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

How to Do Things With Words

From the Publisher: 
A Question of Identity 
Life on the Screen is a fascinating and wide-ranging investigation of the impact of computers and networking on society, peoples' perceptions of themselves, and the individual's relationship to machines. Sherry Turkle, a Professor of the Sociology of Science at MIT and a licensed psychologist, uses Internet MUDs (multi-user domains, or in older gaming parlance multi-user dungeons) as a launching pad for explorations of software design, user interfaces, simulation, artificial intelligence, artificial life, agents, "bots," virtual reality, and "the on-line way of life." 
Turkle's discussion of postmodernism is particularly enlightening. She shows how postmodern concepts in art, architecture, and ethics are related to concrete topics much closer to home, for example AI research (Minsky's "Society of Mind") and even MUDs (exemplified by students with X-window terminals who are doing homework in one window and simultaneously playing out several different roles in the same MUD in other windows). Those of you who have (like me) been turned off by the shallow, pretentious, meaningless paintings and sculptures that litter our museums of modern art may have a different perspective after hearing what Turkle has to say. 
This is a psychoanalytical book, not a technical one. However, software developers and engineers will find it highly accessible because of the depth of the author's technical understanding and credibility. Unlike most other authors in this genre, Turkle does not constantly jar the technically-literate reader with blatant errors or bogus assertions about how things work. Although I personally don't have time or patience for MUDs,view most of AI as snake-oil, and abhor postmodern architecture, I thought the time spent reading this book was an extremely good investment.

스크린 위의 삶 = Life on the screen : identity in the age of the internet

2 1 The innovative transport systems and the CityMobil project 10 1.1 The research questions 10 2 The state of art in the field of automatic transport systems 12 2.1 Case studies and demand studies for innovative transport systems 12 3 The design and implementation of surveys 14 3.1 Definition of experimental design 14 3.2 Questionnaire design and delivery 16 3.3 First analyses on the collected sample 18 4 Calibration of Logit Multionomial demand models 21 4.1 Methodology 21 4.2 Calibration of the “full” model. 22 4.3 Calibration of the “final” model 24 4.4 The demand analysis through the final Multinomial Logit model 25 5 The analysis of interaction between the demand and socioeconomic attributes 31 5.1 Methodology 31 5.2 Application of Mixed Logit models to the demand 31 5.3 Analysis of the interactions between demand and socioeconomic attributes through Mixed Logit models 32 5.4 Mixed Logit model and interaction between age and the demand for the CTS 38 5.5 Demand analysis with Mixed Logit model 39 6 Final analyses and conclusions 45 6.1 Comparison between the results of the analyses 45 6.2 Conclusions 48 6.3 Answers to the research questions and future developments 52

The European commission

Designing interviews to generate rich data for information systems research

The blockchain is a distributed ledger technology in the form of a distributed transactional database, secured by cryptography, and governed by a consensus mechanism. A blockchain is essentially a record of digital events. However, it is not ‘‘just a record,’’ since it can also contain socalled smart contracts, which are programs stored on the blockchain that run as implemented without any risk of downtime, censorship, or fraud (Buterin 2014). While blockchain is now seen mostly as the technology enabling cryptocurrencies such as Bitcoin, it will most likely become an even more valuable enabler of economic and social transactions, for instance as a general purpose digital asset ownership record (Lindman et al. 2017). This is because the distributed transaction data and cryptographic logic that lies at the blockchain’s core make it extraordinarily tamper-resistant. The implications of creating a reliable, trustworthy distributed record system, or ledger, may be fundamental to how we organize interpersonal and interorganizational relationships. The global economic system depends on that individuals and organizations trust other entities to create, store, and distribute essential records. For example, banks construct and maintain the financial records, hospitals construct and maintain health records, and universities construct and maintain education records. Often, records central to our health, social, or professional lives are key records either constructed or maintained by third parties. Such third-party record repositories can be vulnerable to corruption by failure in storage systems or human mischief, which could be mitigated by unbiased and incorruptible blockchain-based digital systems (Nærland et al. 2017). The financial sector leads the way in developing blockchain applications and business models; but also companies in industries from shipping and transportation to healthcare and entertainment are actively using blockchain applications to coordinate the movement of products, facilitate the creation of e-health records, and to securely manage original entertainment content. While substantial activity exists in practice, less academic research has examined the implications of blockchain for how we organize contemporary economies, society or organizations. In this special issue, we present early research results that investigate the positive implications of blockchain for modern organizations, specifically in the financial services industry or to manage physical asset ownership. However, the range of potential blockchain applications goes further to cover a multitude of business and social arrangements from tracking shipping containers and pharmaceuticals to recording gambling winnings and marriages based on smart contracts embedded in blockchain applications. Prof. Dr. R. Beck (&) IT University of Copenhagen, Copenhagen, Denmark e-mail: beck@itu.dk

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Blockchain Technology in Business and Information Systems Research

From generative fit to generative capacity: exploring an emerging dimension of information systems fit and task performance.

Information systems (IS) research has been long concerned with improving task-related performance. The concept of fit is often used to explain how system design can improve performance and overall value. So far, the literature has focused mainly on performance evaluation criteria that are based on measures of task efficiency, accuracy, or productivity. However, nowadays, productivity gain is no longer the single evaluation criterion. In many instances, computer systems are expected to enhance our creativity, reveal opportunities and open new vistas of uncharted frontiers. To address this void, we introduce the concept of generativity in the context of IS design and develop two corresponding design considerations -'generative capacity' that refers to one's ability to produce something ingenious or at least new in a particular context, and 'generative fit' that refers to the extent to which an IT artefact is conducive to evoking and enhancing that generative capacity. We offer an extended view of the concept of fit and realign the prevailing approaches to human-computer interaction design with current leading-edge applications and users' expectations. Our findings guide systems designers who aim to enhance creative work, unstructured syntheses, serendipitous discoveries, and any other form of computer-aided tasks that involve unexplored outcomes or aim to enhance our ability to go boldly where no one has gone before. In this paper, we explore the underpinnings of 'generative capacity' and argue that it should be included in the evaluation of task-related performance. Then, we briefly explore the role of fit in IS research, position 'generative fit' in that context, explain its role and impact on performance, and provide key design considerations that enhance generative fit. Finally, we demonstrate our thesis with an illustrative vignette of good generative fit, and conclude with ideas for further research.

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From generative fit to generative capacity: exploring an emerging dimension of information systems design and task performance

The exponentially growing production of data and the social trend towards openness and sharing are powerful forces that are changing the global economy and society. Governments around the world have become active participants in this evolution, opening up their data for access and re-use by public and private agents alike. The phenomenon of Open Government Data has spread around the world in the last four years, driven by the widely held belief that use of Open Government Data has the ability to generate both economic and social value. However, a cursory review of the popular press, as well as an investigation of academic research and empirical data, reveals the need to further understand the relationship between Open Government Data and value. In this paper, we focus on how use of Open Government Data can bring about new innovative solutions that can generate social and economic value. We apply a critical realist approach to a case study analysis to uncover the mechanisms that can explain how data is transformed to value. We explore the case of Opower, a pioneer in using and transforming data to induce a behavioral change that has resulted in a considerable reduction in energy use over the last six years.

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Michel Avital

Papers

Designing interviews to generate rich data for information systems research

Blockchain Technology in Business and Information Systems Research

From generative fit to generative capacity: exploring an emerging dimension of information systems fit and task performance.

From generative fit to generative capacity: exploring an emerging dimension of information systems design and task performance

Data-driven innovation through open government data