AI in Business and Academia - Information Systems 6 PDF

Information Systems  INFORMATION SYSTEMS CHAPTER 6: ARTIFICIAL INTELLIGENCE IN BUSINESS AND ACADEMIA, & INDUSTRIAL INTERNET OF THINGS (IIOT)  Learning Objectives  You will then learn about the importance of AI to companies, including:  Current and potential AI us...

Information Systems  INFORMATION SYSTEMS CHAPTER 6: ARTIFICIAL INTELLIGENCE IN BUSINESS AND ACADEMIA, & INDUSTRIAL INTERNET OF THINGS (IIOT)  Learning Objectives  You will then learn about the importance of AI to companies, including:  Current and potential AI use cases  The challenges of integrating AI applications into existing business processes  Technological breakthroughs driving the field forward  What is internet of things  Introduction The application of AI in the enterprise is profoundly changing the way businesses work. Companies are incorporating AI technologies into their business operations with the aim of saving money, boosting efficiency, generating insights and creating new markets. There are AI-powered enterprise applications to enhance customer service, maximize sales, sharpen cybersecurity, optimize supply chains, free up workers from mundane tasks, improve existing products and point the way to new products. It is hard to think of an area in the enterprise where AI -- the simulation of human processes by machines, especially computer systems -- will not have an impact. Enterprise leaders determined to use AI to improve their  History of AI  The term "artificial intelligence (AI)" was coined in 1956 at Dartmouth College academic conference  Ancient Mythology and legends beliefs in statues coming to life  Hence they created human-like automata that were believed to possess reason and emotions  By the first millennium B.C., Early Philosophers around the word developing methods for Formal Reasoning  Contributions from various fields over 2,000 years - theologians, mathematicians, engineers, economists, psychologists, computational scientists and  Pioneers of Modern AI Key figures in AI history Alan Turing and transformer neural networks Aristotle's concept of human thought as symbols Al-Khwārizmī's early symbol manipulation Ramon Llull's Theological Contributions and development of symbolic reasoning  Evolution of AI  The Modern Computer: Charles Babbage and Augusta Ada Byron's programmable machine design in 1836.  Stored-Program Computer: John von Neumann's architecture concept in the 1940s.  Neural Network Model: Warren McCulloch and Walter Pitts' mathematical model in 1943.  Turing Test: Alan Turing's development of the Turing Test in 1950.  Dartmouth Conference: The 1956 conference with AI pioneers like John McCarthy, Marvin Minsky, and others.  AI's Early Promise: Significant government and industry support for AI after the Dartmouth conference.  Advances in Early AI: Examples include the GPS algorithm, Lisp programming language, and ELIZA.  The AI Winters  First AI Winter: Occurred from 1974 to 1980, as AI's ambitious goals proved elusive.  Industry Retreat: Government and corporate backing for AI research declined.  Second AI Winter: Lasted until the mid-1990s.  Resurgence in the 1980s: Deep learning and Edward Feigenbaum's expert systems sparked renewed interest.  Funding Challenges: The cyclical pattern of AI enthusiasm followed by funding challenges.  Gradual Revival: Advances in neural networks and the advent of big data propelled AI's resurgence.  Mid-1990s Renaissance: Marked the current era of AI development.  What is AI?  Definition of AI: involves machines performing tasks that require intelligence, which is the capacity to acquire knowledge and apply it to achieve specific outcomes.  Complexity of Intelligence: Intelligence is challenging to define, especially in a work context, as it involves adapting to particular situations, not performing tasks by rote.  Lack of a Single Definition: There is no single or simple definition of AI, as it encompasses a range of behaviours and capabilities.  NSTC Report: The National Science and Technology Council (NSTC) report acknowledges the diversity of AI definitions and behaviours.  Loose Definitions: Some define AI as behaviour requiring intelligence, while others describe it as the capacity to rationally solve complex problems.  Moving Target: The perception of what qualifies as AI shifts over  What is Artificial Intelligence ? THOUGHT Systems that thinkSystems that think like humans rationally Systems that act Systems that act BEHAVIOUR like humans rationally HUMAN RATIONAL  How AI Works  Learning in AI: AI programming focuses on acquiring data and developing algorithms, which are step-by-step instructions for processing data.  Data to Actionable Information: Learning transforms data into actionable information.  Reasoning in AI: AI's ability to select the most appropriate algorithm from a set based on the context.  Contextual Decision-Making: Reasoning helps AI make context- specific decisions efficiently.  Self-Correction in AI: AI's capacity to progressively refine and improve outcomes until they meet the desired goal.  Continuous Improvement: Self-correction allows AI systems to enhance performance over time.  Holistic Approach: AI's functioning combines learning, reasoning,  Four Types of AI 1. Reactive AI  Characteristics: Reactive AI operates based on predefined algorithms, lacking memory and adaptability.  Output Consistency: Given the same input, Reactive AI always produces the same output.  Use Cases: Effective for simple classification and pattern recognition tasks.  Limitations: Unable to handle scenarios with imperfect information or historical context. 