AI & ML report.pptx

studio shodwe ARTIFICIAL INTELLIGE PRESENTED BY: NCE JAMELA TREZ E. BORJA next studio shodwe 02 TOPIC OUTLINE Introduction What is Artificial Intelleg...

studio shodwe ARTIFICIAL INTELLIGE PRESENTED BY: NCE JAMELA TREZ E. BORJA next studio shodwe 02 TOPIC OUTLINE Introduction What is Artificial Intellegence Brief History Purpose / Objectives Prototype and Functionalities Financial Considerations Benefits to the Community Benefits to the Economy next  studio shodwe 03 OBJECTIVES Understand the definitions, history, and evolution of AI. Provide examples of AI/ML in various industries such as healthcare, finance, robotics, and autonomous vehicles. To grasp the economics and community benefits of AI/ML, including their impact on growth, innovation, and social connectivity. next studio shodwe 04 INTRODUCTION Artificial Intelligence (AI) is the development of computer systems capable of performing tasks that typically require human intelligence, such as learning, problem-solving, language understanding, and visual perception. It includes technologies like machine learning, which allows systems to learn from data, and natural language processing, which enables communication in human language. AI applications range from virtual assistants and recommendation systems to autonomous vehicles and robotics. While AI has the potential to revolutionize industries such as healthcare, finance, and manufacturing, it also raises ethical concerns regarding privacy, job displacement, and transparency in decision- next  studio shodwe 05 WHAT IS ARTIFICIAL INTELLIGENCE Artificial Intelligence (AI) is that it refers to the capability of a computer or a machine to perform tasks that typically require human intelligence, like learning, reasoning, problem-solving, and understanding language. next  studio shodwe 06 BRIEF HISTORY 1950 OF AI 1960-1970 1980-1990 2000 - PRESENT The term "Artificial Intelligence" AI research AI has exploded due to the The rise of machine learning dates back to John McCarthy in concentrated on increase in computing power, big and expert systems started 1956, at the Dartmouth problem-solving and data availability, and new the departure from rule- Conference, and this date can be symbolic methods. Early breakthroughs in various based AI to learning-based argued as the birth of AI as a AI programs like ELIZA machine learning techniques with AI. This was also the separate field of study. One of its deep learning becoming popular. and SHRDLU showed beginning for neural founding figures, Alan Turing, in AI is beginning to permeate in primitive natural networks, resulting in the 1950, proposed a measure of creation of further advanced everyday life, forming an integral language processing machine intelligence, the Turing AI models. part of different industries — from and comprehension. Test. healthcare to finance.  studio shodwe 07 PURPOSE / OBJECTIVES The main goal of artificial intelligence is to develop a technique or system that can do exactly what previously only human ECONOMIC GROWTH AND DEVELOPMENT beings could do. This, however, includes the following, among AUTOMATION other things: SAFETY AND DECISION- MAKING ENHANCING PRODUCTIVIY SECURITY PROBLEM SOLVING SUPPORTING ACCESSIBILITY FACILITATING HUMAN- COMPUTER COLLABROATION next  08 PROTOTYPES AND The Smart Healthcare Prototype System FUNCTIONALITIES developed in this study is designed to improve healthcare services, particularly during the COVID-19 pandemic. It integrates artificial intelligence, telemedicine, and the Internet of Medical Things (IoMT) to assist healthcare workers and provide remote care for quarantined patients. The system allows users to upload health data from wearable devices, monitor sleep quality, and track daily activities through webcams. Additionally, it features a conversational robot to facilitate electronic health records and support physical training for patients in isolation.  