One of the requirements for truth seeking AI is that it be trained on substantial works of thought. Social media posts of the past 20 years are abundant, easy to access, and terrible thoughts. I am working on building an initial version of a local AI which will be taught like that of a child with a tutor.
This AI will be tutored by the likes of:
- Ancient Greek and Latin texts for science, philosophy and theology
- An interesting sidebar is that it may be possible to actually have the LLM “read” these in their original language vs modern English translations. This would require a translation layer itself but I wonder what would be the thought process of a classical thinking AI that internally does everything in ancient languages
- Roman orators
- Medieval texts
- Renaissance writings of Western Europe
- Documents of the American revolution and establishment of modern government.
- 19th and 20th century texts of first principled learning like Saxon math and McGuffey readers
The use cases of such a model include:
- Religious Scholar
- “Create a month long devotional from Thomas Aquinas’s confession”
- Critical thinker that can evaluate modern writing for rhetoric and logic
- “Analyze the current immigration policy debate in terms of stoicism”
- Curate wisdom in new data sets
- Organize the onboarding and labeling of new data for the decentralized storage array (Truth cloud
- Tutor for myself and my children as they enter this stage
I hope also to partner with others who can contribute additional data and ideas to better develop this concept in an open and decentralized manner. I am grateful for the work of the large AI companies and simultaneously wary of their direction.
This will run on the DGX spark which fortunately can do this training with all the bells and whistles. Initial estimates seem that I can do version 1 with only a few gigabytes of data and in turn memory requirements on the spark will be small. More to come.
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