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Really depends on the domain and the engine. OpenAI code generation is staggering (https://www.youtube.com/watch?v=SGUCcjHTmGY&t=1214s https://www.youtube.com/w
by howmayiannoyyou 5y ago
Really depends on the domain and the engine. OpenAI code generation is staggering (https://www.youtube.com/watch?v=SGUCcjHTmGY&t=1214s https://www.youtube.com/watch?v=SGUCcjHTmGY&t=1214s), its summarization and classification is still very much a work in progress.
- safog 5y agoI agree with this - when there's an economic incentive to get clean data, you get clean data. For instance, there's a lot of manual clean up work put into things like training data sets for speech recognition because there has been a lot of investment there. Same with self driving I assume because so much $$$ got invested there. Radiology scans or cough based COVID detectors or medical claims on the other hand? I wouldn't expect it. It's just researchers trying to get a quick paper without adequate funding.
- Orou 5y agoMost ML requires collecting, cleaning, and transforming datasets into something that a model can train on for a specific domain. Codex and Copilot aren't good examples of this because they are training on terabytes of public code repos - meaning that there is no code cleaning step. It's relying on the sheer volume of data that is being processed to try and filter the 'unclean' data (think buggy code written by a human) out of the model. These are really the exception rather than the rule when it comes to collecting data for ML/AI applications.