Data Science Weekly - Issue 222
Issue #222 Feb 22 2018
Editor Picks
Why even a moth’s brain is smarter than an AI
A neural network that simulates the way moths recognize odors also shows how they learn so much faster than machines...
Pommerman
Announcing a new Machine Learning Challenge for AI: Pommerman! Based on the classic game Bomberman. Train your team of AI agents. Compete against everyone else's...
For AI to Get Creative, It Must Learn the Rules—Then How to Break ‘Em
New artificial intelligence systems are using “adversarial networks” to develop creativity and originality by more fluidly mixing and matching real-world information...
A Message from this week's Sponsor:
Join data science leaders of today and tomorrow at Rev 2018 in San Francisco
Data science leaders gather on May 30-31 for two days loaded with technical talks, workshops, and strategy discussions. Don’t miss this opportunity to hear from industry leaders and other aspiring innovators that are accelerating data science at their organizations and becoming model-driven. Speakers include:
Jim Guszcza, Chief Data Scientist at Deloitte Consulting
Max Shron, Head of Data Science at Warby Parker
Chad Wilsey, Director of Conservation Science at Audubon
And many more!
Enjoy Early Bird Pricing - $695 (reg: $895, special valid until 3/6/18)
Register Now.
Data Science Articles & Videos
Deep Learning, Structure and Innate Priors
Earlier this month, I had the exciting opportunity to moderate a discussion between Professors Yann LeCun and Christopher Manning, titled “What innate priors should we build into the architecture of deep learning systems?” This discussion topic – about the structural design decisions we build into our neural architectures, and how those correspond to certain assumptions and inductive biases – is an important one in AI right now...
Just How Shallow is the Artificial Intelligence Talent Pool?
Research from Element AI indicates only 22,000 have right skills globally...
Neural Voice Cloning with a Few Samples
Our neural network based system learned to "clone" a voice with less than a minute of audio data from the speaker...
This heated jacket uses AI, Alexa, and other buzzwords to keep you snug
Ministry of Supply’s new Mercury jacket learns about your habits to figure out when to turn up the temperature...
What is a Senior Data Visualization Engineer?
I was asked recently on twitter a question that I’ve been asked in one form or another several times since I became a Senior Data Visualization Engineer at Netflix: can you explain to me what a Senior Data Visualization Engineer exactly does?...
Hey Maggie
A TensorFlow-based Android app for translating baby noises into English! It sounds like science-fiction, but it's written by a child development researcher who taught himself ML...
New benchmarks for approximate nearest neighbors
One of my super nerdy interests include approximate algorithms for nearest neighbors in high-dimensional spaces. The problem is simple. You have say 1M points in some high-dimensional space. Now given a query point, can you find the nearest points out of the 1M set? Doing this fast turns out to be tricky...
UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction
A faster alternative to t-SNE. The paper provides a more detailed account of the theoretical underpinnings of the algorithm, as well as performance benchmarks...
Jobs
VP, Data Science - Diply - Toronto
Diply VP of Data Science, Machine Learning and AI, will have the opportunity to build a superstar data science team from the ground up, both setting the strategy and ensuring tactical execution.
You will partner with business stakeholders to identify and prioritize top Data Science and AI opportunities, create business/technical requirements, transform over 50B monthly records of data into scientific models and AI-driven solutions, lead ML strategy and roadmap planning, and build out the data science and AI teams. The ideal leader will combine expert Data Science/AI/ML knowledge with hands on experience building algorithms/models/programming and outstanding management skills in managing teams and delivering complex/critical projects. Media industry experience is an asset...
Training & Resources
Use TensorFlow Constant Initializer To Do Simple Initialization
Learn how to use the TensorFlow constant_initializer operation to do a simple TensorFlow Variable creation such that the initialized values of the variable get the value that you pass into it, via a screencast video and full tutorial transcript...
Learning Explanatory Rules from Noisy Data
Artificial Neural Networks are powerful function approximators capable of modelling solutions to a wide variety of problems, both supervised and unsupervised. As their size and expressivity increases, so too does the variance of the model, yielding a nearly ubiquitous overfitting problem...
Efficient Neural Architecture Search (ENAS) in PyTorch
PyTorch implementation of "Efficient Neural Architecture Search via Parameters Sharing"...
Books
Bit by Bit: Social Research in the Digital Age
"The book goes well beyond "big data" to unpack the possibilities of doing social science research at a massive scale, and relatively inexpensively. This book should be read by social scientists who want to expand their research horizons, data scientists who want to understand how to incorporate the insights of social science, and anyone in a line of work in which they have potential data that can give them insights into how people behave..."
For a detailed list of books covering Data Science, Machine Learning, AI and associated programming languages check out our resources page.
P.S., Want to reach our audience / fellow readers? Consider sponsoring - grab a spot now; first come first served! All the best, Hannah & Sebastian