• Is a straight line the shortest path?
  Is the shortest path from to the straight line between them? Your first response might be to think it's obviously so. But in fact you know that it's not quite that straightforward...

• Reinforcement Learning with Prediction-Based Rewards
  We’ve developed Random Network Distillation (RND), a prediction-based method for encouraging reinforcement learning agents to explore their environments through curiosity, which for the first time1 exceeds average human performance on Montezuma’s Revenge. RND achieves state-of-the-art performance, periodically finds all 24 rooms and solves the first level without using demonstrations or having access to the underlying state of the game...

• Deep Dreaming with Deep Learning
  Can a machine dream? Yes, it can. A machine dreams or hallucinates by mimicking low-level visual systems of the human brain, in order to perceive patterns and categorise objects. The machine begins to produce outputs even in the absence of any inputs...

 
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• Michelangelo PyML:
  Introducing Uber’s Platform for Rapid Python ML Model Development
  We developed Michelangelo PyML, a platform that enables rapid Python ML model development. As the first step, we built an integration with Michelangelo, Uber’s existing ML platform, that makes serving arbitrary Python-based models at scale for online or offline use cases as easy as requesting a ride...

• A controversial artwork created by AI has hauled in $435,000 at auction
  A portrait created using an AI program has fetched $435,000 in auction at Christie’s, blowing the expected price of $7,000 to $10,000 out of the water...

• Sorry I didn’t get that! — How to understand what your users want
  Using unsupervised AI to automate the definition of your chatbot...

• Humans are still the best lossy image compressors
  Lossy image compression has been studied extensively in the context of typical loss functions such as RMSE, MS-SSIM, etc. However, it is not well understood what loss function might be most appropriate for human perception. Furthermore, the availability of massive public image datasets appears to have hardly been exploited in image compression. In this work, we perform compression experiments in which one human describes images to another, using publicly available images and text instructions...

• Introducing AdaNet:
  Fast and Flexible AutoML with Learning Guarantees
  Today, we’re excited to share AdaNet, a lightweight TensorFlow-based framework for automatically learning high-quality models with minimal expert intervention. AdaNet builds on our recent reinforcement learning and evolutionary-based AutoML efforts to be fast and flexible while providing learning guarantees. Importantly, AdaNet provides a general framework for not only learning a neural network architecture, but also for learning to ensemble to obtain even better models...

• Why Jupyter is data scientists’ computational notebook of choice
  An improved architecture and enthusiastic user base are driving uptake of the open-source web tool...

• How to Decide Which Data Science Projects to Pursue
  In 2018, every organization has a data strategy. But what makes a great one? We all know what failure looks like. Resources are invested, teams are formed, time goes by — but nothing comes of it. No one can necessarily say why; it’s always Someone Else’s Fault. It’s harder to tell the difference between a modest success and excellence. Indeed, in data science they can they look very similar for perhaps a year. After several years, though, an excellent strategy will yield orders of magnitude more valuable results...

• How to Engineer Your Way Out of Slow Models
  In this post I describe how we at Taboola handle performance issues with some of our deep learning models: We find subgraphs that take a lot of calculation time. We extract these subgraphs into a caching mechanism. At train/inference time we query the cache to get instant outputs of these subgraphs. Cache miss? Send a request to another service that will run inference on the subgraphs...

• Create TensorFlow Name Scopes For TensorBoard
  Learn how to use TensorFlow Name Scopes (tf.name_scope) to group graph nodes in the TensorBoard web service so that your graph visualization is legible, via a screencast video and full tutorial transcript...

• sklearn-porter
  Transpile trained scikit-learn estimators to C, Java, JavaScript and others. It's recommended for limited embedded systems and critical applications where performance matters most...

• Data Science in Visual Studio Code using Neuron, a new VS Code extension
  In this post, I’ll give a short explanation of neuron, a Visual Studio Code extension that aims to be a one-stop-shop for data scientists. It’s an extension I developed as part of a team of students at Imperial College London, in collaboration with Microsoft, in the summer of 2018...

• Data Smart: Using Data Science to Transform Information into Insight
  "The best single book on Data Science today. I handle the data analysis and BI for the delivery side of a huge internet-based retail company, and have been a fan of Foreman's since his "Analytics Made Skeezy" blog days. His explanations are clear, his examples are to the point, and throughout it all, he is results-oriented."...
  
  For a detailed list of books covering Data Science, Machine Learning, AI and associated programming languages check out our resources page