Proof isn't always possible

Rather than having been a waste of time, an education can make ChatGPT be far more helpful or specific. “When you have an education ChatGPT does way more for you!” I used a ChatBot to develop the following joke. The written prompt ideas were preparatory, but accumulated during the process. Make sorting x and y Cartesian coordinates ascending on a regression dataset, both humorous AND relatable to a wide audience. Step one, we are gonna NUMBER every x,y pair, 1-50. Somehow the x’s and y’s are going to keep their numbering, and this is how we discover they have been re-associated. R value < 0.5 is the bomb!  Building comments: r value is kind of like pass/fail in school. It is not on a continuum. A continuum represents How ripe a tomato is; a watermelon has a handy double entendre. R values are more like pregnant or not pregnant. So Darren, what have you been doing with yourself lately? We’ve been doing regression analysis on data sets for an AI model. Sounds like a cross between ...

A common complaint about cryptographically random number sequences is that they do not appear satisfactorily random to human perception. No human can predict what comes next, whether true random or pseudorandom. But it does not satisfy human perception for variety. As the Greeks compensated for the perceived overhang of vertically straight skyscrapers in the architecture of the Parthenon, there exists a way to modify a cryptographically random sequence to satisfy human perception for randomness. In my “Magic Eight Ball” program “Wizard,” published some time ago on www indenturedgeek dot com slash recreations, I implemented the following procedure very satisfactorily: As the stream accepts input from the random stream, we collect a filo array of the last four, five or six most recent responses. Before using the random “suggestion,” we reject it, if it is found in the array. We collect the “n” most recent unique responses, and then add each response we syndicate to the end of the arra...

The supermajority of Ai applications are uses of LLMs. I published on LinkedIn (no date available) that Ai is not ONLY LLMs. I compared it to Motion Ai. I said LLMs are like language; Motion Ai is like dance. I went further to ask if chemistry Ai (presumably trained on databases of chemical reactions) was like chemistry and if Chess Ai was not likely similar to Chess in some way. One fruitful area of improving a chemistry Ai, or obtaining more data for an initial training model, would be lists of biological metabolites. Where databases are insufficient in Chess, we can compensate by running large samples of Chess Engine “theory” (as used in Chess as a technical term.)  I speculate that patient students could show enterprise by using the PythonSCF module to apply that (Chess) process to chemistry. An early advertisement for Ai was in cpu overclocking, but I speculate that this was actually more likely to have been rule based, like a Chess engine. Obligatory: [also enumerated ...

When composing a prompt for an LLM, it is possible to specify, “in the style of,” both for images and for prose. When training an Ai/LLM can we develop a proprietary quality improvement, by specifying a particular Dictionary (some specific printing of Webster’s or the OED) to include the carefulness of defining language and the creativity and variety [aka “diversity”] of the example usage sentences ? For another application of the same idea, what is the result of including Cicero, Caesar, Montesquieu, Machiavelli and Tocqueville in specific translations and printings? Expanding on this concept, Jonathan Swift, Daniel Defoe, R. L. Stevenson, Earnest Hemingway and Mark Twain would each offer a unique characteristic. Do we improve or adulterate, when we “build on that success,” by including more gross content, or cross sections for variety?

When we purchase a Dictionary, we can specify “complete,” v “exhaustive.” Exhaustive specifies that every word in every definition is redundantly defined within the same catalog. 100 % Redundant is another metric. Does this give use insight into training Neural Nets?

I asserted that agnostic layer may improve directed search. I might have been more correct to say that Brin and Page brought us “search,” rather than to say Brin and Page  brought us “directed search.” The idea of comparing and contrasting opposing ideas rather than enhancing specialization seems to rest on the axiomatic concept of “Not.” It might difficult to assign a definition or an idea a truth value. However, an LLM suggests a dictionary, and a dictionary suggests antonyms as well as synonyms. For this reason, I want to suggest using a thesaurus or a dictionary to give weighted associations to antonyms.