Far ph d-seminar-dec02-2010

•Download as PPT, PDF•

1 like•241 views

This document reviews artificial intelligence techniques for modeling systems' sustainability. It discusses different approaches to sustainability modeling including pictorial visualizations, physical models, conceptual models, and standardizing models. It also categorizes quantitative sustainability models into macro-econometric models, computable general equilibrium models, optimization models, system dynamics models, and multi-agent simulation models. The document concludes that influence diagrams can provide an intuitive way to model sustainability and its uncertainties, and that Analytica is a tool that can be used to create influence diagrams and model complex sustainability systems.

Technology

Artificial Intelligence Techniques for Modeling Systems' Sustainability: A Review Amin Hosseinian-Far Research Team: Prof. Hamid Jahankhani Mr. Elias Pimenidis Prof. D. C. Wijeyesekera

Contents ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Introduction ,[object Object],[object Object],[object Object],[object Object],[object Object]

Sustainability & Sustainable Development ,[object Object],[object Object],[object Object]

Sustainability & Sustainable Development ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Sustainability Modeling ,[object Object],[object Object],[object Object],[object Object]

Approaches ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Quantitative Modeling ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Analytica-Influence Diagrams ,[object Object],[object Object]

Conclusion ,[object Object],[object Object],[object Object],[object Object]

Key References ,[object Object],[object Object],[object Object],[object Object],[object Object]

Viewers also liked

Aula de música con musica

psmusica

ART

airitilibrary

Al user guide revised(image)

airitilibrary

Al user guide revised(image) 20111209

airitilibrary

Razorlight

pluto093

華藝世界美術資料庫2012

airitilibrary

το εσωτερικο του υπολογιστη

giannis30197

Entrepreneurship development studies 111

Mercy Ejos Isiavwe- OGBARI, PhD.

Al user guide revised

airitilibrary

Wg Cdr Anupam Tiwari Papers

at1972

ZF3 introduction

Vincent Blanchon

TPCR- A Critical Appriasal

at1972

2015 Artificial Intelligence Techniques at Engineering Seminar - Chapter 2 - ...

Enrique Onieva

Decision theory

Jayant Sharma

Viewers also liked (14)

Aula de música con musica

ART

Al user guide revised(image)

Al user guide revised(image) 20111209

Razorlight

華藝世界美術資料庫2012

το εσωτερικο του υπολογιστη

Entrepreneurship development studies 111

Al user guide revised

Wg Cdr Anupam Tiwari Papers

ZF3 introduction

TPCR- A Critical Appriasal

2015 Artificial Intelligence Techniques at Engineering Seminar - Chapter 2 - ...

Decision theory

Similar to Far ph d-seminar-dec02-2010

Venkatesh2003

Ratzman III

SPM_Metrics_WhitePaper_3

Kelsie DeFrancia

In this case study we identify the factors that influence the adoption of a new system in a major company in Saudi Arabia. We develop a theoretical framework to help derive better understanding of system adoption via socio-technical integration. We formulation of 14 hypotheses that were tested via a survey of 42 system users. Management support and change management were found to be significant factors influencing system adoption. As a result, the 14 null hypotheses were rejected due to their statistical significance (p-value < 0.05). Discussions and recommendations for future research are discussed.

