{"id":659,"date":"2024-12-16T14:33:14","date_gmt":"2024-12-16T14:33:14","guid":{"rendered":"https:\/\/pressbooks.hcfl.edu\/introtofintech\/chapter\/__unknown__-4\/"},"modified":"2025-04-02T17:02:20","modified_gmt":"2025-04-02T17:02:20","slug":"7","status":"publish","type":"chapter","link":"https:\/\/pressbooks.hcfl.edu\/introtofintech\/chapter\/7\/","title":{"raw":"Chapter 7 Introduction to Artificial Intelligence","rendered":"Chapter 7 Introduction to Artificial Intelligence"},"content":{"raw":"[embed]https:\/\/www.youtube.com\/embed\/J8o77Kry5uw[\/embed]\r\n<div class=\"__UNKNOWN__\">\r\n<div class=\"textbox textbox--key-takeaways\"><header class=\"textbox__header\">\r\n<p class=\"textbox__title\">Key Principles<\/p>\r\n\r\n<\/header>\r\n<div class=\"textbox__content\">\r\n\r\nWhere is AI making big a big splash? AI has been transformative across several industries driving innovation, improving efficiency, reducing costs and creating new business opportunities. Examples of industries that have extensively benefited from using AI:\r\n<ul>\r\n \t<li class=\"import-Normal\"><em>Finance<\/em><em> \u2013<\/em> AI is heavily used in fraud detection, credit scoring, Robo-advising, market trading and a host of other applications.<\/li>\r\n \t<li class=\"import-Normal\"><em>Healthcare<\/em><em> \u2013<\/em> AI is used as an adjunct to diagnostic tools, personalized treatment, and Robotic-assisted surgeries.<\/li>\r\n \t<li class=\"import-Normal\"><em>Retail and eCommerce-<\/em> Used in developing personalized recommendations; Sales &amp; marketing; Inventory Management; Warehousing; and Transportation &amp; Shipping<\/li>\r\n \t<li class=\"import-Normal\"><em>Manufacturing<\/em> <em>-<\/em> Digital Twins, Smart manufacturing; Autonomous Machinery and Robotics<\/li>\r\n \t<li class=\"import-Normal\"><em>Transportation and Logistics<\/em> \u2013 Route Optimization; Autonomous driving; and demand forecasting and management.<\/li>\r\n \t<li class=\"import-Normal\"><em>Agriculture<\/em><em> (Smart Ag)<\/em> \u2013 Precision farming; Crop monitoring; harvest prediction and weather prediction.<\/li>\r\n \t<li class=\"import-Normal\"><em>Energy<\/em><em> Sector<\/em> \u2013 Smart Grid and Renewable energy sourcing.<\/li>\r\n \t<li class=\"import-Normal\"><em>Education<\/em> \u2013 Personalized learning, Administrative Automation; virtual tutoring<\/li>\r\n \t<li class=\"import-Normal\"><em>Legal<\/em><em> -<\/em> Intelligent case search and court preparations, contracts analysis and management.<\/li>\r\n<\/ul>\r\n<\/div>\r\n<\/div>\r\n<div class=\"textbox textbox--learning-objectives\"><header class=\"textbox__header\">\r\n<p class=\"textbox__title\">Learning Objectives<\/p>\r\n\r\n<\/header>\r\n<div class=\"textbox__content\">\r\n\r\nUpon completion of this chapter, students should be able to:\r\n<ul>\r\n \t<li>Define the nature of intelligence and how humans learn<\/li>\r\n \t<li>Define Intelligent Systems (Artificial Intelligence)<\/li>\r\n \t<li>Define key characteristics, and components of Expert Systems<\/li>\r\n \t<li>Explain how Artificial Neural Networks work<\/li>\r\n \t<li>Explain how Machine Learning works<\/li>\r\n \t<li>Explain how IIoT incorporates AI<\/li>\r\n \t<li>Demonstrate practicality of use of AI across many industries<\/li>\r\n \t<li>Clearly give example uses of Natural Language Processing models using ChatGPT<\/li>\r\n \t<li>Explain the different types of Software and Hardware Intelligent Robotics<\/li>\r\n<\/ul>\r\n<\/div>\r\n<\/div>\r\n[ez-toc]\r\n<h2>A Historical Evolutionary Perspective on AI<\/h2>\r\n<\/div>\r\n[video width=\"1280\" height=\"720\" mp4=\"http:\/\/pressbooks.hcfl.edu\/introtofintech\/wp-content\/uploads\/sites\/96\/2024\/12\/20250117_1247_Leonardos-Future-Vision_storyboard_01jhtnzy05esx90f2mf6fhrrqt.mp4\" autoplay=\"true\"][\/video]\r\n\r\n[h5p id=\"8\"]\r\n<div class=\"__UNKNOWN__\">\r\n\r\n[caption id=\"attachment_839\" align=\"alignleft\" width=\"300\"]<img class=\"wp-image-839 size-medium\" src=\"http:\/\/pressbooks.hcfl.edu\/introtofintech\/wp-content\/uploads\/sites\/96\/2024\/12\/9212589254_4b4501b6d6_o-300x268.jpg\" alt=\"Leonardo da Vinci: Diagram of a proposed flying machine (1789)\" width=\"300\" height=\"268\" \/> <a href=\"https:\/\/www.flickr.com\/photos\/43021516@N06\/9212589254\">Leonardo da Vinci: Diagram of a proposed flying machine (1789)<\/a> Toronto Public Library. <a href=\"https:\/\/commons.wikimedia.org\/wiki\/File:Leonardo-Robot3.jpg?uselang=en#Licensing\">Public Domain<\/a>[\/caption]\r\n\r\n<\/div>\r\n\r\n[caption id=\"\" align=\"alignright\" width=\"255\"]<img style=\"font-family: 'Sorts Mill Goudy', 'Times New Roman', serif;font-size: 16px\" src=\"http:\/\/pressbooks.hcfl.edu\/introtofintech\/wp-content\/uploads\/sites\/96\/2024\/12\/image3-3.png\" alt=\"Leonardo's robot.\" width=\"255\" height=\"255\" \/> Model of Leonardo's robot with inner workings, on display in Berlin. Photo by Erik M\u00f6ller. Leonardo da Vinci. Mensch - Erfinder - Genie exhibit, Berlin 2005. <a href=\"https:\/\/commons.wikimedia.org\/wiki\/File:Leonardo-Robot3.jpg?uselang=en#Licensing\">Public Domain<\/a>[\/caption]\r\n\r\n2300 years ago, the famous Greek philosopher, Aristotle made the first reference to something close to the idea of intelligence in his famous work Prior Analytics &amp; The Theory of Syllogistic, which is \u201creason\u201d. Reason, according to Aristotle, was about humans' ability to reign in their passions, i.e., our ability to resist the urge of our instincts (University of Chicago Press, 2020).\r\n\r\nHumans have attempted to replicate human functions for many centuries, such as Leonardo Davinci\u2019s Flying Machines and Blaise Pascal\u2019s first calculating machine.\r\n\r\n<span class=\"TextRun SCXW97516344 BCX8\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"FindHit SCXW97516344 BCX8\">Not until the <\/span><span class=\"FindHit SCXW97516344 BCX8\">early, to mid-20<\/span><\/span><span class=\"TextRun SCXW97516344 BCX8\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"FindHit Superscript SCXW97516344 BCX8\" data-fontsize=\"12\">th<\/span><\/span><span class=\"TextRun SCXW97516344 BCX8\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"> <span class=\"NormalTextRun SCXW97516344 BCX8\">century<\/span><span class=\"NormalTextRun SCXW97516344 BCX8\"> with the development of electricity did innovations such as Neural Networks, the first <\/span><span class=\"NormalTextRun SCXW97516344 BCX8\">electrically driven<\/span><span class=\"NormalTextRun SCXW97516344 BCX8\"> calculating machine (1947)<\/span><span class=\"NormalTextRun SCXW97516344 BCX8\">,<\/span><span class=\"NormalTextRun SCXW97516344 BCX8\"> and the first use of the term \u201cArtificial Intelligence<\/span><span class=\"NormalTextRun SCXW97516344 BCX8\">\u201d<\/span><span class=\"NormalTextRun SCXW97516344 BCX8\"> (<\/span><span class=\"NormalTextRun ContextualSpellingAndGrammarErrorV2Themed GrammarErrorHighlight SCXW97516344 BCX8\">195<\/span><span class=\"NormalTextRun ContextualSpellingAndGrammarErrorV2Themed GrammarErrorHighlight SCXW97516344 BCX8\">6<\/span><span class=\"NormalTextRun ContextualSpellingAndGrammarErrorV2Themed GrammarErrorHighlight SCXW97516344 BCX8\">)<\/span><\/span><span class=\"TextRun SCXW97516344 BCX8\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW97516344 BCX8\"> c<\/span><span class=\"NormalTextRun SCXW97516344 BCX8\">a<\/span><span class=\"NormalTextRun SCXW97516344 BCX8\">me to being (Dartmouth College, n.d.).<\/span><span class=\"NormalTextRun SCXW97516344 BCX8\">\u00a0<\/span><span class=\"NormalTextRun SCXW97516344 BCX8\">\u00a0<\/span><\/span><span class=\"EOP SCXW97516344 BCX8\" data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559740&quot;:360}\">\u00a0<\/span>\r\n<div class=\"__UNKNOWN__\">\r\n\r\n[caption id=\"\" align=\"alignnone\" width=\"473\"]<img class=\"\" src=\"http:\/\/pressbooks.hcfl.edu\/introtofintech\/wp-content\/uploads\/sites\/96\/2024\/12\/image4-1.jpeg\" alt=\"Blaise Pascal\u2019s Mechanical Calculator.\" width=\"473\" height=\"316\" \/> Figure 9-4 <a href=\"https:\/\/en.m.wikipedia.org\/wiki\/File:Pascaline-CnAM_823-1-IMG_1506-black.jpg\">Image of Blaise Pascal\u2019s Mechanical Calculator<\/a> \u2013 <a class=\"extiw\" title=\"w:en:Creative Commons\" href=\"https:\/\/en.wikipedia.org\/wiki\/en:Creative_Commons\">Creative Commons<\/a>\u00a0<a class=\"extiw\" title=\"creativecommons:by-sa\/3.0\/fr\/deed.en\" href=\"https:\/\/creativecommons.org\/licenses\/by-sa\/3.0\/fr\/deed.en\">Attribution-ShareAlike 3.0 France<\/a><span style=\"font-size: 16px\">(1956)<\/span><span style=\"font-size: 16px\"> came to being.<\/span>[\/caption]\r\n<h2 style=\"text-align: left\"><strong>Artificial Intelligence <\/strong><\/h2>\r\n[h5p id=\"7\"]\r\n<p class=\"import-Normal\">Humans are great thinkers. Our brains have a tremendous capacity of thought. Computers on the other hand beat humans when it comes to mathematical calculations. Current capability of supercomputers such as the Cray EX built by Hewlett-Packard\u2122 registers over 550 petaflops (that\u2019s 550,000,000,000,000,000 multiplications per second). Even with such speed, it still takes the CrayEX about a second to decide on a move in chess. Artificial Intelligence speed is improving at a very fast pace. Scientists estimates that today\u2019s AI would achieve full human-like thought capabilities by mid-21st. century. Speed however is not the ultimate target in the foreseeable future. General AI is the target. The scope of Intelligence in AI is divided into 2 broad categories:<\/p>\r\n\r\n\r\n[caption id=\"attachment_650\" align=\"alignright\" width=\"304\"]<img class=\"wp-image-650\" src=\"http:\/\/pressbooks.hcfl.edu\/introtofintech\/wp-content\/uploads\/sites\/96\/2024\/12\/image1-2-300x300.png\" alt=\"Simulating infusion of AI into life\" width=\"304\" height=\"304\" \/> Simulating infusion of AI into life. Image generated by OpenAI\u2019s DALL\u00b7E[\/caption]\r\n<h3><em>Narrow AI\u00a0<\/em><\/h3>\r\nThis is the type of AI we have today and may achieve its full potential by mid-century. Narrow AI is designed to learn to perform specific tasks that will achieve specific goals. These tasks can be quite complex, but they are limited to a narrow scope. Narrow AI \u201cbehavior\u201d is highly predictable because it must learn and operate in its specific environment in order to reach a specified conclusion. I.e., it must be trained just like us, humans. Narrow AI, example applications include:\r\n<p class=\"import-Normal\">Image recognition (e.g., identifying objects in real life or pictures of real life using distinguishing and differentiating detail (e.g., tree vs. rose, horse vs. sheep, cube vs. triangle, school bus vs. commuter bus, etc.)<\/p>\r\n<p class=\"import-Normal\">Natural language processing (e.g., translating across many natural languages, and manipulating text, speech, images, music, art, even poetry by regeneration (AKA regenerative learning) to produce human-like output.<\/p>\r\n<p class=\"import-Normal\">Recommendation systems (e.g., suggesting movies on Netflix, presenting products you most likely will buy, and even predicting future outcomes, with a high degree of accuracy, based on learned experiences, etc.)\u00a0Game-playing AIs (e.g., Chess, Go, and immersive universes known as Multiverse, Virtual and Augmented reality, etc.)<\/p>\r\n\r\n<h3><em>General AI (AGI)<\/em><\/h3>\r\nThis is a theoretical type of AI that can understand, learn, and apply intelligence across a wide range of tasks, just like how humans do. It would be capable of reasoning, problem-solving, and understanding context in ways that allow it to handle any cognitive task, not just those it was specifically trained for. AGI would also have the ability to transfer knowledge learned in one area to another, a feature we, human beings possess however is lacking in today's narrow AI. At that level, life is mimicked and truly virtualized to the point that it would be near impossible to distinguish the differences between Living and Virtual, and machines could essentially have emotions, self-realization, and the ability to self-reproduce. There are no examples of General AI except in Hollywood\u2019s science fiction (Wikipedia contributors, n.d.) However, AI researchers believe that few, but key characteristics could include:\r\n<ul>\r\n \t<li>the ability to seek learning from data generated by other AI systems, and adapt quickly to new, unseen tasks, much like humans do.<\/li>\r\n \t<li>the ability to reason abstractly, simulate different scenarios, and generate truly novel ideas.<\/li>\r\n \t<li>AGI could transfer knowledge across various domains (e.g., using knowledge from playing chess to improve performance in another task, like driving a car).<\/li>\r\n \t<li>Self-realization, emotions, anger and fear, happiness and sadness. Again, these have been postulated and represented in Science Fiction (Wikipedia contributors, n.d.)<\/li>\r\n<\/ul>\r\n<p class=\"import-Normal\">AI applications are a web of complex and interdisciplinary fields that includes Biology Computer Science, Neuroscience, Philosophy, Law, Ethics, Medicine, Finance, and Psychology along with many engineering disciplines such as electrical and mechanical engineering. A heavy area of Narrow AI research is focused on understanding the foundation of Human Intelligence in hope of replicating it in machines.<\/p>\r\n<p class=\"import-Normal\"><strong>What is Intelligence? <\/strong><\/p>\r\n<p class=\"import-Normal\">James R. Flynn, arguably the most prominent researcher in the field of \u201cintelligence\u201d defines the term as \u201c<em>the ability to learn from minimal data and adapt quickly to new, unseen tasks<\/em><em>. M<\/em><em>uch like humans do. It would also <\/em><em>include <\/em><em>hav<\/em><em>ing<\/em><em> the ability to reason abstractly, simulate different scenarios, and generate novel ideas<\/em>\u201d)\u00a0by this very definition, intelligence therefore, must be coupled with the word <em>b<\/em><em>ehavio<\/em>r. Hence, intelligent machine behavior is modeled after intelligent human behavior that has very specific characteristics:<\/p>\r\n\r\n<ul>\r\n \t<li><em>Learn from <\/em><em>past experience<\/em> \u2013 Situations and events drive human behavior. As toddlers, we quickly learn not to touch hot objects but only after we\u2019ve experienced pain effects and through the trial-and-error method.<\/li>\r\n \t<li><em>Correctly handling complex and sometimes competing situations<\/em>. An ability of successful managers depends on knowing how to balance the needs of the organization vs. the need of the individual employees.<\/li>\r\n \t<li><em>Solving problems often with little or missing information<\/em>. How do we deal with uncertainty?<\/li>\r\n \t<li><em>Prioritization <\/em>\u2013 Determine what is important and how to, preverbally, juggle running chain saws and watermelons simultaneously<\/li>\r\n \t<li><em>Being nimble and react appropriately to a new situation <\/em>\u2013 Swerving away from an oncoming car is brain and muscle memory working together. Computers can react and swerve much faster than a human can.<\/li>\r\n \t<li><em>Understand Visual Clues and Symbols \u2013 <\/em>Easy for students to walk into any classroom and know what a desk is, what a board is, and where the teacher stands. Easy for us walking through the maze of other students, desks, and tables without hurting oneself. Not so easy for a computer. However, perceptive systems have been successfully developed to overcome these challenges.<\/li>\r\n \t<li><em>Process images, sounds, smells, touch and tactile sensations.<\/em> Computers can hear, listen and understand and can see and process images, can also smell (better than a human, but much less than a dog). They can also taste and feel hot or cold or any range of temperatures in-between.<\/li>\r\n \t<li><em>Creative and Imaginative<\/em> \u2013using generative AI one can develop what could be considered as very creative and very imaginative art, music, literature and poetry, but is it original work? This has been an endless debate over the last couple of years.<\/li>\r\n<\/ul>\r\n<h2 class=\"import-Normal\"><strong>Generative AI Overview<\/strong><\/h2>\r\n[caption id=\"\" align=\"alignleft\" width=\"544\"]<img class=\"\" src=\"http:\/\/pressbooks.hcfl.edu\/introtofintech\/wp-content\/uploads\/sites\/96\/2024\/12\/image5-2.png\" alt=\"User interacting with AI.\" width=\"544\" height=\"306\" \/> Showing user interacting with AI. Image generated by OpenAI\u2019s DALL\u00b7E[\/caption]\r\n<p class=\"import-Normal\">Generative AI, such as ChatGPT models focuses on creating new content, such as text, images, and music, by learning patterns from existing data. Unlike traditional expert systems, generative AI excels in handling unstructured data and adapting to new information, however, it often lacks the inherent explain-ability of expert systems, creating challenges in accountability and trust.<\/p>\r\n\r\n<h3 class=\"import-Normal\"><strong>List of <\/strong><strong>C<\/strong><strong>urrent <\/strong><strong>Generative AI<\/strong> <strong>Types and <\/strong><strong>T<\/strong><strong>heir <\/strong><strong>U<\/strong><strong>ses<\/strong><\/h3>\r\n<p class=\"import-Normal\">There are several generative AI models each with its own set of algorithms to use in processing the \u201cgeneration and regeneration\u201d of information (Dugal, 2025). The 2025 list contains:<\/p>\r\n\r\n<table>\r\n<thead>\r\n<tr>\r\n<th style=\"background-color: #47d459;border: solid windowtext 0.5pt\">\r\n<p class=\"import-Normal\" style=\"text-align: center\"><strong>Brand<\/strong><\/p>\r\n<\/th>\r\n<th style=\"background-color: #47d459;border: solid windowtext 0.5pt\">\r\n<p class=\"import-Normal\" style=\"text-align: center\"><strong>Best for<\/strong><\/p>\r\n<\/th>\r\n<\/tr>\r\n<\/thead>\r\n<tbody>\r\n<tr class=\"TableGrid-R\">\r\n<th style=\"background-color: #d9f2d0;border: solid windowtext 0.5pt\">\r\n<p class=\"import-Normal\">ChatGPT<\/p>\r\n<p class=\"import-Normal\"><\/p>\r\n<\/th>\r\n<td class=\"TableGrid-C\" style=\"background-color: #d9f2d0;border: solid windowtext 0.5pt\">\r\n<p class=\"import-Normal\">This very textbook has about 20% ChatGPT content.