rgmwa

How does it decide how the probability maps should be altered to get a better result?

Yes, that’s why I never won Lotto. To be fair that wasn’t really a good test of the methodology. Lotto results are ultimately random, so there was no long term pattern to them that the algorithm could have been trained on to predict the next draw. Had there been an underlying bias it’s possible that the algorithm could eventually have improved the odds in my favour by picking a set of numbers that more likely than not contained next week’s winning numbers. That was my aim at the time. It didn’t work but the programming was fun. While my finite state machine approach was a far less sophisticated AI model than the technology behind ChatGPT or Claude, the principle of using a data set to train an algorithm to mimic human learning by modifying itself by trial and error until it can produce answers to questions that appear logical and intelligent remains the same. Of course it doesn’t mean that the answers are right.

This what it said when I asked it, but don't ask me to explain it: What does A.I. convert human language into, in order to compare the words with the algorithm? AI converts human language into high-dimensional numerical vectors, often called vector embeddings. These are essentially long strings of decimal numbers representing coordinates in a multi-dimensional mathematical "meaning space How Language is Transformed into Math To process your words, AI follows a strict pipeline: Tokenization: The algorithm breaks text down into small pieces called tokens (which can be words, prefixes, or characters) and assigns each a unique ID number. Vector Embeddings: Each token is mapped to a dense list of numbers. A single word can be represented by thousands of dimensions (e.g., 1,536 to 3,072 separate numbers). Each number acts as a coordinate tracking a specific micro-feature of meaning, syntax, or context. Contextual Math: Words with similar meanings or grammatical rules are mathematically positioned close together in this coordinate system. Because language is reduced to geometry, the AI can perform equations like: Vector("King") - Vector("Man") + Vector("Woman") approximates to Vector("Queen") Is it really "just" database matching? No, it is much more dynamic than a static database lookup. While some AI applications use standard vector databases to fetch similar documents (Retrieval-Augmented Generation or RAG), modern Large Language Models (LLMs) operate on active predictive generation, not simple database matching. The Transformer Architecture: Models use Self-Attention mechanisms to dynamically alter a word's vector depending on the words around it. For instance, the word "bank" will be assigned a completely different vector in the phrase "river bank" compared to "investment bank". Probabilistic Computation: The algorithm doesn't copy-paste text from a memory bank. Instead, it processes the text through billions of internal mathematical nodes (weights) to calculate a probability distribution for the next logical token. Emergent Logic: Stacking these layers of math allows the AI to discover the complex geometric patterns of human logic, enabling it to map out reasoning paths rather than merely retrieving matching records. The only part of this that seems close to what I was saying earlier is the Probabilistic Computation bit. Clearly AI models have made some progress since I last used finite state machines to unsuccessfully predict next week's Lotto numbers in the previous century.

I suppose an analogy might be chess. The rules of chess are known (coded) but the process by which each game reaches a conclusion is unknown (the AI model) even though the conclusion is known - it's going to be a win or a draw ( a correct prediction).

Like any other program. The programmer decides what the rules are that govern how the state of the ‘neurons’ in the network should change depending on the outcome of a trial and implements those rules in a programming language like C++ or Python. It’s just a feedback loop. You start by assigning random values, say between 1 and 10 to a node in the network and run a trial that gets it to predict an outcome. If the prediction is correct that node’s value is increased by some amount. If it guesses wrong the value is decreased so its importance in the network is reduced because its opinion is less ‘reliable’. Although I’ve said ‘a’ node, it’s applied to all nodes in the network. That’s the basic theory as I understand it. If you have billions of nodes being ‘trained’ the network may eventually start to seem to produce intelligent answers to questions although nobody really knows exactly what its internal state of ‘mind’ is because although the rules of the game have been coded the process of learning’ is fundamentally random trial and error.

Haven’t seen OT on here for a while. Is he ok?

My understanding is that the neural network uses formally coded algorithms to alter its internal weighting parameters based on whether the model has made a successful prediction or not. If the neural network is big enough and the data it is being trained on is extensive enough its predictions become more accurate to the point where the AI model seems intelligent. Jerry can probably explain it better than I can.

His rusted on's are blind to the obvious, but it's hard to convince them otherwise because they see any criticism of him as a criticism of them. However, some of them are finally starting to see the light. It's beginning to dawn on them that he doesn't particularly care about them or their problems and that all he's interested in is their loyalty and their votes.

He’s right. He’s so transparent the world can see exactly who he is.

No point posting them here then, Nev.

At least the hack was discovered. A rogue AI agent is bad enough but the bigger worry would be if it went undiscovered.

The Trump administration has just instructed the FBI not to investigate any incidents involving ICE anymore, saying that ICE will do that themselves. That’s not going to work out well. ICE is becoming Trump’s racist paramilitary wing like Hitler’s brownshirts.

Worth it to get out of painting!

Sorry OME. I'm still trying to learn this archaic language.

I wouldn’t say survivor’s guilt but it certainly gets your attention when you start to remember more and more people for who they were rather than who they are.