You have to switch off the web search grounding in AI Studio (don't use the Gemini app for AI research)
Bad analogy, there's a very significant difference: excessive warheads above the genuine deterrence requirements can't contribute positively to economy in any way (they can only do so if they contain HEU and it is converted to LEU for the power plants), while even ancient GPUs can be used for some good civilian use (and destroying them irreversibly harms the economy)
Same tokenizers on different training data lead to different glitch tokens, see e. g. comparison of Llama-family models in Yuxi Li et al. 2024 https://arxiv.org/abs/2404.09894
I like two stories of how the US intelligence located both uranium enrichment plants in operation in mid-1950s USSR: one near Tomsk they identified in 1956 by verifying rumors from a German tailor with an isotopic analysis of uranium traces on a fur hat, the other one near Sverdlovsk in 1958 by an analysis of a photo from an electricity dispatching office published in a Soviet magazine! OSINT at its finest in the latter case
Dear Kurt, to be honest, please don't discuss this topic on your citizenship hearing!
— Einstein to Gödel in 1947, probably
Despite what the word seems to suggest, MoE doesn't actually work that way ("experts" are just small parts of one layer in a multi-layer transformer; the term predates deep learning by a couple of decades so you can't really blame its authors).
A better wording would be LLM ensembling, as in https://en.wikipedia.org/wiki/Ensemble_learning
If it's for technical reasons, then it should hit Chinese companies only as soon as they catch up, doesn't it? I'm not sure I understand your argument.
Also, I don't think Chinese companies have any viable business model for future scaling anyway since no one outside China wants to send their data on Chinese servers. Hence they are forced to economize as much as possible, and it's possible they are supported by Chinese authorities for political reasons.
I generally agree but would like to add some minor corrections: not 1/16 of a second but 1/32 because the other half of the time shutter opens or closes, although even exposures much shorter than that were already achieved in the 1870s for the scientific purposes. However, that application used hard plates and thus didn't have to deal with the problem of film tearing in the camera.
Decent lenses and dry gelatine process were also ready by the 1880s, and the idea of making photographic film from oiled paper (Eastman used it initially, but it was very fragile) was present as well. Thus, I think, the actual barrier to inventing cinematography was producing clear transparent nitrocellulose (~1883), a technology transfer to the photographic film soon followed (~1887), then a few more years for figuring out the camera (and projector) mechanics.
Also, I don't think that safety of the projector was solved until well into the 20th c., e. g., see https://en.wikipedia.org/wiki/Bazar_de_la_Charit%C3%A9#Fire_of_1897
1Has anyone tried to test this hypothesis with the glitch token magic?
I wasn't able to elicit anomalous behavior from Gemini 3 Pro in AI Studio neither on temperature 1 (recommended) nor 0 (nonstandard), the only barely interesting thing was (in the latter case)
Anyone trying to research this topic further might try to extract specific tokens from these text with https://docs.cloud.google.com/vertex-ai/generative-ai/docs/multimodal/list-token
There was also a hack how to make Gemini 3 Pro answer without thinking but I can't remember enough details to find it