<\/p>\r\n<\/td>\r\n<\/tr>\r\n<tr class=\"TableGrid-R\">\r\n<th style=\"background-color: #d9f2d0;border: solid windowtext 0.5pt\">\r\n<p class=\"import-Normal\">AlphaCode<\/p>\r\n<p class=\"import-Normal\"><\/p>\r\n<\/th>\r\n<td class=\"TableGrid-C\" style=\"background-color: #d9f2d0;border: solid windowtext 0.5pt\">\r\n<p class=\"import-Normal\">Software development specifically to train other AI models<\/p>\r\n<\/td>\r\n<\/tr>\r\n<tr class=\"TableGrid-R\">\r\n<th style=\"background-color: #d9f2d0;border: solid windowtext 0.5pt\">\r\n<p class=\"import-Normal\">GitHub Copilot<\/p>\r\n<p class=\"import-Normal\"><\/p>\r\n<\/th>\r\n<td class=\"TableGrid-C\" style=\"background-color: #d9f2d0;border: solid windowtext 0.5pt\">\r\n<p class=\"import-Normal\">Authoring software programs<\/p>\r\n<\/td>\r\n<\/tr>\r\n<tr class=\"TableGrid-R\">\r\n<th style=\"background-color: #d9f2d0;border: solid windowtext 0.5pt\">\r\n<p class=\"import-Normal\">Gemini (Previously Bard)<\/p>\r\n<p class=\"import-Normal\"><\/p>\r\n<\/th>\r\n<td class=\"TableGrid-C\" style=\"background-color: #d9f2d0;border: solid windowtext 0.5pt\">\r\n<p class=\"import-Normal\">User Rating - chatbot and content generation tool developed by Google.<\/p>\r\n<\/td>\r\n<\/tr>\r\n<tr class=\"TableGrid-R\">\r\n<th style=\"background-color: #d9f2d0;border: solid windowtext 0.5pt\">\r\n<p class=\"import-Normal\">Cohere Generate<\/p>\r\n<p class=\"import-Normal\"><\/p>\r\n<\/th>\r\n<td class=\"TableGrid-C\" style=\"background-color: #d9f2d0;border: solid windowtext 0.5pt\">\r\n<p class=\"import-Normal\">Custom content emails, website landing pages, and social media product marketing<\/p>\r\n<\/td>\r\n<\/tr>\r\n<tr class=\"TableGrid-R\">\r\n<th style=\"background-color: #d9f2d0;border: solid windowtext 0.5pt\">\r\n<p class=\"import-Normal\">Claude<\/p>\r\n<p class=\"import-Normal\"><\/p>\r\n<\/th>\r\n<td class=\"TableGrid-C\" style=\"background-color: #d9f2d0;border: solid windowtext 0.5pt\">\r\n<p class=\"import-Normal\">Assistant - Research has focused on training AI and is still in research phase. Trains other AI systems to be helpful, fair, and safe to use.<\/p>\r\n<\/td>\r\n<\/tr>\r\n<tr class=\"TableGrid-R\">\r\n<th style=\"background-color: #d9f2d0;border: solid windowtext 0.5pt\">\r\n<p class=\"import-Normal\">DALL-E2<\/p>\r\n<p class=\"import-Normal\"><\/p>\r\n<\/th>\r\n<td class=\"TableGrid-C\" style=\"background-color: #d9f2d0;border: solid windowtext 0.5pt\">\r\n<p class=\"import-Normal\">Image and art generation with photo quality realism<\/p>\r\n<\/td>\r\n<\/tr>\r\n<tr class=\"TableGrid-R\">\r\n<th style=\"background-color: #d9f2d0;border: solid windowtext 0.5pt\">\r\n<p class=\"import-Normal\">Synthesia<\/p>\r\n<\/th>\r\n<td class=\"TableGrid-C\" style=\"background-color: #d9f2d0;border: solid windowtext 0.5pt\">\r\n<p class=\"import-Normal\">Creating high quality video content<\/p>\r\n<\/td>\r\n<\/tr>\r\n<tr class=\"TableGrid-R\">\r\n<th style=\"background-color: #d9f2d0;border: solid windowtext 0.5pt\">\r\n<p class=\"import-Normal\">Copy.AI<\/p>\r\n<\/th>\r\n<td class=\"TableGrid-C\" style=\"background-color: #d9f2d0;border: solid windowtext 0.5pt\">\r\n<p class=\"import-Normal\">Creative writing, marketing slogans, and news headlines,<\/p>\r\n<\/td>\r\n<\/tr>\r\n<tr class=\"TableGrid-R\">\r\n<th style=\"background-color: #d9f2d0;border: solid windowtext 0.5pt\">\r\n<p class=\"import-Normal\">Rephrase.ai<\/p>\r\n<\/th>\r\n<td class=\"TableGrid-C\" style=\"background-color: #d9f2d0;border: solid windowtext 0.5pt\">\r\n<p class=\"import-Normal\">Digital Avatars to be used in Videos<\/p>\r\n<\/td>\r\n<\/tr>\r\n<tr class=\"TableGrid-R\">\r\n<th style=\"background-color: #d9f2d0;border: solid windowtext 0.5pt\">\r\n<p class=\"import-Normal\">Descript<\/p>\r\n<\/th>\r\n<td class=\"TableGrid-C\" style=\"background-color: #d9f2d0;border: solid windowtext 0.5pt\">\r\n<p class=\"import-Normal\">Video publishing, full multitrack editing, transcription, and screen recording.<\/p>\r\n<\/td>\r\n<\/tr>\r\n<tr class=\"TableGrid-R\">\r\n<th style=\"background-color: #d9f2d0;border: solid windowtext 0.5pt\">\r\n<p class=\"import-Normal\">SoundDraw<\/p>\r\n<\/th>\r\n<td class=\"TableGrid-C\" style=\"background-color: #d9f2d0;border: solid windowtext 0.5pt\">\r\n<p class=\"import-Normal\">Music generation<\/p>\r\n<\/td>\r\n<\/tr>\r\n<tr>\r\n<td><\/td>\r\n<td><\/td>\r\n<\/tr>\r\n<\/tbody>\r\n<\/table>\r\n<p class=\"import-Normal\" style=\"text-align: center\"><em>Table: List of AI models and their frequent uses<\/em><\/p>\r\n\r\n<h2 class=\"import-Normal\"><strong>Uses of Generative AI in <\/strong><strong>V<\/strong><strong>arious <\/strong><strong>E<\/strong><strong>conomic Sectors <\/strong><\/h2>\r\n<p class=\"import-Normal\">Generative AI had experienced explosive growth. It has found its way into countless economic sectors. Mainly:<\/p>\r\n<p class=\"import-Normal\"><em>Healthcare:<\/em><\/p>\r\n<p class=\"import-Normal\">Generative AI models integrated with expert system rules are used to generate personalized treatment plans, simulate disease progression, and enhance diagnostic tools.<\/p>\r\n<p class=\"import-Normal\"><strong><em>Example:<\/em><\/strong> AI tools generating patient-specific recommendations based on both expert-defined rules and learned patterns from medical databases.<\/p>\r\n<p class=\"import-Normal\"><em>Finance:<\/em><\/p>\r\n<p class=\"import-Normal\">Hybrid systems are employed to generate investment strategies, identify fraud, and predict market trends.<\/p>\r\n<p class=\"import-Normal\"><strong>Example:<\/strong> AI-driven financial advisors combining historical market data with regulatory compliance rules from expert systems.<\/p>\r\n<p class=\"import-Normal\"><em>Creative Industries:<\/em><\/p>\r\n<p class=\"import-Normal\">Generative AI produces high-quality content such as music, art, and literature, with expert systems ensuring adherence to stylistic or domain-specific constraints.<\/p>\r\n<p class=\"import-Normal\"><strong><em>Example:<\/em><\/strong> Music composition tools using rules from expert systems to replicate the style of classical composers.<\/p>\r\n<p class=\"import-Normal\"><em>Education:<\/em><\/p>\r\n<p class=\"import-Normal\">AI systems create adaptive learning materials and simulations tailored to individual student needs, with expert systems guiding pedagogical principles.<\/p>\r\n<p class=\"import-Normal\"><strong><em>Example:<\/em><\/strong> AI tutors that design curriculum pathways by blending expert rules with generative capabilities.<\/p>\r\nHospitality:\r\n<p class=\"\" data-start=\"0\" data-end=\"207\">Artificial Intelligence is transforming the hospitality industry in multiple ways by enhancing customer experiences, streamlining operations, and driving revenue growth. Example areas impacted by AI include:<\/p>\r\n<p data-start=\"209\" data-end=\"253\">Personalized Customer Experiences - AI analyzes guest preferences and past behaviors to personalize experiences, recommendations, and interactions.<\/p>\r\n<p data-start=\"807\" data-end=\"861\">Revenue Management and Pricing Optimization -\u00a0AI predicts demand fluctuations, helping hotels dynamically price rooms and services to maximize revenue.<\/p>\r\n<p data-start=\"1092\" data-end=\"1140\">Operational Efficiency and Automation -\u00a0 AI automates repetitive administrative tasks like check-ins\/check-outs, booking management, and guest communications.<\/p>\r\n\r\n<h2 class=\"import-Normal\"><strong>Challenges and Concerns <\/strong><strong>With<\/strong> <strong>Generative AI<\/strong><\/h2>\r\n<p class=\"import-Normal\">Despite their promise, there are many challenges and considerations in integrating expert systems into generative AI. The main critique of generative AI include:<\/p>\r\n<p class=\"import-Normal\"><em>Bias<\/em><em> (degree of errors)<\/em><em>:<\/em> Ensuring equity and fairness requires careful curation of the knowledge base and the training data.<\/p>\r\n<p class=\"import-Normal\"><em>Complexity:<\/em> Designing hybrid systems requires balancing rule-based reasoning with the flexibility of generative models.<\/p>\r\n<p class=\"import-Normal\"><em>Scalability:<\/em> Maintaining and updating the knowledge base of expert systems is human and machine resource intensive.<\/p>\r\n\r\n<h3 class=\"import-Normal\"><strong>Machine Learning <\/strong><\/h3>\r\n[caption id=\"\" align=\"alignleft\" width=\"970\"]<img src=\"http:\/\/pressbooks.hcfl.edu\/introtofintech\/wp-content\/uploads\/sites\/96\/2024\/12\/image6.jpeg\" alt=\"Machines Learning.\" width=\"970\" height=\"554\" \/> Machines Learning. Image generated by OpenAI\u2019s DALL\u00b7E[\/caption]\r\n<h3 class=\"import-Normal\"><strong>The Origin of Machine Learning<\/strong><\/h3>\r\n<p class=\"import-Normal\">Machine learning (ML) is a branch of artificial intelligence (AI) that enables systems to learn from data and improve their performance without being explicitly programmed. The origins of machine learning trace back to the mid-20th century, when researchers began to explore the idea of creating machines that could simulate human learning processes. The foundational concept was inspired by Alan Turing, who introduced the notion of a \"learning machine\" in his seminal 1950 paper, <em>Computing Machinery and Intelligence (Turing, 1950)<\/em>. Turing hypothesized that machines could improve their behavior through experience.<\/p>\r\n<p class=\"import-Normal\">In 1959, Arthur Samuel further advanced the concept by developing programs that could play checkers, pioneering the term \"machine learning\u201d. Samuel\u2019s work demonstrated that machines could be trained to optimize their performance through iterative improvements based on data analysis. Since then, ML has grown exponentially, driven by advances in computational power, the availability of large datasets, and the development of sophisticated algorithms.<\/p>\r\n\r\n<h3 class=\"import-Normal\"><strong>How <\/strong><strong>Are<\/strong> <strong>Machines Trained<\/strong><strong>?<\/strong><\/h3>\r\n<p class=\"import-Normal\">Machines are trained using large datasets that serve as inputs to algorithms. The training process involves:<\/p>\r\n<p class=\"import-Normal\">Data Collection: Gathering a comprehensive and relevant dataset.<\/p>\r\n<p class=\"import-Normal\">Data Preprocessing: Cleaning and formatting the data to ensure it is suitable for training.<\/p>\r\n<p class=\"import-Normal\">Algorithm Selection: Choosing an appropriate algorithm based on the problem type.<\/p>\r\n<p class=\"import-Normal\">Model Training: Feeding the data into the algorithm to identify patterns and learn decision rules.<\/p>\r\n<p class=\"import-Normal\">Evaluation: Assessing the model\u2019s performance on unseen data to ensure it generalizes well.<\/p>\r\n<p class=\"import-Normal\">Optimization: Fine-tuning the model to improve accuracy and efficiency.<\/p>\r\n\r\n<h3 class=\"import-Normal\"><strong>The Four Training Modes<\/strong><strong> in Machine Learning <\/strong><\/h3>\r\n<p class=\"import-Normal\">Machine learning uses 4 different modes of training:<\/p>\r\n\r\n<h4 class=\"import-Normal\"><strong>1. <\/strong><strong>Supervised Learning<\/strong><\/h4>\r\n<p class=\"import-Normal\">In supervised learning, the model is trained on a <em>labeled dataset<\/em> (a set of data that has been classified) where the input data is paired with the correct output. It works by learning a mapping function from inputs to outputs by minimizing the path error between a predicted and an actual output(s). Supervised Learning uses applications that <em>c<\/em><em>lassif<\/em><em>y<\/em> (i.e., sort and group) tasks for a desired problem output. (e.g., email spam detection) and regression tasks (e.g., predicting housing prices).<\/p>\r\n\r\n<h4 class=\"import-Normal\"><strong><em>2. <\/em><\/strong><strong><em>Unsupervised Learning<\/em><\/strong><\/h4>\r\n<p class=\"import-Normal\">In unsupervised learning, the model is trained on an <em>unlabeled dataset<\/em>, meaning it must find hidden patterns or structures in the data without the data being organized. It works by applying algorithms the perform functions such as:<\/p>\r\n\r\n<ul>\r\n \t<li><em>C<\/em><em>lustering <\/em>which is a grouping of similar things together based on their features, without anyone telling the computer what the groups should be. For example, all cars are clustered into one group, all airplanes into another group. Clustering would be a result (output) rather than a preemptive input as in Supervised learning. In Fintech, clustering can be applied to classify customers by shopping behavior and banks by lowest percentage rate they charge for processing a transaction. These characteristics are found, and remembered (i.e., stored) by the algorithm.<\/li>\r\n \t<li><em>D<\/em><em>imensionality reduction<\/em> is used to identify groupings through data representations. <em>Dimensionality is about how many features or characteristics a dataset has<\/em>. For example, imagine you are describing a car that has:<\/li>\r\n \t<li>One feature (1 dimension): The car's color.<\/li>\r\n \t<li>Two features (2 dimensions): The car's color and speed.<\/li>\r\n \t<li>Three features (3 dimensions): The car's color, speed, and weight.<\/li>\r\n<\/ul>\r\n<p class=\"import-Normal\">Datasets in the Financial industry are highly complex and have many features. For example, when Banks, or Insurance companies analyze a loan request, or a policy, to determine their risk exposure, their AI system would analyze the customer\u2019s request based on dozens of dimensions such as age, income, employment, credit score, and spending habits, amongst many others. Dimensionality algorithms can call up data accumulated from many of data sources to refine its output. In our example of applying for credit, the algorithm will send requests to review a credit report, callup transactional databases that contain transaction details such as what items the customer spends money on, callup tax records to verify income, etc.<\/p>\r\n\r\n<h4 class=\"import-Normal\"><strong><em>3. <\/em><\/strong><strong><em>Reinforcement Learning<\/em><\/strong><\/h4>\r\n<p class=\"import-Normal\">Reinforcement training uses an AI agent (a software computer program), that is asked to solve an optimal route (solution) using a reward-penalty system. It is a system based on trial-and-error. For example, driving for delivery company, your daily route as designed by the AI system is to travel daily dropping off 50 packages at 50 different locations around the city. In preparation for a daily run, the AI system will solve for an optimal route that saves on time, fuel, vehicle maintenance, reduce probability of getting into an accident, and guarantees on time delivery. The system will be trained to classify all these variables into dimensions, assigning +weights (for rewards) and -weights for (penalties) for each pass through the layers leading to a solution. With each pass, the system \u201clearns\u201d which of the nodes has a greater chance of receiving a reward. Again, through iterative training sessions, the system will develop a solution that will be considered an \u201coptimized delivery\u201d model that can be used for that specific day\u2019s deliveries.<\/p>\r\n\r\n<h4 class=\"import-Normal\"><strong><em>4. <\/em><\/strong><strong><em>Semi-Supervised Learning<\/em><\/strong><\/h4>\r\n<p class=\"import-Normal\">Semi-supervised machine learning is a type of learning that uses both <em>labeled and unlabeled data <\/em>to train a model. It\u2019s like teaching someone a new topic by giving them a few clear examples <strong>(labeled data) <\/strong>and then letting them figure out the rest by exploring on their own <strong>(unlabeled data).<\/strong> Example of labeled data is a set of pictures with a label stating, \u201cfarm animals\u201d with pictures showing Horses, Cows, Chicken, Etc. while unlabeled data would be the same set of pictures, with a possible inclusion of a tractor, a harvester, pitchfork and a grain silo. In the second set of pictures, there will not be a label mentioning what the pictures are, or what is in them.<\/p>\r\n<p class=\"import-Normal\">In Fintech, the use of semi-supervised learning is key due to its economies. There is no need to identify each transaction as fraudulent or not fraudulent. Just few fraudulent examples would suffice for the system to determine common characteristics such as the profile for transactions originating outside of normal locations, number of transactions matching those that are fraudulent, or transactions that started by a known fraudulent merchant or user.<strong><br style=\"clear: both\" \/><\/strong><\/p>\r\n\r\n<h2 class=\"import-Normal\"><strong>Neural Networks and <\/strong><strong>Deep Learning<\/strong><strong> in AI <\/strong><\/h2>\r\n<p class=\"import-Normal\">Neural networks are a type of machine learning model inspired by the structure and function of the human brain. They consist of layers of interconnected nodes (neurons) that process data by assigning <em>weights and biases<\/em> (importance and degree of error), enabling the network to learn patterns and relationships in data. Neural networks are used for tasks like classification, regression, image recognition, natural language processing, and more.<\/p>\r\n\r\n\r\n[caption id=\"\" align=\"alignleft\" width=\"719\"]<img class=\"\" src=\"http:\/\/pressbooks.hcfl.edu\/introtofintech\/wp-content\/uploads\/sites\/96\/2024\/12\/image7-2.png\" alt=\" Input-processing-output layers in a Neural Network\" width=\"719\" height=\"719\" \/> Showing Input-processing-output layers in a Neural Network. Image generated by OpenAI\u2019s DALL\u00b7E[\/caption]\r\n<h3 class=\"import-Normal\"><strong>How do Neural Networks work? <\/strong><\/h3>\r\n<p class=\"import-Normal\">Just like the human brain\u2019s composition of layers of biological neurons, Machine Learning using neural networks, which also has many layers that we call deep neural networks. These network layers mimic the human brain\u2019s structure and function, allowing them to process large and complex datasets. Deep learning excels at feature extraction, automatically identifying the most relevant features in the data without manual intervention. The inner processes that uses neural networks include:<\/p>\r\n\r\n<ul>\r\n \t<li><strong><em>Data Classification Process<\/em><\/strong><strong> \u2013 <\/strong>At the input layer, neural networks classify the data into predefined categories or labels. An example would be feeding an email script which the classification layer processes to categorize it as spam or not spam, hate email or NOT hate email, and so on.