Hello folks, creatives and aspiring individuals! It’s true that modern science not hooded with the veil of welfare and feasibilities offers answers to deep problems where other fields can’t function properly… One of such is artificial intelligence! Let’s begin with an insight, anything that can be computed, meaning, calculated using either numbers or other symbols, can truly be automated. Developing computational methods should exist at the heart of today’s sciences as long as the fundamental component of math, physics or other computational fields, that is the playing with numbers and experiments, exists at the core of our thoughts. How many times we have thought that scientists such as Gauss, Hilbert, John Von Neumann, Pascal and others had a sense of realizing numbers and number attributes that had an impact on the great wonders of nature, whereas today’s scientists may be dogmatic with complicated integrals. Science though being taught at compulsory education may be forgotten by many people in later years… John McKarthy’s key statement about the science of the 21st century that is AI says that “the study is to proceed on the basis of conjecture that every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it!”. The building blocks of very simple mathematics can be synthesized to more complicated structures up until the final concept can be something magnificent, but that requires time, patience and effort. One of such building blocks is mathematic logic that can be sought and investigated with essential education displayed at papers about science, AI and technology. Understanding key logical arguments in riddles, puzzles, games, maps or graphs by using logical keywords and deriving true or false statements is the product of ‘syllogistic logic’. Stepping away from the previous to found simple mathematical arguments of the previous logic, thought and deriving new laws of thought is the product of ‘propositional logic’. Combining of ‘syllogistic logic’ and ‘propositional logic’ to more complicated codex produces ‘first-order logic’. There can also exist 'second-order logic' but that’s beyond the scope of this article. Foundations of AI are being built on math and logic. The description of the universe including AI algorithms can take place through a combination of sentences and laws of thought of syllogistic, propositional and first-order logic! Deriving conclusions from mathematic logic scripts exists at the core of AI. The internet on the other hand that can be the application of an AI problem is a giantic graph of paths for the completion of a final goal where every path incorporates a series of nodes that have to be accomplished, whereas additionally transitions between the nodes are not deterministic but probabilistic. Meaning, there is not perfect information but unknown possibilities, whereas with possibilities, meaning odds and probabilities we finally end up in quantifying and comparing risks, with an emphasis on the comparison that can target creators in other fields as well. There can be two visual tools that can help studying AI problems. Studying graphs and problem solving in terms of getting from point A to point B (for example in the internet) or studying transitions from one state to another developed by transition state diagrams. These visual tools can become complex enough as mind maps coming up with hundreds or thousands of nodes and connection lines but in all cases it’s important to remember that transitions in terms of accomplishing a goal that can be a product of 5 paths with 6 nodes per path are probabilistic. By this and that we realize tools of forming, developing and solving problems. And in order to make an AI algorithm interact with users we have to come up with observations and multiple backgrounds in terms of what in the real world deserves to be solved and why this is unique, fills a gap or is innovative enough to make science move forward at least a little…! Have a great time folks!