System Adoption: Socio-Technical Integration

The International Journal of Business Management and Technology

System Dynamics Modeling for Intellectual Disability Services: A Case Studyjppi_342 112..119 Meri Duryan*,†, Dragan Nikolik‡, Godefridus van Merode§, and Leopold Curfs*,§ *Gouverneur Kremers Centrum; †University of Maastricht; ‡Maastricht School of Management; and §Maastricht University Medical Center, Maastricht, the Netherlands Abstract Organizations providing services to persons with intellectual disabilities (ID) are complex because of many interacting stakeholders with often different and competing interests. The combination of increased consumer demand and diminished resources makes organizational planning a challenge for the managers of such organizations. Such challenges are confounded by significant demands for the optimization of resources and the goal to reduce expenses and to more effectively and efficiently use existing resources while at the same time providing high quality services. The authors explore the possibilities of using “system dynamics modelling” in organizational decision-making processes related to resource allocations. System dynamics suggests the application of generic systems archetypes as a first step in interpreting complex situations in an organization. The authors illustrate the application of this method via a case study in one provider organization in the Netherlands. The authors contend that such a modeling approach can be used by the management of similar organizations serving people with ID as a tool to support decision making that can result in optimal resource allocation. Keywords: allocation of resources, intellectual disabilities, system dynamics modeling, systems thinking, waiting lists INTRODUCTION Healthcare organizations are complex entities as they have multiple stakeholders with often conflicting objectives and goals (Drucker, 1993). Provider organizations specializing in intellectual disabilities (ID) are also complex because of the nature of the care and supports they provide and how they are organized. Some of the complexities relate to the difficulties that adults with ID might have in expressing themselves. Moreover, the specifics of the care often require a deeper involvement of carers with respect to their relationships with families and other sectors of society. Because of their complexity, ID provider organizations, com- pared with healthcare providers, often require a higher level of resource planning, collaboration, and cooperation among social, health, and education services, mental health services, and other sectors (WHO, 2010). To manage the complexities and challenges ID provider organizations face, managers need to analyze and understand complex interdependencies among the systems with which they are dealing. In order to achieve that, ID provider managers need to examine and shift their mental models regarding their role in managing the organization and in establishing relationships with all the stakeholders involved. However, as Forrester (1980) has noted, traditiona.

System Dynamics Modeling for IntellectualDisability Services.docx

mabelf3

rsos.royalsocietypublishing.org Review Cite this article: Calder M etal. 2018 Computational modelling for decision-making: where, why, what, who and how. R.Soc.opensci. 5: 172096. http://dx.doi.org/10.1098/rsos.172096 Received: 6 December 2017 Accepted: 10 May 2018 Subject Category: Computer science Subject Areas: computer modelling and simulation/mathematical modelling Keywords: modelling, decision-making, data, uncertainty, complexity, communication Author for correspondence: Muffy Calder e-mail: [email protected] Computational modelling for decision-making: where, why, what, who and how Muffy Calder1, Claire Craig2, Dave Culley3, Richard de Cani4, Christl A. Donnelly5, Rowan Douglas6, Bruce Edmonds7, Jonathon Gascoigne6, Nigel Gilbert8, Caroline Hargrove9, Derwen Hinds10, David C. Lane11, Dervilla Mitchell4, Giles Pavey12, David Robertson13, Bridget Rosewell14, Spencer Sherwin15, Mark Walport16 and Alan Wilson17 1School of Computing Science, University of Glasgow, Glasgow, UK 2The Royal Society, London, UK 3Improbable, London, UK 4Arup, London, UK 5MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK 6Willis Towers Watson, London, UK 7Centre for Policy Modelling, Manchester Metropolitan University, Manchester, UK 8Centre for Research in Social Simulation, University of Surrey, Guildford, UK 9McLaren Applied Technologies, Woking, UK 10National Cyber Security Centre, UK 11Henley Business School, University of Reading, Reading, UK 12Consultant, UK 13School of Informatics, University of Edinburgh, Edinburgh, UK 14Volterra Partners, London, UK 15Department of Aeronautics, Imperial College London, London, UK 16UK Research and Innovation, London, UK 17The Alan Turing Institute, London, UK BE, 0000-0002-3903-2507 In order to deal with an increasingly complex world, we need ever more sophisticated computational models that can help us make decisions wisely and understand the potential consequences of choices. But creating a model requires far more than just raw data and technical skills: it requires a 2018 The Authors. Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, provided the original author and source are credited. http://crossmark.crossref.org/dialog/?doi=10.1098/rsos.172096&domain=pdf&date_stamp=2018-06-20 mailto:[email protected] http://orcid.org/0000-0002-3903-2507 2 rsos.royalsocietypublishing.org R.Soc.open sci.5:172096 ................................................. close collaboration between model commissioners, developers, users and reviewers. Good modelling requires its users and commissioners to understand more about the whole process, including the different kinds of purpose a model can have and the different technical bases. This paper offers a guide to the process of commissioning, developing and deploying mo ...

rsos.royalsocietypublishing.orgReviewCite this article .docx

healdkathaleen

Tablet PC Adoption Model

arun savukar

Model Based Systems Thinking

sicrcw

Doctors 2.0 & YOU - 2017 - Methodological and ethical issues of connected dev...

