Markov Chain
A Markov chain is a mathematical model describing a sequence of events where the probability of each event depends only on the current state. Markov chains are used in language modeling, page ranking, and MCMC sampling.
Understanding Markov Chain
A Markov chain is a mathematical model describing a sequence of events where the probability of each event depends only on the state of the previous event, a property known as the Markov property or memorylessness. This elegant framework has wide applications in AI and machine learning, from modeling text generation in simple language models to simulating molecular dynamics and financial markets. Markov chain Monte Carlo (MCMC) methods use Markov chains to sample from complex probability distributions that are difficult to compute directly, making them invaluable for Bayesian inference. In reinforcement learning, the environment is often modeled as a Markov decision process, extending Markov chains with actions and rewards. PageRank, Google's original search algorithm, models web browsing as a Markov chain over linked pages. Despite their simplifying assumptions, Markov chains provide powerful tools for reasoning about sequential and stochastic processes.
Category
Fundamentals
Is AI recommending your brand?
Find out if ChatGPT, Perplexity, and Gemini mention you when people search your industry.
Check your brand — $9Related Fundamentals Terms
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.