• Why Is the Human Brain So Efficient?
  How massive parallelism lifts the brain’s performance above that of AI...

• Qualitative before Quantitative:
  How Qualitative Methods Support Better Data Science
  Have you ever been embarrassed by the first iteration of one of your machine learning projects, where you didn’t include obvious and important features? In the practical hustle and bustle of trying to build models, we can often forget about the observation step in the scientific method and jump straight to hypothesis testing...

• Why I've lost faith in p values
  There has been a lot written over the past decade (and even longer) about problems associated with null hypothesis statistical testing (NHST) and p values. Personally, I have found most of these arguments unconvincing. However, one of the problems with p values has been gnawing at me for the past couple years, and it has finally gotten to the point that I'm thinking about abandoning p values. ...

 
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• Artificial Intelligence — The Revolution Hasn’t Happened Yet
  "AI" has become a distraction -- not just to the general public, but also to computer scientists overly focused on imitating human intelligence...

• The Book No One Read
  Why Stanislaw Lem’s futurism deserves attention...

• Toward the Jet Age of machine learning
  Solving the challenges of efficiency, automation, and safety will require cooperation between researchers and engineers spanning both academia and industry...

• Building An AI-Powered Society with AI Fund's Andrew Ng | Greymatter
  In this episode of Greymatter, Greylock’s Sarah Guo and Dr. Andrew Ng, one of the foremost leaders in AI discuss AI and ML techniques being used in industry today, areas of ongoing research, how companies can leverage this technology, and the broader impact on the future workforce...

• Storm damage to forests costs billions – here’s how AI can help
  Europe loses as many trees to storms each year as Poland produces in timber. Until now, the models for predicting which trees are at risk have not been good enough...

• Using machine learning to classify devices on your network
  In this article, we plan to walk readers through using our machine learning code to classify devices on a network... we’ve experimented with classifying devices using packet-capture data...

• Accelerating Deep Neuroevolution:
  Train Atari in Hours on a Single Personal Computer
  Today, we are releasing open source code that makes it possible to train deep neural networks to play Atari, which takes ~1 hour on 720 CPUs, now takes ~4 hours on a single modern desktop...

• Lessons from My First Two Years of AI Research
  A friend of mine who is about to start a career in artificial intelligence research recently asked what I wish I had known when I started two years ago. Below are some lessons I have learned so far. They range from general life lessons to relatively specific tricks of the AI trade. I hope others find them useful...

• Tell PyTorch To Do An In Place Operation
  Learn how to tell PyTorch to do an in-place operation by using an underscore after an operation's name, via a screencast video and full tutorial transcript...

• Modern Pandas Part 8: Scaling pandas
  As I sit down to write this, the third-most popular pandas question on StackOverflow covers how to use pandas for large datasets...

• A Gentle Introduction to Neural Networks for Machine Learning
  Top 10 Neural Network Architectures You Need to Know...

• The Theory That Would Not Die:
  How Bayes' Rule Cracked the Enigma Code, Hunted Down Russian Submarines, and Emerged Triumphant from Two Centuries of Controversy An enjoyable account of the history of Bayesian statistics from Thomas Bayes's first idea to the ultimate (near-)triumph of Bayesian methods in modern statistics...
  
  For a detailed list of books covering Data Science, Machine Learning, AI and associated programming languages check out our resources page...