1. Has DeepMind solved the protein folding problem?

2. Almost everything you need to know about data discovery platforms

Eugene Yan looks at recent data discovery platforms to determine what they're good for, their key features, how they compare, and whether there are open source solutions.

3. Gary Marcus on what's next for AI

Gary Marcus "is well known in AI circles, mostly for his critique on—and ensuing debates around—a number of topics, including the nature of intelligence, what's wrong with deep learning, and whether four lines of code are acceptable as a way to infuse knowledge when seeding algorithms." In this article, he presents his proposal for robust AI.

+ "The Next Decade in AI: Four Steps Towards Robust Artificial Intelligence," a paper by Gary Marcus

4. The future of time series forecasting

Aileen Nielsen shares her thoughts on what’s on the horizon for time series forecasting, from enhanced methodologies to the integration of time series forecasting into everyday life.

+ Practical Time Series Analysis, by Aileen Nielsen

5. Inside an AI-powered savings algorithm

Using supervised machine learning, Digit’s model analyzes a user’s spending patterns, then tucks away unnoticeable amounts into savings. Here's a look at the algorithm that accomplishes it.

IN COLLABORATION WITH Yugabyte

Free ebook: SQL Cookbook, second edition

Where do you turn when you’re looking to tackle day-to-day conundrums with SQL? Based on real-world examples, this new edition of the SQL Cookbook provides a framework to help you construct solutions and executable examples in several flavors of SQL, including Oracle, DB2, SQL Server, MySQL, and PostgreSQL. It's a valuable problem-solving guide for everyday issues—and it's free, courtesy of Yugabyte.

6. Fundamentally flawed

Underspecification is a known issue in statistics. But now, a group of 40 researchers across seven different teams at Google has identified underspecification as a common failure of machine learning models as well and claim it could be an even bigger problem than data shift. “We are asking more of machine-learning models than we are able to guarantee with our current approach,” says Alex D’Amour, who led the study.

7. How to build a production-grade workflow with SQL modeling

Shopify's engineering team explains how they moved to a SQL modeling workflow by leveraging dbt (data build tool) and created tooling for testing and documentation on top of it.

8. Can you smell the vanilla?

Are scented candles an unexpected victim of the COVID-19 pandemic?

TAPP Fellowship applications open

The Technology and Public Purpose (TAPP) Fellowship program is designed to train and mentor today’s practitioners and tomorrow’s leaders in the responsible development and management of emerging technologies. TAPP is now launching applications for their second cohort. The fellowship is a unique opportunity for fellows to spend a year within the Harvard and MIT ecosystem, working on some of the most complex issues of our time. The deadline to apply is January 11, 2021 at 11:59pm ET.