Evolving Role of MIM in an Organization. This slide is part of Course Multimedia INformation Management (Magister Teknologi Infomasi, University of Indonesia)
Data analytics with managerial application ass 2Nishant Kumar
This presentation depicts insights of the article "Data Scientist: The Sexiest Job of the 21st Century", and also how these insight are relevant to a manager in india.
Don't Handicap AI without Explicit KnowledgeAmit Sheth
Keynote at IEEE Services 2021: Abstract: https://conferences.computer.org/services/2021/keynotes/sheth.html
Video: https://lnkd.in/d-r3YXaC
Video of the same keynote given at DEXA2021: https://www.youtube.com/watch?v=u-06kK9TysA
September 9, 2021 15:00 - 16:20 UTC
ABSTRACT
Knowledge representation as expert system rules or using frames and a variety of logics played a key role in capturing explicit knowledge during the hay days of AI in the past century. Such knowledge, aligned with planning and reasoning is part of what we refer to as Symbolic AI. The resurgent AI of this century in the form of Statistical AI has benefitted from massive data and computing. On some tasks, deep learning methods have even exceeded human performance levels. This gave the false sense that data alone is enough, and explicit knowledge is not needed. But as we start chasing machine intelligence that is comparable with human intelligence, there is an increasing realization that we cannot do without explicit knowledge. Neuroscience (role of long-term memory, strong interactions between different specialized regions of data on tasks such as multimodal sensing), cognitive science (bottom brain versus top brain, perception versus cognition), brain-inspired computing, behavioral economics (system 1 versus system 2), and other disciplines point to need for furthering AI to neuro-symbolic AI (i.e., hybrid of Statistical AI and Symbolic AI, also referred to as the third wave of AI). As we make this progress, the role of explicit knowledge becomes more evident. I will specifically look at our endeavor to support human-like intelligence, our desire for AI systems to interact with humans naturally, and our need to explain the path and reasons for AI systems’ workings. Nevertheless, the variety of knowledge needed to support understanding and intelligence is varied and complex. Using the example of progressing from NLP to NLU, I will demonstrate the dimensions of explicit knowledge, which may include, linguistic, language syntax, common sense, general (world model), specialized (e.g., geographic), and domain-specific (e.g., mental health) knowledge. I will also argue that despite this complexity, such knowledge can be scalability created and maintained (even dynamically or continually). Finally, I will describe our work on knowledge-infused learning as an example strategy for fusing statistical and symbolic AI in a variety of ways.
Data analytics with managerial application ass 2Nishant Kumar
This presentation depicts insights of the article "Data Scientist: The Sexiest Job of the 21st Century", and also how these insight are relevant to a manager in india.
Don't Handicap AI without Explicit KnowledgeAmit Sheth
Keynote at IEEE Services 2021: Abstract: https://conferences.computer.org/services/2021/keynotes/sheth.html
Video: https://lnkd.in/d-r3YXaC
Video of the same keynote given at DEXA2021: https://www.youtube.com/watch?v=u-06kK9TysA
September 9, 2021 15:00 - 16:20 UTC
ABSTRACT
Knowledge representation as expert system rules or using frames and a variety of logics played a key role in capturing explicit knowledge during the hay days of AI in the past century. Such knowledge, aligned with planning and reasoning is part of what we refer to as Symbolic AI. The resurgent AI of this century in the form of Statistical AI has benefitted from massive data and computing. On some tasks, deep learning methods have even exceeded human performance levels. This gave the false sense that data alone is enough, and explicit knowledge is not needed. But as we start chasing machine intelligence that is comparable with human intelligence, there is an increasing realization that we cannot do without explicit knowledge. Neuroscience (role of long-term memory, strong interactions between different specialized regions of data on tasks such as multimodal sensing), cognitive science (bottom brain versus top brain, perception versus cognition), brain-inspired computing, behavioral economics (system 1 versus system 2), and other disciplines point to need for furthering AI to neuro-symbolic AI (i.e., hybrid of Statistical AI and Symbolic AI, also referred to as the third wave of AI). As we make this progress, the role of explicit knowledge becomes more evident. I will specifically look at our endeavor to support human-like intelligence, our desire for AI systems to interact with humans naturally, and our need to explain the path and reasons for AI systems’ workings. Nevertheless, the variety of knowledge needed to support understanding and intelligence is varied and complex. Using the example of progressing from NLP to NLU, I will demonstrate the dimensions of explicit knowledge, which may include, linguistic, language syntax, common sense, general (world model), specialized (e.g., geographic), and domain-specific (e.g., mental health) knowledge. I will also argue that despite this complexity, such knowledge can be scalability created and maintained (even dynamically or continually). Finally, I will describe our work on knowledge-infused learning as an example strategy for fusing statistical and symbolic AI in a variety of ways.
