• The Economist's Big Mac Index is calculated with R
  Since its inception in 1986, the Big Mac Index has been compiled and calculated manually, twice a year. But starting with the most recent published index, the index is now calculated with R. This is the first example of a new program at The Economist to publish the data and methods behind its journalism, and here the data and code behind the Big Mac Index have been published as a Github repository...

• Will Compression Be Machine Learning’s Killer App?
  When I talk to people about machine learning on phones and devices I often get asked “What’s the killer application?“. I have a lot of different answers, everything from voice interfaces to entirely new ways of using sensor data, but the one I’m most excited about in the near-team is compression. Despite being fairly well-known in the research community, this seems to surprise a lot of people, so I wanted to share some of my personal thoughts on why I see compression as so promising...

• Your next doctor’s appointment might be with an AI
  A new wave of chatbots are replacing physicians and providing frontline medical advice—but are they as good as the real thing?...

 
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• M.I.T. Plans College for Artificial Intelligence, Backed by $1 Billion
  Every major university is wrestling with how to adapt to the technology wave of artificial intelligence — how to prepare students not only to harness the powerful tools of A.I., but also to thoughtfully weigh its ethical and social implications. A.I. courses, conferences and joint majors have proliferated in the last few years. But the Massachusetts Institute of Technology is taking a particularly ambitious step, creating a new college backed by a planned investment of $1 billion...

• Resource-efficient Machine Learning in 2 KB RAM for the Internet of Things
  This paper develops a novel tree-based algorithm, called Bonsai, for efficient prediction on IoT devices – such as those based on the Arduino Uno board having an 8 bit ATmega328P microcontroller operating at 16 MHz with no native floating point support, 2 KB RAM and 32 KB read-only flash...

• Consistently Beautiful Visualizations with Altair Themes
  In this piece we’ll be digging deeper into one of altair's less known features: themes...

• Applying Deep Learning to Metastatic Breast Cancer Detection
  A pathologist’s microscopic examination of a tumor in patients is considered the gold standard for cancer diagnosis, and has a profound impact on prognosis and treatment decisions. One important but laborious aspect of the pathologic review involves detecting cancer that has spread (metastasized) from the primary site to nearby lymph nodes. Detection of nodal metastasis is relevant for most cancers, and forms one of the foundations of the widely-used TNM cancer staging...

• BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
  We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers. Unlike recent language representation models, BERT is designed to pre-train deep bidirectional representations by jointly conditioning on both left and right context in all layers...

• Neural networks don’t understand what optical illusions are
  Machine-vision systems can match humans at recognizing faces and can even create realistic synthetic faces. But researchers have discovered that the same systems cannot recognize optical illusions, which means they also can’t create new ones...

• Patient2Vec: A Personalized Interpretable Deep Representation of the Longitudinal Electronic Health Record
  The wide implementation of electronic health record (EHR) systems facilitates the collection of large-scale health data from real clinical settings. Despite the significant increase in adoption of EHR systems, this data remains largely unexplored, but presents a rich data source for knowledge discovery from patient health histories in tasks such as understanding disease correlations and predicting health outcomes.In this paper, we propose a computational framework, Patient2Vec, to learn an interpretable deep representation of longitudinal EHR data which is personalized for each patient...

• Image-Based eCommerce Product Discovery: A Deep Learning Case Study
  To further improve discoverability of Macy’s product catalog online we introduced an easy shopping experience for finding products which are hard to describe using text-based search. In this talk, Macy’s engineers share how they’ve implemented visual similarity using CNNs...

• How To Define A Convolutional Layer In PyTorch
  Learn how to use PyTorch nn.Sequential and PyTorch nn.Conv2d to define a convolutional layer in PyTorch, via a screencast video and full tutorial transcript...

• Training Neural Nets on Larger Batches: Practical Tips for 1-GPU, Multi-GPU & Distributed setups
  How can you train your model on large batches when your GPU can’t hold more than a few samples? There are several tools, tips and tricks you can use to do that and I thought it would be nice to gather all the things I use and learned in a post...

• Discriminator Rejection Sampling
  We propose a rejection sampling scheme using the discriminator of a GAN to approximately correct errors in the GAN generator distribution. We show that under quite strict assumptions, this will allow us to recover the data distribution exactly...