2. Limited Memory AI  Characteristics: Limited Memory AI includes a basic form of short- term memory.  Learning from Experience: It can consider recent data, making it suitable for some real-world applications.  Four Types of AI 3. Theory of Mind AI  Characteristics: Theory of Mind AI understands the mental states and intentions of other entities.  Social Interaction: Enables AI to engage in social interactions and understand human emotions and intentions.  Applications: Useful for virtual assistants and human-AI collaboration in various contexts. 4. Self-Aware AI  Characteristics: Self-Aware AI possesses consciousness and self-awareness.  High-Level Intelligence: Demonstrates human-level intelligence and the ability to comprehend complex situations.  Work in Progress: Developing true self-aware AI is a  AI Evolution Progression: AI has evolved from simple reactive systems to more advanced, self- aware AI concepts. Impact: The development of deep learning and machine intelligence has propelled AI advancements. Current State: Most existing AI, including virtual assistants and self-driving cars, falls under the category of narrow or weak AI, with expertise in specific tasks but lacking  Importance of AI in the Enterprise  Data Explosion: By 2025, global data generation is expected to reach 175 zettabytes, a 430% increase from 2018 (33 zettabytes).  Data-Driven Decision-Making: Large datasets fuel data-driven decision-making, enabling improved business operations and the creation of new business opportunities.  Symbiotic Relationship: AI and big data have a symbiotic relationship. AI, particularly deep learning, processes extensive datasets to identify valuable patterns and correlations.  Competitive Edge: Uncovering subtle patterns and correlations provides companies with a competitive edge in the 21st-century business landscape.  High-Quality Data: Quality data is essential for AI to make meaningful predictions and avoid human biases.  Cloud Computing Support: Cloud computing environments offer the  Impact of AI in the Enterprise  Strategic Value: AI's significance in the 21st century is likened to electricity's transformative role in the early 20th century, as it addresses the complexity and dynamism of modern business.  Automation and Augmentation: AI's primary impact lies in its ability to automate and enhance tasks traditionally performed by humans, surpassing the capabilities of current workplace automation tools.  Dynamic Workflow Optimization: AI doesn't merely automate tasks but also dynamically optimizes workflows by analyzing extensive data volumes to achieve maximum efficiency.  Real-world Applications: AI is already augmenting human roles across various sectors, including healthcare, customer service, security, and banking, with applications in medical diagnoses, customer query handling, cybersecurity, and loan processing.  Job Displacement Concerns: While AI augments human work, it also  Benefits of AI in the enterprise Enhanced Customer Service: Personalized and accelerated customer service that has improved overall customer experience Real-time Monitoring: Instantaneous monitoring with AI to improve Quality control in production processes Faster Product Development: Shortened development cycles for Quicker return on investment Improved Quality: Error reduction and enhanced compliance through Automation of tasks previously done manually Efficient Talent Management: Streamlined hiring processes towards Elimination of bias and improved candidate screening Business Model Innovation: Implementation of new business models Fostering innovation and expansion  Risks of AI  Worker mistrust is a major risk in the effective use of AI in the workplace.  Concerns about job elimination due to AI automation are significant.  A wide range of jobs, not just manual labor, will be impacted by AI in the coming years.  Building trust among workers is crucial for AI implementation to succeed.  Complex AI models can be challenging to explain to frontline workers.  Risks of AI  AI errors can occur due to problematic data, training, or algorithm mistakes.  Unethical practices and unintended consequences of AI use must be monitored.  AI can potentially erode essential human skills in certain industries.  Companies should prioritize user needs and explanations in AI development.  Ethical considerations and consequences of AI decisions are vital in business.  Current business applications of AI  AI is widely used across various industries and business departments.  It spans sectors like financial services, manufacturing, agriculture, law, education, and IT.  In financial services, AI enhances back-office operations, automates customer service, and creates new opportunities for banks.  Manufacturing employs AI for collaborative robots, maintenance predictions, and demand forecasting.  Agriculture utilizes AI to improve crop health, reduce workloads, and manage data efficiently.  Current business applications of AI  The legal industry employs AI for data mining, outcome prediction, document classification, and interpreting requests.  AI automates exam grading and personalizes learning in the field of education.  IT service management uses AI for automating user requests and gaining insights into IT infrastructure.  AI applications are diverse, including natural language generation, deep learning, facial recognition, and more.  