Artificial intelligence

Artificial intelligence (46)
Expert system (8)
Machine intelligence (4)
Machine learning (35)
Neural network (21)

Synopsis: Ict: Artificial intelligence:

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

11 1. 2. 2. Artificial intelligence...17 1. 3. Medicine and biotechnologies...23 1. 3. 1. Human spare parts and augmented human...

and Ryszard S. Michalski, 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

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 (Legoâ¢-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 â€oeknowledge†(problem-solving). In this frame of mind, Allen Newell NEW 82 has proposed a new way of modeling knowledge to make it â€oecomprehensible†by computers:

Equipped with artificial intelligence techniques, computers can â€oethinkâ€, 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.

The artificial intelligence techniques may provide an efficient help without however â€oeswitching-off†the users†brain.

Computer vision, medical imaging and machine learning deal with high-dimensional, 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 internetâ€, in MERCIER-LAURENT E.,BOULANGER D. eds), Artificial intelligence for Knowledge management, Revised Selected Papers, Springer IFIP AICT 422, pp. 170†188,2012.

â€oecould artificial intelligence create an unemployment crisis? â€, 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.,â€oethe knowledge levelâ€, 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

, 155 M n o machine learning, 9, 11,20, 90,131, 140 market global, 5, 154 marketplace, 6 measuring benefits, 78†79 mind of plants, 165â€

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

consequences of global business 1. 2. Computer science, the Internet and mass media 1. 2. 1. Example of applying environmental principles 1. 2. 2. Artificial intelligence 1. 2

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. Guâ nther, C, . & Aalst, W. van der (2006).

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

Expert systems with Applications, 37 (4), 3274†3283. Kaplan, R. S, . & Norton, D. P. 1992).

, Germany 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), Saarbruâ 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.

G#1v 7874 Artificial intelligence G#2v 7875 Artificial intelligence 0#3#artificial intelligence Artificial intelligence G#2v 7876 Expert system

0#3#expert system Expert system G#2v 7877 Machine intelligence 0#3#machine intelligence Machine intelligence G#2v 7878 Machine learning

0#3#machine learning Machine learning G#3v 7879 Reinforcement-learning 0#4#reinforcement learning Reinforcement-learning G#2v 7880 Neural network

0#3#neural network Neural network G#1v 6917 Artificial life 0#2#artificial life Artificial life G#1v 6918 Bionics

G#2v 6919 Bionic man 0#3#bionic man Bionic man G#2v 6920 Bionics 0#3#bionics Bionics

G#1v 6921 Brain computer interface 0#2#brain computer interface Brain computer interface G#1v 6922 Breathalyzer 0#2#breathalyzer Breathalyzer

G#1v 6923 Business intelligence 0#2#business intelligence Business intelligence G#1v 6970 Cloud computing 0#2#cloud computing Cloud computing

0#2#cloud storage Cloud computing 0#2#software as a service Cloud computing G#1v 6924 Coding theory 0#2#coding theory Coding theory

G#1v 7881 Cognitive computing 0#2#cognitive computing Cognitive computing G#1v 6971 Communication systems G#2v 6972 Communication system

0#3#communication system Communication system G#2v 6973 Fiber-optic communication G#3v 6974 Fiber optic communication 0#4#fiber optic communication Fiber optic communication

G#3v 6975 Optical fiber 0#4#fibre optic Optical fiber 0#4#optical fiber Optical fiber 0#4#optical fibre Optical fiber

G#2v 6976 Parallel communication 0#3#parallel communication Parallel communication G#2v 6977 Radiocommunication G#3v 6978 CDMA

machines do best, especially in the context of so-called artificial intelligence, and what people do best is constantly shifting,

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

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.

