Chapter 2 Slidesopening Example Intro To Ai Opening Vignette Inri ✓ Solved
Chapter 2 Slides Opening Example /Intro to AI  Opening Vignette  INRIX  Introduction to AI  AI is concerned with two basic ideas: (1) the study of human thought processes (to understand what intelligence is) and (2) the representation and duplication of those thought processes in machines (e.g., computers, robots). That is, the machines are expected to have humanlike thought processes. Major Elements of AI AI  Goals  Drivers  Benefits  Examples of AI at work  Limitations of AI  Three flavors of AI  Assisted  Autonomous  Augmented Human and Computer Intelligence  Content of Intelligence  Capabilities of Intelligence  Comparing AI to Human Intelligence Major AI Technologies and Derivatives  Intelligent Agent  Machine Learning  Machine and Computer Vision  Robotics  NLP  Chatbots AI Support for Decision Making  Issues and Factors in using AI for decision making  AI Support of the Decision-Making process  Problem Identification  Generating of finding alternative solutions  Selecting a solution  Implementing solution  Automated decision making AI Applications In Accounting/ Financial Services/ HRM/Marketing…  Accounting  Examples in book  AI in big accounting companies  Accounting applications small firms  Financial Services  Banking  Customer Recognition  Human Resource Management (HRM)  Talent Acquisition  Chatbots  Marketing  Personalized marketing Wrap Up  Review the Chapter highlights  Review the key terms  Complete the weekly homework Analytics, Data Science, & Artificial Intelligence, 11 Edition Opening Example /Intro to AI Major Elements of AI AI Human and Computer Intelligence Major AI Technologies and Derivatives AI Support for Decision Making AI Applications In Accounting/ Financial Services/ HRM/Marketing… Wrap Up Chapter 2 Slides Opening Example /Intro to AI  Opening Vignette  INRIX  Introduction to AI  AI is concerned with two basic ideas: (1) the study of human thought processes (to understand what intelligence is) and (2) the representation and duplication of those thought processes in machines (e.g., computers, robots).
That is, the machines are expected to have humanlike thought processes. Major Elements of AI AI  Goals  Drivers  Benefits  Examples of AI at work  Limitations of AI  Three flavors of AI  Assisted  Autonomous  Augmented Human and Computer Intelligence  Content of Intelligence  Capabilities of Intelligence  Comparing AI to Human Intelligence Major AI Technologies and Derivatives  Intelligent Agent  Machine Learning  Machine and Computer Vision  Robotics  NLP  Chatbots AI Support for Decision Making  Issues and Factors in using AI for decision making  AI Support of the Decision-Making process  Problem Identification  Generating of finding alternative solutions  Selecting a solution  Implementing solution  Automated decision making AI Applications In Accounting/ Financial Services/ HRM/Marketing…  Accounting  Examples in book  AI in big accounting companies  Accounting applications small firms  Financial Services  Banking  Customer Recognition  Human Resource Management (HRM)  Talent Acquisition  Chatbots  Marketing  Personalized marketing Wrap Up  Review the Chapter highlights  Review the key terms  Complete the weekly homework Analytics, Data Science, & Artificial Intelligence, 11 Edition Opening Example /Intro to AI Major Elements of AI AI Human and Computer Intelligence Major AI Technologies and Derivatives AI Support for Decision Making AI Applications In Accounting/ Financial Services/ HRM/Marketing… Wrap Up
Paper for above instructions
Introduction to Artificial Intelligence and its ApplicationsArtificial Intelligence (AI) plays a pivotal role in transforming industries by enhancing human capabilities and decision-making processes. Specifically, Chapter 2 of "Analytics, Data Science, & Artificial Intelligence" highlights AI's dual focus on understanding human intelligence and replicating those cognitive processes in machines (Sarkar, 2021). This assignment aims to delve into the major elements, technologies, and applications of AI across various industries while discussing the benefits and limitations it brings.
Major Elements of AI
AI encompasses several key aspects including its goals, drivers, benefits, and limitations. The primary goal of AI is to emulate human intelligence, which can be accomplished through three distinct flavors: assisted, autonomous, and augmented human intelligence (Russell & Norvig, 2016). Assisted AI aids human decision-making by providing relevant data and insights, while autonomous AI operates independently, making decisions with minimal human intervention. Augmented intelligence emphasizes collaboration between AI systems and human intelligence for enhanced problem-solving capabilities.
The drivers of AI adoption span business objectives, technological advancements, and competitive pressures. Companies are increasingly integrating AI to improve efficiency and productivity, manage costs, and create personalized experiences for customers (Chui et al., 2018). Benefits of AI include enhanced decision-making capabilities, rapid data analysis, and the ability to process vast amounts of information quickly (Brynjolfsson & McAfee, 2014).
