• Intertwined quest for understanding biological intelligence and creating AI
  On intelligence: its creation and understanding. My new post for the Stanford Human Centered AI Initiative: a personal vision of how neuroscience, psychology, AI, physics, mathematics and other fields can work together to both understand biological intelligence and create artificial intelligence...

• Why Your Office Needs a Laugh Detector
  Play goes hand in hand with innovation. I learned this from Brendan Boyle, director of IDEO's Play Lab, who says that if you measure the amount of laughter in a project space, the teams who chuckle most are also the most successful. As a data scientist, this idea got my gears turning: What if I could use machine learning to build a laugh-detecting algorithm?...

• A radical new neural network design could overcome big challenges in AI
  Researchers borrowed equations from calculus to redesign the core machinery of deep learning so it can model continuous processes like changes in health...

 
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• Comixify: Transform video into a comics
  In this paper, we propose a solution to transform a video into a comics. We approach this task using a neural style algorithm based on Generative Adversarial Networks (GANs). Several recent works in the field of Neural Style Transfer showed that producing an image in the style of another image is feasible. In this paper, we build up on these works and extend the existing set of style transfer use cases with a working application of video comixification...

• AI Index 2018 Report
  The AI Index is an effort to track, collate, distill, and visualize data relating to artificial intelligence. It aspires to be a comprehensive resource of data and analysis for policymakers, researchers, executives, journalists, and the general public to develop intuitions about the complex field of AI...

• World Models: Can agents learn inside of their own dreams?
  We explore building generative neural network models of popular reinforcement learning environments. Our world model can be trained quickly in an unsupervised manner to learn a compressed spatial and temporal representation of the environment...

• Learning to Drive: Beyond Pure Imitation [Waymo]
  In recent years, the supervised training of deep neural networks using large amounts of labeled data has rapidly improved the state-of-the-art in many fields, particularly in the area of object perception and prediction, and these technologies are used extensively at Waymo. Following the success of neural networks for perception, we naturally asked ourselves the question: given that we had millions of miles of driving data (i.e., expert driving demonstrations), can we train a skilled driver using a purely supervised deep learning approach?...

• A new AI method can train on medical records without revealing patient data
  AI researchers have been advancing new techniques for training machine-learning models while keeping the data confidential. The latest method, out of MIT, is called a split neural network: it allows one person to start training a deep-learning model and another person to finish...

• CariGANs: Unpaired Photo-to-Caricature Translation
  Facial caricatures: faces drawn in an exaggerated way to convey humor or sarcasm. "CariGAN" is a Generative adversarial network which models geometric exaggeration and appearance stylization...

• How to build an image duplicate finder for your dataset
  When you download images from the internet you usually find noisy data. Furthermore, popular pictures are all around the place. It is tedious to see them one by one and try to find duplicates to clean you dataset. With this problem in mind, I built a duplicate finder that finds the duplicates for you so you only need to choose if you want to delete them....

• What Bodies Think About:
  Bioelectric Computation Outside the Nervous System
  Fascinating NeurIPS 2018 talk from Michael Levin...

• PyTorch MNIST: Load MNIST Dataset from PyTorch Torchvision
  Learn how to load the MNIST dataset from PyTorch Torchvision and split it into a train data set and a test data set, via a screencast video and full tutorial transcript...

• Keras implementation of the Residual Dense Network for super scaling images
  The goal of this project is to upscale low resolution images (currently only x2 scaling). To achieve this we used the CNN Residual Dense Network described in Residual Dense Network for Image Super-Resolution (Zhang et al. 2018). We wrote a Keras implementation of the network and set up a Docker image to carry training and testing. You can train either locally or on the cloud with AWS and nvidia-docker with only a few commands...

• Advanced cross-validation tips for time series
  Cross-validation for time series (aka backtesting) doesn't work like standard cross-validation. The sequential nature of the data affects training window management, feature engineering and testing procedures quite significantly. We provide practical advice for avoiding classic - and painful - backtesting mistakes...

• Seven Databases in Seven Weeks:
  A Guide to Modern Databases and the NoSQL Movement
  "A book that tries to cover multiple database is a risky endeavor, a book that also provides hands on on each is even riskier but if implemented well leads to a great package. I loved the specific exercises the authors covered. A must read for all big data architects who don’t shy away from coding..."...
  For a detailed list of books covering Data Science, Machine Learning, AI and associated programming languages check out our resources page