The Future of Business Intelligence: Data VisualizationKristen Sosulski
Kristen Sosulski
The future of business intelligence: Data Visualization
How can data visualization be used as a platform to reveal intelligent insights and help business analysts make timely decisions? In this talk, Kristen Sosulski will discuss the opportunities for personalized, location aware, context relevant, and platform independent information visualizations as a toolkit for business analysts.
Knowledge Graphs and their central role in big data processing: Past, Present...Amit Sheth
Keynote at CODS-COMAD 2020, Hyderabad, India, 06 Jan 2020: https://cods-comad.in/keynotes.html
Abstract : Early use of knowledge graphs, before the start of this century, related to building a knowledge graph manually or semi-automatically and applying them for semantic applications, such as search, browsing, personalization, and advertisement. Taalee/Semagix Semantic Search in 2000 had a KG that covered many domains and supported search with an equivalent of today’s infobox. Along with the growth of big data, machine learning became the preferred technique for searching, analyzing and deriving insights from such data. We observed the complementary nature of bottom-up (machine learning-driven) and top-down (semantic, knowledge graph and planning based) techniques. Recently we have seen growing efforts involving the shallow use of a knowledge graph to improve the semantic and conceptual processing of data. The future promises deeper and congruent incorporation or integration of the knowledge graphs in the learning techniques (which we call knowledge-infused learning), where knowledge graphs combining statistical AI (bottom-up) and symbolic AI learning techniques (top-down) play a critical role in hybrid and integrated intelligent systems. Throughout this talk, we will provide real-world examples, products, and applications where the knowledge graph played a pivotal role.
This is just to help my people who wants to pursue their career as a Data Scientist.
I strongly believe that 'We rise by lifting others'.
I made this for one of my project work thought to share it here. Hope you guys will like it. Please feel free to suggest changes for better.
Explains: What is Data Science? What is the difference between Data Science and Data Engineering, and between Data Science and Business Intelligence? What type of work do Data Scientists do, and what types of companies employ them? What is the job outlook for Data Science? What professional education is required?
Explorative computing and consulting - a love affair Ablona AB
Engagement model for combining explorative computing with management consulting. The case is from the "medtech" sector. Also applicable in other domains (Saas, finance, venture capital). The computing platform is Wolfram
Esta es una propuesta para articular el curriculum con las TIC's basada en la experiencia. Se incluyen las barreras que es necesario vencer y las preguntas que toda organización debe hacerse para el logro de este objetivo
The Future of Business Intelligence: Data VisualizationKristen Sosulski
Kristen Sosulski
The future of business intelligence: Data Visualization
How can data visualization be used as a platform to reveal intelligent insights and help business analysts make timely decisions? In this talk, Kristen Sosulski will discuss the opportunities for personalized, location aware, context relevant, and platform independent information visualizations as a toolkit for business analysts.
