HOF Cognitive Traditional Method Transformation (HOF-TMT)

As the quest for Artificial General Intelligence (AGI) intensifies, many researchers have focused on deep learning techniques, such as transformers, which excel at processing vast amounts of data. However, the complexity, interpretability issues, and hardware limitations associated with these models necessitate a fresh look at traditional machine learning methods. A new and clearer path through this challenge is Higher-Order Function (HOF) Cognitive Traditional Method Transformation — a strategy that transforms traditional methods into autonomous, unsupervised learning pipelines that can continuously adapt and generalize across diverse datasets and contexts. This essay presents an approach that functionally decomposes traditional methods and implements them in HOF cognitive computing pipelines, making them highly relevant to AGI development.