Generative AI is not really AI. The only thing generative is in the application of deep learning methods which is all statistics. The broader field of Machine Learning makes up only thirty percent of AI. There is a lot of incorrect words floating around in academia trying to confuse people on AI progress. In last fifty years there has not been any significant ground breaking advancements in AI. Apart from renaming of fields and reusing methods that have been around for decades. For example, Deep Learning basically comes from reusing methods in Neural Networks. Large Language Models is also a trendy topic. However, LLMs are simply an engineering extension of embedding models which come under the sub-area of distributional semantics, another area that has been around for decades in information retrieval. In most cases of Machine Learning methods the machine develops no formal context or understanding apart from the use of an intermediate programming language to translate probabilities into logical form using the computational syntax and semantics. If the machine developed any form of understanding then there wouldn't be any need to use a programming language to build a machine learning model. The other significant issue in the field is the wrong types of people that are hired at organizations who primarily come from math and statistics backgrounds. The correct types of people to be conducting AI research should really be from computer science backgrounds where the full spectrum of subject matter is formally taught in both theory and practice. The Generative AI should really be called Generative Deep Learning as that is pretty much the only area that is covered in application.
