Decision Support Systems Module 2 PDF

Decision Support Systems College of Computing and Informatics 1  Module 2 Chapter 2: Artificial Intelligence Concepts, Drivers, Major Technologies, and Business Applications Analytics, Data Science, & Artificial Intelligence Systems For Decision Support This Pr...

Decision Support Systems College of Computing and Informatics 1  Module 2 Chapter 2: Artificial Intelligence Concepts, Drivers, Major Technologies, and Business Applications Analytics, Data Science, & Artificial Intelligence Systems For Decision Support This Presentation is mainly dependent on this textbook 2  Contents o 2.2 - Introduction to Artificial Intelligence o 2.3 - Human and Computer Intelligence o 2.4 - Major AI Technologies and Some Derivatives o 2.5 - AI Support for Decision Making o 2.6 - AI Applications in Accounting o 2.7 - AI in Human Resource Management (HRM) o 2.8 - AI in Marketing, Advertising, and CRM o 2.9 - AI Applications in Financial Services o 2.10 - AI Applications in Production-Operation Management (POM) 3  Weekly Learning Outcomes 1. Understand the concepts of artificial intelligence (AI) 2. Become familiar with the drivers, capabilities, and benefits of AI 3. Describe human and machine intelligence 4. Describe the major AI technologies and some derivatives 5. Discuss the manner in which AI supports decision making 6. Describe AI applications in accounting, human resource management, marketing, financial Services and in Production-Operation Management (POM) 4  Required Reading  Chapter 2: “Analytics, Data Science, & Artificial Intelligence: Systems for Decision Support” from “Analytics, Data Science, & Artificial Intelligence: Systems for Decision Support”. Recommended Reading  AI-powered decision support systems, what are they? https://blog.pwc.lu/ai-powered-decision-support-systems-what-are-th ey/ Recommended Videos  Artificial intelligence and decision-making (by Thorbjørn Knudsen) https://www.youtube.com/watch?v=Sujww4njwE4 5  2.2 Introduction To Artificial Intelligence Definition of AI Major Characteristics of AI Machines Major Elements of AI AI Applications Major Goals of AI Drivers of AI Benefits of AI Some Limitations of AI Machines Three Flavors of AI Decisions Artificial Brain 6  Definition of AI Artificial intelligence has several definitions that is concerned with two basic ideas: o The study of human thought processes (to understand what intelligence is) o The representation and duplication of those thought processes in machines (e.g., computers, robots) Another definition of AI is “the capabilities of a machine to imitate intelligent human behavior” 7  Major Characteristics of AI Machines There is an increasing trend to make computers “smarter”. o Web 3.0 enables computerized systems that exhibit more intelligence than Web 2.0. Several applications are already based on multiple AI techniques. o Machine translation of languages is helping people who speak different languages to collaborate in real time as well as to buy online products that are advertised in different languages. 8  Major Elements of AI AI components can be divided into two groups: Foundations, and Technologies & Applications. 9  AI Applications Smart or intelligent applications include: Machines to answer customers’ questions asked in natural languages Knowledge-based systems which can provide advice, assist people to make decisions, and even make decisions on their own Automatic generating of online purchasing orders and arranging fulfillment of orders placed online. Shipping prices are determined automatically based on the dimensions, weight, and packaging. 10  Major Goals of AI The overall goal of AI is to create intelligent machines that are capable of executing a variety of tasks currently done by people. AI machines should be able to reason, think abstractly, plan, solve problems, and learn. Some specific goals are to: o Perceive and properly react to changes in the environment that influence specific business processes and operations o Introduce creativity in business processes and decision making 11  Drivers of AI The use of AI has been driven by the following: o People’s interest in smart machines and artificial brains o The low cost of AI applications versus the high cost of manual labor (doing the same work) o The desire of large tech companies to capture competitive advantage and market share of the AI market and their willingness to invest billions of dollars in AI o The pressure on management to increase productivity and speed o The availability of quality data contributing to the progress of AI o The increasing functionalities and reduced cost of computers 12 in general  Benefits of AI o AI has the ability to complete certain tasks faster than humans. o The consistency of AI work. AI machines do not stop, or sleep. o AI systems allow for continuous improvement projects. o AI can be used for predictive analysis via its capability of pattern recognition. o AI can manage delays and blockages in business processes. o AI machines can work autonomously or be assistants to humans. o AI machines can learn, improve its performance, and work in hazardous environments. o AI machines can facilitate innovations by human (i.e., support research and development) o AI excels in fraud detection and in security facilitations. 13 o AI can free employees to work on more complex and productive jobs.  