| Expert system (8) |  |
| Machine intelligence (2) | |
| Machine learning (35) | |
| Neural network (19) | |
| Expert system (8) | |
| Machine intelligence (2) | |
| Machine learning (35) | |
| Neural network (19) | |
11 1. 2. 2. Artificial intelligence...17 1. 3. Medicine and biotechnologies...23 1. 3. 1. Human spare parts and augmented human...
the founder of Machine learning as a Science. Foreword Innovation Biosphere is a very interesting title for a new book intended to raise thoughts beyond the ordinary.
For example, The french service in artificial intelligence was the best in the world in the early 1990s.
We wish to popularize the use of artificial intelligence approaches and techniques with the aim to conceive user-friendly and useful applications that can really help humans in their work instead of replacing them
Other techniques of knowledge discovery, such as neural networks, genetic algorithms, induction or other multistrategy machine learning hybrid tools PIA 91
Google also developed a machine-learning algorithm (artificial intelligence (AI)) that learns from operational data to model plant performance
and scalable devices (Lego-like devices) with the aim to reduce the environmental impact. 1. 2. 2. Artificial intelligence For many, AI means robots.
whereas artificial intelligence enables us to learn how to think about knowledge (problem-solving). In this frame of mind, Allen Newell NEW 82 has proposed a new way of modeling knowledge to make it comprehensible by computers:
Equipped with artificial intelligence techniques, computers can think, solve problems, become experts and accumulate a collective experience, under the condition that we transfer to them the relative knowledge and the necessary reasoning and learning techniques.
Artificial intelligence (AI) is pointed also out for destroying jobs robots are replacing humans FOR 13. According to Ford, advances in AI and robotics will have significant implications for evolving economic systems.
Machine learning, one of the primary techniques used in the development of IBM's Watson, is in essence a way to use statistical analysis of historical data to transform seemingly non-routine tasks into routine operations that can be computerized.
The artificial intelligence techniques may provide an efficient help without however switching-off the users'brain.
Computer vision, medical imaging and machine learning deal with highdimensional, noisy and heterogeneous datasets that are inherently non-Euclidean.
On the machine learning side it is called formalization (intension concept acquisition) or classification (extension class construction.
and learning in the future internet, in MERCIER-LAURENT E.,BOULANGER D. eds), Artificial intelligence for Knowledge management, Revised Selected Papers, Springer IFIP AICT 422, pp. 170 188,2012.
Could artificial intelligence create an unemployment crisis? Communications of the Association for Computing Machinery (ACM), vol. 56, no. 7, pp 37 39,2013, available at http://cacm. acm. org/.
NEW 82 NEWELL A.,The knowledge level, Artificial intelligence, vol. 18,1982. OEC 05a OECD, Definition of biotechnology, available at http://www. oecd. org/sti/biotech/statisticaldefinitionofbiotechnology. htm, 2005.
service, 55,79, 99,101, 121,144, 152,153, 173 social, 12,43, 55,79, 101,120, 121,124, 138,152, 153 intangible benefits, 52,68, 78,173, 176 intelligence artificial intelligence
M n o machine learning, 9, 11,20, 90,131, 140 market global, 5, 154 marketplace, 6 measuring benefits, 78 79 mind of plants, 165 167
We wish to popularize the use of artificial intelligence approaches and techniques in order to conceive friendly and useful applications that aid humans in their work instead of replacing them
In J. Balcazar (Ed.),ECML/PKDD 210 (Lecture Notes in Artificial intelligence, Vol. 6321, pp. 184 199.
Journal of Machine learning Research, 10,1305 1340. Gu nther, C, . & Aalst, W. van der (2006). A generic import framework for process event logs.
Expert systems with Applications, 38 (6), 7029 7040. Rozinat, A, . & Aalst, W. van der (2008). Conformance checking of processes based on monitoring real behavior.
Expert systems and Applications, 39 (5), 6061 6068. Lakshmanan, G. T.,Rozsnyai, S, . & Wang, F. 2013).
