Monte Carlo Method
Monte Carlo methods are computational algorithms that use repeated random sampling to estimate mathematical results. In AI, they are used in reinforcement learning, probabilistic inference, and tree search algorithms.
Understanding Monte Carlo Method
Monte Carlo methods are a class of computational algorithms that use repeated random sampling to estimate numerical results and solve problems that are deterministic in principle but intractable to solve analytically. In machine learning, Monte Carlo approaches appear in Markov chain Monte Carlo for Bayesian inference, Monte Carlo tree search for game AI and planning, and Monte Carlo dropout for estimating model uncertainty. AlphaGo's breakthrough in defeating human Go champions relied heavily on Monte Carlo tree search combined with deep neural networks to evaluate board positions. These methods are also used in reinforcement learning for policy evaluation and in generative models for sampling from complex probability distributions. The power of Monte Carlo methods lies in their ability to provide approximate solutions to high-dimensional problems where exact computation is impossible, with accuracy improving as more samples are drawn.
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Fundamentals
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AGI
Artificial General Intelligence (AGI) refers to a hypothetical AI system with human-level cognitive abilities across all intellectual tasks. Unlike narrow AI, AGI would be able to learn, reason, and solve problems in any domain without task-specific training.
AI Winter
An AI winter is a period of reduced funding, interest, and research progress in artificial intelligence. Historical AI winters occurred in the 1970s and late 1980s, often following inflated expectations and undelivered promises.
Algorithm
An algorithm is a step-by-step procedure or set of rules for solving a computational problem. In AI, algorithms define how models learn from data, make predictions, and optimize their performance.
Artificial General Intelligence
Artificial General Intelligence is a theoretical form of AI that would match or exceed human cognitive abilities across all domains. AGI remains an aspirational goal rather than a current reality in AI research.
Artificial Intelligence
Artificial Intelligence is the broad field of computer science focused on creating systems that can perform tasks requiring human-like intelligence. AI encompasses machine learning, natural language processing, computer vision, and robotics.
Artificial Narrow Intelligence
Artificial Narrow Intelligence (ANI) refers to AI systems designed to perform specific tasks, such as image recognition or language translation. All current AI systems, including large language models, are forms of narrow intelligence.
Artificial Superintelligence
Artificial Superintelligence (ASI) is a hypothetical AI that would surpass human intelligence in every cognitive dimension. The prospect of ASI raises profound questions about control, alignment, and the future of humanity.
Dynamic Programming
Dynamic programming is an algorithmic technique that solves complex problems by breaking them into simpler overlapping subproblems. It is used in reinforcement learning, sequence alignment, and optimal control.