|
> So far the AI use cases that have succeeded are those where that accuracy requirement is not stringent. Image generation and coding assistants, e.g. Cursor, Copilot, and similar, are the only ones that come readily to mind. Or where accuracy does matter and ML-based tools are the most accurate of the available options. Material/product defect detection, weather forecasting/early warning systems, OCR, spam filtering, protein folding, medical segmentation, interaction prediction, etc. > The other side of that coin are the use cases where inaccuracies are not easily tolerated. Legal documents, transcripts, translations, summaries, reporting the news, medical diagnosis, and such. In fact some of the most memorable flops have happened in those domains. Translation (like Google Translate) and transcription (like Whisper) are huge and successful uses of transformers. Albeit, not necessarily because they're more accurate than a human (though they sometimes are) but because there's generally some point, varying by scenario, at which their increased speed/cost/accessibility outweighs disparity in accuracy. > I don't believe that most people have any real need for something that isn't 100% accurate. I can just ask a buddy if I merely wish for an opinion ;) Is a 99% accurate weather forecast, one which is better than available alternates, useless? Is anything 100% accurate? |