Argumentation in Artificial intelligence. Springer. pp. 1-22 Walton, D.,Reed, C. & Macagno, F. 2008) Argumentation Schemes.

big data, machine learning, 3d print -ing, online learning and e-petitions The main technological trends in DSI

The explosion of new types of data analytics and machine learning means that it is no

machine learning, devices), and are imposing their rules of the game. Europe needs to invest in

Artificial intelligence, for example 1 16 THE OPEN BOOK OF SOCIAL INNOVATION has been used in family law in Australia and to help with divorce

crowdfunding, big data, machine learning, 3d printing, online learning, e-petitions and so on Open networks The ability to build bottom-up networking capabilities in every corner or the world and in people†s everyday

The explosion of new types of data analytics and machine learning means that it is no longer only govern

crowdfunding, big data, machine learning, 3d printing, online learning, e-petitions and so on Open networks The ability to build bottom-up networking capabilities in every corner or the world and in people†s everyday

The explosion of new types of data analytics and machine learning means that it is no longer only govern

of the University of Texas applied artificial intelligence (AI) to create an early tool for generating

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. They have limited utility where student learning objectives involve developing new knowledge, solving new problems, and innovation

digital technology, from algorithms to artificial intelligence. These will also help to resolve on a structural level the issue of academic integrity in a digital age

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.

big data, machine learning, 3d print -ing, online learning and e-petitions The main technological trends in DSI

The explosion of new types of data analytics and machine learning means that it is no

machine learning, devices), and are imposing their rules of the game. Europe needs to invest in

which included Z-Scores, ZETA Scores, and Neural networks NN). ) The strengths and weaknesses of each model were exposed

while Z-Scores, ZETA Scores, and Neural networks are examples of models that relate to internal factors

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

such as the ZETA and Neural networks models, require a high information intensity level. Such a requirement can be a problem

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

Neural networks and the mathematics of chaos†an investigation of these methodologies as accurate predictors of corporate bankruptcy.

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.

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.

data effects on the classification accuracy of probit, ID3 and neural networks Contemporary Accounting Research 9 (1), 306†328

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

control and artificial intelligence •Sensing technology †employing sensors to feed control systems with both vehicle-based

machine learning THE IMPACT OF ICT ON EUROPEAN PRODUCTIVITY A principal reason the EU has had lower productivity growth than the United states since

from machine learning, which is primarily about correlation and predictions. 40 Big data are by their very nature observational and can measure

artificial intelligence The increase of the volume of transferable data between the ICT systems The development of new working places by means of

big data, machine learning, 3d print -ing, online learning and e-petitions The main technological trends in DSI

The explosion of new types of data analytics and machine learning means that it is no

machine learning, devices), and are imposing their rules of the game. Europe needs to invest in

systems and machine learning, etc •Knowledge based on mathematics like rela -tional algebra and statistical but also predictive

Artificial intelligence Source: our elaboration from expert opinion European competitiveness: IT and long-term scientific performance Science and Public Policy August 2011 525

systems), psychology (artificial intelligence), visual art (computer graphics), operations management enterprise resource planning), and many other cog -nitive fields.

assistive ICT, Computer science and artificial intelligence Engineering and Technology Electrical and electronic engineering, nanotechnology, materials (specifically

artificial intelligence, will open new business models and opportunities for growth. Future convergence will be defined as convergence of products (Eg

to storing and enforcing high-level information include neural networks, expert systems, statistical association, conditional probability distributions, diï €erent

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.:

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 and to help with divorce

, 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

conference on Artificial intelligence (AAAI2006) Volume 2 (pp 1331†1336. AAAI Press 28. Yu H, Hatzivassiloglou V (2003) Towards answering opinion questions:

machine learning 21. However, Crowdsourcing has been used innovatively for big projects as well. For example, computer scientists at Carnegie mellon Uni

Using powerful machine learning algorithms, it provides extremely accurate profiling and segmentation of consumers based on habits and

Big data analytics, and machine learning promise new solutions to previously intractable problems (e g in healthcare, disaster response, the environment, and

in technologies such as machine learning and natural language processing will also increase, and a gap between the supply and demand for these types of

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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