Any language that facilitates writing vector algebra and numeric calculus over an imperative paradigm works just fine. Once there is no high-level modeling, the process is called Brain Centered. Machine learning, on its turn, is associated with low-level mathematical representations of systems and a set of training data that lead the system toward performance improvement. Commonly, some kind of (backward or forward) logical inference is needed. This kind of manipulation is often associated with expert systems, where high level rules are often provided by humans and used to simulate knowledge, avoiding low-level language details. Knowledge Reasoning’s strategy is usually developed by using functional and logic based programming languages such as Lisp*, Prolog*, and ML* due to their ability to perform symbolic manipulation. However, while machine learning is an approach to AI based on algorithms whose performance improve as they are exposed to more data over time, Knowledge Reasoning is a sibling approach based on symbolic logic. Both machine learning and Knowledge Reasoning have the same concern: the construction of intelligent software. A lot of buzz talks over Internet which suggests that machine learning and Artificial Intelligence (AI) are basically the same thing, but this is a misunderstanding.
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