AI plays a significant role in streamlining operations  Goals of AI To make computers more useful by letting them take over dangerous or tedious tasks from human Understand principles of human intelligence  The Advantages of AI Disadvantages more powerful and increased costs more useful computers difficulty with software new and improved development - slow and interfaces expensive solving new problems few experienced better handling of programmers information few practical products relieves information have reached the overload market as yet. conversion of The Internet Of Things According to the Word Economic Forum ” We stand on the brink of a technological revolution that will fundamentally alter the way we live, work, and relate to one another. In its scale, scope, and complexity, the transformation will be unlike anything humankind has experienced before” CENTRO ESCOLAR UNIVERSITY INDUSTRIAL REVOLUTIONS 1st 2nd 3rd 4th 1760s 1870s 1960s NOW Steam engine Electricity Computers Hyper-connectivity Mechanization Mass production Automation Internet Revolutions have triggered profound changes in economic systems and social structures. CENTRO ESCOLAR UNIVERSITY INDUSTRIAL REVOLUTIONS ru p t ion di S age of accelerations profound and systemic change momentous change CENTRO ESCOLAR UNIVERSITY  Technologies Driving Internet of Things Artificial Intelligence (AI) Biotechnology Robotics Data Analytics Virtual reality (IoT) Quantum computations Blockchain CENTRO ESCOLAR UNIVERSITY What is Internet of Things? CENTRO ESCOLAR UNIVERSITY How Does This Impact You? CENTRO ESCOLAR UNIVERSITY How Does This Impact You? CENTRO ESCOLAR UNIVERSITY IoT-based Smart Farming Utilize wireless IoT applications to collect data regarding the location, well-being, and health of their livestock Monitor pregnant cows: Sensor powered by battery is expelled when its water breaks. This sends an information via the Internet to the rancher. CENTRO ESCOLAR UNIVERSITY IoT for the Elderly With a built-in accelerometer that automatically detects falls Medication reminder With a GPS, which allows an emergency operator to locate and provide directions to the individual. CENTRO ESCOLAR UNIVERSITY HAPIfork The HAPIfork is an electronic fork that helps you monitor and track your eating habits. It also alerts you with the help of indicator lights and gentle vibrations when you are eating too fast. http://www.hapi.com/products-hapifork.asp CENTRO ESCOLAR UNIVERSITY MyVessyl Cup It can hold 13 ounces of liquid. The battery takes 60 minutes to fully charge and will last for 5-7 days. Also has wire-free charging. https://www.myvessyl.com/ CENTRO ESCOLAR UNIVERSITY Smar t Tooth Brush The Beam Brush is a connected toothbrush that engages users with their daily hygiene routine. http://www.beamtoothbrush.com/toothbrush/ CENTRO ESCOLAR UNIVERSITY Smar t Egg Tray Egg Minder syncs with your smartphone to tell you how many eggs you’ve got at home (up to 14 eggs) and when they’re going bad. http://www.quirky.com/shop/619 CENTRO ESCOLAR UNIVERSITY IoT business opportunities CENTRO ESCOLAR UNIVERSITY IoT − Advantages Improved Customer Engagement – Current analytics suffer from blind-spots and significant flaws in accuracy; and as noted, engagement remains passive. IoT completely transforms this to achieve richer and more effective engagement with audiences. CENTRO ESCOLAR UNIVERSITY IoT − Advantages Technology Optimization – The same technologies and data which improve the customer experience also improve device use, and aid in more potent improvements to technology. IoT unlocks a world of critical functional and field data. CENTRO ESCOLAR UNIVERSITY IoT − Advantages Reduced Waste – IoT makes areas of improvement clear. Current analytics give us superficial insight, but IoT provides real- world information leading to more effective management of resources. CENTRO ESCOLAR UNIVERSITY IoT − Advantages Enhanced Data Collection – Modern data collection suffers from its limitations and its design for passive use. IoT breaks it out of those spaces, and places it exactly where humans really want to go to analyze our world. It allows an accurate picture of everything. CENTRO ESCOLAR UNIVERSITY IoT − Disadvantages Security – IoT creates an ecosystem of constantly connected devices communicating over networks. The system offers little control despite any security measures. This leaves users exposed to various kinds of attackers. CENTRO ESCOLAR UNIVERSITY IoT − Disadvantages Privacy – The sophistication of IoT provides substantial personal data in extreme detail without the user's active participation. CENTRO ESCOLAR UNIVERSITY IoT − Disadvantages Complexity – Some find IoT systems complicated in terms of design, deployment, and maintenance given their use of multiple technologies and a large set of new enabling technologies. CENTRO ESCOLAR UNIVERSITY IoT − Disadvantages Compliance – IoT, like any other technology in the realm of business, must comply with regulations. Its complexity makes the issue of compliance seem incredibly challenging when many consider standard software compliance a battle. CENTRO ESCOLAR UNIVERSITY Securing IoT Devices Authentication – IoT devices Network Enforced Policy – connecting to the network controls all elements that route create a trust relationship, and transport endpoint traffic based on validated identity securely over the network through mechanisms such as: through established security passwords, tokens, biometrics, protocols. RFID, X.509 digital certificate, shared secret, or endpoint MAC address. Authorization – a trust Secure Analytics: Visibility relationship is established and Control – provides based on authentication and reconnaissance, threat authorisation of a device that detection, and threat determines what information mitigation for all elements that can be accessed and shared. aggregate and correlate information. CENTRO ESCOLAR UNIVERSITY  THANK YOU! CENTRO ESCOLAR UNIVERSITY