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Artificial Intelligence (AI), defined in its most simple form, is atechnological tool that makes machines intelligent. Since learning is at thecore of intelligence, machine learning poses itself as a core sub-field of AI.Then there comes a subclass of machine learning, known as deep learning, toaddress the limitations of their predecessors. AI has generally acquired itsprominence over the past few years due to its considerable progress in variousfields. AI has vastly invaded the realm of research. This has led physicists toattentively direct their research towards implementing AI tools. Their centralaim has been to gain better understanding and enrich their intuition. Thisreview article is meant to supplement the previously presented efforts tobridge the gap between AI and physics, and take a serious step forward tofilter out the "Babelian" clashes brought about from such gabs. Thisnecessitates first to have fundamental knowledge about common AI tools. To thisend, the review's primary focus shall be on deep learning models calledartificial neural networks. They are deep learning models which trainthemselves through different learning processes. It discusses also the conceptof Markov decision processes. Finally, shortcut to the main goal, the reviewthoroughly examines how these neural networks are capable to construct aphysical theory describing some observations without applying any previousphysical knowledge.

Physicist's Journeys Through the AI World - A Topical Review. There is no royal road to unsupervised learning