A report on Computer science, Natural language processing and Probability
Natural language processing (NLP) is a subfield of linguistics, computer science, and artificial intelligence concerned with the interactions between computers and human language, in particular how to program computers to process and analyze large amounts of natural language data.
- Natural language processingThese concepts have been given an axiomatic mathematical formalization in probability theory, which is used widely in areas of study such as statistics, mathematics, science, finance, gambling, artificial intelligence, machine learning, computer science, game theory, and philosophy to, for example, draw inferences about the expected frequency of events.
- ProbabilityWithin artificial intelligence, computer vision aims to understand and process image and video data, while natural language processing aims to understand and process textual and linguistic data.
- Computer scienceIncreasingly, however, research has focused on statistical models, which make soft, probabilistic decisions based on attaching real-valued weights to each input feature (complex-valued embeddings, and neural networks in general have also been proposed, for e.g. speech ).
- Natural language processingThe cache language model and other statistical language models that are used in natural language processing are also examples of applications of probability theory.
- ProbabilityInformation theory, closely related to probability and statistics, is related to the quantification of information.
- Computer science1 related topic with Alpha
Artificial intelligence
0 linksIntelligence demonstrated by machines, as opposed to the natural intelligence displayed by animals including humans.
Intelligence demonstrated by machines, as opposed to the natural intelligence displayed by animals including humans.
The traditional goals of AI research include reasoning, knowledge representation, planning, learning, natural language processing, perception, and the ability to move and manipulate objects.
To solve these problems, AI researchers have adapted and integrated a wide range of problem-solving techniques—including search and mathematical optimization, formal logic, artificial neural networks, and methods based on statistics, probability and economics.
AI also draws upon computer science, psychology, linguistics, philosophy, and many other fields.