Hackers' Pub수학자들의 연구 결과를 OpenAI가 가로채려 했다는 증언. Sebastien Bubeck 이름이 여기에서 나올 것이라고 과거의 누가 상상할 수 있었을까. Machine learning의 핵심 이론을 연구해 왔던 학자가 지금은 OpenAI를 위해 동료 학자들에게 거짓말과 비방을 하는 사람이 되었다. https://cims.nyu.edu/~tristanb/statement.pdf
lark shared
This statement is one of the most important things to read, for context on how OpenAI is acting towards mathematicians in order to push a narrative and an agenda. Wildly depressing. AI culture has such contagious rot
Today, Levent Alpöge and I have made public three results: finite-time blowup with smooth forcing for incompressible porous media, for Boussinesq, and for 3d incompressible Euler.
https://cims.nyu.edu/~tristanb/statement.pdf
https://cims.nyu.edu/~tristanb/euler.pdf
https://cims.nyu.edu/~tristanb/ipm.pdf
the positive impacts of "AI" with concrete real-world examples it's always classic machine learning techniques and not LLMs
AI 업계가 LLM과 동일시되면서 모든 AI 발전이 LLM이 이룬 것처럼 홍보되고 있고, 정작 LLM이 아닌 AI 분야는 LLM에 밀려 점점 리소스 지원을 받지 못하는 상황이 몇 년째 지속되고 있다.
lark shared
This is very important and I believe it is true for OCR and speech recognition also. Post-OpenAI AI has made pre-OpenAI AI worse.
@mcc precisely. it's the wrong tool for the job. LLM translation produces text that is more intelligible to the reader, but with more frequent and more significant deviations in accuracy of translation compared to traditional approaches. that's truly the worst outcome, because the vast majority of users cannot possibly vet the translation *and* come away feeling more confident in the translation's accuracy because it reads clean. the wonkiness in an imprecise translation is a critical signal.
