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.).

Machine learning

How has Japan become a major economic power, a world leader in the automotive and electronics industries? What is the secret of their success? The consensus has been that, though the Japanese are not particularly innovative, they are exceptionally skilful at imitation, at improving products that already exist. But now two leading Japanese business experts, Ikujiro Nonaka and Hiro Takeuchi, turn this conventional wisdom on its head: Japanese firms are successful, they contend, precisely because they are innovative, because they create new knowledge and use it to produce successful products and technologies. Examining case studies drawn from such firms as Honda, Canon, Matsushita, NEC, 3M, GE, and the U.S. Marines, this book reveals how Japanese companies translate tacit to explicit knowledge and use it to produce new processes, products, and services.

The knowledge-creating company : how Japanese companies create the dynamics of innovation

This article discusses the conduct and evaluatoin of interpretive research in information systems. While the conventions for evaluating information systems case studies conducted according to the natural science model of social science are now widely accepted, this is not the case for interpretive field studies. A set of principles for the conduct and evaluation of interpretive field research in information systems is proposed, along with their philosophical rationale. The usefulness of the principles is illustrated by evaluating three published interpretive field studies drawn from the IS research literature. The intention of the paper is to further reflect and debate on the important subject of grounding interpretive research methodology.

A set of principles for conducting and evaluating interpretive field studies in information systems

Context is a poorly used source of information in our computing environments. As a result, we have an impoverished understanding of what context is and how it can be used. In this paper, we provide an operational definition of context and discuss the different ways in which context can be used by context-aware applications. We also present the Context Toolkit, an architecture that supports the building of these context-aware applications. We discuss the features and abstractions in the toolkit that make the task of building applications easier. Finally, we introduce a new abstraction, a situation which we believe will provide additional support to application designers.

/pdf/understanding-and-using-context-ixmte100q2.pdf

Understanding and Using Context

When humans talk with humans, they are able to use implicit situational information, or context, to increase the conversational bandwidth. Unfortunately, this ability to convey ideas does not transfer well to humans interacting with computers. In traditional interactive computing, users have an impoverished mechanism for providing input to computers. By improving the computer’s access to context, we increase the richness of communication in human-computer interaction and make it possible to produce more useful computational services. The use of context is increasingly important in the fields of handheld and ubiquitous computing, where the user?s context is changing rapidly. In this panel, we want to discuss some of the research challenges in understanding context and in developing context-aware applications.

https://www.cc.gatech.edu/fce/ctk/pubs/HUC99-panel.pdf

Towards a Better Understanding of Context and Context-Awareness

Ethnography has gained considerable prominence as a technique for informing CSCW systems development of the nature of work. Experiences of ethnography reported to date have focused on the use of prolonged on-going enthnography to inform systems design. A considerable number of these studies have taken place within constrained and focused work domain. This paper reflects more generally on the experiences of using ethnography across a number of different projects and in a variety of domains of study. We identify a number of ways in which we have used ethnography to inform design and consider the benefits and problems of each.

/pdf/moving-out-from-the-control-room-ethnography-in-system-2774tr14jb.pdf

Moving out from the control room: ethnography in system design

This paper relates experiences of a project where an ethnographic study of air traffic controllers is being used to inform the design of the controllers’ interface to the flight data base. We outline the current UK air traffic control system, discuss the ethnographic work we have undertaken studying air traffic control as a cooperative activity, describe some of the difficulties in collaboration between software developers and sociologists and show how the ethnographic studies have influenced the systems design process. Our conclusions are that ethnographic studies are helpful in informing the systems design process and may produce insights which contradict conventional thinking in systems design.

/pdf/ethnographically-informed-systems-design-for-air-traffic-53gpum8oqf.pdf

Ethnographically-informed systems design for air traffic control

CVEs can be seen as the result of aconvergence of research interestswithin the VR and computer-sup-ported cooperative work (CSCW)communities. Within the CVEs rep-resent a natural extension of currentcommercial single-user VR technol-ogy to support multiple participants.This extension allows better supportfor a range of applications. For exam-ple, the communication betweeninstructors and trainees central tosimulation and training applicationscan be supported. Visualizationsmay also be shared and discussed byteams of scientists or decision-mak-ers. Finally, the ever-expanding vari-ety of multiplayer games andsimulators demonstrates the poten-tial of CVEs in leisure and entertain-ment, the most notable examplesbeing games such as Doom andQuake. In all of these examples, par-ticipants are often physically dis-persed and communicating over acomputer network. Within the CSCW communityCVEs represent a technology thatmay support some aspects of socialinteraction not readily accommo-dated by technologies such as audioand videoconferencing and shared

Collaborative virtual environments

This article considers the importance of context in mobile systems. It considers a range of context-related issues and focus on location as a key issue for mobile systems. A design framework is described consisting of taxonomies of location, mobility, population, and device awareness. The design framework inorms the construction of a semantic model of space for mobile systems. The semantic model is reflected in a computational model built on a distriuted platform that allows contextual information to be shared across a number of mobile devices. The framework support the design of interactive mobile systems while the platform supports their rapid development.

/pdf/exploiting-space-and-location-as-a-design-framework-for-37wpugbjrd.pdf

Exploiting space and location as a design framework for interactive mobile systems

To address M. Weiser's (ibid., p. 19-25) human-centered vision of ubiquitous computing, the authors focus on physical interaction, general application features and theories of design and evaluation for this new mode of human-computer interaction.

Tom Rodden

Papers

Moving out from the control room: ethnography in system design

Ethnographically-informed systems design for air traffic control

Collaborative virtual environments

Exploiting space and location as a design framework for interactive mobile systems

The human experience [of ubiquitous computing]