08 PROTOTYPES AND The study "Development and Translation of FUNCTIONALITIES Human-AI Interaction Models into Working Prototypes for Clinical Decision-making" by Muhammad Hussain and colleagues explores alternative human-AI interaction models for clinical decision support systems (CDSS) beyond the standard AI recommendation model. Using a co-design approach involving experts from various fields and a 'Wizard of Oz' method, they developed different prototypes to incorporate patient values and enhance clinician involvement. These models aim to address issues of trust, responsibility, and patient-centeredness in AI-based  09 FINANCIAL CONSIDERATION Project Costs: AI technology development involves High Initial Investment: It may be expensive at the start investing in research and development, data acquisition, when establishing the platform, but in the long term, the computing infrastructure, and talent acquisition. The return on investment could be huge with increased costs will depend on the complexity and scale of the AI efficiency, cost reductions on labor, and newly formed system under development. revenue streams created by the product and service of AI. Operational costs: Running AI systems, especially with Market Trends: The global artificial intelligence market is large-scale data processing and those making real-time expected to grow and hit a maximum of $190 billion by decisions, can actually be quite expensive because of the year 2025 with a spur from demands in the health, the high-performance computing resources and finance, and manufacturing industries. next maintenance.  studio shodwe 09 COMMUNITY Education BENEFITS Cultural Preservation Disaster Management: Improved Accessibility: Healthcare Improvements Economic Growth and Job Creation Agriculture and Food Security Transportation and Urban Planning Environmental Sustainability next studio shodwe 09 ECONOMIC ADVANTAGES Creating Jobs Productivity improvement Economic Growth Innovation & Business Improved Customer Experiences next Machine Learning Machine Learning Machine learning (ML) is a branch of artificial intelligence (AI) and computer science that focuses on the using data and algorithms to enable AI to imitate the way that humans learn, gradually improving its accuracy. Brief History of Machine Learning 1943 First mathematical model of neural networks presented in the scientific paper "A logical calculus of the ideas immanent in nervous activity" by Walter Pitts and Warren McCulloch. 1949 The book The Organization of Behavior by Donald Hebb is published 1952 Arthur Samuel developed one of the first machine learning programs, a checkers-playing program that improved its performance through experience 1957 Frank Rosenblatt invented the Perceptron, an early neural network model. 1960s-1980 The nearest neighbor algorithm was developed during this time, laying the groundwork for patter recognition tasks. 1990s The 1990s saw a shift towards data-driven approaches, with the development of algorithms like Support Vector Machines (SVM) and Random Forests. 2000s Deep learning, a subset of machine learning that focuses on neural networks with many layers, became popular. Geoffrey Hinton, Yann LeCun, and Yoshua Bengio are key figures in this revolution. 2010s – present Machine learning became integral to many industries, including healthcare, finance, and technology. Development of frameworks like TensorFlow and PyTorch made machine learning more accessible to a broader audience. Methods of Machine Learning Supervised Machine learning Semi-Supervised Machine learning Unsupervised Machine learning Reinforcement Learning Supervised Machine learning learning, also known as Supervised supervised machine learning, is defined by its use of labeled datasets to train algorithms to classify data or predict outcomes accurately. As input data is fed into the model, the model adjusts its weights until it has been fitted appropriately. This occurs as part of the cross validation process to ensure that the model avoids overfitting or underfitting. Types of Supervised Learning Classification Classification algorithms are used to group data by predicting a categorical label or output variable based on the input data. Classification is used when output variables are categorical, meaning there are two or more classes. Regression Regression algorithms are used to predict a real or continuous value, where the algorithm detects a relationship between two or more variables. Unsupervised Machine learning Unsupervised learning, also known as unsupervised machine learning, uses machine learning algorithms to analyze and cluster unlabeled datasets (subsets called clusters). These algorithms discover hidden patterns or data groupings without the need for human intervention. This method’s ability to discover similarities and differences in information make it ideal for exploratory data analysis, cross-selling strategies, customer