AI was created as part of classical computer science, but both disciplines are operating with different assumptions. Computer science is a well defined engineering discipline which is successfully solving real world problems. For example a mathematician has a need for an electronic calculator and the machine is build and programmed by computer scientists. Or a statistician likes to store data in a table and computer scientists are programming a database which can solve this task. The result is, that doing computer science results mostly into successful projects which are evolving over the years, in the sense that todays computer have more memory and can sum up numbers faster.
In contrast, it remains unclear what the goal of Artificial intelligence is. The missing goal is a structural problem and the only way to solve it is to define goals by itself. That means, the AI community has to find objectives including the solution by itself. There is no higher instance available which has a need for robot hand or vision system, but these projects have to be formulated from scratch.
Let me give some example for self created goals only available within AI research: line following robot, robocup rescue, Tetris playing AI software, chess AI, scene recognition in motion capture recording.
All these problems have only a sense within the AI community. In the reality, nobody has a need for a line following robot or a Tetris playing AI. The only reason why these projects are researched is because of the hope for new knowledge what thinking is about. Especially in the past, successful AI research was always connected to a certain project, for example to build a machine which can play chess. A certain problem allows to benchmark an algorithm. Either the computer is able to do so or not.
In contrast, additional computer science has no need for artificial constructed problems. Computer science has no lack in existing well defined problems, but the missing resource is how to solve all these problems with better hardware and improved algorithms. AI is working the opposite way. There are endless amount of hardware and software but a lack of well defined problems.
Its a bit paradox, but AI engineers have a certain understanding about their tools. A tool isn't a computer e.g. a Unix workstation, but a tool is a problem formulation, e.g. the 15 puzzle problem or the traveling salesman problem is a tool. These tools are helping to guide the discussion into a certain direction. For example, the 15 puzzle problem is a great choice for introducing heuristics, which is not available in classical search algorithms.
The second paradox situation is, that AI isn't evolving in terms of better hardware or more intelligent software, but AI is evolving in terms of well formulated problems. In the year 2025 there are more such problems available than 20 years ago. A very recent problem is Visual question answering which was first described in 2015. The VQA challenge has evolved from similar challanges which are text based question answering which was available since 2000s. Its likely, that in 20 years from now, more advanced robotics problems are available unknown today.
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