your field are, but you also need to write about your past experiences and why they make you a deserving candidate, so many applicants end up claiming that the problems they worked on in the past happen to also be the most important problems in the field. that’s essentially what happened to me last fall - i wrote my essay about language model compute efficiency, interfaces, and interpretability because those were the three things in nlp i’d previously worked on i think this was a step in the right direction (after all i wouldn’t have wanted to work on these problems in the first place if i thought they were unimportant) but it also was not very intellectually honest. after reading more papers and talking to more professors over the last few months, i’ve now converged on what i think are the actual most important problems in nlp: scaling inference-time compute for f

applying to phd programs is funny because you need to write about what you think the most important research directions in your field are, but you also need to write about your past experiences and why they make you a deserving candidate, so many applicants end up claiming that the problems they worked on in the past happen to also be the most important problems in the field. that’s essentially what happened to me last fall - i wrote my essay about language model compute efficiency, interfaces, and interpretability because those were the three things in nlp i’d previously worked on i think thi