Four short links: 11 October 2018
Decentralized Applications, Global Startups, Better Shuffling, and Prolog Text
Decentralized Applications, Global Startups, Better Shuffling, and Prolog Text
Marc Warner and Louis Barson discuss the internal and external uses of AI in the UK government.
Jason Knight offers an overview of the state of the field for scaling training and inference across distributed systems.
Cassie Kozyrkov shares machine learning lessons learned at Google and explains what they mean for applied data science.
Drawing on the McKinsey Global Institute’s research, Michael Chui explores commonly asked questions about AI and its impact on work.
Watch highlights from expert talks covering artificial intelligence, machine learning, automation, and more.
Jonathan Ballon explains why Intel’s AI and computer vision edge technology will drive advances in machine learning and natural language processing.
Ben Lorica and Roger Chen highlight recent trends in data, compute, and machine learning.
Amy Heineike explains how Primer created a self-updating knowledge base that can track factual claims in unstructured text.
Ashok Srivastava draws upon his cross-industry experience to paint an encouraging picture of how AI can solve big problems.
Yangqing Jia talks about what makes AI software unique and its connections to conventional computer science wisdom.
Better Education, Do You Need Blockchain?, Visualization Book, and Hiring Coders
Our bad AI could be the best tool we have for understanding how to be better people.
Lost Lessons, Metaphors to Monads, Future of Work, and Awesome Starts at The Top
Stripe Stats, Worker Ethics, FPGA Futures, and Internet Archive Stats
Supply Chain Security, ML in FB Marketplace, Datasette Ideas, and Scraper DSL
Autonomy and UI, Replicating ML Research, FPGA Dev, and Standard Notes
Tammy Butow explains how companies can use Chaos Days to focus on controlled chaos engineering.