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Obtaining reliable inferences with traditional difference-in-differences (DiD) methods can be difficult. Problems can arise when both outcomes and errors are serially correlated, when there are few clusters or few treated clusters, when cluster sizes vary greatly, and in various other cases. In recent years, recognition of the ``staggered adoption'' problem has shifted the focus away from inference towards consistent estimation of treatment effects. One of the most popular new estimators is the CSDID procedure of Callaway and Sant'Anna (2021). We find that the issues of over-rejection with few clusters and/or few treated clusters are at least as severe for CSDID as for traditional DiD methods. We also propose using a cluster jackknife for inference with CSDID, which simulations suggest greatly improves inference. We provide software packages in Stata csdidjack and R didjack to calculate cluster-jackknife standard errors easily.

A campaigner said: 'This stolen music is almost certain to end up training AI models.' A campaigner said: 'This stolen music is almost certain to end up training AI models.' Platform with 700m users says it is investigating after Anna's Archive claims to have scraped tracks and metadata An activist group has claimed to have scraped millions of tracks from Spotify and is preparing to release them online. Observers said the apparent leak could boost AI companies looking for material to develop their technology. A group called Anna's Archive said it had scraped 86m music files from Spotify and 256m rows of metadata such as artist and album names.

The AI song that gains awareness and turns against its creators is now an old song. Before machines finally catch up with us, the next technology revolution has yet to come. In the game ARG "Acolyte" is already at the door. The assistants are female and female AI assistants. Designed according to the wants and needs of users, they plan appointments or comb the Internet for inquiries or conversations.