+++ With Hanabi-playing bot, Facebook AI reaches a milestone in cooperative AI +++ Baidu is Training an AI to Move Like a Human +++ Google launches E2 family of virtual machines for smaller workloads +++ AI Index 2019 assesses global AI research, investment, and impact +++ Turing Award winner Yoshua Bengio wants AI systems that can reason, plan, and imagine +++ Personalizing the Joke Skill of a Voice-Controlled Virtual Assistant +++ Why scientists need to be better at data visualization +++

+++ An Epidemic of AI Misinformation +++ AWS launches SageMaker Studio, a web-based IDE for machine learning +++ Netflix open-sources Metaflow framework +++ ‘Short window’ to stop AI taking control of society, warns ex-Google employee +++ Deep Learning For Symbolic Mathematics +++ Alibaba: Dynamic Pricing with Deep RL +++ Training multi-agent AI systems to solve complex tasks through cooperation +++ A Visual Guide to Using BERT for the First Time +++ Vaex: How to analyse 100 GB of data on your laptop with Python +++

+++ Introducing TF-GAN: A lightweight GAN library for TensorFlow 2.0 +++ Global AI Survey: AI proves its worth, but few scale impact +++ AR-Net: A simple Auto-Regressive Neural Network for time-series +++ China’s billion-dollar ed-tech companies are planning to export their vision overseas +++ Deep Learning with PyTorch +++ **Corpus Wide Argument Mining - a Working Solution +++ Cost-benefit analysis in R +++ How to apply machine/deep learning to audio analysis, with comet.ml +++ Collection of Jupyter notebooks for quantitative finance +++ Create a streetmap of your favorite city with ggplot2 +++ Tutorial on Graph Neural Networks for Computer Vision and Beyond (in 3 parts) +++

In a previous post, I have delved into how UPS uses Operations Research and Machine Learning in their Network Planning Tools (NPT), and have briefly mentioned their complementary initiatives ORION and EDGE. In this post we will dive deeper into ORION (On-Road Integrated Optimization and Navigation), by summarizing what can publicly be found on the development history as well as current roll-out status of the system.

UPS aims to reroute packages away from snow and other trouble spots in its global network. An app called Network Planning Tools (NPT) is used to view all their facilities and divert their packages around the storms, or to move a particularly large shipment efficiently. It utilizes machine learning to handle the massive amounts of data required to move packages around the world and on time, regardless of the weather.

+++ AI will speed up the hunt for Nazca Lines in southern Peru +++ Increasing transparency with Google Cloud Explainable AI +++ RecSim: Configurable Simulation Platform for Recommender Systems +++ Safety Gym: Reinforcement Learning Agents That Respect Safety Constraints +++ Sony Accounces the Establishment of Sony AI +++ Complete Data Science Project Template with Mlflow for Non-Dummies +++ Summer school on Deep Bayesian methods +++ How to apply machine learning and deep learning methods to audio analysis +++

+++ Machine vision sees behind walls and in darkness +++ Deep Learning deciphers historical Japanese texts +++ Microsoft’s Vision AI Developer Kit is now generally available +++ Intel unleashes three new chips for AI work, challenging Nvidia +++ Looker seeks to go beyond business intelligence +++ Nasdaq is testing a deep learning system to monitor trading +++ The Deep Learning Revolution and Its Implications for Computer Architecture and Chip Design +++ Gaussian Processes, not quite for dummies +++ Self-training with Noisy Student improves ImageNet classification +++ Extensive list of NLP-Models implemented in Tensorflow +++ 74 Summaries of Machine Learning and NLP Research +++

Anyone who works on Python projects and uses various packages will sooner or later have to deal with different versions of packages across different branches and projects. Because each project has its own set of dependencies, it’s a good practice to avoid mixing them. If all the dependencies are installed together in a single Python environment, then it will be difficult to discern where each one came from. In the worst cases, two different projects may depend on two different versions of a package, but with Python you can only have one version of a package installed at one time. What a mess! Virtual environments address this issue. A virtual environment, put simply, is an isolated working copy of Python which allows you to work on a specific project without worry of affecting other projects.

Today I had the pleasure to present at Data Leader Days, a two-day management forum for leaders in the data space. 2019 marked its 5th edition, hosting data practitioners from Retail, Telco, Industrial Goods and Finance. Focus was on practical data science applications, with excursions into more specific areas such as data catalogs or deep learning based image processing. The event was located in the 030 event loft, an unusual venue with direct access to river Spree near Oberbaumbrücke. Here are some impressions:

+++ Experience a live debate between the first AI system to debate humans on complex topics, Project Debater, and a top human debater +++ How Google, Microsoft, and Big Tech Are Automating the Climate Crisis +++ How to train artificial intelligence that won’t destroy the environment +++ Deep learning has a size problem +++ Quantifying the Carbon Emissions of Machine Learning +++