Jack Andersen

Bio: Jack Andersen is an academic researcher from University of Copenhagen. The author has contributed to research in topics: Knowledge organization & Body of knowledge. The author has an hindex of 14, co-authored 38 publications receiving 601 citations.

Sagen, prosaen - og samfundet: Henimod en samfundsteori om sagprosa

Abstract: Denne artikel fremsaetter et bud pa en samfundsteori om sagprosa. Der er tale om en teori, der fokuserer pa sagprosaens medierende effekter og dens indlejring i samfundets virksomhedssfaerer. Med dette fokus fremhaeves sagprosaens funktion som en konsekvens af samfundets made at organisere sig pa. Artiklen diskuterer forst sporgsmalet om, hvordan en sadan teori ser ud.

Genre as digital social action: the case of archiving, tagging and searching in digital media culture

Abstract: PurposeThe purpose of this article is to develop a contemporary understanding of genre as digital social action. Particular emphasis will be on archiving, tagging, and searching as social actions afforded by digital media as a function of their materiality.Design/methodology/approachThe approach is critical analysis and discussion.FindingsIt is shown through an examination and a concrete example of how the genre is understood as digital social action, how the materiality of digital media affords particular communicative actions.Originality/valueThe article contributes with an understanding of the genre as digital social action consisting of two communicating parts: users’ actions and materiality.

Machine learning

Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

The archaeology of knowledge

Abstract: We may not be able to make you love reading, but archaeology of knowledge will lead you to love reading starting from now. Book is the window to open the new world. The world that you want is in the better stage and level. World will always guide you to even the prestige stage of the life. You know, this is some of how reading will give you the kindness. In this case, more books you read more knowledge you know, but it can mean also the bore is full.