Issue 1.1, Summer 2019

doi:doi:10.1162/99608f92.72befc2a

From the Editor-in-Chief

Data Science: An Artificial Ecosystem

by Xiao-Li Meng

Issue 1.1 / Summer 2019

Panorama

Overviews, Visions, and Debates

Data Science: What the Educated Citizen Needs to Know

by Alan M. Garber

The Lives and After Lives of Data

by Christine L. Borgman

Data Governance is Key to Interpretation: Reconceptualizing Data in Data Science

by Sabina Leonelli

The Data Life Cycle

by Jeannette M. Wing

A Balanced Perspective on Prediction and Inference for Data Science in Industry

by Nathan E. Sanders

Artificial Intelligence—The Revolution Hasn’t Happened Yet

by Michael I. Jordan

by Andrew W. Lo

Why Artificial Intelligence May Not Be as Useful or as Challenging as Artificial Stupidity

by Rodney Brooks

Challenges for Human-Level Intelligence

by Emmanuel Candès, John Duchi, and Chiara Sabatti

Statistics and the Oncoming AI Revolution

by Gregory Crane

AI, Language, and the Humanities

by Max Welling

Artificial Intelligence Versus Intelligence Engineering

by Maria Fasli

AI: A New Engineering Discipline or the Alchemy of Our Time?

by Barbara J. Grosz

The AI Revolution Needs Expertise in People, Publics, and Societies

by Maja Matarić

What Kinds of Intelligent Machines Really Make Life Better?

by Brendan McCord

Taking Up the Revolutionary Call: Principles to Guide a Purpose-Driven AI Future

by David Donoho

What’s Missing From Today’s Machine Intelligence Juggernaut?

by Rebecca Willett

Engineering Perspectives on AI

Dr. AI or: How I Learned to Stop Worrying and Love Economics

by Michael I. Jordan

Cornucopia

Impact, Innovation, and Knowledge Transfer

(A) Data in the Life: Authorship Attribution in Lennon-McCartney Songs

by Mark Glickman, Jason Brown, and Ryan Song

Machine Learning With Statistical Imputation for Predicting Drug Approvals

by Andrew W. Lo, Kien Wei Siah, and Chi Heem Wong

Connections

Supplements (1):

Andrew W. Lo & 2 others

Stepping Stones

Learning, Teaching, and Communication

Highlights of the National Academies Report on "Undergraduate Data Science: Opportunities and Options”

by Laura Haas, Alfred Hero, and Robert A. Lue

An interview with Laura Haas and Alfred Hero by Robert Lue

Milestones and Millstones

Foundations, Theories, and Methods

Ambitious Data Science Can Be Painless

by Hatef Monajemi, Riccardo Murri, Eric Jonas, Percy Liang, Victoria Stodden, and David Donoho

A Unified Framework of Five Principles for AI in Society

by Luciano Floridi and Josh Cowls

Diving into Data

Mini Tutorials on Concepts, Methods, and Tools

Column Editor: David Hand

What Is the Purpose of Statistical Modeling?

by David Hand

Mining the Past

Brief Histories of Data Science

Column Co-Editors: Stephanie Dick and Christopher J. Phillips

Artificial Intelligence

by Stephanie Dick

Bits and Bites

The Hiring Gambit: In Search of the Twofer Data Scientist

by Radu V. Craiu