Last year the Google brain team organised a Ask-Me-Anything on reddit. It is an amazing AMA which I encourage everyone to read. However in case you do not have the time to go through the whole thing, I present some of the key take aways and learnings from the AMA.
“our research directions have definitely shifted and evolved based on what we’ve learned. For example, we’re using reinforcement learning quite a lot more than we were five years ago, especially reinforcement learning combined with deep neural nets. We also have a much stronger emphasis on deep recurrent models than we did when we started the project, as we try to solve more complex language understanding problems.”
“Machine learning is equal parts plumbing, data quality and algorithm development. (That’s optimistic. It’s really a lot of plumbing and data :).“
Underrated methods:
- Random Forests and Gradient Boosting
- Evolutionary approaches
- The general problem of intelligent automated collection of training data
Treating neural nets as parametric representations of programs, rather than parametric function approximators. - NEAT
- Careful cleanup of data, e.g. pouring lots of energy into finding systematic problems with metadata
Exciting Work:
- The problem of robotics in unconstrained environments is at the perfect almost-but-not-quite-working spot right now, and that deep learning might just be the missing ingredient to make it work robustly in the real world.
- Architecture search is an area we are very excited about. We could be getting to the point where it may soon be computationally feasible to deploy evolutionary algorithms in large scale to complement traditional deep learning pipelines.
- Excited by the potential for new techniques (particularly generative models) to augment human creativity. For example, neural doodle, artistic style transfer, realistic generative models, the music generation work being done by Magenta.
- All the recent work in unsupervised learning and generative models.
anything related to deep reinforcement learning and low sample complexity algorithms for learning policies. We want intelligent agents that can quickly and easily adapt to new tasks. - Moving beyond supervised learning. I’m especially excited to see research in domains where we don’t have a clear numeric measure of success. But I’m biased… I’m working on Magenta, a Brain effort to generate art and music using deep learning and reinforcement learning
Resources:
- https://keras.io/ : Keras is a minimalist, highly modular neural networks library, written in Python and capable of running on top of either TensorFlow or Theano. It was developed with a focus on enabling fast experimentation. Being able to go from idea to result with the least possible delay is key to doing good research.
- http://www.arxiv-sanity.com/ : Get the best of airxiv; also find similar papers according to tf-idf
- /r/MachineLearning
- https://nucl.ai/blog/neural-doodles/ : Neural Doodles!!