DoctorsTwoPointO And You

Chapters 4,5 and 6 Into policymaking and modeling in a complex world From Building a model to adaptive robust decision- making using systems modelling Features and added value of simulation Models using different modelling approaches supporting policymaking: A comparative analysis. Chapter Goals and Objectives Overall – students will learn and understand consequences of complexity in the real-world, and meaningful ways to understand and manage such situations the implications of complexity and that many social systems are unpredictable by nature, especially when in the presence of structural change (transitions) natural tendency to criticize the approaches that ignore difficulties and pretend to predict using simplistic models that managing a complex system requires a good understanding of the dynamics of the system in question—to know, before they occur, some of the real possibilities that might occur and be ready so they can be reacted to as responsively as possible. 4. Policymaking and modeling in a complex world the word “complexity” can be used to indicate a variety of kinds of difficulties identification of complexity and uncertainty in policy-making in very simple physical systems, interactions may give rise to complex behavior, expressed in different types of behavior, ranging from very stable to chaotic reasons why complex adaptive systems have a strong capacity to self-organize two of the ways systems are oversimplified: quantification and compartmentalization models are assessed by their ability to predict/mirror observed aspects of the environments 5. From building a model to adaptive robust decision-making using systems modeling System Dynamics Modeling and Simulation of Old ✓ methods for modeling and simulating dynamically complex systems ✓ evolutions in modeling and simulation with recent explosive growth in computational power, data, social media, to support decision-making Recent Innovations and Expected Evolutions ✓ Why often seemingly more revolutionary—innovations have been introduced and demonstrated, but that they have not been massively adopted yet Current and Expected Evolutions ✓ Three current evolutions expected to further reinforce - “experiential art” to “computational science.” Future State of Practice of Systems Modeling and Simulation ✓ modeling and simulation with sparse data to modeling and simulation with (near real-time) big data; ✓ simulating and analyzing a few simulation runs to simulating and simultaneously analyzing well-selected ensembles of runs; ✓ using models for intuitive policy testing to using models as instruments for designing adaptive robust robust policies; ✓ developing educational flight simulators to fully integrated decision support. Features and added value of simulation models using different modelling approaches to policy-making: A Comparative analysis Foundations of Simulation Modelling ✓ model simplification definitions—smaller, less detailed, le.

Chapters 4,5 and 6Into policymaking and modeling in a comple.docx

tiffanyd4

Chapters 4,5 and 6Into policymaking and modeling in a comple.docx

mccormicknadine86

MIS 05 Decision Support Systems

Tushar B Kute

Running head: Multi-actor modelling system 1 Multi-actor modelling system3 Multi-actor modelling system Yogesh Dagwale University of the Cumberland’s Ligtenberg, A., Wachowicz, M., Bregt, A. K., Beulens, A., & Kettenis, D. L. (2004). A design and application of a multi-agent system for simulation of multi-actor spatial planning. Journal of environmental management, 72(1-2), 43-55. They talk about the potential and restrictions of the MAS to manufacture models that empower spatial organizers to incorporate the 'actor factor' in their examination. Their structure system contemplates actors who assume a functioning job in the spatial planning. They included actors who can watch and see a spatial domain. Using these perceptions and discernment they produce an inclination for a preferred spatial situation. Actors at that point present and discuss their inclinations amid their exchanges with different actors. The inclinations of the actor fill in as inputs for an official choice making. Finally, ultimate conclusions are actualized in the spatial framework. They found that MAS can produce space utilization designs in light of a portrayal of a multi-actor planning process. It additionally can clear up the impacts of actors under the administration of various planning styles on the space utilization and prove how the relations between actors change amid a planning process and under different orders of coming up with decisions. Unlike the work by Parker, Manson, Janssen, Hoffman & Deadman,2003, cited below, this paper did not include the various challenges associated with the use of MAS. Parker, D. C., Manson, S. M., Janssen, M. A., Hoffmann, M. J., & Deadman, P. (2003). Multi-agent systems for the simulation of land-use and land-cover change: a review. Annals of the association of American Geographers, 93(2), 314-337. In this paper, they studied different models. These models, however, were not thorough enough and therefore they took into account the multi-actor system, dynamic spatial Simulation, which has two components, that is, a cellular model that speaks to biogeophysical and biological parts of a demonstrated framework and an actor-based model to speak to human conclusion making. Because of its nature and ability to model complex situations, they highlighted some of the areas that MAS can be applied where other models cannot be able to deliver. Such areas are modeling of emergent phenomena whereby MAS can model landscape plans, due to its flexibility, MAS can represent complex land use/ cover systems, and they can be used to model dynamic paths. They also outlined the various challenges to Multi-actor systems. Such challenges include an understanding of complexity, individual decision making, empirical parameterization and model validation, and communication. Faber, N. R., & Jorna, R. J. (2011, June). The use of multi-actor systems for studying social sustainability: Theoretical backgrounds and pseudo-specifications. In Com.

