Phil 2.22.19

7:00 – 4:00 ASRC

  • Running Ellen’s dungeon tonight
  • Wondering what to do next. Look at text analytics? List is in this post.
  • But before we do that, I need to extract from the DB posts as text. And now I have something to do!
    • Sheesh – tried to update the database and had all kinds of weird problems. I wound up re-injesting everything from the Slack files. This seems to work fine, so I exported that to replace the .sql file that may have been causing all the trouble.
  • Here’s a thing using the JAX library, which I’m becoming interested in: Meta-Learning in 50 Lines of JAX
    • The focus of Machine Learning (ML) is to imbue computers with the ability to learn from data, so that they may accomplish tasks that humans have difficulty expressing in pure code. However, what most ML researchers call “learning” right now is but a very small subset of the vast range of behavioral adaptability encountered in biological life! Deep Learning models are powerful, but require a large amount of data and many iterations of stochastic gradient descent (SGD). This learning procedure is time-consuming and once a deep model is trained, its behavior is fairly rigid; at deployment time, one cannot really change the behavior of the system (e.g. correcting mistakes) without an expensive retraining process. Can we build systems that can learn faster, and with less data?
  • Meta-Learning: Learning to Learn Fast
    • A good machine learning model often requires training with a large number of samples. Humans, in contrast, learn new concepts and skills much faster and more efficiently. Kids who have seen cats and birds only a few times can quickly tell them apart. People who know how to ride a bike are likely to discover the way to ride a motorcycle fast with little or even no demonstration. Is it possible to design a machine learning model with similar properties — learning new concepts and skills fast with a few training examples? That’s essentially what meta-learning aims to solve.
  • Meta learning is everywhere! Learning to Generalize from Sparse and Underspecified Rewards
    • In “Learning to Generalize from Sparse and Underspecified Rewards“, we address the issue of underspecified rewards by developing Meta Reward Learning (MeRL), which provides more refined feedback to the agent by optimizing an auxiliary reward function. MeRL is combined with a memory buffer of successful trajectories collected using a novel exploration strategy to learn from sparse rewards.
  • Lingvo: A TensorFlow Framework for Sequence Modeling
    • While Lingvo started out with a focus on NLP, it is inherently very flexible, and models for tasks such as image segmentation and point cloud classification have been successfully implemented using the framework. Distillation, GANs, and multi-task models are also supported. At the same time, the framework does not compromise on speed, and features an optimized input pipeline and fast distributed training. Finally, Lingvo was put together with an eye towards easy productionisation, and there is even a well-defined path towards porting models for mobile inference.
  • Working on white paper. Still reading Command Dysfunction and making notes. I think I’ll use the idea of C&C combat as the framing device of the paper. Started to write more bits
  • What, if anything, can the Pentagon learn from this war simulator?
    • It is August 2010, and Operation Glacier Mantis is struggling in the fictional Saffron Valley. Coalition forces moved into the valley nine years ago, but peace negotiations are breaking down after a series of airstrikes result in civilian casualties. Within a few months, the Coalition abandons Saffron Valley. Corruption sapped the reputation of the operation. Troops are called away to a different war. Operation Glacier Mantis ends in total defeat.
  • Created a post for Command Dysfunction here. Finished.