jreynar

I completely agree. It's hard to imagine this ever being perfect both because of AI and because people make mistakes in communications all the time. In our own usage of it we've found it essential to check things that seem suspicious once they're in the company brain. But, the more important gate, and one we expect may remain longer than people would like, is that we ask people to approve changes to a company brain that are proposed. Even this isn't perfect but it is a key defense from the brain turning into slop. And it's different than this demo, which was much closer to fully automated.

Cool idea. If a journalist wants to try this on an email corpus they have (legitimate) access to, send them my way. I'd be happy to help.

Thanks, really appreciate you bringing it back from the dead and hope it provides some helpful context that didn't fit naturally into the web site.

Ha, I'm a human and we made this for other humans. But I won't complain if we get some AI readers too.

I'm Jeff, one of the founders of this+that. I've worked on named entity detection, fact extraction, knowledge management and NLP throughout my career, starting in the NLP stone age of the 1990s. I wanted to see whether a model could now write a useful wiki straight from a company's email.

Enron's mail is an obvious test because it's public, so anyone can check the pages against the messages and the history. We used 45,047 emails from eight mailboxes. Every fact quotes the sentence it came from, and a fact expires on the date the mail shows it stopped being true.

The hardest part was that the model already knows Enron. It kept adding details from the public record that the email didn't contain. The methodology page shows how we caught those, what it still gets wrong and what it cost: about $11,000 at list prices (though we used a subscription), with no attempt to bring that down.

Happy to answer questions.