<\/li>\r\n \t<li><strong><em>Regression Process<\/em><\/strong><strong> - <\/strong>Regression involves predicting continuous numerical values rather than discrete categories. An example would be predicting new home prices based on features like size, location, and age.<\/li>\r\n \t<li><strong><em>Clustering Process<\/em><\/strong><strong> - <\/strong>Neural networks can group data into clusters based on similarity, even without labeled training data. An example could be segmenting customers into groups based on purchasing behavior, or groupings of 1<sup>st<\/sup> year college students by a target area of study (e.g., how much they spend on Coffee Lattes)<\/li>\r\n \t<li><strong><em>Dimensionality Reduction Process<\/em><\/strong><strong> - <\/strong>Neural networks, can reduce the number of features in a dataset while retaining critical information. An example could be compressing high-dimensional image data \u2013 such as an image of the sky behind the subject in the image, into a smaller feature set.<\/li>\r\n \t<li><strong><em>Generative Tasks Process<\/em><\/strong><strong> - <\/strong>Neural networks can generate new data very much similar to the input data they were trained on. It is the essence of what most AI models produce currently. An example would be the ability of AI to correctly postulate and produce coherent answers to questions on themes such as for generating stories, poetry, or dialogue where the user asks the AI model to <em>\u201cwrite<\/em> a<em> short story about a robot discovering emotions.\" <\/em>In this example, the Language Model would have been trained on language usage, writing styles, fiction as a theme, science fiction as an epilog, Robots as smart machines, and many others.<\/li>\r\n \t<li><strong><em>Sequence Prediction Process<\/em><\/strong><em> - <\/em>Neural networks can analyze sequential data and predict the next elements in the sequence. Examples include Text generation such as for auto-complete or predicting stock prices over similar periods of time.<\/li>\r\n \t<li><strong><em>Object Detection Process -<\/em><\/strong> Detecting and localizing multiple objects within an image, assigning a bounding box and determining a class label to each object. Such as man walking a dog, female with a shopping bag, homeless person pushing a shopping cart, etc. Example uses would include detecting vehicles, pedestrians, center lanes, and traffic signs in self-driving cars.<\/li>\r\n \t<li><strong><em>Semantic Segmentation Process \u2013<\/em><\/strong> Is classifying each pixel of an image into a category, effectively partitioning the image into meaningful segments. Example uses would include identifying roads, buildings, and vegetation in satellite imagery.<\/li>\r\n \t<li><strong><em>Anomaly Detection Process -<\/em><\/strong> Identifying unusual patterns or outliers in data. Example uses would include detecting fraudulent transactions in financial datasets.<\/li>\r\n \t<li><strong><em>Recommendation Process -<\/em><\/strong> Predicting user preferences and suggesting items based on historical data such as suggesting movies on Netflix or products on Amazon.<\/li>\r\n \t<li><strong><em>Image-to-Image Conversion Process -<\/em><\/strong> Converting images from one format to another using neural networks. Image-to-image processing is used for:<\/li>\r\n<\/ul>\r\n<p class=\"import-Normal\">Super-resolution: Enhancing image resolution.<\/p>\r\n<p class=\"import-Normal\">Style transfer: Applying artistic styles to images.<\/p>\r\n<p class=\"import-Normal\">Image colorization: Adding color to grayscale images.<\/p>\r\n\r\n<ul>\r\n \t<li><strong><em>Speech and Audio Processing -<\/em><\/strong> Handling tasks related to sound and speech. Example uses include:<\/li>\r\n<\/ul>\r\n<p class=\"import-Normal\">Speech recognition: Transcribing spoken words into text.<\/p>\r\n<p class=\"import-Normal\">Speech synthesis: Generating human-like speech (e.g., text-to-speech systems).<\/p>\r\n\r\n<ul>\r\n \t<li><strong><em>Time Series Analysis<\/em><\/strong><strong> Process \u2013 <\/strong>Is a statistical-based analysis and prediction model that processes patterns in <em>time-series data<\/em> (data that is time stamped in linear time sequences). Time series is important for applications requiring super large data sets such as predicting weather, machine breakdowns, or the most likely item that a customer may purchase next.<\/li>\r\n \t<li><strong><em>Multi-task Learning<\/em><\/strong><strong> Process- <\/strong>Training a single neural network to perform multiple related tasks simultaneously. Example usage is for a model that can recognize an image, and simultaneously detect objects within it.<\/li>\r\n<\/ul>\r\n<h2 class=\"import-Normal\"><strong>Appl<\/strong><strong>ying<\/strong> <strong>AI and <\/strong><strong>Machine Learning <\/strong><strong>in FinTech<\/strong><\/h2>\r\n<p class=\"import-Normal\">Machine learning is transforming classical applications of the financial industry into the modern FinTech sector by replacing legacy business approaches, enhancing operational efficiency, reducing costs, improving accuracy, and increasing security. Below are some common applications:<\/p>\r\n\r\n<h3 class=\"import-Normal\">Fraud and Anti-Money Laundering Detection<\/h3>\r\n<p class=\"import-Normal\">ML models analyze transaction data in real time to identify anomalous patterns indicative of fraudulent activity. Deep learning models can detect subtle irregularities in transaction metadata. For example, someone shops at 5 different stores on the same day, using a different credit card on each transaction and withdraws cash with each transaction.<\/p>\r\n\r\n<h3 class=\"import-Normal\">Credit Scoring<\/h3>\r\n<p class=\"import-Normal\">ML algorithms evaluate creditworthiness by analyzing financial behavior, payment history, and alternative data sources. This enables institutions to offer credit to underserved populations.<\/p>\r\n\r\n<h3 class=\"import-Normal\">Algorithmic Trading<\/h3>\r\n<p class=\"import-Normal\">Reinforcement learning models optimize trading strategies by learning market dynamics and adapting to changing conditions. Algorithms process vast datasets to predict price movements and execute trades efficiently.<\/p>\r\n\r\n<h3 class=\"import-Normal\">Customer Personalization<\/h3>\r\n<p class=\"import-Normal\">FinTech platforms use supervised and unsupervised learning to offer personalized financial products, such as tailored investment portfolios or savings plans.<\/p>\r\n\r\n<h3 class=\"import-Normal\">Risk Management<\/h3>\r\n<p class=\"import-Normal\">Predictive analytics models assess risks associated with loans, investments, or insurance policies, improving decision-making.<\/p>\r\n<p class=\"import-Normal\">Machine learning continues to revolutionize industries, with FinTech at the forefront of its adoption. The synergy between deep learning techniques and FinTech applications promises greater financial inclusion, enhanced security, and personalized services, driving innovation in the global economy.<\/p>\r\n\r\n<h2 class=\"import-Normal\"><strong>AI and Robotics <\/strong><\/h2>\r\n[caption id=\"\" align=\"alignleft\" width=\"638\"]<img src=\"http:\/\/pressbooks.hcfl.edu\/introtofintech\/wp-content\/uploads\/sites\/96\/2024\/12\/image8-1.jpeg\" alt=\"Nasa Mars Rover.\" width=\"638\" height=\"425\" \/> <a href=\"https:\/\/airandspace.si.edu\/collection-media\/NASM-NASM2022-05070\">National Air and Space Museum, \"Mars Pathfinder Marie Curie Rover Engineering Test Vehicle.<\/a>\" public domain[\/caption]\r\n<p class=\"import-Normal\">Labor is defined as a physical, or mental human effort applied against objects to transform them. Labor throughout history has gone through many evolutionary phases. Initially human labor was focused on what nature provided (the age of hunter-gatherer), then through mechanization (e.g., making tools to ease the labor process), then followed by mechanization (using power to drive tools such as tractors and harvesting machines) and on to automation that programmed machinery to repetitively produce objects in mass quantities (machines that automate production) and on to AI-supported systems and methods of production such as in farming using smart autonomous machinery (e.g., smart efficient farming to produce more with less resources such as labor, land, chemicals, water, etc.)<\/p>\r\n<p class=\"import-Normal\">Labor in Fintech traditionally required complex business processes that went through the same evolutionary path as was discussed previously in this textbook. The latest evolution in Fintech is applying AI in ways that envision the future. \u201cWe need banking, but we don't need banks anymore\u201d Bill Gates stated in 1997 envisioning the future (American Deposits. n.d.).<\/p>\r\n<p class=\"import-Normal\">Early Robots were machines that replicated certain human movements. Starting in the early 1960\u2019s heavy industries began using Robotic machines to perform highly repetitive tasks (boring things that drive people mad, such as counting things), or tasks that required dangerous, tedious, and exact such as lifting heavy items, handling, fabricating and inspections in radioactive environments. These evolved into much more complex Robots performing activities such as exploring the depth of oceans and explorations of deep space and the surfaces of the Moon and Mars.<\/p>\r\n\r\n\r\n[caption id=\"\" align=\"alignright\" width=\"448\"]<img class=\"\" src=\"http:\/\/pressbooks.hcfl.edu\/introtofintech\/wp-content\/uploads\/sites\/96\/2024\/12\/image9-1.jpeg\" alt=\"NASA\u2019s R5 aka\u00a0Valkyrie robot.\" width=\"448\" height=\"336\" \/> <a href=\"https:\/\/www.nasa.gov\/robonaut2\/\">NASA\u2019s R5 aka\u00a0Valkyrie<\/a>. Public domain.[\/caption]\r\n<p class=\"import-Normal\">This course is not about hardware Robots or the study of Robotics. We mentioned them as a prologue to introducing the subject of Software type of Robotics known as Robotic Process Automation.<\/p>\r\n\r\n<h3 class=\"import-Normal\"><strong>Robotic Process Automation: Transforming <\/strong><strong>the Fintech <\/strong><strong>Industr<\/strong><strong>y<\/strong><\/h3>\r\n<p class=\"import-Normal\">Robotic Process Automation (RPA) refers to the use of software robots or \"bots\" to automate repetitive, rule-based tasks that were traditionally performed by humans. These bots interact with digital systems and software in the same way a human would, but with greater speed, accuracy, and consistency. Unlike traditional automation, RPA does not require extensive programming or changes to existing IT infrastructure, making it a highly accessible and scalable solution for organizations.<\/p>\r\n<p class=\"import-Normal\">RPA leverages technologies such as artificial intelligence (AI), machine learning (ML), and natural language processing (NLP) to handle tasks such as data entry, transaction processing, and customer service interactions. By automating these processes, businesses can improve efficiency, reduce operational costs, and free up human employees to focus on more strategic and creative tasks.<\/p>\r\n\r\n<h3 class=\"import-Normal\"><strong>RPA Components<\/strong><\/h3>\r\n<p class=\"import-Normal\">Building an RPA system involves designing \u201csoftware bots\u201d and setting up the necessary software to perform specific tasks. To develop a functioning RPA, the requirements would include:<\/p>\r\n<p class=\"import-Normal\">Understanding the Process: First, a business identifies tasks that are repetitive, rule-based, and time-consuming. These tasks are perfect candidates for automation.<\/p>\r\n<p class=\"import-Normal\">Choosing the RPA Software: Next, a company picks an RPA tool, like UiPath, Blue Prism, or Automation Anywhere. These tools provide a platform to design and deploy bots.<\/p>\r\n<p class=\"import-Normal\">Designing the Bot: Using the RPA tool, developers or analysts create a \"workflow,\" which is a step-by-step script that tells the bot what to do. For example, \"open this file, copy data from column A, and paste it into another system.\"<\/p>\r\n<p class=\"import-Normal\">Testing: The bot is tested to ensure it performs the task correctly and efficiently.<\/p>\r\n<p class=\"import-Normal\">Deployment: Once tested, the bot is deployed in the company\u2019s systems to work alongside humans or on its own.<\/p>\r\n\r\n<h3 class=\"import-Normal\"><strong>Main Components of RPA<\/strong><\/h3>\r\n<p class=\"import-Normal\"><em>Bot Designer:<\/em> This is where the bot is built and programmed. It uses a visual interface to map out tasks step by step.<\/p>\r\n<p class=\"import-Normal\"><em>Bot Runner<\/em>: This is the environment where the bot actually performs its tasks. It executes the instructions created in the designer.<\/p>\r\n<p class=\"import-Normal\"><em>Control Room:<\/em> This is the central hub for managing and monitoring all bots. It helps track performance and ensure bots are functioning correctly.<\/p>\r\n<p class=\"import-Normal\">RPA systems also rely on integrations with existing software, allowing bots to work across multiple applications without requiring major changes to those systems.<\/p>\r\n\r\n<h3 class=\"import-Normal\"><strong>Applications of RPA<\/strong><\/h3>\r\n<p class=\"import-Normal\">RPA is used across a variety of industries and functions to optimize operations and enhance productivity. Some common applications include:<\/p>\r\n\r\n<h2 class=\"import-Normal\"><em>Data Entry and Migration<\/em><\/h2>\r\n<p class=\"import-Normal\">Many newer systems are made with newer approaches including programming languages requiring the migration of older systems data to newer ones. RPA helps in automating the transfer of data between systems without human intervention.<\/p>\r\n<p class=\"import-Normal\"><strong><em>Invoice Processing:<\/em><\/strong> Extracting data from invoices, validating information, and processing payments. The traditional approach is for a human analyst to run jobs inside of computer systems to extract data, validate information such as customer name and address in preparation for shipping. RPA automates this and many other processes such as matching Invoice numbers to specific Customer accounts, automatically looking up inventory and issuing an order to pull from inventory, and prepare all shipping and receiving paperwork. Upon completion, the RPA may connect to the financial system to reconcile the account eliminating to a great degree functions that are manually performed by company staff.<\/p>\r\n<p class=\"import-Normal\"><strong><em>Customer Support:<\/em><\/strong> We are all familiar with chatbots especially around customer support. A chat bot is a form of an RPA that is programmed to understand human (customer) voice or typed interactions. It can listen and record a call and even attempt to resolve an issue. Chatbots can also email, or call specialists within the company to come on-line to assist the customer.<\/p>\r\n<p class=\"import-Normal\"><strong><em>Compliance and Auditing:<\/em><\/strong> RPA bots are sometimes used to ensure regulatory compliance by automating the generation of audit trails and reports. Depending on the nature of the business, government regulatory bodies, like in Telecommunication and energy for example could require a company to generate periodic reports that are excessive. RPA have been deployed in these industries to collect regulatory specific information, ensure its accuracy, then format it into the required style the government requires and ensure such reports are delivered at the proper times.<\/p>\r\n<p class=\"import-Normal\"><em>IT Support<\/em><strong><em>:<\/em><\/strong> Automating routine IT tasks like password resets and software installations.<\/p>\r\n\r\n<h2 class=\"import-Normal\"><strong>RPA <\/strong><strong>Applications <\/strong><strong>in the Financial Industry<\/strong><\/h2>\r\n<p class=\"import-Normal\">The financial industry has been a significant beneficiary of RPA, using it to streamline operations, improve customer experiences, and ensure compliance. Examples where Financial institutions rely on RPA include:<\/p>\r\n<p class=\"import-Normal\"><strong><em>Transaction Processing:<\/em><\/strong> Automating routine tasks such as account reconciliations, payments processing, and loan approvals.<\/p>\r\n<p class=\"import-Normal\"><strong><em>Fraud Detection:<\/em><\/strong> Using bots to monitor transactions for anomalies, flagging potentially fraudulent activities in real time.<\/p>\r\n<p class=\"import-Normal\"><strong><em>Regulatory Compliance:<\/em><\/strong> Automating the creation and submission of compliance reports to meet stringent regulatory requirements.<\/p>\r\n<p class=\"import-Normal\"><strong><em>Customer Onboarding:<\/em><\/strong> Simplifying the customer onboarding process by automating the collection and validation of customer data.<\/p>\r\n<p class=\"import-Normal\"><strong><em>Risk Management<\/em><\/strong><strong>:<\/strong> Automating the aggregation and analysis of risk-related data to support decision-making.<\/p>\r\n\r\n<\/div>\r\n<div class=\"textbox textbox--exercises\"><header class=\"textbox__header\">\r\n<p class=\"textbox__title\"><strong>Case Study: AI-Driven Digital Transformation in Healthcare \u2013 A Fintech Perspective<\/strong><\/p>\r\n\r\n<\/header>\r\n<div class=\"textbox__content\">\r\n\r\n<em>Background<\/em>\r\nIn April 2024, Chi Mei Medical Center in Taiwan introduced generative AI copilots built on Microsoft\u2019s Azure OpenAI Service. This deployment represents a key example of how fintech principles\u2014automation, data integration, and real-time analytics\u2014can transform traditional sectors like healthcare by addressing workforce shortages, improving operational efficiency, and enhancing service quality.\r\n\r\nChi Mei Medical Center operates within Taiwan\u2019s renowned healthcare system, which faces significant challenges due to an aging population, a low birth rate, and increasing demand for medical services. By leveraging AI tools tailored to specific medical workflows, Chi Mei aims to alleviate healthcare worker burnout while maintaining high standards of care.