P. Loos(*)P. Fettke J. Walter T. Thaler P. Ardalani German Research center for Artificial intelligence (DFKI), Saarland University, Stuhlsatzenhausweg 3, 66123 Saarbru
Expert systems with Applications, 37 (4), 3274 3283. Kaplan, R. S, . & Norton, D. P. 1992).
Peyman Ardalani has been doing his academical research as a Ph d. student since 2012 at the Institute for Information systems (IWI) at the German Research Institute for Artificial intelligence (DFKI.
Currently, he is the deputy chair of the Institute for Information systems (IWI) at the German Research center for Artificial intelligence (DFKI), Saarbru cken.
Germany Peter Loos is Director of the Institute for Information systems (IWI) at the German Research center for Artificial intelligence (DFKI)
for Artificial intelligence (DFKI) and research project lead at Saarland University. His research activities include business process management, process mining, software development as well as implementation of information systems.
the use of Bayesian networks is becoming increasingly common in bioinformatics, artificial intelligence and decision-support systems; 45 however, their theoretical complexity and the amount of computer power required to perform relatively simple graph searches make them difficult to implement in a convenient manner.
'Thus, the boundary between what machines do best, especially in the context of so-called artificial intelligence,
Many technology trends (such as ambient intelligent space, artificial intelligence and intelligent agents, cloud based services, the semantic web and the internet of things, mobile and mobile apps, social media,
MOOCS famously emerged from a Stanford experiment with a course on artificial intelligence and Professor Why is a private sector initiative.
Argumentation in Artificial intelligence. Springer. pp. 1-22. Walton, D.,Reed, C. & Macagno, F. 2008) Argumentation Schemes.
and he has made numerous presentations at various international conferences on a wide range of topics such as music, film, aesthetics, semiology, neuroscience, sociology, education, artificial intelligence, religion,
Argumentation in Artificial intelligence. Springer. pp. 1-22. Walton, D.,Reed, C. & Macagno, F. 2008) Argumentation Schemes.
social media, crowdsourcing, crowdfunding, big data, machine learning, 3d printing, online learning and e-petitions. The main technological trends in DSI 0100 200 300 400 Arduino Smart Citizen Kit Fairphone Safecast OPEN NETWORKS Tor Confine Guifi. net Smart
and from the environment The explosion of new types of data analytics and machine learning means that it is no longer only government
The Internet ecosystem today is highly centralised The current Internet is dominated by a handful of mainly US companies that control all the layers of the ecosystem (app store, cloud, machine learning, devices),
Artificial intelligence, for example, 1 16 THE OPEN BOOK OF SOCIAL INNOVATION has been used in family law in Australia
The internet ecosystem today is highly centralised The current Internet is dominated by a handful of mainly US companies that control all the layers of the tecosystem (app store, cloud, machine learning, devices),
and digital services adopted by DSI activities such as social networking, social media, crowdsourcing, crowdfunding, big data, machine learning, 3d printing, online learning,
Open Data The explosion of new types of data analytics and machine learning means that it is no longer only government
and digital services adopted by DSI activities such as social networking, social media, crowdsourcing, crowdfunding, big data, machine learning, 3d printing, online learning,
Open Data The explosion of new types of data analytics and machine learning means that it is no longer only government
Wayne Danielson of the University of Texas applied artificial intelligence (AI) to create an early tool for generating computer-written haikus.
when learning analytics and artificial intelligence are used effectively to optimize and customize student engagement and learning in real time (Fournier, 2011).
While an increasing number of MOOCS integrate artificial intelligence and expert systems to provide student feedback and learning customization,
the ability of these systems to function effectively is limited largely to courses designed to advance subject matter mastery.
Educators need to develop new assessment methods using the unique capabilities of digital technology, from algorithms to artificial intelligence.
Digital tools using artificial intelligence can enable real-time customization of learning as they are beginning to do with some MOOCS.
