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.

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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

In a few short years, Oink grew into a massive community of like-minded music enthusiasts, offering high-quality downloads of virtually every album in existence. “It was like opening a secret door to this incredible world,” Sheridan reflects. “File sharing was everywhere, but you'd never seen this level of care and detail.

What.CD, Oink, and lost communities - what an era I still think about often - via kottke.org

To that end, here’s an even more interesting question around distillation: why exactly is it bad? After all, what are large language models but the distillation of all of the knowledge on the open Internet, scraped by the frontier labs and distilled into the models that are themselves being distilled? Who is exactly being wronged here?

In fact, this paradox is the solution. I believe that open weight models are good for innovation (and, per the above, I think that labs on the frontier will be fine), but it’s a problem to be dependent on China. The U.S. should pass a law that (1) makes explicit that collecting data for training models is fair use, and (2) bars terms of service that forbid distillation, for U.S. companies at a minimum. Stopping distillation — which is literally just querying the API — is nearly impossible; the U.S. should go the other way and lean into a new copyright policy that both indemnifies the labs and also guarantees that what they learned fuels further innovation for everyone else.

Stratechery on Chinese AI models. Good points all around on R&D costs versus COGs, distillation and open source in general.