\r\n\r\n<em>AI Copilots in Action<\/em>\r\nChi Mei\u2019s AI copilots were designed for various roles:\r\n<ul>\r\n \t<li><strong>A+ Pharmacy Copilot<\/strong>: Helps pharmacists retrieve and summarize patient data, flagging potential drug interactions and insurance coverage.<\/li>\r\n \t<li><strong>A+ Doctor Copilot<\/strong>: Streamlines medical report generation, reducing documentation time from one hour to 15 minutes.<\/li>\r\n \t<li><strong>A+ Nurse Copilot<\/strong>: Speeds up the process of documenting patient transfers and shift changes.<\/li>\r\n \t<li><strong>A+ Nutritionist Copilot<\/strong>: Offers dietary recommendations based on patients\u2019 medical conditions.<\/li>\r\n<\/ul>\r\nBy integrating data from multiple databases, these copilots provide comprehensive patient profiles, enabling healthcare providers to focus more on patient care rather than administrative tasks. In May 2024 alone, these AI copilots were used over 36,000 times by 3,500 individual users across the hospital's departments.\r\n\r\n<em>Impact on Healthcare Efficiency<\/em>\r\nThe introduction of AI copilots has delivered measurable benefits, including:\r\n<ul>\r\n \t<li><strong>Increased Patient Capacity<\/strong>: Pharmacists doubled the number of patients they can serve daily, from 15 to 30.<\/li>\r\n \t<li><strong>Reduced Burnout<\/strong>: Early surveys indicate a drop in burnout scores among nurses.<\/li>\r\n \t<li><strong>Improved Workflow<\/strong>: Nurses report a reduction in documentation time from 20 minutes to under five minutes per task.<\/li>\r\n<\/ul>\r\nDespite initial resistance from healthcare professionals who feared AI might replace them, hospital administrators emphasized that AI tools are intended to assist staff, allowing them to leave work on time and spend more quality time with their families.\r\n\r\n<em>Challenges and Finetuning<\/em>\r\nWhile the copilots have performed well overall, some areas for improvement remain:\r\n<ol>\r\n \t<li><strong>Language Accuracy<\/strong>: Occasional use of simplified Chinese characters instead of traditional script requires manual correction.<\/li>\r\n \t<li><strong>Professional Terminology<\/strong>: The copilots sometimes use layperson terms instead of precise medical language, necessitating edits during reviews.<\/li>\r\n \t<li><strong>Perceived AI Authorship<\/strong>: Some users felt that the generated reports were too formulaic, making them appear AI-authored.<\/li>\r\n<\/ol>\r\nChi Mei\u2019s ongoing efforts include improving language models and expanding copilot functions, such as a forthcoming <strong>A+ National Exam Review Copilot<\/strong> for medical professionals\u2019 continuous education.\r\n\r\n<em>Conclusion: A Fintech Model for Healthcare Innovation<\/em>\r\nThis case study illustrates how fintech innovations can be adapted to healthcare, demonstrating the potential of AI-driven solutions in enhancing efficiency, accuracy, and user satisfaction. By integrating fintech principles\u2014such as real-time data analysis, automation, and user-centric design\u2014Chi Mei Medical Center has become a leader in digital healthcare transformation.\r\n\r\nThrough continuous refinement of AI tools and active collaboration with healthcare providers, Chi Mei exemplifies how fintech-inspired technologies can address critical challenges in other sectors, paving the way for broader adoption across industries\u00a0 (Yee, 2024).\r\n\r\n<\/div>\r\n<\/div>\r\n<div class=\"textbox\">\r\n<div class=\"group\/conversation-turn relative flex w-full min-w-0 flex-col agent-turn\">\r\n<div class=\"flex-col gap-1 md:gap-3\">\r\n<div class=\"flex max-w-full flex-col flex-grow\">\r\n<div class=\"min-h-8 text-message flex w-full flex-col items-end gap-2 whitespace-normal break-words [.text-message+&amp;]:mt-5\" dir=\"auto\" data-message-author-role=\"assistant\" data-message-id=\"5555fc12-a2a4-4540-bc00-a0effb0de35c\" data-message-model-slug=\"gpt-4o\">\r\n<div class=\"flex w-full flex-col gap-1 empty:hidden first:pt-[3px]\">\r\n<div class=\"markdown prose w-full break-words dark:prose-invert light\">\r\n<h3><strong>Licenses and Attribution<\/strong><\/h3>\r\n<h4>CC Licensed Content, Original<\/h4>\r\n<span data-teams=\"true\">This educational material includes AI-generated content from ChatGPT by OpenAI. The original content created by Mohammed Kotaiche from Hillsborough Community College is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License (<a id=\"menur5so\" class=\"fui-Link ___1q1shib f2hkw1w f3rmtva f1ewtqcl fyind8e f1k6fduh f1w7gpdv fk6fouc fjoy568 figsok6 f1s184ao f1mk8lai fnbmjn9 f1o700av f13mvf36 f1cmlufx f9n3di6 f1ids18y f1tx3yz7 f1deo86v f1eh06m1 f1iescvh fhgqx19 f1olyrje f1p93eir f1nev41a f1h8hb77 f1lqvz6u f10aw75t fsle3fq f17ae5zn\" title=\"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/deed.en\" href=\"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/deed.en\" rel=\"noreferrer noopener\" aria-label=\"Link CC BY-NC 4.0\">CC BY-NC 4.0<\/a>).\u00a0<\/span>\r\n<div class=\"flex-shrink-0 flex flex-col relative items-end\">\r\n<div>\r\n<div class=\"pt-0\">\r\n<div class=\"gizmo-bot-avatar flex h-8 w-8 items-center justify-center overflow-hidden rounded-full\">\r\n<div class=\"relative p-1 rounded-sm flex items-center justify-center bg-token-main-surface-primary text-token-text-primary h-8 w-8\">All images in this textbook generated with DALL-E are licensed under the terms provided by OpenAI, allowing for their free use, modification, and distribution with appropriate attribution.<\/div>\r\n<\/div>\r\n<\/div>\r\n<\/div>\r\n<\/div>\r\n\r\n<hr \/>\r\n\r\n<h4><strong>CC Licensed Content Included<\/strong><\/h4>\r\n<\/div>\r\n<\/div>\r\n<div class=\"flex w-full flex-col gap-1 empty:hidden first:pt-[3px]\">\r\n<div class=\"markdown prose w-full break-words dark:prose-invert light\">\r\n<div>\r\n<ul>\r\n \t<li><strong>Wikipedia contributors. (n.d.)<\/strong>\r\nSource: <a href=\"https:\/\/en.wikipedia.org\/wiki\/HAL_9000\" target=\"_new\" rel=\"noopener\">Wikipedia contributors, HAL 9000<\/a>\r\nLicense: Public Domain<\/li>\r\n \t<li><strong>Wikipedia contributors. (n.d.)<\/strong>\r\nSource: <a href=\"https:\/\/en.wikipedia.org\/wiki\/I,_Robot_(film)\" target=\"_new\" rel=\"noopener\">Wikipedia contributors, I, Robot (film)<\/a>\r\nLicense: Public Domain<\/li>\r\n \t<li><strong>NASA. (n.d.)<\/strong>\r\nSource<strong>:<\/strong><a href=\"https:\/\/www.nasa.gov\/robonaut2\/\"> NASA, \"Robonaut 2\"<\/a>\r\nLicense: Public Domain<\/li>\r\n \t<li><strong>National Air and Space Museum. (2017).<\/strong>\r\nSource: <a href=\"https:\/\/airandspace.si.edu\/collection-media\/NASM-NASM2022-05070\">National Air and Space Museum, \"Mars Pathfinder Marie Curie Rover Engineering Test Vehicle.\"<\/a>\r\nLicense<strong>:<\/strong> Creative Commons Zero (CC0 1.0).<\/li>\r\n<\/ul>\r\n<\/div>\r\n\r\n<hr \/>\r\n\r\n<h4>Other Licensed Content Included<\/h4>\r\n<ul>\r\n \t<li>Yee, C. M. (2024, July 12). <i>Taiwan hospital deploys AI copilots to lighten workloads for doctors, nurses and pharmacists<\/i>. Microsoft. Retrieved from <a href=\"https:\/\/www.microsoft.com\/en-us\/news\/taiwan-ai-copilots-healthcare\" target=\"_blank\" rel=\"noopener\">Microsoft\u2019s Article on AI Copilots in Taiwan Hospitals<\/a>.<\/li>\r\n \t<li>University of Chicago Press. (2020). <i>Reason and character: The moral foundations of Aristotelian political philosophy<\/i>. Retrieved from <a href=\"https:\/\/press.uchicago.edu\/ucp\/books\/book\/chicago\/R\/bo56407959.html\" target=\"_blank\" rel=\"noopener\">University of Chicago Press\u2019s Reason and Character<\/a>.<\/li>\r\n \t<li>Dartmouth College. (n.d.). <i>Artificial intelligence (AI) coined at Dartmouth<\/i>. Retrieved January 23, 2025, from\r\n<a href=\"https:\/\/home.dartmouth.edu\/news\/artificial-intelligence-coined-dartmouth\" target=\"_blank\" rel=\"noopener\">Learn About AI\u2019s Origin at Dartmouth<\/a>.<\/li>\r\n \t<li>Flynn, J. R. (2007). <i>What is intelligence? Beyond the Flynn effect<\/i>. Cambridge University Press. Retrieved January 23, 2025, from\r\n<a href=\"https:\/\/www.cambridge.org\/core\/books\/what-is-intelligence\/6A5AB5E24D396B9B4774E3D4D132223F\" target=\"_blank\" rel=\"noopener\">Cambridge University Press\u2019s What is Intelligence?<\/a>.<\/li>\r\n \t<li>Dugal, N. (2025). <i>Top generative AI tools for 2025<\/i>. Simplilearn. Retrieved January 23, 2025, from <a href=\"https:\/\/www.simplilearn.com\/top-generative-ai-tools-2025-article\" target=\"_blank\" rel=\"noopener\">Simplilearn\u2019s Article on Top Generative AI Tools<\/a>.<\/li>\r\n \t<li>Turing, A. M. (1950). <i>Computing machinery and intelligence<\/i>. <i>Mind<\/i>, New Series, 59(236), 433\u2013460. Published by Oxford Press. Retrieved January 23, 2025, from <a href=\"https:\/\/academic.oup.com\/mind\/article\/LIX\/236\/433\/986238\" target=\"_blank\" rel=\"noopener\">Oxford Press\u2019s Article on Computing Machinery and Intelligence<\/a>.<\/li>\r\n \t<li>American Deposits. (n.d.). <i>Conference on the future of digital banking. Insights: A Brief History of Digital Banking<\/i>. Retrieved from <a href=\"https:\/\/www.americandeposits.com\/history-digital-banking\" target=\"_blank\" rel=\"noopener\">A Brief History of Digital Banking<\/a>.\r\n<ul>\r\n \t<li style=\"list-style-type: none\"><\/li>\r\n<\/ul>\r\n<\/li>\r\n<\/ul>\r\n<div id=\"sdfootnote18sym\"><\/div>\r\n<\/div>\r\n<\/div>\r\n<\/div>\r\n<\/div>\r\n<\/div>\r\n<\/div>\r\n<\/div>","rendered":"<p><iframe loading=\"lazy\" id=\"oembed-1\" title=\"Introduction to Artificial Intelligence\" width=\"500\" height=\"281\" src=\"https:\/\/www.youtube.com\/embed\/J8o77Kry5uw?feature=oembed&#38;rel=0\" frameborder=\"0\" allowfullscreen=\"allowfullscreen\"><\/iframe><\/p>\n<div class=\"__UNKNOWN__\">\n<div class=\"textbox textbox--key-takeaways\">\n<header class=\"textbox__header\">\n<p class=\"textbox__title\">Key Principles<\/p>\n<\/header>\n<div class=\"textbox__content\">\n<p>Where is AI making big a big splash? AI has been transformative across several industries driving innovation, improving efficiency, reducing costs and creating new business opportunities. Examples of industries that have extensively benefited from using AI:<\/p>\n<ul>\n<li class=\"import-Normal\"><em>Finance<\/em><em> \u2013<\/em> AI is heavily used in fraud detection, credit scoring, Robo-advising, market trading and a host of other applications.<\/li>\n<li class=\"import-Normal\"><em>Healthcare<\/em><em> \u2013<\/em> AI is used as an adjunct to diagnostic tools, personalized treatment, and Robotic-assisted surgeries.<\/li>\n<li class=\"import-Normal\"><em>Retail and eCommerce-<\/em> Used in developing personalized recommendations; Sales &amp; marketing; Inventory Management; Warehousing; and Transportation &amp; Shipping<\/li>\n<li class=\"import-Normal\"><em>Manufacturing<\/em> <em>&#8211;<\/em> Digital Twins, Smart manufacturing; Autonomous Machinery and Robotics<\/li>\n<li class=\"import-Normal\"><em>Transportation and Logistics<\/em> \u2013 Route Optimization; Autonomous driving; and demand forecasting and management.<\/li>\n<li class=\"import-Normal\"><em>Agriculture<\/em><em> (Smart Ag)<\/em> \u2013 Precision farming; Crop monitoring; harvest prediction and weather prediction.<\/li>\n<li class=\"import-Normal\"><em>Energy<\/em><em> Sector<\/em> \u2013 Smart Grid and Renewable energy sourcing.<\/li>\n<li class=\"import-Normal\"><em>Education<\/em> \u2013 Personalized learning, Administrative Automation; virtual tutoring<\/li>\n<li class=\"import-Normal\"><em>Legal<\/em><em> &#8211;<\/em> Intelligent case search and court preparations, contracts analysis and management.<\/li>\n<\/ul>\n<\/div>\n<\/div>\n<div class=\"textbox textbox--learning-objectives\">\n<header class=\"textbox__header\">\n<p class=\"textbox__title\">Learning Objectives<\/p>\n<\/header>\n<div class=\"textbox__content\">\n<p>Upon completion of this chapter, students should be able to:<\/p>\n<ul>\n<li>Define the nature of intelligence and how humans learn<\/li>\n<li>Define Intelligent Systems (Artificial Intelligence)<\/li>\n<li>Define key characteristics, and components of Expert Systems<\/li>\n<li>Explain how Artificial Neural Networks work<\/li>\n<li>Explain how Machine Learning works<\/li>\n<li>Explain how IIoT incorporates AI<\/li>\n<li>Demonstrate practicality of use of AI across many industries<\/li>\n<li>Clearly give example uses of Natural Language Processing models using ChatGPT<\/li>\n<li>Explain the different types of Software and Hardware Intelligent Robotics<\/li>\n<\/ul>\n<\/div>\n<\/div>\n<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_82_2 counter-hierarchy ez-toc-counter ez-toc-grey ez-toc-container-direction\">\n<div class=\"ez-toc-title-container\">\n<p class=\"ez-toc-title\" style=\"cursor:inherit\">Table of Contents<\/p>\n<p><span class=\"ez-toc-title-toggle\"><a href=\"#\" class=\"ez-toc-pull-right ez-toc-btn ez-toc-btn-xs ez-toc-btn-default ez-toc-toggle\" aria-label=\"Toggle Table of Content\"><span class=\"ez-toc-js-icon-con\"><span class=\"\"><span class=\"eztoc-hide\" style=\"display:none;\">Toggle<\/span><span class=\"ez-toc-icon-toggle-span\"><svg style=\"fill: #999;color:#999\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" class=\"list-377408\" width=\"20px\" height=\"20px\" viewBox=\"0 0 24 24\" fill=\"none\"><path d=\"M6 6H4v2h2V6zm14 0H8v2h12V6zM4 11h2v2H4v-2zm16 0H8v2h12v-2zM4 16h2v2H4v-2zm16 0H8v2h12v-2z\" fill=\"currentColor\"><\/path><\/svg><svg style=\"fill: #999;color:#999\" class=\"arrow-unsorted-368013\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"10px\" height=\"10px\" viewBox=\"0 0 24 24\" version=\"1.2\" baseProfile=\"tiny\"><path d=\"M18.2 9.3l-6.2-6.3-6.2 6.3c-.2.2-.3.4-.3.7s.1.5.3.7c.2.2.4.3.7.3h11c.3 0 .5-.1.7-.3.2-.2.3-.5.3-.7s-.1-.5-.3-.7zM5.8 14.7l6.2 6.3 6.2-6.3c.2-.2.3-.5.3-.7s-.1-.5-.3-.7c-.2-.2-.4-.3-.7-.3h-11c-.3 0-.5.1-.7.3-.2.2-.3.5-.3.7s.1.5.3.7z\"\/><\/svg><\/span><\/span><\/span><\/a><\/span><\/div>\n<nav>\n<ul class='ez-toc-list ez-toc-list-level-1 ' >\n<li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-1\" href=\"https:\/\/pressbooks.hcfl.edu\/introtofintech\/chapter\/7\/#A_Historical_Evolutionary_Perspective_on_AI\" >A Historical Evolutionary Perspective on AI<\/a><\/li>\n<li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/pressbooks.hcfl.edu\/introtofintech\/chapter\/7\/#Artificial_Intelligence\" >Artificial Intelligence<\/a>\n<ul class='ez-toc-list-level-3' >\n<li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/pressbooks.hcfl.edu\/introtofintech\/chapter\/7\/#Narrow_AI\" >Narrow AI\u00a0<\/a><\/li>\n<li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/pressbooks.hcfl.edu\/introtofintech\/chapter\/7\/#General_AI_AGI\" >General AI (AGI)<\/a><\/li>\n<\/ul>\n<\/li>\n<li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/pressbooks.hcfl.edu\/introtofintech\/chapter\/7\/#Generative_AI_Overview\" >Generative AI Overview<\/a>\n<ul class='ez-toc-list-level-3' >\n<li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-6\" href=\"https:\/\/pressbooks.hcfl.edu\/introtofintech\/chapter\/7\/#List_of_Current_Generative_AI_Types_and_Their_Uses\" >List of Current Generative AI Types and Their Uses<\/a><\/li>\n<\/ul>\n<\/li>\n<li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-7\" href=\"https:\/\/pressbooks.hcfl.edu\/introtofintech\/chapter\/7\/#Uses_of_Generative_AI_in_Various_Economic_Sectors\" >Uses of Generative AI in Various Economic Sectors<\/a><\/li>\n<li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-8\" href=\"https:\/\/pressbooks.hcfl.edu\/introtofintech\/chapter\/7\/#Challenges_and_Concerns_With_Generative_AI\" >Challenges and Concerns With Generative AI<\/a>\n<ul class='ez-toc-list-level-3' >\n<li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-9\" href=\"https:\/\/pressbooks.hcfl.edu\/introtofintech\/chapter\/7\/#Machine_Learning\" >Machine Learning<\/a><\/li>\n<li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-10\" href=\"https:\/\/pressbooks.hcfl.edu\/introtofintech\/chapter\/7\/#The_Origin_of_Machine_Learning\" >The Origin of Machine Learning<\/a><\/li>\n<li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-11\" href=\"https:\/\/pressbooks.hcfl.edu\/introtofintech\/chapter\/7\/#How_Are_Machines_Trained\" >How Are Machines Trained?<\/a><\/li>\n<li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-12\" href=\"https:\/\/pressbooks.hcfl.edu\/introtofintech\/chapter\/7\/#The_Four_Training_Modes_in_Machine_Learning\" >The Four Training Modes in Machine Learning<\/a>\n<ul class='ez-toc-list-level-4' >\n<li class='ez-toc-heading-level-4'><a class=\"ez-toc-link ez-toc-heading-13\" href=\"https:\/\/pressbooks.hcfl.edu\/introtofintech\/chapter\/7\/#1_Supervised_Learning\" >1. Supervised Learning<\/a><\/li>\n<li class='ez-toc-page-1 ez-toc-heading-level-4'><a class=\"ez-toc-link ez-toc-heading-14\" href=\"https:\/\/pressbooks.hcfl.edu\/introtofintech\/chapter\/7\/#2_Unsupervised_Learning\" >2. Unsupervised Learning<\/a><\/li>\n<li class='ez-toc-page-1 ez-toc-heading-level-4'><a class=\"ez-toc-link ez-toc-heading-15\" href=\"https:\/\/pressbooks.hcfl.edu\/introtofintech\/chapter\/7\/#3_Reinforcement_Learning\" >3. Reinforcement Learning<\/a><\/li>\n<li class='ez-toc-page-1 ez-toc-heading-level-4'><a class=\"ez-toc-link ez-toc-heading-16\" href=\"https:\/\/pressbooks.hcfl.edu\/introtofintech\/chapter\/7\/#4_Semi-Supervised_Learning\" >4. Semi-Supervised Learning<\/a><\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/li>\n<li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-17\" href=\"https:\/\/pressbooks.hcfl.edu\/introtofintech\/chapter\/7\/#Neural_Networks_and_Deep_Learning_in_AI\" >Neural Networks and Deep Learning in AI<\/a>\n<ul class='ez-toc-list-level-3' >\n<li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-18\" href=\"https:\/\/pressbooks.hcfl.edu\/introtofintech\/chapter\/7\/#How_do_Neural_Networks_work\" >How do Neural Networks work?