segmentation, and Methods of Unsupervised Learning Clustering o Exclusive clustering o Overlapping clustering o Hierarchical clustering o Probabilistic clustering Dimension reduction Association Semi-Supervised Machine learning Semi-supervised learning offers a happy medium between supervised and unsupervised learning. During training, it uses a smaller labeled data set to guide classification and feature extraction from a larger, unlabeled data set. Semi-supervised learning can solve the problem of not having enough labeled data for a supervised learning algorithm. It also helps if it’s too costly to label enough data. Reinforcement Learning machine Reinforcement learning is a machine learning model that is similar to supervised learning, but the algorithm isn’t trained using sample data. This model learns as it goes by using trial and error. A sequence of successful outcomes will be reinforced to develop the best recommendation or policy for a given problem.  FINANCIAL CONSIDERATION Initial ROI and Cost- Investment Benefit Efficiency Gains Analysis Infrastructure Improved Decision- Costs Talent Acquisition Making Personalization and Ongoing Risk and Retention Customer Operational Costs Data Management Uncertainty Algorithm Bias Model Maintenance Failure to Deliver and Updates  BENEFIT OF MACHINE LEARNING TO THE COMMUNITY AND ECONOMY Healthcare Improvement Enhanced Public Services Boost to Education Machine learning can analyze Machine learning has already Personalized learning vast amounts of medical data demonstrated considerable powered by machine to predict disease outbreaks, potential to enhance the learning adapts educational assist in diagnostics, and effectiveness and accuracy of content to individual personalize treatment plans. many decisions-making scenarios students' needs, improving This leads to better patient ranging from medical diagnosis, educational outcomes and outcomes and overall public granting mortgages, tax evasion, closing achievement gaps. health improvement. and terrorist activities identification A better-educated (Kononenko, 2001; Nowshath et workforce drives innovation al., 2019; Rodríguez et al., 2019; and economic growth by Mantari et al., 2020).. increasing productivity and adaptability in various industries. In healthcare, common machine learning advances have been evolving for years. The application of AI has the capacity to assist with case triage and diagnoses , enhance image scanning and segmentation , support decision making , predict the risk of Economic Growth Through Innovation Environmental Sustainability ML drives innovation in industries like ML can optimize energy usage in manufacturing, agriculture, and finance smart grids, reduce waste in by automating complex tasks, leading to manufacturing, and monitor more efficient production processes and environmental changes. This leads better products. This innovation leads to to a more sustainable and eco- the creation of new jobs, markets, and friendly community. Sustainable industries, driving economic growth and practices often lead to long-term cost increasing national GDP. savings and create green jobs, contributing positively to the economy.  reference 10 McKinsey & Company. (2019). The Global AI Agenda: Promise, Reality, and a Blueprint for the Future PwC. (2017). Sizing the Prize: What’s the Real Value of AI for Your Business and How Can You Capitalise? Brynjolfsson, E., & McAfee, A. (2014). The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies. W. W. Norton & Company. Turing, A. M. (1950). "Computing Machinery and Intelligence." Mind, 59(236), 433-460. McCarthy, J., Minsky, M. L., Rochester, N., & Shannon, C. E. (1956). "A Proposal for the Dartmouth Summer Research Project on Artificial Intelligence." Russell, S., & Norvig, P. (2020). Artificial Intelligence: A Modern Approach. Pearson. Russell, S., & Norvig, P. (2016). *Artificial Intelligence: A Modern Approach* (3rd ed.). Pearson Education National Institute of Standards and Technology (NIST). (2021). "Artificial Intelligence Risk Management Framework." World Economic Forum. (2020). "The Future of Jobs Report 2020." Grand View Research. (2021). "Artificial Intelligence Market Size Worth $190.61 Billion By 2025."  