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People have tried (and continue to try) that human-in-the-loop data growth. Basically any applied AI company is doing something like that every day, if they're
by heyitsguay 4y ago
People have tried (and continue to try) that human-in-the-loop data growth. Basically any applied AI company is doing something like that every day, if they're getting their own training data in the course of business. It helps but it won't turn your bag-of-words model into GPT3.
Companies like Google have even spent huge amounts of time and money on enormous labeled datasets -- JFT-300M or something like that for computer vision tasks, as you might guess, ~300M labeled images. It creates value, but it creates more value for larger models with higher capacity.
- deleted 4y ago[deleted]
- z3c0 4y agoI "have tried (and continue to try) that human-in-the-loop data growth" to enormous success, bringing logistic regression models to greater than 99% accuracy. And you can chain vectorization strategies to create more input features than simply a bag-of-words, like morphology, shape, etc. We (the software company that I work for) don't need GPT-3, because it is a specialized model geared towards generating human-like text. Most NLP problems are just parsing text for actionable information, and oftentimes, supervised models can be chained to create something far more effective towards your needs than trying to shoehorn a massive general-purpose unsupervised model into a specialized problem.