Running head Multi-actor modelling system 1Multi-actor mod.docx

todd581

Running head Multi-actor modelling system 1Multi-actor mod.docx

glendar3

If System Dynamics (SD) models are constructed based solely on decision makers' mental models and u n- derstanding of the context subject to study, then the resulting systems must necessarily bear some d e gree of deficiency due to the subjective, limited, and internally inconsistent mental models which led to t he conce p- tion of these systems. As such, a systematic method for constructing SD models could be esse n tially helpful in overcoming the biases dictated by the human mind's limited understanding and conceptualization of complex systems. This paper proposes a novel combined method to su p port SD model construction. The classical Dec i sion Making Trial and Evaluation Laboratory (DEMATEL) technique is used to define causal relationships among variables of a system, and to construct the corresponding Impact Relatio n Maps (IRMs). The novelty of this paper stems from the use of the resulting total influence m a trix to derive the system dynamic's Causal Loop Diagram (CLD) and then define variable weights in the stock - flow chart equations. This new method allows to overc ome the subjectivity bias of SD mode ling while projecting D E- MATEL in a more d y namic simulation environment, which could significantly improve the strategic choices made by an a lysts and policy makers

T OWARDS A S YSTEM D YNAMICS M ODELING M E- THOD B ASED ON DEMATEL

ijcsit

The DeLone and McLean Model of Information Systems Success: A Ten-Year Update WILLIAM H. D E L O N E AND EPHRAIM R. McLEAN WILLIAM DELONE is an Associate Professor of Information Systems and Chair of the Information Technology Department at the Kogod School of Business at American University in Washington, DC. Professor DeLone's primary areas of research include the assessment of information systems effectiveness and value, the implementation and use of information technology in small and medium-sized businesses, and the global management of information technology. He has been published in various journals including Information Systems Research, MIS Quarterly. DATABASE, Jour- nal of Global Information Management, and Journal of Information Technology Management. Professor DeLone earned a B.S. in mathematics from Villanova Uni- versity, an M.S. in industrial administration from Cai'negie Mellon University, and a Ph.D. in Computers and Information Systems from the University of California, Los Angeles. EPHRAIM R. MCLE.'\N is a Regents' Professor and George E. Smith Eminent Scholar's Chair in Information Systems in the Robinson College of Business at Georgia State University, Atlanta. Prior to coming to Georgia State University in 1987, he was on the faculty of the University of California, Los Angeles (UCLA) for 18 years. Dr. McLean's research focuses on the management of information sen/ices, the value of IS investments, and career issues for IS professionals. He has published over 125 papers in such journals as Information Systems Research, Journal of Management Information Systems, MIS Quarterly, Management Science, Communications of the ACM. DATABASE. Han>aril Business Review, Sloan Management Review, and others; and his coauthored book. Information Technology for Management, now in its third edition, is currently the second largest selling IS textbook in the world. Dr. McLean earned his B.M.E. in mechanical engineering from Cornell University and his S.M. and Ph.D. degrees from the Sloan School of Management at the Massachusetts Insti- tute of Technology (MIT). He is also the Executive Director of the Association for Information Systems (AISj and in 1999 was made a Eellow of the AIS. ABSTRACT: Ten years ago, we presented the DeLone and McLean Information Sys- tems (IS) Success Model as a framework and model for measuring the complex- dependent variable in IS research. In this paper, we discuss many of the important IS success research contributions of the last decade, focusing especially on research efforts that apply, validate, challenge, and propose enhancements to our original model. Based on our evaluation of those contributions, we propose minor i efinements to the model and propose an updated DeLone and McLean IS Success Model. We discuss the utility of the updated model for measuring e-commerce system success. Finally, we make a series of recommendations regarding current and future measurement of IS success. Journal of Mantif(.

