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It's a really smart application of ML: helping around the edges and not letting the ML algo invent pheonems or even whole words by accident. ML transcription h
by indolering 3y ago
It's a really smart application of ML: helping around the edges and not letting the ML algo invent pheonems or even whole words by accident. ML transcription has a similar trade-off of performing better on some benchmarks but also hallucinating results.
- kolinko 3y agoA nice story about Xerox discovering this issue in 2003, when their copiers began slightly changing random numbers in copied documents https://www.theverge.com/2013/8/6/4594482/xerox-copiers-randomly-replacing-numbers-in-documents https://www.theverge.com/2013/8/6/4594482/xerox-copiers-rand...
- h4x0rr 3y agohttps://youtu.be/zXXmhxbQ-hk https://youtu.be/zXXmhxbQ-hk Interesting yet funny CCC video about this
- cedilla 3y agoI don't think machine learning was involved there at all. As I understand it, it was an issue of a specifically implemented feature (reusing a single picture of a glyph for all other instances to save space) being turned on in archive-grade settings, despite the manual stating otherwise.
- yalok 3y agofwiw, in ASR/speech transcription world, it looks reverse to me - in the past, there was lots of custom non-ML code & separate ML models for audio modeling and language modeling - but current SOTA ASRs are all e2e, and that's what's used even in mobile applications, iiuc. I still think the pendulum will swing back there again, to have even better battery/larger models on mobile.