Business Analytics Course Introduction with AI Concepts PDF

MS365 Business Analytics  Business Analytics Business Analytics – – Using quantitative methodology to make data driven decision – Can be as simple as descriptive statistics or as complex Business Intelligence reporting and Data Mining....

MS365 Business Analytics  Business Analytics Business Analytics – – Using quantitative methodology to make data driven decision – Can be as simple as descriptive statistics or as complex Business Intelligence reporting and Data Mining. 2  Business Intelligence Business Intelligence – – Data aggregation, visualization, and reporting for understanding. Who, what, when, where, and why? – Often presented as dashboards. – Popular self serve BI solutions include PowerBI and Tableau. 3 BI Dashboard Examples 4 BI Dashboard Examples Check out your classmates in action 5  Data Mining and Predictive Analytics Business analytics include methodology the moves past explanatory modeling. – Analytics can be used to create predictive models used for business decision making. 6  Analytics in practice Example 1 – Credit score 7 Analytics that make college students happy 8  Data Mining Where statistics and machine learning meet – Data mining can be used to cluster customers into persona. Example: – Netflix recommendations, Amazon shopping, Spotify, email marketing. 9  Big Data – The four V’s Volume – The amount of data Veracity – The truthfulness of the data Variety – The diversity of the data Velocity – The rate at which data is being generated 10  So is this a Data Science Class? Data Science – A mix of skills in statistics, IT, math, programming, and business 11 The World of AI J. Davidson 12  The World of AI Artificial Intelligence (AI) – The science of engineering and making intelligent machines (Mccarthy, 1956). – An umbrella term that encompasses areas such as machine learning and cognitive computing. (Guenole and Feinzig, 2021) – Machine Learning Classification Models Regression Models Clustering Association Rules & Recommenders – Deep Learning Computer Vision Natural Language Processing Generative AI 13 Real World Applications Classification Models Regression Models Recommenders and Association Rules Autonomous Vehicles Computer Vision Models Natural Language Processing – Large Language Models – Chatbots Generative AI – GPT – GAN 14 AI Categorical Visualization 15 The History of AI – The Father of AI Dr. John Mccarthy – Computer Scientist – Professor at Dartmouth, Stanford, MIT, and Princeton – Created the programming language List Processing (LISP) – Coined the term AI – Primary organizer of the Dartmouth Summer Research Project on Artificial Intelligence 16 Dartmouth Summer Research Project on Artificial Intelligence Organized by John McCarthy, Marvin Minsky, Claude Shannon, and Nathaniel Rochester. Convened a little over 30 thought leaders and computer scientist to solve the problem of AI. Met from June – August Defined and made popular the term AI. 17 Alan Turing – The father of computing Computer Scientist Code breaker – Created the bombe machine Best known for his seminal paper Computing Machinery and Intelligence. – Published in 1950 – Known for the introduction of the imitation game now known as the Turing Test. 18 The BOMBE Machine Enigma Machine Bombe Machine VS 19 The Turing Test A novel theory called on measuring the intelligence of machines called the Imitation Game proposed by Turing in 1950. Compromised of three players A real agent A machine pretending to be the real agent An interrogator The goal of the interrogator is to blindly interview the real and machine agents and determine which one is the machine. If the agent is unable to tell the difference between the two agents, the machine is deemed intelligent. 20  General AI vs Narrow AI Narrow AI – Also known as weak AI. Narrow AI designs create models that perform one specific task. The current state of the art is narrow AI. General AI is a theoretical design that would allow AI models to be created to perform a broad array of general actions. Think iRobot or HAL from 2001 a space odyssey. Fun fact HAL was a subtle joke at IBM. 21  How Did We Get Here A majority of the math behind modern deep learning and machine learning algorithms are 40 plus years old or older. When the original theories were created computer hardware was limited. Original computers were large, expensive, and power hungry. Computers could be given commands but did not have the ability to store information. AI requires large amounts of big data which could not be captured or stored. 22  Big Data and Computing Power Moore’s Law – Named after cofounder of Intel Gordon Moore – The number of transistors in an integrated circuit (IS) doubles about every two years. 23 Big Data Storage Kryder’s Law – Named after Seagate Senior Vice President Mark Kryder. – The density of hard drives increases 1,000 every 10.5 years. – Increasing at a larger rate than Moore’s Law. 24  Data Generation 1.7 mb per second 25 26 Generative AI 27 IP issues with Generative AI 28  Key Take Aways What is Business Analytics? What is Business Intelligence? What is Data Mining? What is the difference between Predictive Modeling and Classification modeling? What is Moore’s Law? What is Kryder’s Law? General AI vs Narrow AI What is Generative AI? What is the Turing Test? 29 Morning Motivation 30