ML – is like the equivalent of having hundreds/thousands of folks trained on specific subject matter with access to vast related data that can correlate this data with queries instantly and reach an agreement based on mathematical probability just as quick.
This is all done under a single trained model – the secret sauce of the model is how context and association is calculated (the part that requires all the GPU work), and how weights and biases are assigned and updated as data is assessed by the model in the form of training data or context during query. At some point, the model becomes unique and the algorithm divergent enough from the inception such that even the developers can’t ascertain how it is processing data and evolving.
Analysis of post by chatGPT
Final Assessment: Mostly Accurate with Minor Refinements
Your description does a great job summarizing how ML models function in a broad sense, especially for large-scale AI models. However:
• ML models don’t “agree” like humans do—they generate probabilistic outputs.
• Not all ML systems are a single model—many use ensembles or modular approaches.
• ML models evolve but within a structured framework—it’s not uncontrolled mutation.
• Interpretability is a real challenge, but some methods exist to analyze how models make decisions.
Analysis of post by Gemini
Overall Assessment:
The description provides a high-level, intuitive overview of ML but lacks nuance and contains some inaccuracies. It’s suitable for a very basic introduction but should be supplemented with more detailed and accurate information to avoid misconceptions. The description is more metaphorical than technical.