reference 10 PwC. (2018). "AI to contribute $15.7 trillion to the global economy by 2030." European Commission. (2020). "White Paper on Artificial Intelligence - A European Approach to Excellence and Trust." McKinsey & Company. (2018). "Notes from the AI frontier: Insights from hundreds of use cases." Batty, M. (2018). Artificial Intelligence and Smart Cities. Environment and Planning B: Urban Analytics and City Science, Wolfert, S., et al. (2017). Big Data in Smart Farming – A Review. Agricultural Systems Sun, Z., & Liu, W. (2018). Disaster Management in the Era of Artificial Intelligence. Journal of Intelligent & Fuzzy Systems Janssen, M. A., & Ostrom, E. (2006). A Framework for Analyzing the Robustness of Social-ecological Systems from an Institutional Perspective. Ecology and Society Topol, E. J. (2019). Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again. Basic Books Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial Intelligence in Education: Promises and Implications for Teaching and Learning. Center for Curriculum Redesign  reference 10 Brynjolfsson, E., & McElheran, K. (2016). The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies. W. W. Norton & Company. Rolnick, D., et al. (2019). Tackling Climate Change with Machine Learning. arXiv preprint Brynjolfsson, E., & McAfee, A. (2014). The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies. W. W. Norton & Company. McKinsey & Company. (2019). The Global AI Agenda: Promise, Reality, and a Blueprint for the Future George Firican (2022).The History of Machine Learning. https://www.lightsondata.com/the-history-of-machine-learning Boris B., I. G. Cohen, Theodoros E., & Sara G. (2021). When Machine learning goes off the rails. When Machine Learning Goes Off the Rails (hbr.org) Ginsburg, Boris. (2017). Programming Massively Parallel Processors || Application case study—machine learning. , (), 345–367. doi:10.1016/B978-0-12- 811986-0.00016-9 Zuo, Analysis of e-commerce characteristics based on edge algorithm and cox model, Wireless Commun. Mobile Comput., № 2021, с. 1 Katrina Wakefield, Marketing, SAS UK(2020). A guide to the types of machine learning algorithms and their application. Keith D. F. (2021). A Brief History of Machine Learning. https://www.dataversity.net/a-brief-history-of-machine-learning Kate Dion (2023). How machine learning is transforming the healthcare industry. https://healthcaretransformers.com/digital-health/current-trends/machine-learning-transforming-healthcare studio shodwe 10 THANK YOU!  studio shodwe ARTIFICIAL INTELLIGE PRESENTED BY: NCE JAMELA TREZ E. BORJA next studio shodwe 02 TOPIC OUTLINE Introduction What is Artificial Intellegence Brief History Purpose / Objectives Prototype and Functionalities Financial Considerations Benefits to the Community Benefits to the Economy next  studio shodwe 03 OBJECTIVES Understand the definitions, history, and evolution of AI. Provide examples of AI/ML in various industries such as healthcare, finance, robotics, and autonomous vehicles. To grasp the economics and community benefits of AI/ML, including their impact on growth, innovation, and social connectivity. next studio shodwe 04 INTRODUCTION Artificial Intelligence (AI) is the development of computer systems capable of performing tasks that typically require human intelligence, such as learning, problem-solving, language understanding, and visual perception. It includes technologies like machine learning, which allows systems to learn from data, and natural language processing, which enables communication in human language. AI applications range from virtual assistants and recommendation systems to autonomous vehicles and robotics. While AI has the potential to revolutionize industries such as healthcare, finance, and manufacturing, it also raises ethical concerns regarding privacy, job displacement, and transparency in decision- next  studio shodwe 05 WHAT IS ARTIFICIAL INTELLIGENCE Artificial Intelligence (AI) is that it refers to the capability of a computer or a machine to perform tasks that typically require human intelligence, like learning, reasoning, problem-solving, and understanding language. next  studio shodwe 06 BRIEF HISTORY 1950 OF AI 1960-1970 1980-1990 2000 - PRESENT The term "Artificial Intelligence" AI research AI has exploded due to the The rise of machine learning dates back to John McCarthy in concentrated on increase in computing power, big and expert systems started 1956, at the Dartmouth problem-solving and data availability, and new the departure from rule- Conference, and this date can be symbolic methods. Early breakthroughs in various based AI to learning-based argued as the birth of AI as a AI programs like ELIZA machine learning techniques with AI. This was also the separate field of study. One of its deep learning becoming popular. and SHRDLU showed beginning for neural founding figures, Alan Turing, in AI is beginning to permeate in primitive natural networks, resulting in the 1950, proposed a measure of creation of further advanced everyday life, forming an integral language processing machine intelligence, the Turing AI models. part of different industries — from and comprehension. Test. healthcare to finance.  