Microsoft’s Struggles Enter the Metaverse wsj.com/articles/micro…

The Company That Lost $800 Billion wsj.com/articles/meta-…

Can You Leave Twitter and Still Get Twitter? I Tried Mastodon. wsj.com/articles/dont-…

A Verifiable Mess: Twitter Users Create Havoc by Impersonating Brands nyti.ms/3Grai8r

The difference between a snafu, a shitshow, and a clusterfuck qz-com.cdn.ampproject.org/c/s/qz.com/wor…

New York City Wants to Pay a ‘Rat Czar’ $170,000 to Solve Its Rodent Problem wsj.com/articles/new-y…

A New Chat Bot Is a ‘Code Red’ for Google’s Search Business nyti.ms/3v5LuMj

The Food Expiration Dates You Should Actually Follow nyti.ms/2Voi2ii

scopeblog.stanford.edu/2020/10/07/how…

Meta, Long an A.I. Leader, Tries Not to Be Left Out of the Boom nyti.ms/3YrmueW

Why Are You Seeing So Many Bad Digital Ads Now? nyti.ms/3liJG0A

AI Becomes Silicon Valley’s Next Buzzy Bandwagon wsj.com/articles/ai-si…

Complain at Work the Right Way and Get Ahead wsj.com/articles/how-t…

AI Is Teaching Us New Things About the Human Mind wsj.com/articles/artif…

Finding the Words That Get Results wsj.com/articles/findi…

The Downside of Success? It Can Lead to Failure wsj.com/articles/succe…

Why Does Your Boss Reject All of Your Good Ideas? wsj.com/articles/boss-…

newyorker.com/science/annals…

How Netflix’s Algorithms and Tech Feed Its Success wsj.com/articles/how-n…

Pearl enables reinforcement learning researchers and practitioners to develop AI agents that are tailored to problems involving limited observability, sparse feedback, safety-critical scenarios and many more. 2/8