Knowledge Graphs and their central role in big data processing: Past, Present...Amit Sheth
Keynote at CODS-COMAD 2020, Hyderabad, India, 06 Jan 2020: https://cods-comad.in/keynotes.html
Abstract : Early use of knowledge graphs, before the start of this century, related to building a knowledge graph manually or semi-automatically and applying them for semantic applications, such as search, browsing, personalization, and advertisement. Taalee/Semagix Semantic Search in 2000 had a KG that covered many domains and supported search with an equivalent of today’s infobox. Along with the growth of big data, machine learning became the preferred technique for searching, analyzing and deriving insights from such data. We observed the complementary nature of bottom-up (machine learning-driven) and top-down (semantic, knowledge graph and planning based) techniques. Recently we have seen growing efforts involving the shallow use of a knowledge graph to improve the semantic and conceptual processing of data. The future promises deeper and congruent incorporation or integration of the knowledge graphs in the learning techniques (which we call knowledge-infused learning), where knowledge graphs combining statistical AI (bottom-up) and symbolic AI learning techniques (top-down) play a critical role in hybrid and integrated intelligent systems. Throughout this talk, we will provide real-world examples, products, and applications where the knowledge graph played a pivotal role.
This is just to help my people who wants to pursue their career as a Data Scientist.
I strongly believe that 'We rise by lifting others'.
I made this for one of my project work thought to share it here. Hope you guys will like it. Please feel free to suggest changes for better.
Explains: What is Data Science? What is the difference between Data Science and Data Engineering, and between Data Science and Business Intelligence? What type of work do Data Scientists do, and what types of companies employ them? What is the job outlook for Data Science? What professional education is required?
Explorative computing and consulting - a love affair Ablona AB
Engagement model for combining explorative computing with management consulting. The case is from the "medtech" sector. Also applicable in other domains (Saas, finance, venture capital). The computing platform is Wolfram
Esta es una propuesta para articular el curriculum con las TIC's basada en la experiencia. Se incluyen las barreras que es necesario vencer y las preguntas que toda organización debe hacerse para el logro de este objetivo
Data science is the study of where information comes from, what it represents and how it can be turned into a valuable resource in the creation of business and IT strategies. Mining large amounts of structured and unstructured data to identify patterns can help an organization rein in costs, increase efficiencies, recognize new market opportunities and increase the organization's competitive advantage.
מצגת הסוקרת נקודות מרכזיות בתחום ניהול הידע כיום.
למה מתכוונים כשאומרים ניהול ידע? מה זה ידע? מה נופל תחת הכותרת של ניהול ידע? מהם סוגי הידע? היכן נמצא המידע? כיצד מתמודדים עם הצפת מידע? כיצד האינטרנט מסייע בתהליך? מהם המכשולים בדרך? למה לשתף בידע? התשתית הנדרשת, תרבות ארגונית, תפקיד מנהל הידע, וכלים לבחירת טכנולוגיה.
המצגת באנגלית מאת שמעון ברק מנהל ידע באמדוקס הוצגה בקורס ניהול ידע.
Knowledge is more than power , its a game changer and it is the means of sustainable progression and development... Its a quick glance and short introduction of knowledge and knowledge management.
Understanding Data Science: Unveiling the Basics
What is Data Science?
Data science is an interdisciplinary field that combines techniques from statistics, mathematics, computer science, and domain knowledge to extract insights and knowledge from data. It involves collecting, processing, analyzing, and interpreting large and complex datasets to solve real-world problems.
Importance of Data Science
In today's data-driven world, organizations are inundated with data from various sources. Data science allows them to convert this raw data into actionable insights, enabling informed decision-making, improved efficiency, and innovation.
Intersection of Data Science, Statistics, and Computer Science
Data science borrows heavily from statistics and computer science. Statistical methods help in understanding data patterns, while computer science provides the tools to process and analyze large datasets efficiently.
Key Components of Data Science
Data Collection and Storage
The first step in data science is gathering relevant data from various sources. This data is then stored in databases or data warehouses for further processing.
Data Cleaning and Preprocessing
Raw data is often messy and inconsistent. Data cleaning involves removing errors, duplicates, and irrelevant information. Preprocessing includes transforming data into a usable format.
Exploratory Data Analysis (EDA)
EDA involves visualizing and summarizing data to uncover patterns, trends, and anomalies. It helps in forming hypotheses and guiding further analysis.