studio shodwe 07 PURPOSE / OBJECTIVES The main goal of artificial intelligence is to develop a technique or system that can do exactly what previously only human ECONOMIC GROWTH AND DEVELOPMENT beings could do. This, however, includes the following, among AUTOMATION other things: SAFETY AND DECISION- MAKING ENHANCING PRODUCTIVIY SECURITY PROBLEM SOLVING SUPPORTING ACCESSIBILITY FACILITATING HUMAN- COMPUTER COLLABROATION next  08 PROTOTYPES AND The Smart Healthcare Prototype System FUNCTIONALITIES developed in this study is designed to improve healthcare services, particularly during the COVID-19 pandemic. It integrates artificial intelligence, telemedicine, and the Internet of Medical Things (IoMT) to assist healthcare workers and provide remote care for quarantined patients. The system allows users to upload health data from wearable devices, monitor sleep quality, and track daily activities through webcams. Additionally, it features a conversational robot to facilitate electronic health records and support physical training for patients in isolation.  08 PROTOTYPES AND The study "Development and Translation of FUNCTIONALITIES Human-AI Interaction Models into Working Prototypes for Clinical Decision-making" by Muhammad Hussain and colleagues explores alternative human-AI interaction models for clinical decision support systems (CDSS) beyond the standard AI recommendation model. Using a co-design approach involving experts from various fields and a 'Wizard of Oz' method, they developed different prototypes to incorporate patient values and enhance clinician involvement. These models aim to address issues of trust, responsibility, and patient-centeredness in AI-based  09 FINANCIAL CONSIDERATION Project Costs: AI technology development involves High Initial Investment: It may be expensive at the start investing in research and development, data acquisition, when establishing the platform, but in the long term, the computing infrastructure, and talent acquisition. The return on investment could be huge with increased costs will depend on the complexity and scale of the AI efficiency, cost reductions on labor, and newly formed system under development. revenue streams created by the product and service of AI. Operational costs: Running AI systems, especially with Market Trends: The global artificial intelligence market is large-scale data processing and those making real-time expected to grow and hit a maximum of $190 billion by decisions, can actually be quite expensive because of the year 2025 with a spur from demands in the health, the high-performance computing resources and finance, and manufacturing industries. next maintenance.  studio shodwe 09 COMMUNITY Education BENEFITS Cultural Preservation Disaster Management: Improved Accessibility: Healthcare Improvements Economic Growth and Job Creation Agriculture and Food Security Transportation and Urban Planning Environmental Sustainability next studio shodwe 09 ECONOMIC ADVANTAGES Creating Jobs Productivity improvement Economic Growth Innovation & Business Improved Customer Experiences next Machine Learning Machine Learning Machine learning (ML) is a branch of artificial intelligence (AI) and computer science that focuses on the using data and algorithms to enable AI to imitate the way that humans learn, gradually improving its accuracy. Brief History of Machine Learning 1943 First mathematical model of neural networks presented in the scientific paper "A logical calculus of the ideas immanent in nervous activity" by Walter Pitts and Warren McCulloch. 1949 The book The Organization of Behavior by Donald Hebb is published 1952 Arthur Samuel developed one of the first machine learning programs, a checkers-playing program that improved its performance through experience 1957 Frank Rosenblatt invented the Perceptron, an early neural network model. 