The DeLone and McLean Model ofInformation Systems Success.docx

arnoldmeredith47041

Resilience: a brief view on the state of the art

Henry Muccini

IDS Impact Innovation and Learning Workshop March 2013: Day 1, Paper Session ...

Institute of Development Studies

Systems Thinking Tools for Climate Resilience Programming Workshop - Nov 2015

Eric Momanyi

RUNNING HEADER: Analytics Ecosystem 1 Analytics Ecosystem 4 Analytics Ecosystem Lisa Garay Rasmussen College Authors Note This paper is being submitted for Anastasia Rashtchian’s B288 Business Analytics Course. This paper looks at the nine clusters of the ecosystem. Clustering refers to a system of grouping functions that are similar so as to set them out from others. It begins by highlighting them before proceeding to defining them. It then identifies clusters that represent technology developers and technology users. Peer reviewed materials are used in this endeavor. They include executive sponsor cluster which contains information that concerns administrators for directing the system. Another one is end-user tools and dashboards cluster that is made of functions that facilitate ability of persons to ultimately engage the system. Data owners cluster is made up of programs that are related to persons who have data in the system. Business users’ cluster is made up of functions that are related to clients of the system. Business applications and systems cluster is made up programs related to features of a given system. Developers cluster is made of programs that are related to the development of programs in the system. Analyst cluster is made up of materials that are related to analysis of data in the system. SME cluster that is made up switches that run SME applications in the system. Lastly, operational data stores that are made up of programs that are concerned with storage of data in a system (Pitelis, 2012). While developers cluster is made up of technology developers in the system, business users’ cluster is made up of technology users in the system. In conclusion, clustering serves to bring roles together as well as separating roles that are not related in a system (Cameron, Gelbach & Miller, 2012). They can be represented as follows:- References Cameron, A. C., Gelbach, J. B., & Miller, D. L. (2012). Robust inference with multiway clustering. Journal of Business & Economic Statistics. Pitelis, C. (2012). Clusters, entrepreneurial ecosystem co-creation, and appropriability: a conceptual framework. Industrial and Corporate Change, dts008. Infrastructure Executive Sponsor Cluster End-user tools and dashboards cluster operational data stores Data Owners Cluster Business users' cluster Business systems and applications cluster Developers Cluster Analysts Cluster SME cluster 4 Running head: Sentiment analysis Sentiment Analysis Lisa Garay Rasmussen College Authors Note This paper is being submitted for Anastashia Rashtcian’s B288 Business Analytics course. Sentiment analysis has played a significant role in the concurrent marketing field, specifically in product marketing. According to Somasundaran, Swapna, (2010), the process’ operational module is structured on a data mining sequence, whereby the end users of given particulars the feedback pertaining a used.

