message: Reword the goals to be a little clearer.
  Some rewording of existing goals
  Add a goal for providing data to the community.
diff: Minigo: A minimalist Go engine modeled after AlphaGo Zero, built on MuGo
  ==================================================
  -This is a pure Python implementation of a neural-network based Go AI, using TensorFlow.
  +This is a pure Python implementation of a neural-network based Go AI, using
  +TensorFlow. While inspired by Deepmind's AlphaGo algorithm, this project is not
  +a Deepmind project.
  -This is based off of Brian Lee's "MuGo" -- a pure Python implementation of the
  +Minigo is based off of Brian Lee's "MuGo" -- a pure Python implementation of the
  first AlphaGo paper ["Mastering the Game of Go with Deep Neural Networks and
  Tree Search"](https://www.nature.com/articles/nature16961) published in
  *Nature*. This implementation adds features and architecture changes present in
  @@ -12,18 +14,27 @@ Knowledge"](https://www.nature.com/articles/nature24270). More recently, this
  architecture was extended for Chess and Shogi in ["Mastering Chess and Shogi by
  Self-Play with a General Reinforcement Learning
  Algorithm"](https://arxiv.org/abs/1712.01815). These papers will often be
  -abridged in documentation as *AG* (for AlphaGo), *AGZ* (for AlphaGo Zero), and
  -*AZ* (for AlphaZero) respectively.
  -
  -The goal of this project is to reproduce the results of the original paper
  -through an open-source implementation using the Google Cloud Platform and
  -open-source pipeline tools. A secondary goal is to provide a clear set of
  -learning examples for establishing RL pipelines on various hardware
  -accelerators available in TensorFlow.
  -
  -An explicit non-goal is to produce a competitive program, and the original
  -non-goal of MuGo -- "diving into the fiddly bits of optimizing" -- is preserved
  -in the service of the secondary goal above :)
  +abridged in Minigo documentation as *AG* (for AlphaGo), *AGZ* (for AlphaGo
  +Zero), and *AZ* (for AlphaZero) respectively.
  +
  +
  +## Goals of the Project
  +
  +1. Provide a clear set of learning examples using Tensorflow, Kubernetes, and
  + Google Cloud Platform for establishing Reinforcement Learning pipelines on
  + various hardware accelerators.
  +
  +1. Reproduce the methods of the original Deepmind AlphaGo papers as faithfully
  + as possible, through an open-source implementation and open-source pipeline
  + tools.
  +
  +1. Provide our data, results, and discoveries in the open to benefit the go,
  + machine learning communities, and Kubernetes communities.
  +
  +An explicit non-goal of the project is to produce a competitive go program that
  +establishes itself as the top Go AI. Instead, we strive for a readable,
  +understandable implementation that can benefit the community, even if that
  +means our implementation is not as fast or efficient as possible.
  Getting Started
  ===============
  