Machine Learning and Predictive Modeling
Machine learning algorithms are used to build predictive models from data. These models can make predictions and decisions based on new, unseen data.
Data Visualization
Visual representations of data, such as graphs and charts, help in understanding complex information quickly. Data visualization aids in conveying insights effectively.
The Data Science Process
Problem Definition
The data science process begins with understanding the problem you want to solve and defining clear objectives.
Data Collection and Understanding
Collect relevant data and understand its context. This step is crucial as the quality of the analysis depends on the quality of the data.
Data Preparation
Clean, preprocess, and transform the data into a suitable format for analysis. This step ensures that the data is accurate and ready for modeling.
Model Building
Select appropriate algorithms and build predictive models using machine learning techniques. This step involves training and fine-tuning the models.
Model Evaluation and Deployment
Evaluate the model's performance using metrics and test datasets. If the model performs well, deploy it for making predictions on new data.
Technologies Driving Data Science
Programming Languages
Languages like Python and R are widely used in data science due to their extensive libraries and versatility.
Machine Learning Libraries
Libraries like Scikit-Learn and TensorFlow prov
Strategic Workforce Planning: Key Principles and Objectives, Paul TurnerThe HR Observer
Making sure that we have the right people in the right place with the right level of skills at the right time to deliver both short and long term objectives requires information and insight. This need has sparked a growing interest in workforce planning. Organisations have identified a compelling need to be able to ‘shape’ and skill themselves to deal with both expected and unexpected events: as well as to control costs without damaging competitiveness. Strategic Workforce Planning (SWP) supports these objectives in the quest to become flexible and agile. SWP is a core process of human resource management. It helps HR Professionals to provide insight to an organisation’s competitive advantage through people. This session will cover some of the objectives, principles and models used in SWP, together with case studies of best practice.
This presentation was used at HR Summit and Expo 2013 www.hrsummitexpo.com
Definition of Knowledge Management
Forces Driving Knowledge Management Data, Information and Knowledge
Importance of Knowledge
Managing Knowledge
Organizational Learning
Through Knowledge Management
Materi webinar yang diselenggarakan oleh PHP Indonesia secara live di Facebook PHP-ID. Topik yang dibahas mengenai Machine Learning dengan PHP, baik sisi konsep maupun implementasinya.
Webinar Data Mining dengan Rapidminer | Universitas Budi LuhurAchmad Solichin
Materi Webinar Data Mining dengan Rapidminer di Universitas Budi Luhur yang diselenggarakan oleh mahasiswa S2 Ilmu Komputer Universitas Budi Luhur pada hari Jumat, 8 Januari 2021
TREN DAN IDE RISET BIDANG DATA MINING TERBARUAchmad Solichin
Presentasi ini menyajikan Tren dan Ide Riset Bidang DATA MINING Tahun ini. Wajib disimak! Disertai juga puluhan Contoh Paper Penelitian Terkini di bidang Data Mining. Saya menjelaskan banyak ide penelitian untuk skripsi, tesis, disertasi, dll. Simak sampe akhir ya.
Materi Seminar: Artificial Intelligence dengan PHPAchmad Solichin
Materi Seminar: Artificial Intelligence dengan PHP ini disampaikan pada Seminar online yang diselenggarakan oleh Lab ICT Universitas Budi Luhur pada hari Rabu, 12 Agustus 2020
Slide ini menjelaskan mengenai konsep dan langkah-langkah Algoritma Depth First Search (BFS) pada Graph.
Slide disusun oleh Achmad Solichin (http://achmatim.net)
Slide ini menjelaskan mengenai konsep dan langkah-langkah Algoritma Breadth First Search (BFS) pada Graph.
Slide disusun oleh Achmad Solichin | http://achmatim.net
Materi seminar ini menjelaskan mengenai konsep dasar computer vision dan aplikasinya di era Industri 4.0. Materi seminar ini disampaikan pada acara Seminar Tahunan IT yang diselenggarakan oleh Lab ICT Universitas Budi Luhur
Unit 8 - Information and Communication Technology (Paper I).pdfThiyagu K
This slides describes the basic concepts of ICT, basics of Email, Emerging Technology and Digital Initiatives in Education. This presentations aligns with the UGC Paper I syllabus.