Despite these advantages, AI comes with limitations such as ethical concerns, biases inherent in training data, and the potential for job displacement (Binns, 2018). Furthermore, while AI can process information efficiently, it lacks the emotional intelligence and ethical judgment present in human decision-making environments.
Major AI Technologies and Derivatives
Several key AI technologies are at the forefront of this revolution. Intelligent agents refer to systems that perceive their environment and take actions to maximize their chances of success (Russell & Norvig, 2016). Machine Learning (ML), a subset of AI, is particularly vital, focusing on the development of algorithms that can learn from and make predictions based on data.
Computer vision and natural language processing (NLP) are other essential technologies. Computer vision enables machines to interpret and understand visual information, which can be applied in various fields such as healthcare for diagnosing medical images (Jha et al., 2020). NLP allows machines to understand, interpret, and generate human language, facilitating communication with chatbots and virtual assistants.
Robotics is an area heavily influenced by AI, particularly in industries requiring automation. Robots equipped with AI capabilities can perform tasks ranging from manufacturing to logistics, enhancing operational efficiencies (Gerla, 2019).
AI Support for Decision Making
AI can significantly enhance the decision-making process within organizations by supporting various stages including problem identification, generating alternative solutions, selection, and implementation (Hofmann et al., 2019).
1. Problem Identification: AI systems can sift through large datasets to identify trends, aiding in recognizing problems that may require attention.
2. Generating Alternative Solutions: By simulating various scenarios or leveraging historical data and machine learning models, AI can propose multiple alternatives for consideration.
3. Selecting a Solution: Algorithms can assess alternatives based on predefined criteria and recommend optimal choices.
4. Implementing Solutions: AI can assist in the execution of decisions through automation, ensuring efficient processes and reduced error rates.
Furthermore, AI enables automated decision-making, wherein critical decisions can be executed without human input, particularly in high-speed environments such as financial trading (Davenport et al., 2020).
AI Applications Across Various Industries
AI has found diverse applications across different sectors, notably in accounting, financial services, human resources, and marketing.
1. Accounting: Major accounting firms have adopted AI for tasks such as auditing and compliance (Dyer et al., 2020). AI can analyze financial records and flag discrepancies, enhancing accuracy and speed.
2. Financial Services: In banking, AI improves customer experience through personalization and fraud detection. Machine learning algorithms analyze transaction patterns to identify anomalies indicative of fraud (Matz & Netzer, 2017).
3. Human Resource Management (HRM): AI streamlines the talent acquisition process by utilizing chatbots for initial candidate screening and automated scheduling (Guenole et al., 2018). Predictive analytics can also identify suitable candidates based on historical hiring data.
4. Marketing: Personalized marketing has transformed through AI, where algorithms analyze consumer behavior and preferences to tailor marketing strategies effectively (López-Miguens et al., 2019).
Wrap Up
In conclusion, AI is revolutionizing industries by providing powerful tools that enhance human capabilities and facilitate improved decision-making processes. Its major technologies, such as machine learning, NLP, and robotics, have a wide array of applications across various sectors. However, organizations must navigate the ethical implications and technical limitations associated with AI to ensure responsible implementation. As AI continues to evolve, its integration will be pivotal in shaping the future landscape of work and technology.
References
1. Binns, R. (2018). Fairness in Machine Learning: Lessons from Political Philosophy. In Proceedings of the 2018 ICML Workshop on Fairness, Accountability, and Transparency (pp. 149-158).
2. Brynjolfsson, E., & McAfee, A. (2014). The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies. W.W. Norton & Company.
3. Chui, M., Manyika, J., & Miremadi, M. (2018). Where machines could replace humans—and where they can’t (yet). McKinsey Quarterly.
4. Davenport, T. H., Guha, A., Grewal, D., & Bressgott, T. (2020). How AI Will Change the Future of Marketing. Harvard Business Review.
5. Dyer, J. H., Godfrey, D., & Dyer, J. (2020). Artificial Intelligence: How AI is Transforming Accounting. The CPA Journal.
6. Gerla, M. (2019). Revolutionizing Robotic Process Automation. IEEE Network.
7. Guenole, N., Ferrar, J., & Feinzig, S. (2018). The Power of Data Analytics in HR. Harvard Business Review.
8. Hofmann, J., & Dutta, S. (2019). Decision-Making and Artificial Intelligence: An Empirical Investigation. Journal of Business Research.
9. Jha, D., et al. (2020). Computer Vision and IoT: Applications in Healthcare. Journal of Healthcare Engineering.
10. López-Miguens, M. J., & Pita, M. (2019). Artificial Intelligence in Marketing: A Creative Framework for Product Development. International Marketing Review.