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Non-AI specific 'synthetic data generation': historically used for processes which make use of time-series / simulations & modeling / forcasting. aka weather f

Non-AI specific 'synthetic data generation': historically used for processes which make use of time-series / simulations & modeling / forcasting. aka weather forcasting, related points in [0] 2) a) Testing with actual 'sensitive' data may not be possible for security reasons (aka payroll information, stock market price influences)[1]. b) insufficent/incomplete information. aka figure out how well what's known matches 'reality' and/or may suggest areas to look for 'missing' pieces in model. ----- [0] : https://www.oreilly.com/library/view/practical-time-series/9781492041641/ch04.html https://www.oreilly.com/library/view/practical-time-series/9... [1] : https://www.k2view.com/what-is-synthetic-data-generation/ https://www.k2view.com/what-is-synthetic-data-generation/

cpard 1y ago
This is great. Synthetic data has been around for a long time, I think the difference with LLM related cases is that in the past it was primarily structured data that was a bit easier to approximate with some distribution or some grammar. With synthetic data for large languages models it’s more about QA pairs and reasoning trails for solving complicated problems
- sargstuff 1y ago
  Non-physics Much Ado about Shrodinger's Cat. Just tool(s) for quickly building higher order associations/abstractions from 'base term information'.[1][2][3]. aka dynamically generate a unique catlan number(s) for given Tromp lambda calcui as way of reducing tree height/lisp parentheses down to a single pair while dynamically computing/recomputing the determinant (appropriate base / number symbols ratio) to minimize length between parentheses. ---------------- [1] : I told AI to make me a protein. Here's what it came up with : https://www.nature.com/articles/d41586-025-01586-y https://www.nature.com/articles/d41586-025-01586-y [2] : AI Models for Protein Structure Prediction : https://frontlinegenomics.com/ai-models-for-protein-structure-prediction/ https://frontlinegenomics.com/ai-models-for-protein-structur... [3] : AI model deciphers the code in proteins that tells them where to go : https://news.mit.edu/2025/ai-model-deciphers-code-proteins-tells-them-where-to-go-0213 https://news.mit.edu/2025/ai-model-deciphers-code-proteins-t...
  - cpard 1y ago
    Love the references but I’m having a hard time deciphering your comment. Quantum physics where always fascinating to me but not always easy to comprehend I guess
    - sargstuff 1y ago
      yes/no. Don't know what Shrodinger's cat's state is until put into context. aka fundamental theorm of calculus provides symbolic evaluation context; but without expressions / 'numbers', theorm is just a 'shrodinger's cat' in different clothing. One can escape the original NIL, NULL, None issue by using boolean logic, but that implies rules. The 'strange thing' about shrodinger's cat, one can never be certain that one didn't pick the context before the cat existed and/or the references after the deceased remains were no longer visible. So, exercise is arguably statistically skewed toward 3/4 deceased, 1/4 alive. Add statistical sampling, and one can get an approximation of where things might be relative to cat life span. Only works one finds at least one instance of an 'alive cat' first. Much easier to just start with Boole's cat to avoid shrodinger's cat issues (aka lambda term). LLM's will happily supply relevant Boole's cat expression with/or without shrodinger's cat input. Pauli might consider that a half baked cracker.

cpard 1y ago

This is great. Synthetic data has been around for a long time, I think the difference with LLM related cases is that in the past it was primarily structured data that was a bit easier to approximate with some distribution or some grammar. With synthetic data for large languages models it’s more about QA pairs and reasoning trails for solving complicated problems

sargstuff 1y ago
Non-physics Much Ado about Shrodinger's Cat. Just tool(s) for quickly building higher order associations/abstractions from 'base term information'.[1][2][3]. aka dynamically generate a unique catlan number(s) for given Tromp lambda calcui as way of reducing tree height/lisp parentheses down to a single pair while dynamically computing/recomputing the determinant (appropriate base / number symbols ratio) to minimize length between parentheses. ---------------- [1] : I told AI to make me a protein. Here's what it came up with : https://www.nature.com/articles/d41586-025-01586-y https://www.nature.com/articles/d41586-025-01586-y [2] : AI Models for Protein Structure Prediction : https://frontlinegenomics.com/ai-models-for-protein-structure-prediction/ https://frontlinegenomics.com/ai-models-for-protein-structur... [3] : AI model deciphers the code in proteins that tells them where to go : https://news.mit.edu/2025/ai-model-deciphers-code-proteins-tells-them-where-to-go-0213 https://news.mit.edu/2025/ai-model-deciphers-code-proteins-t...
- cpard 1y ago
  Love the references but I’m having a hard time deciphering your comment. Quantum physics where always fascinating to me but not always easy to comprehend I guess
  - sargstuff 1y ago
    yes/no. Don't know what Shrodinger's cat's state is until put into context. aka fundamental theorm of calculus provides symbolic evaluation context; but without expressions / 'numbers', theorm is just a 'shrodinger's cat' in different clothing. One can escape the original NIL, NULL, None issue by using boolean logic, but that implies rules. The 'strange thing' about shrodinger's cat, one can never be certain that one didn't pick the context before the cat existed and/or the references after the deceased remains were no longer visible. So, exercise is arguably statistically skewed toward 3/4 deceased, 1/4 alive. Add statistical sampling, and one can get an approximation of where things might be relative to cat life span. Only works one finds at least one instance of an 'alive cat' first. Much easier to just start with Boole's cat to avoid shrodinger's cat issues (aka lambda term). LLM's will happily supply relevant Boole's cat expression with/or without shrodinger's cat input. Pauli might consider that a half baked cracker.