Re:
https://religionnews.com/2023/04/14/bin ... -chatbots/
"Bing has a testimony of the Book of Mormon! And other adventures with AI chatbots."
"The technical explanation for this is that
Bing is simply amalgamating countless data points and going with 'majority rule' here, parroting the formula for a testimony that can be found just about everywhere online. And that when Bing says they read (believable!) and pray (not quite believable), that’s because
Bing is just regurgitating all those human beings who have tried to express their LDS faith in just such language. And that the Church of Jesus Christ has been really, really successful in its ongoing attempts to make sure that faithful voices are crowding out any others when people try to search for information, because those seem to be the only voices Bing is hearing."
![Idea :idea:](./images/smilies/icon_idea.gif)
So, AI responses alone are not so valuable unless we know
how those responses were influenced by users' prompts ...
and what AI platform was the Guru Of Determination.
![Laughing :lol:](./images/smilies/icon_lol.gif)
The new
WYSIWYG (What You Say Is What You Get)
... then share with others as AI brilliance.
![study :study:](./images/smilies/icon_study.gif)
About "data annotation", human instruction for AI on how to think about words and their associations, and a heads-up
about new AI-related jobs for humans: data annotator, machine learning monitor (human ground truth data becomes monitor):
Re:
https://toloka.ai/blog/what-does-a-data-annotator-do/
"The role of a data annotator in machine learning (ML)"
"Data annotation is the process of labeling elements of data (images, videos, text, or any other format) by adding contextual information which ML models can learn from. It helps ML models understand what exactly is important about each piece of data ... Data comes in many different forms – from images and videos to text and audio files – but in almost all cases, this data has to be processed to render itself usable. What it means is that this data has to be organized and made 'clear' to whomever is using it, or as we say, it has to be 'labeled' ... the data annotator turns 'raw data' into 'labeled data'."
"each and every machine learning model requires adequately labeled data at multiple points in its life cycle. And normally not just some high-quality training data – lots of it! Such ground truth data is used to train an ML model initially, as well as to monitor that it continues to produce accurate results over time."
Rod
![Very Happy :D](./images/smilies/icon_biggrin.gif)