Macroeconomics- Movie Location
This will be used as part of your Personal Professional Portfolio once graded.
Objective:
Prepare a presentation or a paper using research, basic comparative analysis, data organization and application of economic information. You will make an informed assessment of an economic climate outside of the United States to accomplish an entertainment industry objective.
Safalta Digital marketing institute in Noida, provide complete applications that encompass a huge range of virtual advertising and marketing additives, which includes search engine optimization, virtual communication advertising, pay-per-click on marketing, content material advertising, internet analytics, and greater. These university courses are designed for students who possess a comprehensive understanding of virtual marketing strategies and attributes.Safalta Digital Marketing Institute in Noida is a first choice for young individuals or students who are looking to start their careers in the field of digital advertising. The institute gives specialized courses designed and certification.
for beginners, providing thorough training in areas such as SEO, digital communication marketing, and PPC training in Noida. After finishing the program, students receive the certifications recognised by top different universitie, setting a strong foundation for a successful career in digital marketing.
This slide is special for master students (MIBS & MIFB) in UUM. Also useful for readers who are interested in the topic of contemporary Islamic banking.
Biological screening of herbal drugs: Introduction and Need for
Phyto-Pharmacological Screening, New Strategies for evaluating
Natural Products, In vitro evaluation techniques for Antioxidants, Antimicrobial and Anticancer drugs. In vivo evaluation techniques
for Anti-inflammatory, Antiulcer, Anticancer, Wound healing, Antidiabetic, Hepatoprotective, Cardio protective, Diuretics and
Antifertility, Toxicity studies as per OECD guidelines
Executive Directors Chat Leveraging AI for Diversity, Equity, and InclusionTechSoup
Let’s explore the intersection of technology and equity in the final session of our DEI series. Discover how AI tools, like ChatGPT, can be used to support and enhance your nonprofit's DEI initiatives. Participants will gain insights into practical AI applications and get tips for leveraging technology to advance their DEI goals.
Read| The latest issue of The Challenger is here! We are thrilled to announce that our school paper has qualified for the NATIONAL SCHOOLS PRESS CONFERENCE (NSPC) 2024. Thank you for your unwavering support and trust. Dive into the stories that made us stand out!
A review of the growth of the Israel Genealogy Research Association Database Collection for the last 12 months. Our collection is now passed the 3 million mark and still growing. See which archives have contributed the most. See the different types of records we have, and which years have had records added. You can also see what we have for the future.
Operation “Blue Star” is the only event in the history of Independent India where the state went into war with its own people. Even after about 40 years it is not clear if it was culmination of states anger over people of the region, a political game of power or start of dictatorial chapter in the democratic setup.
The people of Punjab felt alienated from main stream due to denial of their just demands during a long democratic struggle since independence. As it happen all over the word, it led to militant struggle with great loss of lives of military, police and civilian personnel. Killing of Indira Gandhi and massacre of innocent Sikhs in Delhi and other India cities was also associated with this movement.
1. Evolving Role of MIM in an O rganization By: Zainal A. Hasibuan Magister of Information Technology Faculty of Computer Science, University of Indonesia [email_address]
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5. Evolving Data to Wisdom Wisdom Knowledge Information + Judgment Information Data + Context Data Unorganized Facts Less Structured More Structured 60 90 50 80 70 70 Rata-rata Ujian Standard Deviasi 70 15 Lulus: >= 70-15 Gagal: < 70-15 60, 70, 80, 90 50 Yang tidak lulus, diberi kesempatan mengulang sekali lagi
6. To what extend IT can play its role?? Less Structured More Structured Less Robust More Robust
7. Strategic Level Wisdom Knowledge Information + Judgment Information Data + Context Data Unorganized Facts More Strategic TPS MIS EIS