1960s-1980 The nearest neighbor algorithm was developed during this time, laying the groundwork for patter recognition tasks. 1990s The 1990s saw a shift towards data-driven approaches, with the development of algorithms like Support Vector Machines (SVM) and Random Forests. 2000s Deep learning, a subset of machine learning that focuses on neural networks with many layers, became popular. Geoffrey Hinton, Yann LeCun, and Yoshua Bengio are key figures in this revolution. 2010s – present Machine learning became integral to many industries, including healthcare, finance, and technology. Development of frameworks like TensorFlow and PyTorch made machine learning more accessible to a broader audience. Methods of Machine Learning Supervised Machine learning Semi-Supervised Machine learning Unsupervised Machine learning Reinforcement Learning Supervised Machine learning learning, also known as Supervised supervised machine learning, is defined by its use of labeled datasets to train algorithms to classify data or predict outcomes accurately. As input data is fed into the model, the model adjusts its weights until it has been fitted appropriately. This occurs as part of the cross validation process to ensure that the model avoids overfitting or underfitting. Types of Supervised Learning Classification Classification algorithms are used to group data by predicting a categorical label or output variable based on the input data. Classification is used when output variables are categorical, meaning there are two or more classes. Regression Regression algorithms are used to predict a real or continuous value, where the algorithm detects a relationship between two or more variables. Unsupervised Machine learning Unsupervised learning, also known as unsupervised machine learning, uses machine learning algorithms to analyze and cluster unlabeled datasets (subsets called clusters). These algorithms discover hidden patterns or data groupings without the need for human intervention. This method’s ability to discover similarities and differences in information make it ideal for exploratory data analysis, cross-selling strategies, customer segmentation, and Methods of Unsupervised Learning Clustering o Exclusive clustering o Overlapping clustering o Hierarchical clustering o Probabilistic clustering Dimension reduction Association Semi-Supervised Machine learning Semi-supervised learning offers a happy medium between supervised and unsupervised learning. During training, it uses a smaller labeled data set to guide classification and feature extraction from a larger, unlabeled data set. Semi-supervised learning can solve the problem of not having enough labeled data for a supervised learning algorithm. It also helps if it’s too costly to label enough data. Reinforcement Learning machine Reinforcement learning is a machine learning model that is similar to supervised learning, but the algorithm isn’t trained using sample data. This model learns as it goes by using trial and error. A sequence of successful outcomes will be reinforced to develop the best recommendation or policy for a given problem.  FINANCIAL CONSIDERATION Initial ROI and Cost- Investment Benefit Efficiency Gains Analysis Infrastructure Improved Decision- Costs Talent Acquisition Making Personalization and Ongoing Risk and Retention Customer Operational Costs Data Management Uncertainty Algorithm Bias Model Maintenance Failure to Deliver and Updates  BENEFIT OF MACHINE LEARNING TO THE COMMUNITY AND ECONOMY Healthcare Improvement Enhanced Public Services Boost to Education Machine learning can analyze Machine learning has already Personalized learning vast amounts of medical data demonstrated considerable powered by machine to predict disease outbreaks, potential to enhance the learning adapts educational assist in diagnostics, and effectiveness and accuracy of content to individual personalize treatment plans. many decisions-making scenarios students' needs, improving This leads to better patient ranging from medical diagnosis, educational outcomes and outcomes and overall public granting mortgages, tax evasion, closing achievement gaps. health improvement. and terrorist activities identification A better-educated (Kononenko, 2001; Nowshath et workforce drives innovation al., 2019; Rodríguez et al., 2019; and economic growth by Mantari et al., 2020).. increasing productivity and adaptability in various industries. In healthcare, common machine learning