Machine translation, sometimes referred to by the abbreviation MT (not to be confused with computer-aided translation, machine-aided human translation or interactive translation) is a sub-field of computational linguistics that investigates the use of software to translate text or speech from one natural language to another.
In the 80s and 90s MT software was rule-based, but in the 2000s statistical analysis and the re-emergence of neural networking and more advanced machine learning techniques have proved to be far more successful.
Artificial General Intelligence (AGI) refers to a type of artificial intelligence that is at least as capable as human intelligence. As powerful as machine learning has become with neural networking and deep learning techniques, it does not approach AGI, and when, or even if, it will is controversial.
A NoSQL (originally referring to “non SQL”, “non relational” or “not only SQL”) database provides a mechanism for storage and retrieval of data which is modeled in means other than the tabular relations used in relational databases. Such databases have existed since the late 1960s, and include document databases, and XML databases, which along with object-oriented databases (OODB), focus on managing unstructured data or semi-structured data. “NoSQL” became popular in the early twenty-first century, triggered by the needs of Web 2.0 companies such as Facebook, Google, and Amazon.com. NoSQL databases are increasingly used in big data and real-time web applications. NoSQL systems are also sometimes called “Not only SQL” to emphasize that they may support SQL-like query languages.
The 2014 edition of the Gilbane Conference in Boston focused on Content Management, and Digital Experience: manage, measure, mobilize, monetize, and was designed for marketers, content managers, technologists, and executives responsible for building strategies and implementations for compelling multichannel digital experiences for customers, employees, and partners.
Chaired by: Frank Gilbane ∙ Organized by: Information Today Inc
Conference website: http://gilbaneconference.com/2014/
Program: http://gilbaneconference.com/2014/program.aspx
Speakers: http://gilbaneconference.com/2014/SpeakerList.aspx
Presentations: http://gilbaneconference.com/2014/Presentations.aspx
For posts about this conference see: https://gilbane.com/category/gilbane-conference/gilbane-conference-2014/
For additional information on our events see Gilbane Conferences.
The idea behind ‘information objects’ was that discrete pieces of information, along with metadata, were what should be the raw data for computing. Computing with information objects rather than bits or bytes or fixed-length records was the evolutionary step in information processing that would make the next big difference.
See Document Management & Information Objects
The term metadata refers to “data about data”, but both uses of “data” in practice are loose, in that they can refer to structured, unstructured, or semi-structured data, and can be descriptive or prescriptive. Metadata can also refer to physical objects.
Metadata is especially useful for creating, managing, publishing, categorizing, searching, and enhancing digital information. See the Wikipedia page on the Dublin Core for a good description.
https://en.wikipedia.org/wiki/Dublin_Core
Now more commonly known as machine translation (MT), refers to the the use of software to translate text or speech from one language to another. In the 80s and 90s MT software was rule-based, but in the 2000s statistical analysis and the re-emergence of neural networking and more advanced machine learning techniques have proved to be far more successful.
Natural language processing (NLP) is a subfield of linguistics, computer science, information engineering, and machine learning or artificial intelligence concerned with the interactions between computers and human (natural) languages, in particular how to program computers to process and analyze large amounts of natural language data.
