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Computational linguistics is an interdisciplinary field concerned with the computational modelling of natural language, as well as the study of appropriate computational approaches to linguistic questions. In general, computational linguistics draws upon linguistics, computer science, artificial intelligence, mathematics, logic, philosophy, cognitive science, cognitive psychology, psycholinguistics, anthropology and neuroscience, among others.
Data mining is the process of extracting and discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems. Data mining is an interdisciplinary subfield of computer science and statistics with an overall goal of extracting information from a data set and transforming the information into a comprehensible structure for further use. Data mining is the analysis step of the "knowledge discovery in databases" process, or KDD. Aside from the raw analysis step, it also involves database and data management aspects, data pre-processing, model and inference considerations, interestingness metrics, complexity considerations, post-processing of discovered structures, visualization, and online updating.
Machine learning (ML) is a field of inquiry devoted to understanding and building methods that 'learn', that is, methods that leverage data to improve performance on some set of tasks. It is seen as a part of artificial intelligence. Machine learning algorithms build a model based on sample data, known as training data, in order to make predictions or decisions without being explicitly programmed to do so. Machine learning algorithms are used in a wide variety of applications, such as in medicine, email filtering, speech recognition, and computer vision, where it is difficult or unfeasible to develop conventional algorithms to perform the needed tasks.
Text mining, also referred to as text data mining, similar to text analytics, is the process of deriving high-quality information from text. It involves "the discovery by computer of new, previously unknown information, by automatically extracting information from different written resources." Written resources may include websites, books, emails, reviews, and articles. High-quality information is typically obtained by devising patterns and trends by means such as statistical pattern learning. According to Hotho et al. (2005) we can distinguish between three different perspectives of text mining: information extraction, data mining, and a KDD process. Text mining usually involves the process of structuring the input text, deriving patterns within the structured data, and finally evaluation and interpretation of the output. 'High quality' in text mining usually refers to some combination of relevance, novelty, and interest. Typical text mining tasks include text categorization, text clustering, concept/entity extraction, production of granular taxonomies, sentiment analysis, document summarization, and entity relation modeling.
Collaborative filtering (CF) is a technique used by recommender systems. Collaborative filtering has two senses, a narrow one and a more general one.
A recommender system, or a recommendation system, is a subclass of information filtering system that provide suggestions for items that are most pertinent to a particular user. Typically, the suggestions refer to various decision-making processes, such as what product to purchase, what music to listen to, or what online news to read. Recommender systems are particularly useful when an individual needs to choose an item from a potentially overwhelming number of items that a service may offer.
Automatic image annotation is the process by which a computer system automatically assigns metadata in the form of captioning or keywords to a digital image. This application of computer vision techniques is used in image retrieval systems to organize and locate images of interest from a database.
A comparison shopping website, sometimes called a price comparison website, price analysis tool, comparison shopping agent, shopbot, aggregator or comparison shopping engine, is a vertical search engine that shoppers use to filter and compare products based on price, features, reviews and other criteria. Most comparison shopping sites aggregate product listings from many different retailers but do not directly sell products themselves, instead earning money from affiliate marketing agreements. In the United Kingdom, these services made between £780m and £950m in revenue in 2005. Hence, E-commerce accounted for an 18.2 percent share of total business turnover in the United Kingdom in 2012. Online sales already account for 13% of the total UK economy, and its expected to increase to 15% by 2017. There is a huge contribution of comparison shopping websites in the expansion of the current E-commerce industry.
Adaptive hypermedia (AH) uses hypermedia which is adaptive according to a user model. In contrast to linear media, where all users are offered a standard series of hyperlinks, adaptive hypermedia (AH) tailors what the user is offered based on a model of the user's goals, preferences and knowledge, thus providing links or content most appropriate to the current user.
Cold start is a potential problem in computer-based information systems which involves a degree of automated data modelling. Specifically, it concerns the issue that the system cannot draw any inferences for users or items about which it has not yet gathered sufficient information.
Computer audition (CA) or machine listening is general field of study of algorithms and systems for audio understanding by machine. Since the notion of what it means for a machine to "hear" is very broad and somewhat vague, computer audition attempts to bring together several disciplines that originally dealt with specific problems or had a concrete application in mind. The engineer Paris Smaragdis, interviewed in Technology Review, talks about these systems --"software that uses sound to locate people moving through rooms, monitor machinery for impending breakdowns, or activate traffic cameras to record accidents."