The coalescence of learning analytics and artificial intelligence holds promise. Consider the case of Narrative Science (Northwestern university Innovation and New Ventors Office, 2014.
and insights through its proprietary artificial intelligence authoring system. The algorithms the system uses are highly effective
social media, crowdsourcing, crowdfunding, big data, machine learning, 3d printing, online learning and e-petitions. The main technological trends in DSI 0100 200 300 400 Arduino Smart Citizen Kit Fairphone Safecast OPEN NETWORKS Tor Confine Guifi. net Smart
and from the environment The explosion of new types of data analytics and machine learning means that it is no longer only government
The Internet ecosystem today is highly centralised The current Internet is dominated by a handful of mainly US companies that control all the layers of the ecosystem (app store, cloud, machine learning, devices),
or the‘Bangalore University'with 250,000 students as well as a number of public research institutes covering various areas such as IT, artificial intelligence, production technologies, aircraft-/aerospace (Fromhold-Eisebith and Eisebith 1999).
Contrary to the hype surrounding"intelligent agents"and"artificial intelligence, the fact remains that search results are only as good as the query we pose
Tacit-to-explicit has been the Holy grail for many years spawning the field of expert systems. The aim has been to somehow capture the subtleties of tacit knowledge
which included Z-Scores, ZETA Scores, and Neural networks (NN). The strengths and weaknesses of each model were exposed
and Neural networks are examples of models that relate to internal factors. Utilizing SMES indiscriminately will negatively affect the outcome of the majority of SME studies.
such as the ZETA and Neural networks models, require a high level of information intensity. That implies the need for detailed data,
Examples for such models are the ZETA model, the Neural networks model, and the SIV model.
The other group includes Z-Scores, ZETA Scores, Neural networks, and the SIV model. These are more suitable to the investigation of firm performance in relation to the internal environment of an enterprise.
such as the ZETA and Neural networks models, require a high information intensity level. Such a requirement can be a problem
Comparisons using linear discriminant analysis and neural networks (the Italian experience. Journal of Banking and Finance 18 (3), 505 529.
Neural networks versus logistic regression in predicting bank failure. In R. P. Srivastava (ed.)Auditing Symposium. Vol:
Data mining with neural networks: Solving business problems from application development to decision support. Mcgraw-hill, Inc. Hightstown, New jersey, USA.
Neural networks and the mathematics of chaos an investigation of these methodologies as accurate predictors of corporate bankruptcy.
The First International Conference on Artificial intelligence Applications on Wall street (Proceedings. IEEE, 52 57. Cainelli, G.,Evangelista, R. and Savona, M. 2004.
Generalization with neural networks. Decision Support systems 11 (5), 527 545. Edvinsson, L. and Malone, M. S. 1997.
Forecasting small air carrier bankruptcies using a neural network approach. Journal of Financial Management and Analysis 13 (19), 44 49.
Performance evaluation of neural network decision models. Journal of Management Information systems 14 (2), 201 216. Jaques, E. 1951.
An empirical investigation of some data effects on the classification accuracy of probit, ID3 and neural networks.
Trading equity index futures with a neural network: A machine learning-enhanced trading strategy. The Journal of Portfolio Management 19 (1), 27 33.
Trist, E. I. 1981. The evolution of sociotechnical systems as a conceptual framework and as an action research program.
and more costly microprocessors, allowing for more sophisticated applications such as model-based process control and artificial intelligence;
improved self-serve kiosks, 3d printing, location awareness, and machine learning. THE IMPACT OF ICT ON EUROPEAN PRODUCTIVITY A principal reason the EU has had lower productivity growth than the United states
who previously had worked together at MIT's Artificial intelligence Laboratory. It was incorporated in 2000 when it merged with Real world Interface
Further advances have been in combining machine intelligence with innovative gripper and sensor technologies to expand applications and industrial sectors.
ready to create new, revolutionary paradigms of production, within artificial intelligence, advanced biotechnology, green energy technology, material technology or other promising and sophisticated fields.