RUNNING HEADER Analytics Ecosystem1Analytics Ecosystem4.docx

anhlodge

Reflective Journal 9: Benefits and Dangers of Social Networks Write a 3/4 to 1 page journal entry (300 to 500 words) in which you: 1. Discuss two or three (2-3) benefits you or others have experienced with social networks. 2. Discuss one or two (1-2) dangers you or others have experienced with social networks. 3. Complete the page requirement. 4. Write with clarity, following mechanics and formatting requirements. The specific course learning outcome(s) associated with this assignment are: · Apply critical thinking skills to the analysis of issues involving mass media and society. · Analyze ways in which different types of media content reflect and / or influence society’s attitudes and behaviors. · Analyze various issues affecting the media business. · Evaluate the effects of new forms of media (e.g., online services) on social interactions. · Write clearly and concisely about media and society using proper writing mechanics. Grading for this assignment will be based on answer quality, and language and writing skills, using the following rubric. Click here to view the grading rubric. Data Analysis and Reporting Chapter 15 Data ManagementIncludes coding, cleaning, and organizing data into a usable format (preparing for analysis) Coding – assigning labels so data can be read and understood by a computer (e.g., 1=yes, 2=no) Cleaning – values are valid and consistent (e.g., 1=true, 2=false, there should be no 3s); Also, need to deal with missing data Data AnalysisBegins with being able to identify the variables Variables – a characteristic or attribute that can be measured or observed (Creswell, 2002) Types of variables: independent (controlled or cause or exert some influence) and dependent (are outcome variables that are being studied) Also, the level(s) of data collected are importantNominal OrdinalNumerical (interval and ratio) Data Analysis (cont.)Descriptive statistics – used to organize, summarize and describe characteristics Inferential statistics – concerned with relationships and causality to make generalizations about a population based on a sample AnalysesUnivariate (1 variable) Bivariate (2 variables)Multivariate (More than 2 variables) Examples of Evaluation Questions Answered Univariate Data AnalysesOne variable at a time Summary counts (frequency distributions) Measures of central tendency – e.g., mean, median, and mode Measures of spread or variation – e.g., range, standard deviation, variance Bivariate AnalysesCan be non-statistical comparisonsExample of non-statistical comparisons (eyeballing the data) Male Female Yes 35 62 No 50 46 Bivariate Analyses (cont.)HypothesesNull: statement of no significant difference Type I error – rejecting the null hypothesis when it is trueType II error – failing to reject the null hypothesis when it is not true (accepting a false null hypothesis)Level of significance (alpha level) – probability of making a type I error; e.g., p<.01A.

Reflective Journal 9 Benefits and Dangers of Social NetworksW.docx

carlt3

Similar to Far ph d-seminar-dec02-2010 (20)

Venkatesh2003

SPM_Metrics_WhitePaper_3

System Adoption: Socio-Technical Integration

System Dynamics Modeling for IntellectualDisability Services.docx

rsos.royalsocietypublishing.orgReviewCite this article .docx

Tablet PC Adoption Model

Model Based Systems Thinking

Doctors 2.0 & YOU - 2017 - Methodological and ethical issues of connected dev...

Chapters 4,5 and 6Into policymaking and modeling in a comple.docx

MIS 05 Decision Support Systems

Running head Multi-actor modelling system 1Multi-actor mod.docx

T OWARDS A S YSTEM D YNAMICS M ODELING M E- THOD B ASED ON DEMATEL

The DeLone and McLean Model ofInformation Systems Success.docx

Resilience: a brief view on the state of the art

IDS Impact Innovation and Learning Workshop March 2013: Day 1, Paper Session ...