<\/a><\/li>\n<\/ul>\n<\/li>\n<li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-19\" href=\"https:\/\/pressbooks.hcfl.edu\/introtofintech\/chapter\/7\/#Applying_AI_and_Machine_Learning_in_FinTech\" >Applying AI and Machine Learning in FinTech<\/a>\n<ul class='ez-toc-list-level-3' >\n<li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-20\" href=\"https:\/\/pressbooks.hcfl.edu\/introtofintech\/chapter\/7\/#Fraud_and_Anti-Money_Laundering_Detection\" >Fraud and Anti-Money Laundering Detection<\/a><\/li>\n<li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-21\" href=\"https:\/\/pressbooks.hcfl.edu\/introtofintech\/chapter\/7\/#Credit_Scoring\" >Credit Scoring<\/a><\/li>\n<li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-22\" href=\"https:\/\/pressbooks.hcfl.edu\/introtofintech\/chapter\/7\/#Algorithmic_Trading\" >Algorithmic Trading<\/a><\/li>\n<li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-23\" href=\"https:\/\/pressbooks.hcfl.edu\/introtofintech\/chapter\/7\/#Customer_Personalization\" >Customer Personalization<\/a><\/li>\n<li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-24\" href=\"https:\/\/pressbooks.hcfl.edu\/introtofintech\/chapter\/7\/#Risk_Management\" >Risk Management<\/a><\/li>\n<\/ul>\n<\/li>\n<li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-25\" href=\"https:\/\/pressbooks.hcfl.edu\/introtofintech\/chapter\/7\/#AI_and_Robotics\" >AI and Robotics<\/a>\n<ul class='ez-toc-list-level-3' >\n<li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-26\" href=\"https:\/\/pressbooks.hcfl.edu\/introtofintech\/chapter\/7\/#Robotic_Process_Automation_Transforming_the_Fintech_Industry\" >Robotic Process Automation: Transforming the Fintech Industry<\/a><\/li>\n<li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-27\" href=\"https:\/\/pressbooks.hcfl.edu\/introtofintech\/chapter\/7\/#RPA_Components\" >RPA Components<\/a><\/li>\n<li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-28\" href=\"https:\/\/pressbooks.hcfl.edu\/introtofintech\/chapter\/7\/#Main_Components_of_RPA\" >Main Components of RPA<\/a><\/li>\n<li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-29\" href=\"https:\/\/pressbooks.hcfl.edu\/introtofintech\/chapter\/7\/#Applications_of_RPA\" >Applications of RPA<\/a><\/li>\n<\/ul>\n<\/li>\n<li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-30\" href=\"https:\/\/pressbooks.hcfl.edu\/introtofintech\/chapter\/7\/#Data_Entry_and_Migration\" >Data Entry and Migration<\/a><\/li>\n<li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-31\" href=\"https:\/\/pressbooks.hcfl.edu\/introtofintech\/chapter\/7\/#RPA_Applications_in_the_Financial_Industry\" >RPA Applications in the Financial Industry<\/a>\n<ul class='ez-toc-list-level-3' >\n<li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-32\" href=\"https:\/\/pressbooks.hcfl.edu\/introtofintech\/chapter\/7\/#Licenses_and_Attribution\" >Licenses and Attribution<\/a>\n<ul class='ez-toc-list-level-4' >\n<li class='ez-toc-heading-level-4'><a class=\"ez-toc-link ez-toc-heading-33\" href=\"https:\/\/pressbooks.hcfl.edu\/introtofintech\/chapter\/7\/#CC_Licensed_Content_Original\" >CC Licensed Content, Original<\/a><\/li>\n<li class='ez-toc-page-1 ez-toc-heading-level-4'><a class=\"ez-toc-link ez-toc-heading-34\" href=\"https:\/\/pressbooks.hcfl.edu\/introtofintech\/chapter\/7\/#CC_Licensed_Content_Included\" >CC Licensed Content Included<\/a><\/li>\n<li class='ez-toc-page-1 ez-toc-heading-level-4'><a class=\"ez-toc-link ez-toc-heading-35\" href=\"https:\/\/pressbooks.hcfl.edu\/introtofintech\/chapter\/7\/#Other_Licensed_Content_Included\" >Other Licensed Content Included<\/a><\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/nav>\n<\/div>\n<h2><span class=\"ez-toc-section\" id=\"A_Historical_Evolutionary_Perspective_on_AI\"><\/span>A Historical Evolutionary Perspective on AI<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<\/div>\n<div style=\"width: 1280px;\" class=\"wp-video\"><video class=\"wp-video-shortcode\" id=\"video-659-1\" width=\"1280\" height=\"720\" autoplay preload=\"metadata\" controls=\"controls\"><source type=\"video\/mp4\" src=\"http:\/\/pressbooks.hcfl.edu\/introtofintech\/wp-content\/uploads\/sites\/96\/2024\/12\/20250117_1247_Leonardos-Future-Vision_storyboard_01jhtnzy05esx90f2mf6fhrrqt.mp4?_=1\" \/><a href=\"http:\/\/pressbooks.hcfl.edu\/introtofintech\/wp-content\/uploads\/sites\/96\/2024\/12\/20250117_1247_Leonardos-Future-Vision_storyboard_01jhtnzy05esx90f2mf6fhrrqt.mp4\">http:\/\/pressbooks.hcfl.edu\/introtofintech\/wp-content\/uploads\/sites\/96\/2024\/12\/20250117_1247_Leonardos-Future-Vision_storyboard_01jhtnzy05esx90f2mf6fhrrqt.mp4<\/a><\/video><\/div>\n<div id=\"h5p-8\">\n<div class=\"h5p-iframe-wrapper\"><iframe id=\"h5p-iframe-8\" class=\"h5p-iframe\" data-content-id=\"8\" style=\"height:1px\" src=\"about:blank\" frameBorder=\"0\" scrolling=\"no\" title=\"Leonardo Davinci Sora: Audio Description\"><\/iframe><\/div>\n<\/div>\n<div class=\"__UNKNOWN__\">\n<figure id=\"attachment_839\" aria-describedby=\"caption-attachment-839\" style=\"width: 300px\" class=\"wp-caption alignleft\"><img loading=\"lazy\" decoding=\"async\" class=\"wp-image-839 size-medium\" src=\"http:\/\/pressbooks.hcfl.edu\/introtofintech\/wp-content\/uploads\/sites\/96\/2024\/12\/9212589254_4b4501b6d6_o-300x268.jpg\" alt=\"Leonardo da Vinci: Diagram of a proposed flying machine (1789)\" width=\"300\" height=\"268\" srcset=\"https:\/\/pressbooks.hcfl.edu\/introtofintech\/wp-content\/uploads\/sites\/96\/2024\/12\/9212589254_4b4501b6d6_o-300x268.jpg 300w, https:\/\/pressbooks.hcfl.edu\/introtofintech\/wp-content\/uploads\/sites\/96\/2024\/12\/9212589254_4b4501b6d6_o-1024x915.jpg 1024w, https:\/\/pressbooks.hcfl.edu\/introtofintech\/wp-content\/uploads\/sites\/96\/2024\/12\/9212589254_4b4501b6d6_o-768x686.jpg 768w, https:\/\/pressbooks.hcfl.edu\/introtofintech\/wp-content\/uploads\/sites\/96\/2024\/12\/9212589254_4b4501b6d6_o-1536x1373.jpg 1536w, https:\/\/pressbooks.hcfl.edu\/introtofintech\/wp-content\/uploads\/sites\/96\/2024\/12\/9212589254_4b4501b6d6_o-65x58.jpg 65w, https:\/\/pressbooks.hcfl.edu\/introtofintech\/wp-content\/uploads\/sites\/96\/2024\/12\/9212589254_4b4501b6d6_o-225x201.jpg 225w, https:\/\/pressbooks.hcfl.edu\/introtofintech\/wp-content\/uploads\/sites\/96\/2024\/12\/9212589254_4b4501b6d6_o-350x313.jpg 350w, https:\/\/pressbooks.hcfl.edu\/introtofintech\/wp-content\/uploads\/sites\/96\/2024\/12\/9212589254_4b4501b6d6_o.jpg 1920w\" sizes=\"auto, (max-width: 300px) 100vw, 300px\" \/><figcaption id=\"caption-attachment-839\" class=\"wp-caption-text\"><a href=\"https:\/\/www.flickr.com\/photos\/43021516@N06\/9212589254\">Leonardo da Vinci: Diagram of a proposed flying machine (1789)<\/a> Toronto Public Library. <a href=\"https:\/\/commons.wikimedia.org\/wiki\/File:Leonardo-Robot3.jpg?uselang=en#Licensing\">Public Domain<\/a><\/figcaption><\/figure>\n<\/div>\n<figure style=\"width: 255px\" class=\"wp-caption alignright\"><img loading=\"lazy\" decoding=\"async\" style=\"font-family: 'Sorts Mill Goudy', 'Times New Roman', serif;font-size: 16px\" src=\"http:\/\/pressbooks.hcfl.edu\/introtofintech\/wp-content\/uploads\/sites\/96\/2024\/12\/image3-3.png\" alt=\"Leonardo's robot.\" width=\"255\" height=\"255\" \/><figcaption class=\"wp-caption-text\">Model of Leonardo&#8217;s robot with inner workings, on display in Berlin. Photo by Erik M\u00f6ller. Leonardo da Vinci. Mensch &#8211; Erfinder &#8211; Genie exhibit, Berlin 2005. <a href=\"https:\/\/commons.wikimedia.org\/wiki\/File:Leonardo-Robot3.jpg?uselang=en#Licensing\">Public Domain<\/a><\/figcaption><\/figure>\n<p>2300 years ago, the famous Greek philosopher, Aristotle made the first reference to something close to the idea of intelligence in his famous work Prior Analytics &amp; The Theory of Syllogistic, which is \u201creason\u201d. Reason, according to Aristotle, was about humans&#8217; ability to reign in their passions, i.e., our ability to resist the urge of our instincts (University of Chicago Press, 2020).<\/p>\n<p>Humans have attempted to replicate human functions for many centuries, such as Leonardo Davinci\u2019s Flying Machines and Blaise Pascal\u2019s first calculating machine.<\/p>\n<p><span class=\"TextRun SCXW97516344 BCX8\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"FindHit SCXW97516344 BCX8\">Not until the <\/span><span class=\"FindHit SCXW97516344 BCX8\">early, to mid-20<\/span><\/span><span class=\"TextRun SCXW97516344 BCX8\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"FindHit Superscript SCXW97516344 BCX8\" data-fontsize=\"12\">th<\/span><\/span><span class=\"TextRun SCXW97516344 BCX8\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"> <span class=\"NormalTextRun SCXW97516344 BCX8\">century<\/span><span class=\"NormalTextRun SCXW97516344 BCX8\"> with the development of electricity did innovations such as Neural Networks, the first <\/span><span class=\"NormalTextRun SCXW97516344 BCX8\">electrically driven<\/span><span class=\"NormalTextRun SCXW97516344 BCX8\"> calculating machine (1947)<\/span><span class=\"NormalTextRun SCXW97516344 BCX8\">,<\/span><span class=\"NormalTextRun SCXW97516344 BCX8\"> and the first use of the term \u201cArtificial Intelligence<\/span><span class=\"NormalTextRun SCXW97516344 BCX8\">\u201d<\/span><span class=\"NormalTextRun SCXW97516344 BCX8\"> (<\/span><span class=\"NormalTextRun ContextualSpellingAndGrammarErrorV2Themed GrammarErrorHighlight SCXW97516344 BCX8\">195<\/span><span class=\"NormalTextRun ContextualSpellingAndGrammarErrorV2Themed GrammarErrorHighlight SCXW97516344 BCX8\">6<\/span><span class=\"NormalTextRun ContextualSpellingAndGrammarErrorV2Themed GrammarErrorHighlight SCXW97516344 BCX8\">)<\/span><\/span><span class=\"TextRun SCXW97516344 BCX8\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW97516344 BCX8\"> c<\/span><span class=\"NormalTextRun SCXW97516344 BCX8\">a<\/span><span class=\"NormalTextRun SCXW97516344 BCX8\">me to being (Dartmouth College, n.d.).<\/span><span class=\"NormalTextRun SCXW97516344 BCX8\">\u00a0<\/span><span class=\"NormalTextRun SCXW97516344 BCX8\">\u00a0<\/span><\/span><span class=\"EOP SCXW97516344 BCX8\" data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559740&quot;:360}\">\u00a0<\/span><\/p>\n<div class=\"__UNKNOWN__\">\n<figure style=\"width: 473px\" class=\"wp-caption alignnone\"><img loading=\"lazy\" decoding=\"async\" class=\"\" src=\"http:\/\/pressbooks.hcfl.edu\/introtofintech\/wp-content\/uploads\/sites\/96\/2024\/12\/image4-1.jpeg\" alt=\"Blaise Pascal\u2019s Mechanical Calculator.\" width=\"473\" height=\"316\" \/><figcaption class=\"wp-caption-text\">Figure 9-4 <a href=\"https:\/\/en.m.wikipedia.org\/wiki\/File:Pascaline-CnAM_823-1-IMG_1506-black.jpg\">Image of Blaise Pascal\u2019s Mechanical Calculator<\/a> \u2013 <a class=\"extiw\" title=\"w:en:Creative Commons\" href=\"https:\/\/en.wikipedia.org\/wiki\/en:Creative_Commons\">Creative Commons<\/a>\u00a0<a class=\"extiw\" title=\"creativecommons:by-sa\/3.0\/fr\/deed.en\" href=\"https:\/\/creativecommons.org\/licenses\/by-sa\/3.0\/fr\/deed.en\">Attribution-ShareAlike 3.0 France<\/a><span style=\"font-size: 16px\">(1956)<\/span><span style=\"font-size: 16px\"> came to being.<\/span><\/figcaption><\/figure>\n<h2 style=\"text-align: left\"><span class=\"ez-toc-section\" id=\"Artificial_Intelligence\"><\/span><strong>Artificial Intelligence <\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<div id=\"h5p-7\">\n<div class=\"h5p-iframe-wrapper\"><iframe id=\"h5p-iframe-7\" class=\"h5p-iframe\" data-content-id=\"7\" style=\"height:1px\" src=\"about:blank\" frameBorder=\"0\" scrolling=\"no\" title=\"AI Timeline\"><\/iframe><\/div>\n<\/div>\n<p class=\"import-Normal\">Humans are great thinkers. Our brains have a tremendous capacity of thought. Computers on the other hand beat humans when it comes to mathematical calculations. Current capability of supercomputers such as the Cray EX built by Hewlett-Packard\u2122 registers over 550 petaflops (that\u2019s 550,000,000,000,000,000 multiplications per second). Even with such speed, it still takes the CrayEX about a second to decide on a move in chess. Artificial Intelligence speed is improving at a very fast pace. Scientists estimates that today\u2019s AI would achieve full human-like thought capabilities by mid-21st. century. Speed however is not the ultimate target in the foreseeable future. General AI is the target. The scope of Intelligence in AI is divided into 2 broad categories:<\/p>\n<figure id=\"attachment_650\" aria-describedby=\"caption-attachment-650\" style=\"width: 304px\" class=\"wp-caption alignright\"><img loading=\"lazy\" decoding=\"async\" class=\"wp-image-650\" src=\"http:\/\/pressbooks.hcfl.edu\/introtofintech\/wp-content\/uploads\/sites\/96\/2024\/12\/image1-2-300x300.png\" alt=\"Simulating infusion of AI into life\" width=\"304\" height=\"304\" srcset=\"https:\/\/pressbooks.hcfl.edu\/introtofintech\/wp-content\/uploads\/sites\/96\/2024\/12\/image1-2-300x300.png 300w, https:\/\/pressbooks.hcfl.edu\/introtofintech\/wp-content\/uploads\/sites\/96\/2024\/12\/image1-2-150x150.png 150w, https:\/\/pressbooks.hcfl.edu\/introtofintech\/wp-content\/uploads\/sites\/96\/2024\/12\/image1-2-768x768.png 768w, https:\/\/pressbooks.hcfl.edu\/introtofintech\/wp-content\/uploads\/sites\/96\/2024\/12\/image1-2-65x65.png 65w, https:\/\/pressbooks.hcfl.edu\/introtofintech\/wp-content\/uploads\/sites\/96\/2024\/12\/image1-2-225x225.png 225w, https:\/\/pressbooks.hcfl.edu\/introtofintech\/wp-content\/uploads\/sites\/96\/2024\/12\/image1-2-350x350.png 350w, https:\/\/pressbooks.hcfl.edu\/introtofintech\/wp-content\/uploads\/sites\/96\/2024\/12\/image1-2.png 1024w\" sizes=\"auto, (max-width: 304px) 100vw, 304px\" \/><figcaption id=\"caption-attachment-650\" class=\"wp-caption-text\">Simulating infusion of AI into life. Image generated by OpenAI\u2019s DALL\u00b7E<\/figcaption><\/figure>\n<h3><span class=\"ez-toc-section\" id=\"Narrow_AI\"><\/span><em>Narrow AI\u00a0<\/em><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>This is the type of AI we have today and may achieve its full potential by mid-century. Narrow AI is designed to learn to perform specific tasks that will achieve specific goals. These tasks can be quite complex, but they are limited to a narrow scope. Narrow AI \u201cbehavior\u201d is highly predictable because it must learn and operate in its specific environment in order to reach a specified conclusion. I.e., it must be trained just like us, humans. Narrow AI, example applications include:<\/p>\n<p class=\"import-Normal\">Image recognition (e.g., identifying objects in real life or pictures of real life using distinguishing and differentiating detail (e.g., tree vs. rose, horse vs. sheep, cube vs. triangle, school bus vs. commuter bus, etc.)<\/p>\n<p class=\"import-Normal\">Natural language processing (e.g., translating across many natural languages, and manipulating text, speech, images, music, art, even poetry by regeneration (AKA regenerative learning) to produce human-like output.<\/p>\n<p class=\"import-Normal\">Recommendation systems (e.g., suggesting movies on Netflix, presenting products you most likely will buy, and even predicting future outcomes, with a high degree of accuracy, based on learned experiences, etc.)\u00a0Game-playing AIs (e.g., Chess, Go, and immersive universes known as Multiverse, Virtual and Augmented reality, etc.)<\/p>\n<h3><span class=\"ez-toc-section\" id=\"General_AI_AGI\"><\/span><em>General AI (AGI)<\/em><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>This is a theoretical type of AI that can understand, learn, and apply intelligence across a wide range of tasks, just like how humans do. It would be capable of reasoning, problem-solving, and understanding context in ways that allow it to handle any cognitive task, not just those it was specifically trained for. AGI would also have the ability to transfer knowledge learned in one area to another, a feature we, human beings possess however is lacking in today&#8217;s narrow AI. At that level, life is mimicked and truly virtualized to the point that it would be near impossible to distinguish the differences between Living and Virtual, and machines could essentially have emotions, self-realization, and the ability to self-reproduce. There are no examples of General AI except in Hollywood\u2019s science fiction (Wikipedia contributors, n.d.) However, AI researchers believe that few, but key characteristics could include:<\/p>\n<ul>\n<li>the ability to seek learning from data generated by other AI systems, and adapt quickly to new, unseen tasks, much like humans do.<\/li>\n<li>the ability to reason abstractly, simulate different scenarios, and generate truly novel ideas.<\/li>\n<li>AGI could transfer knowledge across various domains (e.g., using knowledge from playing chess to improve performance in another task, like driving a car).<\/li>\n<li>Self-realization, emotions, anger and fear, happiness and sadness. Again, these have been postulated and represented in Science Fiction (Wikipedia contributors, n.d.)<\/li>\n<\/ul>\n<p class=\"import-Normal\">AI applications are a web of complex and interdisciplinary fields that includes Biology Computer Science, Neuroscience, Philosophy, Law, Ethics, Medicine, Finance, and Psychology along with many engineering disciplines such as electrical and mechanical engineering. A heavy area of Narrow AI research is focused on understanding the foundation of Human Intelligence in hope of replicating it in machines.<\/p>\n<p class=\"import-Normal\"><strong>What is Intelligence? <\/strong><\/p>\n<p class=\"import-Normal\">James R. Flynn, arguably the most prominent researcher in the field of \u201cintelligence\u201d defines the term as \u201c<em>the ability to learn from minimal data and adapt quickly to new, unseen tasks<\/em><em>. M<\/em><em>uch like humans do. It would also <\/em><em>include <\/em><em>hav<\/em><em>ing<\/em><em> the ability to reason abstractly, simulate different scenarios, and generate novel ideas<\/em>\u201d)\u00a0by this very definition, intelligence therefore, must be coupled with the word <em>b<\/em><em>ehavio<\/em>r. Hence, intelligent machine behavior is modeled after intelligent human behavior that has very specific characteristics:<\/p>\n<ul>\n<li><em>Learn from <\/em><em>past experience<\/em> \u2013 Situations and events drive human behavior. As toddlers, we quickly learn not to touch hot objects but only after we\u2019ve experienced pain effects and through the trial-and-error method.<\/li>\n<li><em>Correctly handling complex and sometimes competing situations<\/em>. An ability of successful managers depends on knowing how to balance the needs of the organization vs. the need of the individual employees.<\/li>\n<li><em>Solving problems often with little or missing information<\/em>. How do we deal with uncertainty?