Existing tutorials about Artificial Intelligence were mostly written with a certain bias. Either the goal is to explain the social implication of intelligent robots for example that they will improve the society. Or in the second approach, AI is explained as enabling technology for realizing breakthrough innovation in healthcare, material science and logistics.
The problem with such an approach is, that it redirects the reader away from AI towards non AI subjects located in society, application or marketing. On the long run it will weaken the understanding and doesn't explain what AI is about.
This introduction text prefers a computer science focus in which AI is explained in the same discourse space used by AI experts on the field over decades. AI was never imagined as a powerful technology to enabling automation but computer scientists are defining AI as unsolvable problem namely np hard.
An np hard problem can't be solved by existing algorithms nor computer hardware because such a problem has no answer. These complex problems are very fascinating for computer scientists and there are endless amount of papers available about the subject. The typical np hard problem is surprisingly easy to explain to non experts, for example the videogame Lemmings is often referenced as np hard.[1] Another more abstract problem is the traveling salesman problem which has its roots in mathematics. The goal is to find the shortest path in a graph.[2] Another important unsolvable problem in the context of robotics is motion planning. According to most experts it is unsolvable with current algorithms and hardware.[3].
The history of Artificial Intelligence is mostly the history of unsolvable problems. It is not an exaggeration to claim, that endless amount of papers were written why a certain problem is np hard, and why existing algorithms are not powerful enough to solve such a problem on a computer. The discourse space around pspace, np complete and np hard is the center of gravity in the AI debate over the last decades. It shows the limitation of computer science.
AI in the past was trying to solve these problem, but it wasn't successful. The overall workflow is similar to the myth of Sisyphus, in which a hero in greek mythology struggles in solving a problem.
References:
* [1] Viglietta, Giovanni. "Lemmings is PSPACE-complete." Theoretical Computer Science 586 (2015): 120-134.
* [2] Zambito, Leonardo. "The traveling salesman problem: a comprehensive survey." Project for CSE 4080 (2006): 11.
* [3] Hoffmann, Michael. "Motion planning amidst movable square blocks: Push-* is NP-hard." Canadian Conference on Computational Geometry. 2000.
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