Systems Thinking Tools for Climate Resilience Programming Workshop - Nov 2015

RUNNING HEADER Analytics Ecosystem1Analytics Ecosystem4.docx

Reflective Journal 9 Benefits and Dangers of Social NetworksW.docx

Recently uploaded

Top 10 Most Downloaded Games on Play Store in 2024

SynarionITSolutions

Tata AIG General Insurance Company - Insurer Innovation Award 2024

The Digital Insurer

AWS Community Day CPH - Three problems of Terraform

Andrey Devyatkin

The presentation explores the development and application of artificial intelligence (AI) from its inception to its current status in the modern world. The term "artificial intelligence" was first coined by John McCarthy in 1956 to describe efforts to develop computer programs capable of performing tasks that typically require human intelligence. This concept was first introduced at a conference held at Dartmouth College, where programs demonstrated capabilities such as playing chess, proving theorems, and interpreting texts. In the early stages, Alan Turing contributed to the field by defining intelligence as the ability of a being to respond to certain questions intelligently, proposing what is now known as the Turing Test to evaluate the presence of intelligent behavior in machines. As the decades progressed, AI evolved significantly. The 1980s focused on machine learning, teaching computers to learn from data, leading to the development of models that could improve their performance based on their experiences. The 1990s and 2000s saw further advances in algorithms and computational power, which allowed for more sophisticated data analysis techniques, including data mining. By the 2010s, the proliferation of big data and the refinement of deep learning techniques enabled AI to become mainstream. Notable milestones included the success of Google's AlphaGo and advancements in autonomous vehicles by companies like Tesla and Waymo. A major theme of the presentation is the application of generative AI, which has been used for tasks such as natural language text generation, translation, and question answering. Generative AI uses large datasets to train models that can then produce new, coherent pieces of text or other media. The presentation also discusses the ethical implications and the need for regulation in AI, highlighting issues such as privacy, bias, and the potential for misuse. These concerns have prompted calls for comprehensive regulations to ensure the safe and equitable use of AI technologies. Artificial intelligence has also played a significant role in healthcare, particularly highlighted during the COVID-19 pandemic, where it was used in drug discovery, vaccine development, and analyzing the spread of the virus. The capabilities of AI in healthcare are vast, ranging from medical diagnostics to personalized medicine, demonstrating the technology's potential to revolutionize fields beyond just technical or consumer applications. In conclusion, AI continues to be a rapidly evolving field with significant implications for various aspects of society. The development from theoretical concepts to real-world applications illustrates both the potential benefits and the challenges that come with integrating advanced technologies into everyday life. The ongoing discussion about AI ethics and regulation underscores the importance of managing these technologies responsibly to maximize their their benefits while minimizing potential harms.

Artificial Intelligence: Facts and Myths

Joaquim Jorge

Effective data discovery is crucial for maintaining compliance and mitigating risks in today's rapidly evolving privacy landscape. However, traditional manual approaches often struggle to keep pace with the growing volume and complexity of data. Join us for an insightful webinar where industry leaders from TrustArc and Privya will share their expertise on leveraging AI-powered solutions to revolutionize data discovery. You'll learn how to: - Effortlessly maintain a comprehensive, up-to-date data inventory - Harness code scanning insights to gain complete visibility into data flows leveraging the advantages of code scanning over DB scanning - Simplify compliance by leveraging Privya's integration with TrustArc - Implement proven strategies to mitigate third-party risks Our panel of experts will discuss real-world case studies and share practical strategies for overcoming common data discovery challenges. They'll also explore the latest trends and innovations in AI-driven data management, and how these technologies can help organizations stay ahead of the curve in an ever-changing privacy landscape.

TrustArc Webinar - Unlock the Power of AI-Driven Data Discovery

TrustArc

Discord is a free app offering voice, video, and text chat functionalities, primarily catering to the gaming community. It serves as a hub for users to create and join servers tailored to their interests. Discord’s ecosystem comprises servers, each functioning as a distinct online community with its own channels dedicated to specific topics or activities. Users can engage in text-based discussions, voice calls, or video chats within these channels. Understanding Discord Servers Discord servers are virtual spaces where users congregate to interact, share content, and build communities. Servers may revolve around gaming, hobbies, interests, or fandoms, providing a platform for like-minded individuals to connect. Communication Features Discord offers a range of communication tools, including text channels for messaging, voice channels for real-time audio conversations, and video channels for face-to-face interactions. These features facilitate seamless communication and collaboration. What Does NSFW Mean? The acronym NSFW stands for “Not Safe For Work,” indicating content that may be inappropriate for professional or public settings. NSFW Content NSFW content encompasses material that is sexually explicit, violent, or otherwise graphic in nature. It often includes nudity, profanity, or depictions of sensitive topics.

Understanding Discord NSFW Servers A Guide for Responsible Users.pdf

UK Journal

Polkadot JAM Slides - Token2049 - By Dr. Gavin Wood

Juan lago vázquez

Repurposing LNG terminals for Hydrogen Ammonia: Feasibility and Cost Saving

Edi Saputra

This presentation explores the impact of HTML injection attacks on web applications, detailing how attackers exploit vulnerabilities to inject malicious code into web pages. Learn about the potential consequences of such attacks and discover effective mitigation strategies to protect your web applications from HTML injection vulnerabilities. for more information visit https://bostoninstituteofanalytics.org/category/cyber-security-ethical-hacking/

