Four short links: 6 September 2018
BS in AI, Visual Exploration, Bad Predictions, and USB-C Development
BS in AI, Visual Exploration, Bad Predictions, and USB-C Development
Ben Lorica and Roger Chen provide a glimpse into tools and trends poised to accelerate AI innovation.
Atomic Receiver, Nerdery as AR, Open Access, and Journey Maps
New Hardware, Image Discovery, Interactive SQL, and Fooling Object Detection
Detecting Skimmers, Forecasting, The Quantum Race, and USB C
Magic Leap One Teardown, SIGGRAPH, Formats and Protocols, and Nifty Tricks
Financial Modeling, Deductive Database, Good Memes, and Product Management
Online Harassment, Deployment Software, Text to Commandline, and RL Prototyping
3D Learning, Trie DB, Robolawyer Ethics, and Security Controls
Fernando Perez talks about UC Berkeley's transition into an environment where many undergraduates use Jupyter and the open data ecosystem as naturally as they use email.
Tracy Teal explains how to bring people to data and empower them to address their questions.
Ryan Abernathey makes the case for the large-scale migration of scientific data and research to the cloud.
David Schaaf explains how data science and data engineering can work together to deliver results to decision makers.
Michelle Gill discusses how data science methods and tools can link information from different scientific fields and accelerate discovery.
Notebook Future, Arduino CLI, Robot Mind, and Conscious Computers
Watch keynotes covering Jupyter's role in business, data science, higher education, open source, journalism, and other domains, from JupyterCon in New York 2018.
Will Farr offers lessons about the many advantages and few disadvantages of using Jupyter for global scientific collaborations.
Paco Nathan shares a few unexpected things that emerged in Jupyter in 2018.