Some Limitations of AI Machines The following are the major limitations of AI machines: o Lack human touch and feel o Lack attention to non-task surroundings o Can lead people to rely on AI machines (e.g., people may stop to think on their own) o Can be programmed to create destruction o Can cause many people to lose their jobs o Can start to think by themselves, diminishing with time. However, risks exist. Therefore, it is necessary to properly causing significant damage Some of the limitations are diminishing with time. However, 14 risks exist. Therefore, it is necessary to improve AI development and minimize the risks.  Artificial Brain The artificial brain is a machine that is desired to be as intelligent, creative, and self-aware as humans. To date, no one has created such a machine. The following are some differences between traditional and augmented AI: o Augmented machines extend rather than replace human decision making o Augmentation excels in solving complex human and industry problems in specific domains in contrast with strong, general AI. o In contrast with a “black box” model of some AI and analytics, augmented intelligence provides insights and 15 recommendations, including explanations.  2.3 Human and Computer Intelligence A. What Is Intelligence? B. How Intelligent Is AI? C. Measuring AI 16  What Is Intelligence? Intelligence is a broad term measured by an IQ test. To understand what artificial intelligence is, it is useful to first examine those abilities that are considered signs of human intelligence: o Learning or understanding from experience o Making sense out of ambiguous, incomplete, or even contradictory messages and information o Responding quickly and successfully to a new situation o Understanding/inferring in a rational way, and solving problems o Applying knowledge to manipulate environments and 17 situations  How Intelligent Is AI? AI machines have demonstrated superiority over humans in playing complex games such as chess, Jeopardy!, and Go by defeating the world’s best players. Despite this many AI applications still show significantly less intelligence than humans. 18  Measuring AI The Turing Test is a well-known attempt to measure the intelligence level of AI machines. It aims to determine whether a computer exhibits intelligent behavior. A computer can be considered smart only when a human interviewer asking the same questions to both an unseen human and an unseen computer cannot determine which is which. To pass the Turing Test, a computer needs to be able to understand a human language (NLP), to possess human intelligence (e.g., have a knowledge base), to reason using its stored knowledge, and to be able to learn 19 from its experiences (machine  2.4 Major AI Technologies And Some Derivatives Intelligent Agents Machine Learning Deep Learning Machine and Computer Vision Robotic Systems Natural Language Processing Knowledge and Expert Systems and Recommenders Chatbots 20 Emerging AI Technologies  Intelligent Agents An intelligent agent (IA) is a small computer software program that observes and acts upon changes in its environment by running specific tasks autonomously. An IA directs an agent’s activities to achieve specific goals related to the changes in the surrounding environment. IAs have the ability to learn by using their knowledge Example 1: An example of an intelligent software agent is a virus detection program. It resides in a computer, scans incoming data, and removes viruses automatically while learning to detect new virus types and detection methods. Example 2: Allstate Business Insurance uses an intelligent agent to 21 reduce call center traffic and provide human insurance agents during  Machine Learning Machine Learning (ML) is a discipline concerned with design & development of algorithms that allow computers to learn based on incoming data. ML allows computer systems to monitor and sense their environment, so that the machines can adjust their behavior to deal with the changes ML scientists teach computers to identify patterns and make connections by showing the machines a large volume of examples and related data. ML used for predicting, recognizing patterns, & supporting decision makers. An example is computers detecting credit card fraud. ML applications are expanding due to the availability of Big Data 22 sources, especially those provided by the IoT.  Deep Learning One subset of machine learning is called deep learning; a technology tries to mimic how the human brain works. Deep learning (DL) uses artificial neural technology and deals with complex applications that regular machine learning and AI technologies can not handle. For example, DL is a key technology in autonomous vehicles by helping to interpret road signs and road obstacles. DL is mostly useful in real-time interactive applications in the areas of machine vision, scene recognition, robotics, and speech and voice processing. 23  Machine and Computer Vision Machine vision includes “technology and methods used to provide imaging-based automated inspection and analysis for applications such as robot guidance, process control, autonomous vehicles, and inspection.” Computer vision “is an interdisciplinary field that deals with how computers can be made for gaining high-level understanding from digital images or videos. From the perspective of engineering, it seeks to automate tasks that the human visual system can do.” 24  Machine and Computer Vision The two technologies are combined with image processing that facilitates complex applications, such as visual quality control. Applied area of machine vision is scene recognition, done by computer vision. Video analytics is a derivative application of computer vision, where techniques are applied to videos to enable pattern recognition and identify events. Example of Applications: o The machine vision wood identification project developed a prototype machine vision system for wood identification to help identify illegal logging. 