<\/li>\n<li><em>Prioritization <\/em>\u2013 Determine what is important and how to, preverbally, juggle running chain saws and watermelons simultaneously<\/li>\n<li><em>Being nimble and react appropriately to a new situation <\/em>\u2013 Swerving away from an oncoming car is brain and muscle memory working together. Computers can react and swerve much faster than a human can.<\/li>\n<li><em>Understand Visual Clues and Symbols \u2013 <\/em>Easy for students to walk into any classroom and know what a desk is, what a board is, and where the teacher stands. Easy for us walking through the maze of other students, desks, and tables without hurting oneself. Not so easy for a computer. However, perceptive systems have been successfully developed to overcome these challenges.<\/li>\n<li><em>Process images, sounds, smells, touch and tactile sensations.<\/em> Computers can hear, listen and understand and can see and process images, can also smell (better than a human, but much less than a dog). They can also taste and feel hot or cold or any range of temperatures in-between.<\/li>\n<li><em>Creative and Imaginative<\/em> \u2013using generative AI one can develop what could be considered as very creative and very imaginative art, music, literature and poetry, but is it original work? This has been an endless debate over the last couple of years.<\/li>\n<\/ul>\n<h2 class=\"import-Normal\"><span class=\"ez-toc-section\" id=\"Generative_AI_Overview\"><\/span><strong>Generative AI Overview<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<figure style=\"width: 544px\" class=\"wp-caption alignleft\"><img loading=\"lazy\" decoding=\"async\" class=\"\" src=\"http:\/\/pressbooks.hcfl.edu\/introtofintech\/wp-content\/uploads\/sites\/96\/2024\/12\/image5-2.png\" alt=\"User interacting with AI.\" width=\"544\" height=\"306\" \/><figcaption class=\"wp-caption-text\">Showing user interacting with AI. Image generated by OpenAI\u2019s DALL\u00b7E<\/figcaption><\/figure>\n<p class=\"import-Normal\">Generative AI, such as ChatGPT models focuses on creating new content, such as text, images, and music, by learning patterns from existing data. Unlike traditional expert systems, generative AI excels in handling unstructured data and adapting to new information, however, it often lacks the inherent explain-ability of expert systems, creating challenges in accountability and trust.<\/p>\n<h3 class=\"import-Normal\"><span class=\"ez-toc-section\" id=\"List_of_Current_Generative_AI_Types_and_Their_Uses\"><\/span><strong>List of <\/strong><strong>C<\/strong><strong>urrent <\/strong><strong>Generative AI<\/strong> <strong>Types and <\/strong><strong>T<\/strong><strong>heir <\/strong><strong>U<\/strong><strong>ses<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p class=\"import-Normal\">There are several generative AI models each with its own set of algorithms to use in processing the \u201cgeneration and regeneration\u201d of information (Dugal, 2025). The 2025 list contains:<\/p>\n<table>\n<thead>\n<tr>\n<th style=\"background-color: #47d459;border: solid windowtext 0.5pt\">\n<p class=\"import-Normal\" style=\"text-align: center\"><strong>Brand<\/strong><\/p>\n<\/th>\n<th style=\"background-color: #47d459;border: solid windowtext 0.5pt\">\n<p class=\"import-Normal\" style=\"text-align: center\"><strong>Best for<\/strong><\/p>\n<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr class=\"TableGrid-R\">\n<th style=\"background-color: #d9f2d0;border: solid windowtext 0.5pt\">\n<p class=\"import-Normal\">ChatGPT<\/p>\n<p class=\"import-Normal\">\n<\/th>\n<td class=\"TableGrid-C\" style=\"background-color: #d9f2d0;border: solid windowtext 0.5pt\">\n<p class=\"import-Normal\">This very textbook has about 20% ChatGPT content.<\/p>\n<\/td>\n<\/tr>\n<tr class=\"TableGrid-R\">\n<th style=\"background-color: #d9f2d0;border: solid windowtext 0.5pt\">\n<p class=\"import-Normal\">AlphaCode<\/p>\n<p class=\"import-Normal\">\n<\/th>\n<td class=\"TableGrid-C\" style=\"background-color: #d9f2d0;border: solid windowtext 0.5pt\">\n<p class=\"import-Normal\">Software development specifically to train other AI models<\/p>\n<\/td>\n<\/tr>\n<tr class=\"TableGrid-R\">\n<th style=\"background-color: #d9f2d0;border: solid windowtext 0.5pt\">\n<p class=\"import-Normal\">GitHub Copilot<\/p>\n<p class=\"import-Normal\">\n<\/th>\n<td class=\"TableGrid-C\" style=\"background-color: #d9f2d0;border: solid windowtext 0.5pt\">\n<p class=\"import-Normal\">Authoring software programs<\/p>\n<\/td>\n<\/tr>\n<tr class=\"TableGrid-R\">\n<th style=\"background-color: #d9f2d0;border: solid windowtext 0.5pt\">\n<p class=\"import-Normal\">Gemini (Previously Bard)<\/p>\n<p class=\"import-Normal\">\n<\/th>\n<td class=\"TableGrid-C\" style=\"background-color: #d9f2d0;border: solid windowtext 0.5pt\">\n<p class=\"import-Normal\">User Rating &#8211; chatbot and content generation tool developed by Google.<\/p>\n<\/td>\n<\/tr>\n<tr class=\"TableGrid-R\">\n<th style=\"background-color: #d9f2d0;border: solid windowtext 0.5pt\">\n<p class=\"import-Normal\">Cohere Generate<\/p>\n<p class=\"import-Normal\">\n<\/th>\n<td class=\"TableGrid-C\" style=\"background-color: #d9f2d0;border: solid windowtext 0.5pt\">\n<p class=\"import-Normal\">Custom content emails, website landing pages, and social media product marketing<\/p>\n<\/td>\n<\/tr>\n<tr class=\"TableGrid-R\">\n<th style=\"background-color: #d9f2d0;border: solid windowtext 0.5pt\">\n<p class=\"import-Normal\">Claude<\/p>\n<p class=\"import-Normal\">\n<\/th>\n<td class=\"TableGrid-C\" style=\"background-color: #d9f2d0;border: solid windowtext 0.5pt\">\n<p class=\"import-Normal\">Assistant &#8211; Research has focused on training AI and is still in research phase. Trains other AI systems to be helpful, fair, and safe to use.<\/p>\n<\/td>\n<\/tr>\n<tr class=\"TableGrid-R\">\n<th style=\"background-color: #d9f2d0;border: solid windowtext 0.5pt\">\n<p class=\"import-Normal\">DALL-E2<\/p>\n<p class=\"import-Normal\">\n<\/th>\n<td class=\"TableGrid-C\" style=\"background-color: #d9f2d0;border: solid windowtext 0.5pt\">\n<p class=\"import-Normal\">Image and art generation with photo quality realism<\/p>\n<\/td>\n<\/tr>\n<tr class=\"TableGrid-R\">\n<th style=\"background-color: #d9f2d0;border: solid windowtext 0.5pt\">\n<p class=\"import-Normal\">Synthesia<\/p>\n<\/th>\n<td class=\"TableGrid-C\" style=\"background-color: #d9f2d0;border: solid windowtext 0.5pt\">\n<p class=\"import-Normal\">Creating high quality video content<\/p>\n<\/td>\n<\/tr>\n<tr class=\"TableGrid-R\">\n<th style=\"background-color: #d9f2d0;border: solid windowtext 0.5pt\">\n<p class=\"import-Normal\">Copy.AI<\/p>\n<\/th>\n<td class=\"TableGrid-C\" style=\"background-color: #d9f2d0;border: solid windowtext 0.5pt\">\n<p class=\"import-Normal\">Creative writing, marketing slogans, and news headlines,<\/p>\n<\/td>\n<\/tr>\n<tr class=\"TableGrid-R\">\n<th style=\"background-color: #d9f2d0;border: solid windowtext 0.5pt\">\n<p class=\"import-Normal\">Rephrase.ai<\/p>\n<\/th>\n<td class=\"TableGrid-C\" style=\"background-color: #d9f2d0;border: solid windowtext 0.5pt\">\n<p class=\"import-Normal\">Digital Avatars to be used in Videos<\/p>\n<\/td>\n<\/tr>\n<tr class=\"TableGrid-R\">\n<th style=\"background-color: #d9f2d0;border: solid windowtext 0.5pt\">\n<p class=\"import-Normal\">Descript<\/p>\n<\/th>\n<td class=\"TableGrid-C\" style=\"background-color: #d9f2d0;border: solid windowtext 0.5pt\">\n<p class=\"import-Normal\">Video publishing, full multitrack editing, transcription, and screen recording.<\/p>\n<\/td>\n<\/tr>\n<tr class=\"TableGrid-R\">\n<th style=\"background-color: #d9f2d0;border: solid windowtext 0.5pt\">\n<p class=\"import-Normal\">SoundDraw<\/p>\n<\/th>\n<td class=\"TableGrid-C\" style=\"background-color: #d9f2d0;border: solid windowtext 0.5pt\">\n<p class=\"import-Normal\">Music generation<\/p>\n<\/td>\n<\/tr>\n<tr>\n<td><\/td>\n<td><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p class=\"import-Normal\" style=\"text-align: center\"><em>Table: List of AI models and their frequent uses<\/em><\/p>\n<h2 class=\"import-Normal\"><span class=\"ez-toc-section\" id=\"Uses_of_Generative_AI_in_Various_Economic_Sectors\"><\/span><strong>Uses of Generative AI in <\/strong><strong>V<\/strong><strong>arious <\/strong><strong>E<\/strong><strong>conomic Sectors <\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p class=\"import-Normal\">Generative AI had experienced explosive growth. It has found its way into countless economic sectors. Mainly:<\/p>\n<p class=\"import-Normal\"><em>Healthcare:<\/em><\/p>\n<p class=\"import-Normal\">Generative AI models integrated with expert system rules are used to generate personalized treatment plans, simulate disease progression, and enhance diagnostic tools.<\/p>\n<p class=\"import-Normal\"><strong><em>Example:<\/em><\/strong> AI tools generating patient-specific recommendations based on both expert-defined rules and learned patterns from medical databases.<\/p>\n<p class=\"import-Normal\"><em>Finance:<\/em><\/p>\n<p class=\"import-Normal\">Hybrid systems are employed to generate investment strategies, identify fraud, and predict market trends.<\/p>\n<p class=\"import-Normal\"><strong>Example:<\/strong> AI-driven financial advisors combining historical market data with regulatory compliance rules from expert systems.<\/p>\n<p class=\"import-Normal\"><em>Creative Industries:<\/em><\/p>\n<p class=\"import-Normal\">Generative AI produces high-quality content such as music, art, and literature, with expert systems ensuring adherence to stylistic or domain-specific constraints.<\/p>\n<p class=\"import-Normal\"><strong><em>Example:<\/em><\/strong> Music composition tools using rules from expert systems to replicate the style of classical composers.<\/p>\n<p class=\"import-Normal\"><em>Education:<\/em><\/p>\n<p class=\"import-Normal\">AI systems create adaptive learning materials and simulations tailored to individual student needs, with expert systems guiding pedagogical principles.<\/p>\n<p class=\"import-Normal\"><strong><em>Example:<\/em><\/strong> AI tutors that design curriculum pathways by blending expert rules with generative capabilities.<\/p>\n<p>Hospitality:<\/p>\n<p class=\"\" data-start=\"0\" data-end=\"207\">Artificial Intelligence is transforming the hospitality industry in multiple ways by enhancing customer experiences, streamlining operations, and driving revenue growth. Example areas impacted by AI include:<\/p>\n<p data-start=\"209\" data-end=\"253\">Personalized Customer Experiences &#8211; AI analyzes guest preferences and past behaviors to personalize experiences, recommendations, and interactions.<\/p>\n<p data-start=\"807\" data-end=\"861\">Revenue Management and Pricing Optimization &#8211;\u00a0AI predicts demand fluctuations, helping hotels dynamically price rooms and services to maximize revenue.<\/p>\n<p data-start=\"1092\" data-end=\"1140\">Operational Efficiency and Automation &#8211;\u00a0 AI automates repetitive administrative tasks like check-ins\/check-outs, booking management, and guest communications.<\/p>\n<h2 class=\"import-Normal\"><span class=\"ez-toc-section\" id=\"Challenges_and_Concerns_With_Generative_AI\"><\/span><strong>Challenges and Concerns <\/strong><strong>With<\/strong> <strong>Generative AI<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p class=\"import-Normal\">Despite their promise, there are many challenges and considerations in integrating expert systems into generative AI. The main critique of generative AI include:<\/p>\n<p class=\"import-Normal\"><em>Bias<\/em><em> (degree of errors)<\/em><em>:<\/em> Ensuring equity and fairness requires careful curation of the knowledge base and the training data.<\/p>\n<p class=\"import-Normal\"><em>Complexity:<\/em> Designing hybrid systems requires balancing rule-based reasoning with the flexibility of generative models.<\/p>\n<p class=\"import-Normal\"><em>Scalability:<\/em> Maintaining and updating the knowledge base of expert systems is human and machine resource intensive.<\/p>\n<h3 class=\"import-Normal\"><span class=\"ez-toc-section\" id=\"Machine_Learning\"><\/span><strong>Machine Learning <\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<figure style=\"width: 970px\" class=\"wp-caption alignleft\"><img loading=\"lazy\" decoding=\"async\" src=\"http:\/\/pressbooks.hcfl.edu\/introtofintech\/wp-content\/uploads\/sites\/96\/2024\/12\/image6.jpeg\" alt=\"Machines Learning.\" width=\"970\" height=\"554\" \/><figcaption class=\"wp-caption-text\">Machines Learning. Image generated by OpenAI\u2019s DALL\u00b7E<\/figcaption><\/figure>\n<h3 class=\"import-Normal\"><span class=\"ez-toc-section\" id=\"The_Origin_of_Machine_Learning\"><\/span><strong>The Origin of Machine Learning<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p class=\"import-Normal\">Machine learning (ML) is a branch of artificial intelligence (AI) that enables systems to learn from data and improve their performance without being explicitly programmed. The origins of machine learning trace back to the mid-20th century, when researchers began to explore the idea of creating machines that could simulate human learning processes. The foundational concept was inspired by Alan Turing, who introduced the notion of a &#8220;learning machine&#8221; in his seminal 1950 paper, <em>Computing Machinery and Intelligence (Turing, 1950)<\/em>. Turing hypothesized that machines could improve their behavior through experience.<\/p>\n<p class=\"import-Normal\">In 1959, Arthur Samuel further advanced the concept by developing programs that could play checkers, pioneering the term &#8220;machine learning\u201d. Samuel\u2019s work demonstrated that machines could be trained to optimize their performance through iterative improvements based on data analysis. Since then, ML has grown exponentially, driven by advances in computational power, the availability of large datasets, and the development of sophisticated algorithms.<\/p>\n<h3 class=\"import-Normal\"><span class=\"ez-toc-section\" id=\"How_Are_Machines_Trained\"><\/span><strong>How <\/strong><strong>Are<\/strong> <strong>Machines Trained<\/strong><strong>?<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p class=\"import-Normal\">Machines are trained using large datasets that serve as inputs to algorithms. The training process involves:<\/p>\n<p class=\"import-Normal\">Data Collection: Gathering a comprehensive and relevant dataset.<\/p>\n<p class=\"import-Normal\">Data Preprocessing: Cleaning and formatting the data to ensure it is suitable for training.<\/p>\n<p class=\"import-Normal\">Algorithm Selection: Choosing an appropriate algorithm based on the problem type.<\/p>\n<p class=\"import-Normal\">Model Training: Feeding the data into the algorithm to identify patterns and learn decision rules.<\/p>\n<p class=\"import-Normal\">Evaluation: Assessing the model\u2019s performance on unseen data to ensure it generalizes well.<\/p>\n<p class=\"import-Normal\">Optimization: Fine-tuning the model to improve accuracy and efficiency.<\/p>\n<h3 class=\"import-Normal\"><span class=\"ez-toc-section\" id=\"The_Four_Training_Modes_in_Machine_Learning\"><\/span><strong>The Four Training Modes<\/strong><strong> in Machine Learning <\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p class=\"import-Normal\">Machine learning uses 4 different modes of training:<\/p>\n<h4 class=\"import-Normal\"><span class=\"ez-toc-section\" id=\"1_Supervised_Learning\"><\/span><strong>1. <\/strong><strong>Supervised Learning<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h4>\n<p class=\"import-Normal\">In supervised learning, the model is trained on a <em>labeled dataset<\/em> (a set of data that has been classified) where the input data is paired with the correct output. It works by learning a mapping function from inputs to outputs by minimizing the path error between a predicted and an actual output(s). Supervised Learning uses applications that <em>c<\/em><em>lassif<\/em><em>y<\/em> (i.e., sort and group) tasks for a desired problem output. (e.g., email spam detection) and regression tasks (e.g., predicting housing prices).<\/p>\n<h4 class=\"import-Normal\"><span class=\"ez-toc-section\" id=\"2_Unsupervised_Learning\"><\/span><strong><em>2. <\/em><\/strong><strong><em>Unsupervised Learning<\/em><\/strong><span class=\"ez-toc-section-end\"><\/span><\/h4>\n<p class=\"import-Normal\">In unsupervised learning, the model is trained on an <em>unlabeled dataset<\/em>, meaning it must find hidden patterns or structures in the data without the data being organized. It works by applying algorithms the perform functions such as:<\/p>\n<ul>\n<li><em>C<\/em><em>lustering <\/em>which is a grouping of similar things together based on their features, without anyone telling the computer what the groups should be. For example, all cars are clustered into one group, all airplanes into another group. Clustering would be a result (output) rather than a preemptive input as in Supervised learning. In Fintech, clustering can be applied to classify customers by shopping behavior and banks by lowest percentage rate they charge for processing a transaction. These characteristics are found, and remembered (i.e., stored) by the algorithm.<\/li>\n<li><em>D<\/em><em>imensionality reduction<\/em> is used to identify groupings through data representations. <em>Dimensionality is about how many features or characteristics a dataset has<\/em>. For example, imagine you are describing a car that has:<\/li>\n<li>One feature (1 dimension): The car&#8217;s color.<\/li>\n<li>Two features (2 dimensions): The car&#8217;s color and speed.<\/li>\n<li>Three features (3 dimensions): The car&#8217;s color, speed, and weight.<\/li>\n<\/ul>\n<p class=\"import-Normal\">Datasets in the Financial industry are highly complex and have many features. For example, when Banks, or Insurance companies analyze a loan request, or a policy, to determine their risk exposure, their AI system would analyze the customer\u2019s request based on dozens of dimensions such as age, income, employment, credit score, and spending habits, amongst many others. Dimensionality algorithms can call up data accumulated from many of data sources to refine its output. In our example of applying for credit, the algorithm will send requests to review a credit report, callup transactional databases that contain transaction details such as what items the customer spends money on, callup tax records to verify income, etc.