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About this paper
Bruno Buchberger
Bruno Buchberger articulates a comprehensive personal perspective on the fundamental scope of symbolic computation within software science, offering revised definitions and integrating machine learning as a numerical automatic programming method. The work also critically re-evaluates artificial intelligence, identifying key research challenges and opportunities within this interdisciplinary field.
Key insights
- Software science, focused on automating the software development process, is fundamentally an application domain for symbolic computation, as software objects are formal, finitary representations.
- Machine learning, specifically supervised learning, can be effectively understood and integrated as a *numerical* approach within automatic programming, characterized by example-based problem specification and parameter optimization.
- Many aspects commonly attributed to "Artificial Intelligence" can be re-contextualized within established mathematical and algorithmic frameworks, challenging notions of AI being entirely distinct or "beyond mathematics."
Summary by alphaXiv.
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