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List of unsolved problems in computer science

List of unsolved computational problems From Wikipedia, the free encyclopedia

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This article is a list of notable unsolved problems in computer science. A problem in computer science is considered unsolved when no solution is known or when experts in the field disagree about proposed solutions.

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AI safety

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Artificial intelligence (AI) safety is an interdisciplinary field focused on preventing accidents, misuse, risks, or other harmful consequences arising from AI systems. Problems here are considered unsolved if no answer is known or if there is significant disagreement among experts about a proposed solution.

Risk

AI risk concerns the probability and magnitude of harmful outcomes caused by artificial intelligence systems, particularly as systems gain greater autonomy and influence over society. [1]

  • How likely are the various pathways through which AI could cause significant, catastrophic, or existential harm? [2][3]
  • What follows after creating artificial general intelligence? [4][5]
  • What follows after creating superintelligence? [6][7]

Alignment

AI alignment is the problem of building machines that faithfully try to do what we want them to do (or what we ought to want them to do). [8]

  • What are the human values or intentions that AI should be aligned to? [9][10]
  • How do we align increasingly capable systems? [11][12]
  • How can we understand and verify the objectives and reasoning processes of complex AI models? [13][14]

Control

AI control relates to the technical and procedural measures designed to prevent AI systems from causing unacceptable outcomes, even if these systems actively attempt to subvert safety measures. It focuses on maintaining human oversight, regardless of whether the AI's objectives align with human intentions.[15]

  • Can a sufficiently intelligent AI be controlled? [6][16][17]

Ethics

Ethical issues in AI safety concern fairness, accountability, transparency, and the moral status of AI systems. These questions overlap with but are distinct from technical safety, focusing on the societal consequences of AI deployment. [18]

  • How can algorithmic biases be overcome? [19][20]
  • How can the environmental impact of AI be reduced? [21][22]
  • How can the moral status of AI systems be evaluated?[23][24]

Governance

AI governance examines institutional, legal, and policy mechanisms for managing risks and ensuring the safe development and deployment of AI technologies. [25]

  • How can AI be safely developed, evaluated, and deployed? [26][27]
  • How can society balance innovations in AI with the prevention of irreversible harms? [28][29]
  • Who is responsible for the actions of an AI model? [30][31]
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Computational complexity

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Polynomial versus nondeterministic-polynomial time for specific algorithmic problems

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The graph isomorphism problem involves determining whether two finite graphs are isomorphic, meaning there is a one-to-one correspondence between their vertices and edges that preserves adjacency. While the problem is known to be in NP, it is not known whether it is NP-complete or solvable in polynomial time. This uncertainty places it in a unique complexity class, making it a significant open problem in computer science.[33]

Algorithmic number theory

Other algorithmic problems

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Programming language theory

Other problems

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Many other problems in coding theory are also listed among the unsolved problems in mathematics.

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References

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