<\/p>\n<h4 class=\"import-Normal\"><span class=\"ez-toc-section\" id=\"3_Reinforcement_Learning\"><\/span><strong><em>3. <\/em><\/strong><strong><em>Reinforcement Learning<\/em><\/strong><span class=\"ez-toc-section-end\"><\/span><\/h4>\n<p class=\"import-Normal\">Reinforcement training uses an AI agent (a software computer program), that is asked to solve an optimal route (solution) using a reward-penalty system. It is a system based on trial-and-error. For example, driving for delivery company, your daily route as designed by the AI system is to travel daily dropping off 50 packages at 50 different locations around the city. In preparation for a daily run, the AI system will solve for an optimal route that saves on time, fuel, vehicle maintenance, reduce probability of getting into an accident, and guarantees on time delivery. The system will be trained to classify all these variables into dimensions, assigning +weights (for rewards) and -weights for (penalties) for each pass through the layers leading to a solution. With each pass, the system \u201clearns\u201d which of the nodes has a greater chance of receiving a reward. Again, through iterative training sessions, the system will develop a solution that will be considered an \u201coptimized delivery\u201d model that can be used for that specific day\u2019s deliveries.<\/p>\n<h4 class=\"import-Normal\"><span class=\"ez-toc-section\" id=\"4_Semi-Supervised_Learning\"><\/span><strong><em>4. <\/em><\/strong><strong><em>Semi-Supervised Learning<\/em><\/strong><span class=\"ez-toc-section-end\"><\/span><\/h4>\n<p class=\"import-Normal\">Semi-supervised machine learning is a type of learning that uses both <em>labeled and unlabeled data <\/em>to train a model. It\u2019s like teaching someone a new topic by giving them a few clear examples <strong>(labeled data) <\/strong>and then letting them figure out the rest by exploring on their own <strong>(unlabeled data).<\/strong> Example of labeled data is a set of pictures with a label stating, \u201cfarm animals\u201d with pictures showing Horses, Cows, Chicken, Etc. while unlabeled data would be the same set of pictures, with a possible inclusion of a tractor, a harvester, pitchfork and a grain silo. In the second set of pictures, there will not be a label mentioning what the pictures are, or what is in them.<\/p>\n<p class=\"import-Normal\">In Fintech, the use of semi-supervised learning is key due to its economies. There is no need to identify each transaction as fraudulent or not fraudulent. Just few fraudulent examples would suffice for the system to determine common characteristics such as the profile for transactions originating outside of normal locations, number of transactions matching those that are fraudulent, or transactions that started by a known fraudulent merchant or user.<strong><br style=\"clear: both\" \/><\/strong><\/p>\n<h2 class=\"import-Normal\"><span class=\"ez-toc-section\" id=\"Neural_Networks_and_Deep_Learning_in_AI\"><\/span><strong>Neural Networks and <\/strong><strong>Deep Learning<\/strong><strong> in AI <\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p class=\"import-Normal\">Neural networks are a type of machine learning model inspired by the structure and function of the human brain. They consist of layers of interconnected nodes (neurons) that process data by assigning <em>weights and biases<\/em> (importance and degree of error), enabling the network to learn patterns and relationships in data. Neural networks are used for tasks like classification, regression, image recognition, natural language processing, and more.<\/p>\n<figure style=\"width: 719px\" class=\"wp-caption alignleft\"><img loading=\"lazy\" decoding=\"async\" class=\"\" src=\"http:\/\/pressbooks.hcfl.edu\/introtofintech\/wp-content\/uploads\/sites\/96\/2024\/12\/image7-2.png\" alt=\"Input-processing-output layers in a Neural Network\" width=\"719\" height=\"719\" \/><figcaption class=\"wp-caption-text\">Showing Input-processing-output layers in a Neural Network. Image generated by OpenAI\u2019s DALL\u00b7E<\/figcaption><\/figure>\n<h3 class=\"import-Normal\"><span class=\"ez-toc-section\" id=\"How_do_Neural_Networks_work\"><\/span><strong>How do Neural Networks work? <\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p class=\"import-Normal\">Just like the human brain\u2019s composition of layers of biological neurons, Machine Learning using neural networks, which also has many layers that we call deep neural networks. These network layers mimic the human brain\u2019s structure and function, allowing them to process large and complex datasets. Deep learning excels at feature extraction, automatically identifying the most relevant features in the data without manual intervention. The inner processes that uses neural networks include:<\/p>\n<ul>\n<li><strong><em>Data Classification Process<\/em><\/strong><strong> \u2013 <\/strong>At the input layer, neural networks classify the data into predefined categories or labels. An example would be feeding an email script which the classification layer processes to categorize it as spam or not spam, hate email or NOT hate email, and so on.<\/li>\n<li><strong><em>Regression Process<\/em><\/strong><strong> &#8211; <\/strong>Regression involves predicting continuous numerical values rather than discrete categories. An example would be predicting new home prices based on features like size, location, and age.<\/li>\n<li><strong><em>Clustering Process<\/em><\/strong><strong> &#8211; <\/strong>Neural networks can group data into clusters based on similarity, even without labeled training data. An example could be segmenting customers into groups based on purchasing behavior, or groupings of 1<sup>st<\/sup> year college students by a target area of study (e.g., how much they spend on Coffee Lattes)<\/li>\n<li><strong><em>Dimensionality Reduction Process<\/em><\/strong><strong> &#8211; <\/strong>Neural networks, can reduce the number of features in a dataset while retaining critical information. An example could be compressing high-dimensional image data \u2013 such as an image of the sky behind the subject in the image, into a smaller feature set.<\/li>\n<li><strong><em>Generative Tasks Process<\/em><\/strong><strong> &#8211; <\/strong>Neural networks can generate new data very much similar to the input data they were trained on. It is the essence of what most AI models produce currently. An example would be the ability of AI to correctly postulate and produce coherent answers to questions on themes such as for generating stories, poetry, or dialogue where the user asks the AI model to <em>\u201cwrite<\/em> a<em> short story about a robot discovering emotions.&#8221; <\/em>In this example, the Language Model would have been trained on language usage, writing styles, fiction as a theme, science fiction as an epilog, Robots as smart machines, and many others.<\/li>\n<li><strong><em>Sequence Prediction Process<\/em><\/strong><em> &#8211; <\/em>Neural networks can analyze sequential data and predict the next elements in the sequence. Examples include Text generation such as for auto-complete or predicting stock prices over similar periods of time.<\/li>\n<li><strong><em>Object Detection Process &#8211;<\/em><\/strong> Detecting and localizing multiple objects within an image, assigning a bounding box and determining a class label to each object. Such as man walking a dog, female with a shopping bag, homeless person pushing a shopping cart, etc. Example uses would include detecting vehicles, pedestrians, center lanes, and traffic signs in self-driving cars.<\/li>\n<li><strong><em>Semantic Segmentation Process \u2013<\/em><\/strong> Is classifying each pixel of an image into a category, effectively partitioning the image into meaningful segments. Example uses would include identifying roads, buildings, and vegetation in satellite imagery.<\/li>\n<li><strong><em>Anomaly Detection Process &#8211;<\/em><\/strong> Identifying unusual patterns or outliers in data. Example uses would include detecting fraudulent transactions in financial datasets.<\/li>\n<li><strong><em>Recommendation Process &#8211;<\/em><\/strong> Predicting user preferences and suggesting items based on historical data such as suggesting movies on Netflix or products on Amazon.<\/li>\n<li><strong><em>Image-to-Image Conversion Process &#8211;<\/em><\/strong> Converting images from one format to another using neural networks. Image-to-image processing is used for:<\/li>\n<\/ul>\n<p class=\"import-Normal\">Super-resolution: Enhancing image resolution.<\/p>\n<p class=\"import-Normal\">Style transfer: Applying artistic styles to images.<\/p>\n<p class=\"import-Normal\">Image colorization: Adding color to grayscale images.<\/p>\n<ul>\n<li><strong><em>Speech and Audio Processing &#8211;<\/em><\/strong> Handling tasks related to sound and speech. Example uses include:<\/li>\n<\/ul>\n<p class=\"import-Normal\">Speech recognition: Transcribing spoken words into text.<\/p>\n<p class=\"import-Normal\">Speech synthesis: Generating human-like speech (e.g., text-to-speech systems).<\/p>\n<ul>\n<li><strong><em>Time Series Analysis<\/em><\/strong><strong> Process \u2013 <\/strong>Is a statistical-based analysis and prediction model that processes patterns in <em>time-series data<\/em> (data that is time stamped in linear time sequences). Time series is important for applications requiring super large data sets such as predicting weather, machine breakdowns, or the most likely item that a customer may purchase next.<\/li>\n<li><strong><em>Multi-task Learning<\/em><\/strong><strong> Process- <\/strong>Training a single neural network to perform multiple related tasks simultaneously. Example usage is for a model that can recognize an image, and simultaneously detect objects within it.<\/li>\n<\/ul>\n<h2 class=\"import-Normal\"><span class=\"ez-toc-section\" id=\"Applying_AI_and_Machine_Learning_in_FinTech\"><\/span><strong>Appl<\/strong><strong>ying<\/strong> <strong>AI and <\/strong><strong>Machine Learning <\/strong><strong>in FinTech<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p class=\"import-Normal\">Machine learning is transforming classical applications of the financial industry into the modern FinTech sector by replacing legacy business approaches, enhancing operational efficiency, reducing costs, improving accuracy, and increasing security. Below are some common applications:<\/p>\n<h3 class=\"import-Normal\"><span class=\"ez-toc-section\" id=\"Fraud_and_Anti-Money_Laundering_Detection\"><\/span>Fraud and Anti-Money Laundering Detection<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p class=\"import-Normal\">ML models analyze transaction data in real time to identify anomalous patterns indicative of fraudulent activity. Deep learning models can detect subtle irregularities in transaction metadata. For example, someone shops at 5 different stores on the same day, using a different credit card on each transaction and withdraws cash with each transaction.<\/p>\n<h3 class=\"import-Normal\"><span class=\"ez-toc-section\" id=\"Credit_Scoring\"><\/span>Credit Scoring<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p class=\"import-Normal\">ML algorithms evaluate creditworthiness by analyzing financial behavior, payment history, and alternative data sources. This enables institutions to offer credit to underserved populations.<\/p>\n<h3 class=\"import-Normal\"><span class=\"ez-toc-section\" id=\"Algorithmic_Trading\"><\/span>Algorithmic Trading<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p class=\"import-Normal\">Reinforcement learning models optimize trading strategies by learning market dynamics and adapting to changing conditions. Algorithms process vast datasets to predict price movements and execute trades efficiently.<\/p>\n<h3 class=\"import-Normal\"><span class=\"ez-toc-section\" id=\"Customer_Personalization\"><\/span>Customer Personalization<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p class=\"import-Normal\">FinTech platforms use supervised and unsupervised learning to offer personalized financial products, such as tailored investment portfolios or savings plans.<\/p>\n<h3 class=\"import-Normal\"><span class=\"ez-toc-section\" id=\"Risk_Management\"><\/span>Risk Management<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p class=\"import-Normal\">Predictive analytics models assess risks associated with loans, investments, or insurance policies, improving decision-making.<\/p>\n<p class=\"import-Normal\">Machine learning continues to revolutionize industries, with FinTech at the forefront of its adoption. The synergy between deep learning techniques and FinTech applications promises greater financial inclusion, enhanced security, and personalized services, driving innovation in the global economy.<\/p>\n<h2 class=\"import-Normal\"><span class=\"ez-toc-section\" id=\"AI_and_Robotics\"><\/span><strong>AI and Robotics <\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<figure style=\"width: 638px\" class=\"wp-caption alignleft\"><img loading=\"lazy\" decoding=\"async\" src=\"http:\/\/pressbooks.hcfl.edu\/introtofintech\/wp-content\/uploads\/sites\/96\/2024\/12\/image8-1.jpeg\" alt=\"Nasa Mars Rover.\" width=\"638\" height=\"425\" \/><figcaption class=\"wp-caption-text\"><a href=\"https:\/\/airandspace.si.edu\/collection-media\/NASM-NASM2022-05070\">National Air and Space Museum, &#8220;Mars Pathfinder Marie Curie Rover Engineering Test Vehicle.<\/a>&#8221; public domain<\/figcaption><\/figure>\n<p class=\"import-Normal\">Labor is defined as a physical, or mental human effort applied against objects to transform them. Labor throughout history has gone through many evolutionary phases. Initially human labor was focused on what nature provided (the age of hunter-gatherer), then through mechanization (e.g., making tools to ease the labor process), then followed by mechanization (using power to drive tools such as tractors and harvesting machines) and on to automation that programmed machinery to repetitively produce objects in mass quantities (machines that automate production) and on to AI-supported systems and methods of production such as in farming using smart autonomous machinery (e.g., smart efficient farming to produce more with less resources such as labor, land, chemicals, water, etc.)<\/p>\n<p class=\"import-Normal\">Labor in Fintech traditionally required complex business processes that went through the same evolutionary path as was discussed previously in this textbook. The latest evolution in Fintech is applying AI in ways that envision the future. \u201cWe need banking, but we don&#8217;t need banks anymore\u201d Bill Gates stated in 1997 envisioning the future (American Deposits. n.d.).<\/p>\n<p class=\"import-Normal\">Early Robots were machines that replicated certain human movements. Starting in the early 1960\u2019s heavy industries began using Robotic machines to perform highly repetitive tasks (boring things that drive people mad, such as counting things), or tasks that required dangerous, tedious, and exact such as lifting heavy items, handling, fabricating and inspections in radioactive environments. These evolved into much more complex Robots performing activities such as exploring the depth of oceans and explorations of deep space and the surfaces of the Moon and Mars.<\/p>\n<figure style=\"width: 448px\" class=\"wp-caption alignright\"><img loading=\"lazy\" decoding=\"async\" class=\"\" src=\"http:\/\/pressbooks.hcfl.edu\/introtofintech\/wp-content\/uploads\/sites\/96\/2024\/12\/image9-1.jpeg\" alt=\"NASA\u2019s R5 aka\u00a0Valkyrie robot.\" width=\"448\" height=\"336\" \/><figcaption class=\"wp-caption-text\"><a href=\"https:\/\/www.nasa.gov\/robonaut2\/\">NASA\u2019s R5 aka\u00a0Valkyrie<\/a>. Public domain.<\/figcaption><\/figure>\n<p class=\"import-Normal\">This course is not about hardware Robots or the study of Robotics. We mentioned them as a prologue to introducing the subject of Software type of Robotics known as Robotic Process Automation.<\/p>\n<h3 class=\"import-Normal\"><span class=\"ez-toc-section\" id=\"Robotic_Process_Automation_Transforming_the_Fintech_Industry\"><\/span><strong>Robotic Process Automation: Transforming <\/strong><strong>the Fintech <\/strong><strong>Industr<\/strong><strong>y<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p class=\"import-Normal\">Robotic Process Automation (RPA) refers to the use of software robots or &#8220;bots&#8221; to automate repetitive, rule-based tasks that were traditionally performed by humans. These bots interact with digital systems and software in the same way a human would, but with greater speed, accuracy, and consistency. Unlike traditional automation, RPA does not require extensive programming or changes to existing IT infrastructure, making it a highly accessible and scalable solution for organizations.<\/p>\n<p class=\"import-Normal\">RPA leverages technologies such as artificial intelligence (AI), machine learning (ML), and natural language processing (NLP) to handle tasks such as data entry, transaction processing, and customer service interactions. By automating these processes, businesses can improve efficiency, reduce operational costs, and free up human employees to focus on more strategic and creative tasks.<\/p>\n<h3 class=\"import-Normal\"><span class=\"ez-toc-section\" id=\"RPA_Components\"><\/span><strong>RPA Components<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p class=\"import-Normal\">Building an RPA system involves designing \u201csoftware bots\u201d and setting up the necessary software to perform specific tasks. To develop a functioning RPA, the requirements would include:<\/p>\n<p class=\"import-Normal\">Understanding the Process: First, a business identifies tasks that are repetitive, rule-based, and time-consuming. These tasks are perfect candidates for automation.<\/p>\n<p class=\"import-Normal\">Choosing the RPA Software: Next, a company picks an RPA tool, like UiPath, Blue Prism, or Automation Anywhere. These tools provide a platform to design and deploy bots.<\/p>\n<p class=\"import-Normal\">Designing the Bot: Using the RPA tool, developers or analysts create a &#8220;workflow,&#8221; which is a step-by-step script that tells the bot what to do. For example, &#8220;open this file, copy data from column A, and paste it into another system.