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Data News and Articles
The Case for AI Insurance — Most major companies have had their artificial intelligence (AI) and machine learning (ML) systems tricked, evaded, or unintentionally misled. Yet despite these high profile failures, most organizations’ leaders are largely unaware of their own risk when creating and using AI and ML technologies. Existing cyber insurance generally doesn’t fully cover ML systems, and legal remedies (e.g., copyright, liability, and anti-hacking laws) may not cover such situations. An emerging solution is AI/ML-specific insurance. Tags: Policy, Machine Learning
Monitoring Data Quality at Scale with Statistical Modeling — Good business decisions cannot be made with bad data. At Uber, they use aggregated and anonymized data to guide decision-making, ranging from financial planning to letting drivers know the best location for ride requests at a given time. But how can they ensure high quality for the data powering these decisions? Tags: Infrastructure
Data Science & Expertise — Video talk from GOTO Chicago were Rajiv shares two projects for predicting COVID cases at the county level in the United States and using chest x-rays for detecting COVID. While explaining how data scientists build predictive modeling, Rajiv also points out the importance of subject matter expertise for validating and improving these models. Tags: COVID, Video
A Call to Honesty in Pandemic Modeling — A critical evaluation of pandemic modeling is not using honest models. Tags: COVID
Our Weird Behavior During the Pandemic is Messing with AI Models — Machine-learning models trained on normal behavior are showing cracks —forcing humans to step in to set them straight. When everyone rushed to buy items such as toilet paper, face masks, and disinfectants, the AI models responded, causing hiccups for the algorithms that run behind the scenes in inventory management, fraud detection, marketing, and more. Tags: COVID, Machine Learning
How to Serve Models — A detailed description of three different architectures/patterns to serve ML models: databases, microservices, and applications. Tags: Infrastructure, Machine Learning
Make Deep Learning Models Run Fast on Embedded Hardware — There are huge benefits to running deep learning models “at the edge”, on hardware that is connected directly to sensors. Using deep learning to analyze data where it comes from—instead of sending it to remote servers—allows products to preserve privacy, avoid network latency or bandwidth requirements, and even save power, since processor cycles are cheaper than radio comms. Tags: Infrastructure, Machine Learning
Google's AI Blog — An often-updated blog of about all the interesting AI-related items occurring at Google. Recent articles discuss Google Translate, reinforcement learning, and federated analytics. Tags: Infrastructure, Machine Learning
Professor James Zou and Dr. Irena Fisher-Hwang on Data Science and AI for COVID-19 — Stanford AI Lab PhD Andrey Kurenkov interviews Professor James Zou and Doctor Irena Fisher-Hwang about their new class CS472: Data Science and AI for COVID-19. Tags: COVID, Podcast
How-To's and Tutorials
Laws of UX —
A collection of the maxims and principles that designers can consider when building user interfaces, created by our next meetup speaker, Jon Yabolinski. Tags: Design
Mental Models for Designers —
Curious about product design at Dropbox? Here’s a look at tools we use for solving problems, making decisions, and communicating ideas. Tags: Design
Doing Freelance Data Science Consulting in 2019 —
A great post on what it is like to leave your job as an ML lead to work as an independent data scientist. Answers questions such as what freelancing is, what a freelancer data scientist does, and what is good and bad about this career. Tags: Career
Explainable Deep Learning: A Field Guide for the Uninitiated —
This article offers a "field guide" to deep learning explainability for those uninitiated in the field. The field guide: i) Discusses the traits of a deep learning system that researchers enhance in explainability research, ii) places explainability in the context of other related deep learning research areas, and iii) introduces three simple dimensions defining the space of foundational methods that contribute to explainable deep learning. The guide is designed as an easy-to-digest starting point for those just embarking in the field. Tags: Deep Learning
Calculating Streaks in Pandas —
In this tutorial, we’re going to learn how to calculate streaks in Python using the pandas library and visualize them using Matplotlib. A streak is when several events happen in a row consecutively. In this post, we’re going to be working with NBA shot data and looking at players who made or missed a number of shots in a row. That said, streaks can take many forms. You can just as easily use this technique to detect and measure other streaks like consecutive days logging in to an app or website. Tags: Pandas
Data Tools and Resources
US City COVID-19 ZIP Code Analysis —
This repository contains data and code supporting a BuzzFeed News article about city-level ZIP code demographics and COVID-19 cases, published May 7, 2020. See below for details. Tags: COVID, Data Set
Apache Pinot —
Built at LinkedIn, Pinot is an open-source, distributed, and scalable OLAP data store that we use as our de-facto near-real-time analytics service. Tags: Tools
25 Hot New Data Tools and What They Don't Do —
There are dozens of new tools in the fast-growing data ecosystem today. Together, they are reshaping data work in exciting, productive and often surprising ways. The seeds of the data landscape for the next decade have been planted, and they’re growing wildly. Turns out, cultivating a new ecosystem is messy. Tags: Tools
ReviewNB: Diff & Commenting for Jupyter Notebooks —
Having trouble using Jupyter Notebooks effectively in your team? Join 200+ organizations like Amazon, Microsoft, Tensorflow, fast.ai in using ReviewNB for notebook code reviews. ReviewNB provides a complete code review workflow for notebooks. Tags: Tools