HTML Injection Attacks: Impact and Mitigation Strategies

Boston Institute of Analytics

Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024

The Digital Insurer

Axa Assurance Maroc - Insurer Innovation Award 2024

The Digital Insurer

Scaling API-first – The story of a global engineering organization

Radu Cotescu

Automating Google Workspace (GWS) & more with Apps Script

wesley chun

Artificial Intelligence Chap.5 : Uncertainty

Khushali Kathiriya

With more memory available, system performance of three Dell devices increased, which can translate to a better user experience Conclusion When your system has plenty of RAM to meet your needs, you can efficiently access the applications and data you need to finish projects and to-do lists without sacrificing time and focus. Our test results show that with more memory available, three Dell PCs delivered better performance and took less time to complete the Procyon Office Productivity benchmark. These advantages translate to users being able to complete workflows more quickly and multitask more easily. Whether you need the mobility of the Latitude 5440, the creative capabilities of the Precision 3470, or the high performance of the OptiPlex Tower Plus 7010, configuring your system with more RAM can help keep processes running smoothly, enabling you to do more without compromising performance.

Boost PC performance: How more available memory can improve productivity

Principled Technologies

ProductAnonymous-April2024-WinProductDiscovery-MelissaKlemke

Product Anonymous

How to Troubleshoot Apps for the Modern Connected Worker

ThousandEyes

The Good, the Bad and the Governed - Why is governance a dirty word? David O'Neill, Chief Operating Officer - APIContext Apidays New York 2024: The API Economy in the AI Era (April 30 & May 1, 2024) ------ Check out our conferences at https://www.apidays.global/ Do you want to sponsor or talk at one of our conferences? https://apidays.typeform.com/to/ILJeAaV8 Learn more on APIscene, the global media made by the community for the community: https://www.apiscene.io Explore the API ecosystem with the API Landscape: https://apilandscape.apiscene.io/

Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...

apidays

In this session, we will delve into strategic approaches for optimizing knowledge management within Microsoft 365, amidst the evolving landscape of Copilot. From leveraging automatic metadata classification and permission governance with SharePoint Premium, to unlocking Viva Engage for the cultivation of knowledge and communities, you will gain actionable insights to bolster your organization's knowledge-sharing initiatives. In this session, we will also explore how to facilitate solutions to enable your employees to find answers and expertise within Microsoft 365. You will leave equipped with practical techniques and a deeper understanding of how there is more to effective knowledge management than just enabling Copilot, but building actual solutions to prepare the knowledge that Copilot and your employees can use.

Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...

Drew Madelung

Partners Life - Insurer Innovation Award 2024

The Digital Insurer

Recently uploaded (20)

Top 10 Most Downloaded Games on Play Store in 2024

Tata AIG General Insurance Company - Insurer Innovation Award 2024

AWS Community Day CPH - Three problems of Terraform

Artificial Intelligence: Facts and Myths

TrustArc Webinar - Unlock the Power of AI-Driven Data Discovery

Understanding Discord NSFW Servers A Guide for Responsible Users.pdf

Polkadot JAM Slides - Token2049 - By Dr. Gavin Wood

Repurposing LNG terminals for Hydrogen Ammonia: Feasibility and Cost Saving

HTML Injection Attacks: Impact and Mitigation Strategies

Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024

Axa Assurance Maroc - Insurer Innovation Award 2024

Scaling API-first – The story of a global engineering organization

Automating Google Workspace (GWS) & more with Apps Script

Artificial Intelligence Chap.5 : Uncertainty

Boost PC performance: How more available memory can improve productivity

ProductAnonymous-April2024-WinProductDiscovery-MelissaKlemke

How to Troubleshoot Apps for the Modern Connected Worker

Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...

Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...

Partners Life - Insurer Innovation Award 2024

Far ph d-seminar-dec02-2010

1. Artificial Intelligence Techniques for Modeling Systems' Sustainability: A Review Amin Hosseinian-Far Research Team: Prof. Hamid Jahankhani Mr. Elias Pimenidis Prof. D. C. Wijeyesekera

8. Pictorial Visualizations Models

9. Physical Models

10. Conceptual Models

11. Standardizing Models

12.

13. Categories of Intelligent Systems

14. Influence Diagrams

15. Analytica

16.

17.

18.

19.