25 o AI computer vision mixed with deep learning identifies illegal animal poachers.  Robotic Systems A robot is a device guided by a program to perform manual/mental tasks. An “intelligent” robot has a sensory apparatus (e.g., camera) to collects information about the surroundings and can respond to the changes in the environment. Autonomous robots (programmed to do tasks completely on their own, even repair themselves), are equipped with AI intelligent agents. Example: Walmart Is Using Robots o In Walmart, 2-foot-tall robots use cameras/sensors to scan the shelves for misplaced, missing, or mispriced 26 items. The results are transmitted to  Natural Language Processing Natural language processing (NLP) allows users to communicate with a computer in their native language. NLP includes two subfields: o NLP that investigates methods of enabling computers to comprehend instructions or queries provided in English or other human languages. o NLP generation that strives to have computers produce ordinary spoken language so that people can understand the computers more easily. Speech/Voice Understanding: recognition & comprehension of spoken languages by a computer. This has been adopted in automated call centers. 27 Machine Translation of Languages: uses computer  Knowledge and Expert Systems and Recommenders These systems are computer programs that store knowledge, which their applications use to generate expert advice and/or perform problem solving. Knowledge-based expert systems help people to verify information and make certain types of automated routine decisions. Recommendation systems are knowledge-based systems that make recommendations to people. Another knowledge system is chatbots. Knowledge Sources And Acquisition For Intelligent: Intelligent systems must gain 28 knowledge through knowledge acquisition.  Chatbots Robots come in several shapes and types, of which is a chatbot. A chatbot is a conversional robot that is used for chatting with people using NLP technology. Depending on the purpose of the chat, which can be done in writing or by voice, bots can be in the form of intelligent agents that retrieve information or personal assistants that provide advice. 29  Emerging AI Technologies Several new AI technologies are emerging. Here are a few examples: o Effective computing. Technologies that detect emotional conditions of people and suggest how to deal with discovered problems o Biometric analysis. Technologies that verify an identity based on unique biological traits that are compared to stored ones (e.g., facial recognition). Cognitive Computing: The application of knowledge derived from cognitive science so that computers can exhibit and/or support decision-making and problem-solving capabilities. Augmented Reality: Augmented reality (AR) refers to the real 30 time integration of digital information with a users environment  2.5 AI Support For Decision Making Issues and Factors in Using AI in Decision Making AI Support of the Decision-Making Process Automated Decision Making 31  Issues and Factors in Using AI in Decision Making These factors determine the justification of AI usage and its chance of success: o The nature of the decision (E.g., routine decisions = likely automated) o The method of support, what technologies are used Cost-benefit & Risk Analyses: necessary for large-scale decisions, but hard to compute with AI models due to difficulties in measuring costs, risks, & benefits. Using Business Rule: AI systems can be based on business rules, whose quality determines that of the automated. AI Algorithms: are the basis for automated decisions & 32 decision support.  AI Support of the Decision-Making Process  AI support can be applied to the various steps of the decision- making process: 1. Problem Identification: collecting data through technology that can be used by AI algorithms. Performance levels of machines are compared to standards, and trend analysis can point to opportunities. 2. Generating/Finding Alternative Solutions: matching problem characteristics with best practice, or proven solutions stored in databases. Tools such as case-based reasoning and neural computing are for this purpose. 3. Selecting a Solution: evaluate proposed solutions, predict future impacts, asses chance of success, or predict a reply to actions taken by a competitor. 33 4. Implementing the Solutions: demonstrates the superiority of  Automated Decision Making The process of automated decision making starts with knowledge acquisition and creation of a knowledge repository. The system generates and submits responses to user’s questions. Solutions are evaluated to improve the knowledge repository, and complex situations are forwarded to humans. Companies use automated decision making for both their external operations (e.g., sales) and internal operations (e.g., resource allocation) Example: Supporting Nurses’ Diagnosis Decisions Researchers used AI tools to conduct data mining to predict the probable success of automated nursing diagnoses based 34 on patient characteristics.  Main Reference  Chapter 2: “Analytics, Data Science, & Artificial Intelligence: Systems for Decision Support” from “Analytics, Data Science, & Artificial Intelligence: Systems for Decision Support”. Week self-review exercises  Application Case 2.1 - 2.7 from “Analytics, Data Science, & Artificial Intelligence: Systems for Decision Support” 35  Thank You 36