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I don't have a lot of time, so. I will try to be quick (given I have only skimmed the posts since OME asked the question). The algorithm is not predictive in the sense of predicting the future. The predict what you meant by what you typed, and whether or not their response will make sense. For example, if you asked how many legs a dog normally has, it will predict that 4 is the correct answer; not that my some genetic mutation caused by increasing radiation in the atmosphere, they will grow 5 legs in the future. Well, not unless you ask it, anyway. But even if you did, it would use existing data to try and predict the correct answer based on the data it has, not to perform its own scientific analysis and determine for itself. It may take the factors and apply it to an existing theory to mimic scientific analysis and intelligent conclusion drawing, but it is not doing that itself. In terms of AI implementation, it is likely to be a lower level language such as C or C++ = not python because it is too slow; the end point AI functions can be done in Python, but that is on a precomputed large dataset - usually. The algorithm will be surprising small.. usual Bayes theorem or something similar. It is able to be repeated over billions of nodes - or data points. The code will likely be recursive in building the precomputed data set to apply simpler probability maps across the data you provide. And it will likely be recursive in using that map to answer your question. By recursion we mean the same code calls itself.. So, you will be able to crawl heterogenous data sets, over and over using the same code developing deep nested stacks (well, heaps to be accurate) of responses from which it will map as many of the different types of data that can be used to probably represent what ever bit of data it actually is. Tagging data is old these days.. That is what these maps do. This of something like if you type the number 4 into your LLM. That is easy, because it is represented by whatever coding mechanism you are using... and there are only a handful of coding mechanisms - ASCII, EBCDIC (not used anymore) UTF-8, UTF-16 and a few others. But, they are translated to the discrete representation of 4 to AI. No thinking required. Now, say you provide it a scanned doc, which contains the number 4 somewhere. It has to read all of the pixels and look at the shape of each character to determine if it is a letter, number, whitespace, part of a diagram, etc. Say it worked out it was a digit. Now it was to work out what digit it is? It has to look through its maps/nodes and get the most likely digit it is.. it predicts what digit is is because it spits out a probability index of what it has been taught what that digit could possibly be, across any digit which it may match. Is it a 4, or is it a H.. It also has to take into account font. As you know, a 4 can be represented very differently in different fonts... So, the sort answer to your question is that there will be a set of probability algorithms that will read data, build probability maps, and then apply the same probability algorithms to inputs and compare to what it has learned. Machine learning means it constantly refines its probability maps. The technical implementation is likely to to be a lower level language (a lot of Python maths/stats libraries are written in C++).. and there will be a lot of recursion.

Poor dog. Imagine being a dog and living with a human being that has less sense than you.

I did a search in case I missed it being posted, but Bill Oddie of Goodies fame (and later a wildlife show presenter) passed away yesterday aged 85.