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 AI Roles Some Companies Forget to Fill
“However, AI talent goes far beyond machine learning Ph.D’s. Equally important and less understood are the set of talent issues emerging around AI product development and engineering. Most firms have not filled these roles, and their AI projects are suffering as a result.”
- Facial recognition’s ‘dirty little secret’: Millions of online photos scraped without consent
People’s faces are being used without their permission, in order to power technology that could eventually be used to surveil them, legal experts say.
- Beware the data science pin factory
The power of the full-stack data science generalist and the perils of division of labor through function
- The AI-Art Gold Rush Is Here
An artificial-intelligence “artist” got a solo show at a Chelsea gallery. Will it reinvent art, or destroy it?
- Here’s How We’ll Know an AI Is Conscious
- Why Model Explainability is The Next Data Science Superpower
Some people think machine learning models are black boxes, useful for making predictions but otherwise unintelligible; but the best data scientists know techniques to extract real-world insights from any model.
A curated list of awesome machine learning interpretability resources.
- m2cgen: Transform ML models into a native code (Java, C, Python, etc.) with zero dependencies
- Exploring Neural Networks with Activation Atlases
By using feature inversion to visualize millions of activations from an image classification network, we create an explorable activation atlas of features the network has learned which can reveal how the network typically represents some concepts.
- Designing agent incentives to avoid side effects
A major challenge in AI safety is reliably specifying human preferences to AI systems. An incorrect or incomplete specification of the objective can result in undesirable behavior.
- The Promise of Hierarchical Reinforcement Learning
Hierarchical reinforcement learning (HRL) is a computational approach intended to address these issues by learning to operate on different levels of temporal abstraction
- Reconstructing Twitter’s Firehose
How to reconstruct over 99% of Twitter’s firehose for any time period
- Python Data Science Handbook: full text in Jupyter Notebooks
- Loc2vec — a fast pytorch implementation
It is possible to learn about geography by training in an unsupervised manner similar to Word2Vec and arrive at impressive embedding for each location
- ThunderGBM: Fast GBDTs and Random Forests on GPUs
- xforest: A super-fast and scalable Random Forest library based on fast histogram decision tree algorithm and distributed bagging framework
- A gallery of interesting Jupyter Notebooks
- 10 things R can do that might surprise you