An information filtering system is a system that removes redundant or unwanted information from an information stream using (semi)automated or computerized methods prior to presentation to a human user. Its main goal is the management of the information overload and increment of the semantic signal-to-noise ratio. To do this the user's profile is compared to some reference characteristics. These characteristics may originate from the information item or the user's social environment.
User modeling is the subdivision of human–computer interaction which describes the process of building up and modifying a conceptual understanding of the user. The main goal of user modeling is customization and adaptation of systems to the user's specific needs. The system needs to "say the 'right' thing at the 'right' time in the 'right' way". To do so it needs an internal representation of the user. Another common purpose is modeling specific kinds of users, including modeling of their skills and declarative knowledge, for use in automatic software-tests. User-models can thus serve as a cheaper alternative to user testing but should not replace user testing.
Activity recognition aims to recognize the actions and goals of one or more agents from a series of observations on the agents' actions and the environmental conditions. Since the 1980s, this research field has captured the attention of several computer science communities due to its strength in providing personalized support for many different applications and its connection to many different fields of study such as medicine, human-computer interaction, or sociology.
Adaptive learning, also known as adaptive teaching, is an educational method which uses computer algorithms as well as artificial intelligence to orchestrate the interaction with the learner and deliver customized resources and learning activities to address the unique needs of each learner. In professional learning contexts, individuals may "test out" of some training to ensure they engage with novel instruction. Computers adapt the presentation of educational material according to students' learning needs, as indicated by their responses to questions, tasks and experiences. The technology encompasses aspects derived from various fields of study including computer science, AI, psychometrics, education, psychology, and brain science.
Learning analytics is the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning and the environments in which it occurs. The growth of online learning since the 1990s, particularly in higher education, has contributed to the advancement of Learning Analytics as student data can be captured and made available for analysis. When learners use an LMS, social media, or similar online tools, their clicks, navigation patterns, time on task, social networks, information flow, and concept development through discussions can be tracked. The rapid development of massive open online courses (MOOCs) offers additional data for researchers to evaluate teaching and learning in online environments.
The fields of marketing and artificial intelligence converge in systems which assist in areas such as market forecasting, and automation of processes and decision making, along with increased efficiency of tasks which would usually be performed by humans. The science behind these systems can be explained through neural networks and expert systems, computer programs that process input and provide valuable output for marketers.
The Allen Institute for AI is a research institute founded by late Microsoft co-founder Paul Allen. The institute seeks to achieve scientific breakthroughs by constructing AI systems with reasoning, learning, and reading capabilities. Oren Etzioni was appointed by Paul Allen in September 2013 to direct the research at the institute. AI2 has programs in the University of California, Irvine, and in Tel Aviv, Israel.
Oren Etzioni is an American entrepreneur, Professor Emeritus of computer science, and founding CEO of the Allen Institute for Artificial Intelligence (AI2). On June 15, 2022, he announced that he will step down as CEO of AI2 effective September 30, 2022. After that time, he will continue as a board member and advisor. Etzioni will also take the position of Technical Director of the AI2 Incubator. Oren joined the University of Washington faculty in 1991, where he became the Washington Research Foundation Entrepreneurship Professor in the Department of Computer Science and Engineering. In May 2005, he founded and became the director of the university's Turing Center. The center investigated problems in data mining, natural language processing, the Semantic Web and other web search topics. Etzioni coined the term machine reading and helped to create the first commercial comparison shopping agent.
The following outline is provided as an overview of and topical guide to machine learning. Machine learning is a subfield of soft computing within computer science that evolved from the study of pattern recognition and computational learning theory in artificial intelligence. In 1959, Arthur Samuel defined machine learning as a "field of study that gives computers the ability to learn without being explicitly programmed". Machine learning explores the study and construction of algorithms that can learn from and make predictions on data. Such algorithms operate by building a model from an example training set of input observations in order to make data-driven predictions or decisions expressed as outputs, rather than following strictly static program instructions.
References
- J.D. Velásquez and V. Palade, "Adaptive Web Sites: A Knowledge Extraction from Web Data Approach", IOS Press, 2008
- Mike Perkowitz, Oren Etzioni, "Towards adaptive Web sites: Conceptual framework and case study", Artificial Intelligence 118(1-2), 2000