Big data draws many of its techniques from machine learning, which is primarily about correlation and predictions. 40 Big data are by their very nature observational
and constraints The increase of the requests with respect to autonomous systems which include elements of artificial intelligence The increase of the volume of transferable data between the ICT systems The development of new working places by means of the development
sensing computer-based instruments and measurement and process control improvements numerical modelling and simulation machine learning data centres, data transmission networks etc.
social media, crowdsourcing, crowdfunding, big data, machine learning, 3d printing, online learning and e-petitions. The main technological trends in DSI 0100 200 300 400 Arduino Smart Citizen Kit Fairphone Safecast OPEN NETWORKS Tor Confine Guifi. net Smart
and from the environment The explosion of new types of data analytics and machine learning means that it is no longer only government
The Internet ecosystem today is highly centralised The current Internet is dominated by a handful of mainly US companies that control all the layers of the ecosystem (app store, cloud, machine learning, devices),
With the exponential growth of computing power and developments in genetics, nanotechnology and artificial intelligence, humanity will soon overcome biological limitations.
The cross-over point of human and artificial intelligence, the Technological Singularity. Image by Futurebuff 11 This moment has implications for almost all the important areas of our lives,
Computer skills as query languages, database design, mining and interactive data analysis, scripting or programming languages, expert systems and machine learning, etc.
Artificial intelligence Source: our elaboration from expert opinion European competitiveness: IT and long-term scientific performance Science and Public Policy August 2011 525 the mountains of pure theory down to the sea of market competitiveness.
but also biology and chemistry (bioinformatics), earth sciences (geographic information systems), psychology (artificial intelligence), visual art (computer graphics), operations management (enterprise resource planning),
artificial intelligence, WEB technologies (web mining), robotics, integrated systems, producing systems and planning production systems, calculation systems, voice recognition, images'processing, graphics processing, telemonitoring
electronics, embedded system design, personal health system, ICT for energy efficiency and accessible and assistive ICT, Computer science and artificial intelligence.
and artificial intelligence, will open new business models and opportunities for growth. Future convergence will be defined as convergence of products (Eg. electric car
However, the name ontology was used first only in the seventeenth century by Johannes Clauberg 2. In the area of technology its initial use was performed by Mealy in 1967 20 and expanded especially in areas of artificial intelligence, database
AAAI Spring Symposium Linked Data Meets Artificial intelligence, March 2010, AAAI Press, Menlo Park (2010) 10.
Popular techniques related to storing and enforcing high-level information include neural networks, expert systems, statistical association, conditional probability distributions, different kinds of monotonic and nonmonotonic, fuzzy logic, decision trees, static and dynamic
Neural networks are employed in this approach to classify features extracted from video blobs for their classification task.
Machine learning 9 (4), 309 347 (1992) 400 Q. Zhang and E. Izquierdo 9. Fan, J.,Gao, Y.,Luo, H.,Jain, R.:
IEEE Transactions on Neural networks 13 (4), 793 810 (2002) 18. Qian, R.,Haering, N.,Sezan, I.:
IEEE Transactions on pattern analysis and machine intelligence 22 (12), 1349 1380 (2000) 20. Vailaya, A.,Figueiredo, M. A t.,Jain, A k.,Zhang, H. J.:
Expert systems with Applications, 27,459 465. Cohen, WM, & Levinthal, DA. 1990). ) Absorptive capacity: a new perspective on learning and innovation.
Artificial intelligence, for example, 1 16 THE OPEN BOOK OF SOCIAL INNOVATION has been used in family law in Australia
, machine learning, statistics, and operations research, among others. Furthermore, centralization of the staff is motivated by three factors:
/Finally, statistical approaches are used for machine learning such as Support vector machines (SVM) and Elastic-net Logistic Regressions (ENETS.
In the 21st national conference on Artificial intelligence (AAAI2006) Volume 2 (pp 1331 1336. AAAI Press 28.
finding contact information or labeling data to prepare it for the use in machine learning 21.
Using powerful machine learning algorithms, it provides extremely accurate profiling and segmentation of consumers based on habits and spending preferences.
Big data analytics, and machine learning promise new solutions to previously intractable problems (e g.,, in healthcare, disaster response, the environment, and transportation;
The demand for engineers who specialize in technologies such as machine learning and natural language processing will also increase,
mining big data requires an extremely diverse set of skills deep business insights, data visualization, statistics, machine learning, and computer programming.
machine learning (systems that learn from data) and data warehousing. Big data professionals are expected to be familiar with both disciplines,
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