We're breaking five stories today based on verifiable financial data which everyone missed for years.

Effort.News automates investigative journalism with five verified financial stories via marginalrevolution.com

Why do I think I will be able to handle all scams in a few years, when I am already struggling to detect simple AI slop, increasingly ignore cold emails and have to write off whole swathes of social media as a source of information, and I can already see how eager all my peers are to offload all their thinking and writing to chatbot assistants unworthy of that trust, and how many projects or mailing lists have had to clamp down on unvetted contributions (eg. today as I write this, ⁠Project Ladybird)? In a few years, won’t I be the equivalent of a rich old person with declining faculties getting a call from the IRS about how I owe them fines, conveniently payable via gift cards…? And if not, why and how not—concretely?

Gwern announces Guardian Angels.

The BBC Sound Effects Archive is available for personal, educational or research purposes. There are over 33,000 clips from across the world from the past 100 years. These include clips made by the BBC Radiophonic workshop, recordings from the Blitz in London, special effects made for BBC TV and Radio productions, as well as 15,000 recordings from the Natural History Unit archive. You can explore sounds from every continent - from the college bells ringing in Oxford to a Patagonian waterfall - or listen to a submarine klaxon or the sound of a 1969 Ford Cortina door slamming shut.

...

The earliest recording in the collection is probably the world’s earliest natural history sound recording – made onto wax cylinder in 1889 by Ludwig Koch, and later bequeathed to the BBC. Some of the early BBC field recordings were made by David Attenborough, and others by recordist’s such as David Toombs and Nigel Tucker in the 1980s, and Chris Watson in more recent years.

BBC Sound Effects provides thousands of free, downloadable audio clips via DG

If a market researcher wants to know how people will respond to a new product, or a political pollster wants to know how people will vote in an election, the models will give plausible answers. That’s what they do, but the answers will only be plausible, no more. To the extent that the situation were are asking about is novel, exactly when we might gain from accurate predictions, they will be useless.

There’s more in the talk I haven’t covered, it really is an excellent introduction to both the limitations of LLMs and some deeper issues about why psychological science is so hard. Recommended.

Dokyun Lee's talk on why LLMs fail at predicting human behavior with a great write-up from tomstafford.substack.com