advances have been evolving for years. The application of AI has the capacity to assist with case triage and diagnoses , enhance image scanning and segmentation , support decision making , predict the risk of Economic Growth Through Innovation Environmental Sustainability ML drives innovation in industries like ML can optimize energy usage in manufacturing, agriculture, and finance smart grids, reduce waste in by automating complex tasks, leading to manufacturing, and monitor more efficient production processes and environmental changes. This leads better products. This innovation leads to to a more sustainable and eco- the creation of new jobs, markets, and friendly community. Sustainable industries, driving economic growth and practices often lead to long-term cost increasing national GDP. savings and create green jobs, contributing positively to the economy.  reference 10 McKinsey & Company. (2019). The Global AI Agenda: Promise, Reality, and a Blueprint for the Future PwC. (2017). Sizing the Prize: What’s the Real Value of AI for Your Business and How Can You Capitalise? Brynjolfsson, E., & McAfee, A. (2014). The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies. W. W. Norton & Company. Turing, A. M. (1950). "Computing Machinery and Intelligence." Mind, 59(236), 433-460. McCarthy, J., Minsky, M. L., Rochester, N., & Shannon, C. E. (1956). "A Proposal for the Dartmouth Summer Research Project on Artificial Intelligence." Russell, S., & Norvig, P. (2020). Artificial Intelligence: A Modern Approach. Pearson. Russell, S., & Norvig, P. (2016). *Artificial Intelligence: A Modern Approach* (3rd ed.). Pearson Education National Institute of Standards and Technology (NIST). (2021). "Artificial Intelligence Risk Management Framework." World Economic Forum. (2020). "The Future of Jobs Report 2020." Grand View Research. (2021). "Artificial Intelligence Market Size Worth $190.61 Billion By 2025."  reference 10 PwC. (2018). "AI to contribute $15.7 trillion to the global economy by 2030." European Commission. (2020). "White Paper on Artificial Intelligence - A European Approach to Excellence and Trust." McKinsey & Company. (2018). "Notes from the AI frontier: Insights from hundreds of use cases." Batty, M. (2018). Artificial Intelligence and Smart Cities. Environment and Planning B: Urban Analytics and City Science, Wolfert, S., et al. (2017). Big Data in Smart Farming – A Review. Agricultural Systems Sun, Z., & Liu, W. (2018). Disaster Management in the Era of Artificial Intelligence. Journal of Intelligent & Fuzzy Systems Janssen, M. A., & Ostrom, E. (2006). A Framework for Analyzing the Robustness of Social-ecological Systems from an Institutional Perspective. Ecology and Society Topol, E. J. (2019). Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again. Basic Books Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial Intelligence in Education: Promises and Implications for Teaching and Learning. Center for Curriculum Redesign  reference 10 Brynjolfsson, E., & McElheran, K. (2016). The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies. W. W. Norton & Company. Rolnick, D., et al. (2019). Tackling Climate Change with Machine Learning. arXiv preprint Brynjolfsson, E., & McAfee, A. (2014). The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies. W. W. Norton & Company. McKinsey & Company. (2019). The Global AI Agenda: Promise, Reality, and a Blueprint for the Future George Firican (2022).The History of Machine Learning. https://www.lightsondata.com/the-history-of-machine-learning Boris B., I. G. Cohen, Theodoros E., & Sara G. (2021). When Machine learning goes off the rails. When Machine Learning Goes Off the Rails (hbr.org) Ginsburg, Boris. (2017). Programming Massively Parallel Processors || Application case study—machine learning. , (), 345–367. doi:10.1016/B978-0-12- 811986-0.00016-9 Zuo, Analysis of e-commerce characteristics based on edge algorithm and cox model, Wireless Commun. Mobile Comput., № 2021, с. 1 Katrina Wakefield, Marketing, SAS UK(2020). A guide to the types of machine learning algorithms and their application. Keith D. F. (2021). A Brief History of Machine Learning. https://www.dataversity.net/a-brief-history-of-machine-learning Kate Dion (2023). How machine learning is transforming the healthcare industry. https://healthcaretransformers.com/digital-health/current-trends/machine-learning-transforming-healthcare studio shodwe 10 THANK YOU!