A computer expert, he said he'd learned from computer programming that if you put garbage in, you get garbage out. Now the paradigm is – Probable output from questionable input. said to mean that if you produce something using poor quality materials, the thing you produce will also be of poor quality. It's just that the kinds of data quality problems are looking different. It's not the data quality problems are magically solved. But figuring out the right prompts and coaxing information out of the vast network is now the problem. In some ways, many data quality issues are solved by recent AI advancements – just like we can perform super-resolution on photos to upscale them programmatically. Hallucination is a problem, but is it a data problem?ĬhatGPT, GPT-3, or even Grammarly will clean up your writing and ideas. Significado de garbage in, garbage outen inglés. Stable Diffusion's original training set (LAION) was not the most pristine (see my post on two approaches to prompt engineering to search the datasets). Even the best algorithms perform poorly with poorly labeled data.īut is the idea still true in this new age of AI? GPT learns unsupervised – it doesn't need humans to label data (although human feedback can make these algorithms much better – see Reinforcement Learning from Human Feedback, RLHF). It's why data cleaning and data quality are such important parts of most data workflows. The idea is that a system with bad input will produce bad output. 'Garbage in, garbage out' is a phrase just as old as computing.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |