Every two weeks, we find the most interesting data science links from around the web and collect them in Data Science Briefings, the DataMiningApps newsletter. Subscribe now for free if you want to be the first to get up to speed on interesting resources.
- The NIPS (Neural Information Processing Systems) 2016 conference is just past, and many people are reflecting on the many great works presenting there. See NIPS 2016 Highlights – Sebastian Ruder, Some general take aways from #NIPS2016, 50 things I learned at NIPS 2016, Post NIPS Reflections, All the available code repos for the NIPS 2016’s top papers for what people are saying, as well as Le Cun’s slides.
- The great AI awakening
How Google used artificial intelligence to transform Google Translate, one of its more popular services — and how machine learning is poised to reinvent computing itself.
- In the race to build the best AI, there’s already one clear winner
As Google, Facebook, Microsoft, and Baidu take turns leapfrogging each other in artificial intelligence innovation, one company stands to profit from any outcome: Nvidia.
- The World’s Largest Hedge Fund Is Building an Algorithmic Model From its Employees’ Brains
Bridgewater wants day-to-day management—hiring, firing, decision-making—to be guided by software that doles out instructions.
- Crime Prediction software joins Dubai Police Force
In addition to its fleet of supercars, the Dubai Police are now enlisting the help of Crime Prediction software.
- What I learned creating one chart with 24 tools
Finding the best tool means thinking hard about your goals and needs.
- The Most Boring/Valuable Data Science Advice
“I’m going to make this quick. You do a carefully thought through analysis. You present it to all the movers and shakers at your company. Everyone loves it. Six months later someone asks you a question you didn’t cover so you need to reproduce your analysis…”
- The major advancements in Deep Learning in 2016
“In this article, we will go through the advancements we think have contributed the most (or have the potential) to move the field forward and how organizations and the community are making sure that these powerful technologies are going to be used in a way that is beneficial for all.”
- US starts asking foreign travelers for their social media info
Homeland Security approved the controversial proposal a few days ago.
- Wall Street wants algorithms that trade based on Trump’s tweets
Trump’s volatility is a market opportunity.
- Tourists Vs Locals: 20 Cities Based On Where People Take Photos
Tourists and locals experience cities in strikingly different ways. Great maps!
- Tool AI’s want to be Agent AI’s
“Tool AIs limited purely to inferential tasks will be less intelligent, efficient, and economically valuable than independent reinforcement-learning AIs learning actions over computation / data / training / architecture / hyperparameters / external-resource use.”
- Building Jarvis
Wondering how Zuckerberg creates an AI? “My personal challenge for 2016 was to build a simple AI to run my home — like Jarvis in Iron Man.”
- A non-comprehensive list of awesome things other people did in 2016
Some people always manage to stick an ungodly amount of work in a year!
- Finding MLB Anomalies with CADE
“Over the Summer, while an intern at Elder Research, I learned about a very intuitive anomaly detection algorithm called CADE, or Classifier-Adjusted Density Estimation. The algorithm seemed very simple, so I wanted to try and implement it myself and try to find anomalous players in the MLB.”
- A Guide to Solving Social Problems with Machine Learning
“We have learned that some of the most important challenges fall within the cracks between the discipline that builds algorithms (computer science) and the disciplines that typically work on solving policy problems (such as economics and statistics). As a result, few of these key challenges are even on anyone’s radar screen.”
- A Visual and Interactive Guide to the Basics of Neural Networks
Simple explanation with great interactive visualizations.
- Top 10 Python libraries of 2016
“Again, we try to avoid most established choices such as Django, Flask, etc. that are kind of standard nowadays.”
- Hamiltonian Monte Carlo explained
MCMC (Markov chain Monte Carlo) is a family of methods that are applied in computational physics and chemistry and also widely used in bayesian machine learning.
- Data science and critical thinking (pdf)
Some great stats and thoughts in this presentation!
- Speed up your code with multidplyr
“There’s nothing more frustrating than waiting for long-running R scripts to iteratively run. I’ve recently come across a new-ish package for parallel processing that plays nicely with the tidyverse: multidplyr.”
- Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling
“We study the problem of 3D object generation. We propose a novel framework, namely 3D Generative Adversarial Network (3D-GAN), which generates 3D objects from a probabilistic space by leveraging recent advances in volumetric convolutional networks and generative adversarial nets.”
- China invents the digital totalitarian state
Big data, meet big brother.
- How we learn how you learn
“In this post, we’ll take a look at the science behind the Duolingo skill strength meter, which we published in an Association of Computational Linguistics article earlier this year….”
- Machine learning model to production (presentation)
As explained by Georg Heiler.
- Anomaly Detection at Scale (presentation)
Jeff Henrikson presents at the first annual O’Reilly Security Conference, in New York City, 2016.
