Discussion on "Techniques for Massive-Data Machine Learning in
  Astronomy" by A. Gray
  Astronomy is increasingly encountering two fundamental truths: (1) The field
is faced with the task of extracting useful information from extremely large,
complex, and high dimensional datasets; (2) The techniques of astroinformatics
and astrostatistics are the only way to make this tractable, and bring the
required level of sophistication to the analysis. Thus, an approach which
provides these tools in a way that scales to these datasets is not just
desirable, it is vital. The expertise required spans not just astronomy, but
also computer science, statistics, and informatics. As a computer scientist and
expert in machine learning, Alex's contribution of expertise and a large number
of fast algorithms designed to scale to large datasets, is extremely welcome.
We focus in this discussion on the questions raised by the practical
application of these algorithms to real astronomical datasets. That is, what is
needed to maximally leverage their potential to improve the science return?
This is not a trivial task. While computing and statistical expertise are
required, so is astronomical expertise. Precedent has shown that, to-date, the
collaborations most productive in producing astronomical science results (e.g,
the Sloan Digital Sky Survey), have either involved astronomers expert in
computer science and/or statistics, or astronomers involved in close, long-term
collaborations with experts in those fields. This does not mean that the
astronomers are giving the most important input, but simply that their input is
crucial in guiding the effort in the most fruitful directions, and coping with
the issues raised by real data. Thus, the tools must be useable and
understandable by those whose primary expertise is not computing or statistics,
even though they may have quite extensive knowledge of those fields.
