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| compai [2024/10/14 09:45] – [Induction and the computational complexity classes] pedroortega | compai [2024/10/14 14:15] (current) – [Conclusions] pedroortega |
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| Though universal pattern recognition is incomputable, it's possible within finite limits. Perfect pattern recognition works when restricted to a finite set of candidate programs. This means that while AI can solve problems within specific, bounded domains (e.g., diagnosing diseases or navigating traffic), **building better AIs will forever remain open-ended**. | Though universal pattern recognition is incomputable, it's possible within finite limits. Perfect pattern recognition works when restricted to a finite set of candidate programs. This means that while AI can solve problems within specific, bounded domains (e.g., diagnosing diseases or navigating traffic), **building better AIs will forever remain open-ended**. |
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| **The challenge of inverting computation**: | **The challenge of inverting computation and the general structure of AI algorithms**: |
| The **P vs NP** problem fundamentally revolves around the difficulty of pattern recognition. In class **P**, given an input, computing the output is efficient. In class **NP**, however, the challenge is reversed: you need to find the input (or "program") that produces a specific output, a task that is much harder. This highlights a general framework for pattern recognition algorithms: first, analyze the data, then iteratively hypothesize potential programs, verifying at each step whether a given program generates the observed data. | The **P vs NP** problem fundamentally revolves around the difficulty of pattern recognition. In class **P**, given an input, computing the output is efficient. In class **NP**, however, the challenge is reversed: you need to find the input (or "program") that produces a specific output, a task that is much harder. This highlights a general framework for pattern recognition algorithms: first, analyze the data, then iteratively hypothesize potential programs, verifying at each step whether a given program generates the observed data. |
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| **AI as a core branch of computer science**: | **AI as a core branch of computer science**: |
| AI isn't just an engineering branch about practical problem-solving—it's deeply rooted in computer science theory. Concepts like computation, logic, and complexity are essential for understanding and advancing AI, though their importance is often underplayed. Strengthening these links could unlock new AI breakthroughs by helping researchers better understand the limits and possibilities of computation and pattern recognition. AI's future lies in its deep integration with computer science, focusing more on foundational concepts like algorithmic complexity. | AI isn't just an engineering branch about practical problem-solving—it's deeply rooted in computer science theory. Concepts like computation, logic, and complexity are essential for understanding and advancing AI, though their importance is often underplayed. Strengthening these links could unlock new AI breakthroughs by helping researchers better understand the limits and possibilities of computation and pattern recognition. AI's future lies in its deep integration with computer science, focusing more on foundational concepts like algorithmic complexity. |