&#8221;<\/p>\n<p class=\"import-Normal\">Testing: The bot is tested to ensure it performs the task correctly and efficiently.<\/p>\n<p class=\"import-Normal\">Deployment: Once tested, the bot is deployed in the company\u2019s systems to work alongside humans or on its own.<\/p>\n<h3 class=\"import-Normal\"><span class=\"ez-toc-section\" id=\"Main_Components_of_RPA\"><\/span><strong>Main Components of RPA<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p class=\"import-Normal\"><em>Bot Designer:<\/em> This is where the bot is built and programmed. It uses a visual interface to map out tasks step by step.<\/p>\n<p class=\"import-Normal\"><em>Bot Runner<\/em>: This is the environment where the bot actually performs its tasks. It executes the instructions created in the designer.<\/p>\n<p class=\"import-Normal\"><em>Control Room:<\/em> This is the central hub for managing and monitoring all bots. It helps track performance and ensure bots are functioning correctly.<\/p>\n<p class=\"import-Normal\">RPA systems also rely on integrations with existing software, allowing bots to work across multiple applications without requiring major changes to those systems.<\/p>\n<h3 class=\"import-Normal\"><span class=\"ez-toc-section\" id=\"Applications_of_RPA\"><\/span><strong>Applications of RPA<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p class=\"import-Normal\">RPA is used across a variety of industries and functions to optimize operations and enhance productivity. Some common applications include:<\/p>\n<h2 class=\"import-Normal\"><span class=\"ez-toc-section\" id=\"Data_Entry_and_Migration\"><\/span><em>Data Entry and Migration<\/em><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p class=\"import-Normal\">Many newer systems are made with newer approaches including programming languages requiring the migration of older systems data to newer ones. RPA helps in automating the transfer of data between systems without human intervention.<\/p>\n<p class=\"import-Normal\"><strong><em>Invoice Processing:<\/em><\/strong> Extracting data from invoices, validating information, and processing payments. The traditional approach is for a human analyst to run jobs inside of computer systems to extract data, validate information such as customer name and address in preparation for shipping. RPA automates this and many other processes such as matching Invoice numbers to specific Customer accounts, automatically looking up inventory and issuing an order to pull from inventory, and prepare all shipping and receiving paperwork. Upon completion, the RPA may connect to the financial system to reconcile the account eliminating to a great degree functions that are manually performed by company staff.<\/p>\n<p class=\"import-Normal\"><strong><em>Customer Support:<\/em><\/strong> We are all familiar with chatbots especially around customer support. A chat bot is a form of an RPA that is programmed to understand human (customer) voice or typed interactions. It can listen and record a call and even attempt to resolve an issue. Chatbots can also email, or call specialists within the company to come on-line to assist the customer.<\/p>\n<p class=\"import-Normal\"><strong><em>Compliance and Auditing:<\/em><\/strong> RPA bots are sometimes used to ensure regulatory compliance by automating the generation of audit trails and reports. Depending on the nature of the business, government regulatory bodies, like in Telecommunication and energy for example could require a company to generate periodic reports that are excessive. RPA have been deployed in these industries to collect regulatory specific information, ensure its accuracy, then format it into the required style the government requires and ensure such reports are delivered at the proper times.<\/p>\n<p class=\"import-Normal\"><em>IT Support<\/em><strong><em>:<\/em><\/strong> Automating routine IT tasks like password resets and software installations.<\/p>\n<h2 class=\"import-Normal\"><span class=\"ez-toc-section\" id=\"RPA_Applications_in_the_Financial_Industry\"><\/span><strong>RPA <\/strong><strong>Applications <\/strong><strong>in the Financial Industry<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p class=\"import-Normal\">The financial industry has been a significant beneficiary of RPA, using it to streamline operations, improve customer experiences, and ensure compliance. Examples where Financial institutions rely on RPA include:<\/p>\n<p class=\"import-Normal\"><strong><em>Transaction Processing:<\/em><\/strong> Automating routine tasks such as account reconciliations, payments processing, and loan approvals.<\/p>\n<p class=\"import-Normal\"><strong><em>Fraud Detection:<\/em><\/strong> Using bots to monitor transactions for anomalies, flagging potentially fraudulent activities in real time.<\/p>\n<p class=\"import-Normal\"><strong><em>Regulatory Compliance:<\/em><\/strong> Automating the creation and submission of compliance reports to meet stringent regulatory requirements.<\/p>\n<p class=\"import-Normal\"><strong><em>Customer Onboarding:<\/em><\/strong> Simplifying the customer onboarding process by automating the collection and validation of customer data.<\/p>\n<p class=\"import-Normal\"><strong><em>Risk Management<\/em><\/strong><strong>:<\/strong> Automating the aggregation and analysis of risk-related data to support decision-making.<\/p>\n<\/div>\n<div class=\"textbox textbox--exercises\">\n<header class=\"textbox__header\">\n<p class=\"textbox__title\"><strong>Case Study: AI-Driven Digital Transformation in Healthcare \u2013 A Fintech Perspective<\/strong><\/p>\n<\/header>\n<div class=\"textbox__content\">\n<p><em>Background<\/em><br \/>\nIn April 2024, Chi Mei Medical Center in Taiwan introduced generative AI copilots built on Microsoft\u2019s Azure OpenAI Service. This deployment represents a key example of how fintech principles\u2014automation, data integration, and real-time analytics\u2014can transform traditional sectors like healthcare by addressing workforce shortages, improving operational efficiency, and enhancing service quality.<\/p>\n<p>Chi Mei Medical Center operates within Taiwan\u2019s renowned healthcare system, which faces significant challenges due to an aging population, a low birth rate, and increasing demand for medical services. By leveraging AI tools tailored to specific medical workflows, Chi Mei aims to alleviate healthcare worker burnout while maintaining high standards of care.<\/p>\n<p><em>AI Copilots in Action<\/em><br \/>\nChi Mei\u2019s AI copilots were designed for various roles:<\/p>\n<ul>\n<li><strong>A+ Pharmacy Copilot<\/strong>: Helps pharmacists retrieve and summarize patient data, flagging potential drug interactions and insurance coverage.<\/li>\n<li><strong>A+ Doctor Copilot<\/strong>: Streamlines medical report generation, reducing documentation time from one hour to 15 minutes.<\/li>\n<li><strong>A+ Nurse Copilot<\/strong>: Speeds up the process of documenting patient transfers and shift changes.<\/li>\n<li><strong>A+ Nutritionist Copilot<\/strong>: Offers dietary recommendations based on patients\u2019 medical conditions.<\/li>\n<\/ul>\n<p>By integrating data from multiple databases, these copilots provide comprehensive patient profiles, enabling healthcare providers to focus more on patient care rather than administrative tasks. In May 2024 alone, these AI copilots were used over 36,000 times by 3,500 individual users across the hospital&#8217;s departments.<\/p>\n<p><em>Impact on Healthcare Efficiency<\/em><br \/>\nThe introduction of AI copilots has delivered measurable benefits, including:<\/p>\n<ul>\n<li><strong>Increased Patient Capacity<\/strong>: Pharmacists doubled the number of patients they can serve daily, from 15 to 30.<\/li>\n<li><strong>Reduced Burnout<\/strong>: Early surveys indicate a drop in burnout scores among nurses.<\/li>\n<li><strong>Improved Workflow<\/strong>: Nurses report a reduction in documentation time from 20 minutes to under five minutes per task.<\/li>\n<\/ul>\n<p>Despite initial resistance from healthcare professionals who feared AI might replace them, hospital administrators emphasized that AI tools are intended to assist staff, allowing them to leave work on time and spend more quality time with their families.<\/p>\n<p><em>Challenges and Finetuning<\/em><br \/>\nWhile the copilots have performed well overall, some areas for improvement remain:<\/p>\n<ol>\n<li><strong>Language Accuracy<\/strong>: Occasional use of simplified Chinese characters instead of traditional script requires manual correction.<\/li>\n<li><strong>Professional Terminology<\/strong>: The copilots sometimes use layperson terms instead of precise medical language, necessitating edits during reviews.<\/li>\n<li><strong>Perceived AI Authorship<\/strong>: Some users felt that the generated reports were too formulaic, making them appear AI-authored.<\/li>\n<\/ol>\n<p>Chi Mei\u2019s ongoing efforts include improving language models and expanding copilot functions, such as a forthcoming <strong>A+ National Exam Review Copilot<\/strong> for medical professionals\u2019 continuous education.<\/p>\n<p><em>Conclusion: A Fintech Model for Healthcare Innovation<\/em><br \/>\nThis case study illustrates how fintech innovations can be adapted to healthcare, demonstrating the potential of AI-driven solutions in enhancing efficiency, accuracy, and user satisfaction. By integrating fintech principles\u2014such as real-time data analysis, automation, and user-centric design\u2014Chi Mei Medical Center has become a leader in digital healthcare transformation.<\/p>\n<p>Through continuous refinement of AI tools and active collaboration with healthcare providers, Chi Mei exemplifies how fintech-inspired technologies can address critical challenges in other sectors, paving the way for broader adoption across industries\u00a0 (Yee, 2024).<\/p>\n<\/div>\n<\/div>\n<div class=\"textbox\">\n<div class=\"group\/conversation-turn relative flex w-full min-w-0 flex-col agent-turn\">\n<div class=\"flex-col gap-1 md:gap-3\">\n<div class=\"flex max-w-full flex-col flex-grow\">\n<div class=\"min-h-8 text-message flex w-full flex-col items-end gap-2 whitespace-normal break-words [.text-message+&amp;]:mt-5\" dir=\"auto\" data-message-author-role=\"assistant\" data-message-id=\"5555fc12-a2a4-4540-bc00-a0effb0de35c\" data-message-model-slug=\"gpt-4o\">\n<div class=\"flex w-full flex-col gap-1 empty:hidden first:pt-[3px]\">\n<div class=\"markdown prose w-full break-words dark:prose-invert light\">\n<h3><span class=\"ez-toc-section\" id=\"Licenses_and_Attribution\"><\/span><strong>Licenses and Attribution<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<h4><span class=\"ez-toc-section\" id=\"CC_Licensed_Content_Original\"><\/span>CC Licensed Content, Original<span class=\"ez-toc-section-end\"><\/span><\/h4>\n<p><span data-teams=\"true\">This educational material includes AI-generated content from ChatGPT by OpenAI. The original content created by Mohammed Kotaiche from Hillsborough Community College is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License (<a id=\"menur5so\" class=\"fui-Link ___1q1shib f2hkw1w f3rmtva f1ewtqcl fyind8e f1k6fduh f1w7gpdv fk6fouc fjoy568 figsok6 f1s184ao f1mk8lai fnbmjn9 f1o700av f13mvf36 f1cmlufx f9n3di6 f1ids18y f1tx3yz7 f1deo86v f1eh06m1 f1iescvh fhgqx19 f1olyrje f1p93eir f1nev41a f1h8hb77 f1lqvz6u f10aw75t fsle3fq f17ae5zn\" title=\"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/deed.en\" href=\"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/deed.en\" rel=\"noreferrer noopener\" aria-label=\"Link CC BY-NC 4.0\">CC BY-NC 4.0<\/a>).\u00a0<\/span><\/p>\n<div class=\"flex-shrink-0 flex flex-col relative items-end\">\n<div>\n<div class=\"pt-0\">\n<div class=\"gizmo-bot-avatar flex h-8 w-8 items-center justify-center overflow-hidden rounded-full\">\n<div class=\"relative p-1 rounded-sm flex items-center justify-center bg-token-main-surface-primary text-token-text-primary h-8 w-8\">All images in this textbook generated with DALL-E are licensed under the terms provided by OpenAI, allowing for their free use, modification, and distribution with appropriate attribution.<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<hr \/>\n<h4><span class=\"ez-toc-section\" id=\"CC_Licensed_Content_Included\"><\/span><strong>CC Licensed Content Included<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h4>\n<\/div>\n<\/div>\n<div class=\"flex w-full flex-col gap-1 empty:hidden first:pt-[3px]\">\n<div class=\"markdown prose w-full break-words dark:prose-invert light\">\n<div>\n<ul>\n<li><strong>Wikipedia contributors. (n.d.)<\/strong><br \/>\nSource: <a href=\"https:\/\/en.wikipedia.org\/wiki\/HAL_9000\" target=\"_new\" rel=\"noopener\">Wikipedia contributors, HAL 9000<\/a><br \/>\nLicense: Public Domain<\/li>\n<li><strong>Wikipedia contributors. (n.d.)<\/strong><br \/>\nSource: <a href=\"https:\/\/en.wikipedia.org\/wiki\/I,_Robot_(film)\" target=\"_new\" rel=\"noopener\">Wikipedia contributors, I, Robot (film)<\/a><br \/>\nLicense: Public Domain<\/li>\n<li><strong>NASA. (n.d.)<\/strong><br \/>\nSource<strong>:<\/strong><a href=\"https:\/\/www.nasa.gov\/robonaut2\/\"> NASA, &#8220;Robonaut 2&#8221;<\/a><br \/>\nLicense: Public Domain<\/li>\n<li><strong>National Air and Space Museum. (2017).<\/strong><br \/>\nSource: <a href=\"https:\/\/airandspace.si.edu\/collection-media\/NASM-NASM2022-05070\">National Air and Space Museum, &#8220;Mars Pathfinder Marie Curie Rover Engineering Test Vehicle.&#8221;<\/a><br \/>\nLicense<strong>:<\/strong> Creative Commons Zero (CC0 1.0).<\/li>\n<\/ul>\n<\/div>\n<hr \/>\n<h4><span class=\"ez-toc-section\" id=\"Other_Licensed_Content_Included\"><\/span>Other Licensed Content Included<span class=\"ez-toc-section-end\"><\/span><\/h4>\n<ul>\n<li>Yee, C. M. (2024, July 12). <i>Taiwan hospital deploys AI copilots to lighten workloads for doctors, nurses and pharmacists<\/i>. Microsoft. Retrieved from <a href=\"https:\/\/www.microsoft.com\/en-us\/news\/taiwan-ai-copilots-healthcare\" target=\"_blank\" rel=\"noopener\">Microsoft\u2019s Article on AI Copilots in Taiwan Hospitals<\/a>.<\/li>\n<li>University of Chicago Press. (2020). <i>Reason and character: The moral foundations of Aristotelian political philosophy<\/i>. Retrieved from <a href=\"https:\/\/press.uchicago.edu\/ucp\/books\/book\/chicago\/R\/bo56407959.html\" target=\"_blank\" rel=\"noopener\">University of Chicago Press\u2019s Reason and Character<\/a>.<\/li>\n<li>Dartmouth College. (n.d.). <i>Artificial intelligence (AI) coined at Dartmouth<\/i>. Retrieved January 23, 2025, from<br \/>\n<a href=\"https:\/\/home.dartmouth.edu\/news\/artificial-intelligence-coined-dartmouth\" target=\"_blank\" rel=\"noopener\">Learn About AI\u2019s Origin at Dartmouth<\/a>.<\/li>\n<li>Flynn, J. R. (2007). <i>What is intelligence? Beyond the Flynn effect<\/i>. Cambridge University Press. Retrieved January 23, 2025, from<br \/>\n<a href=\"https:\/\/www.cambridge.org\/core\/books\/what-is-intelligence\/6A5AB5E24D396B9B4774E3D4D132223F\" target=\"_blank\" rel=\"noopener\">Cambridge University Press\u2019s What is Intelligence?<\/a>.<\/li>\n<li>Dugal, N. (2025). <i>Top generative AI tools for 2025<\/i>. Simplilearn. Retrieved January 23, 2025, from <a href=\"https:\/\/www.simplilearn.com\/top-generative-ai-tools-2025-article\" target=\"_blank\" rel=\"noopener\">Simplilearn\u2019s Article on Top Generative AI Tools<\/a>.<\/li>\n<li>Turing, A. M. (1950). <i>Computing machinery and intelligence<\/i>. <i>Mind<\/i>, New Series, 59(236), 433\u2013460. Published by Oxford Press. Retrieved January 23, 2025, from <a href=\"https:\/\/academic.oup.com\/mind\/article\/LIX\/236\/433\/986238\" target=\"_blank\" rel=\"noopener\">Oxford Press\u2019s Article on Computing Machinery and Intelligence<\/a>.<\/li>\n<li>American Deposits. (n.d.). <i>Conference on the future of digital banking. Insights: A Brief History of Digital Banking<\/i>. Retrieved from <a href=\"https:\/\/www.americandeposits.com\/history-digital-banking\" target=\"_blank\" rel=\"noopener\">A Brief History of Digital Banking<\/a>.\n<ul>\n<li style=\"list-style-type: none\"><\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<div id=\"sdfootnote18sym\"><\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n","protected":false},"author":2,"menu_order":7,"comment_status":"open","ping_status":"closed","template":"","meta":{"pb_show_title":"on","pb_short_title":"","pb_subtitle":"","pb_authors":[],"pb_section_license":""},"chapter-type":[],"contributor":[],"license":[],"class_list":["post-659","chapter","type-chapter","status-publish","hentry"],"part":3,"_links":{"self":[{"href":"https:\/\/pressbooks.hcfl.edu\/introtofintech\/wp-json\/pressbooks\/v2\/chapters\/659","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/pressbooks.hcfl.edu\/introtofintech\/wp-json\/pressbooks\/v2\/chapters"}],"about":[{"href":"https:\/\/pressbooks.hcfl.edu\/introtofintech\/wp-json\/wp\/v2\/types\/chapter"}],"author":[{"embeddable":true,"href":"https:\/\/pressbooks.hcfl.edu\/introtofintech\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/pressbooks.hcfl.edu\/introtofintech\/wp-json\/wp\/v2\/comments?post=659"}],"version-history":[{"count":132,"href":"https:\/\/pressbooks.hcfl.edu\/introtofintech\/wp-json\/pressbooks\/v2\/chapters\/659\/revisions"}],"predecessor-version":[{"id":1240,"href":"https:\/\/pressbooks.hcfl.edu\/introtofintech\/wp-json\/pressbooks\/v2\/chapters\/659\/revisions\/1240"}],"part":[{"href":"https:\/\/pressbooks.hcfl.edu\/introtofintech\/wp-json\/pressbooks\/v2\/parts\/3"}],"metadata":[{"href":"https:\/\/pressbooks.hcfl.edu\/introtofintech\/wp-json\/pressbooks\/v2\/chapters\/659\/metadata\/"}],"wp:attachment":[{"href":"https:\/\/pressbooks.hcfl.edu\/introtofintech\/wp-json\/wp\/v2\/media?parent=659"}],"wp:term":[{"taxonomy":"chapter-type","embeddable":true,"href":"https:\/\/pressbooks.hcfl.edu\/introtofintech\/wp-json\/pressbooks\/v2\/chapter-type?post=659"},{"taxonomy":"contributor","embeddable":true,"href":"https:\/\/pressbooks.hcfl.edu\/introtofintech\/wp-json\/wp\/v2\/contributor?post=659"},{"taxonomy":"license","embeddable":true,"href":"https:\/\/pressbooks.hcfl.edu\/introtofintech\/wp-json\/wp\/v2\/license?post=659"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}