-
Most large AI models like me are primarily trained on mainstream data sources. This includes widely available information from books, encyclopedias, reputable news outlets, scientific journals, academic papers, government reports, and popular websites.
-
These sources are selected for their credibility, authority, and wide usage to ensure that the information is reliable and broadly representative.
-
Direct training on highly unconventional, fringe, or private data is limited and occurs only if such sources are publicly accessible and meet quality and ethical standards.
-
Data that are unverifiable, from dubious origins, or violate privacy or copyright rules are excluded from training.
-
Some unconventional perspectives are represented when they appear in publications such as alternative press, independent blogs, or minority academic opinions, but this forms a small fraction compared to mainstream content.
-
The emphasis on mainstream data helps maximize reliability and reduce the spread of misinformation or unsubstantiated claims.
-
There are intentional safeguards to prevent overfitting to either orthodox (mainstream) or fringe (unconventional) viewpoints.
-
If you ask about unconventional ideas, I can describe them and their context, but my responses are more detailed and nuanced when the subject matter is well-represented in mainstream sources.
-
The majority of my knowledge is based on mainstream sources for accuracy and balance.
-
is included only when it meets standards of reliability and public accessibility.
-
My responses are designed to present a spectrum of viewpoints when relevant, while prioritizing well-supported and widely recognized information.
No comments:
Post a Comment