IBM Auditing PDF

IBM auditing Ms. Prakriti SJCC July 2024 1 IBM Auditing: IBM audits refer to the process by which IBM (International Business Machines Corporation), a major multinational technology and consulting compa...

IBM auditing Ms. Prakriti SJCC July 2024 1 IBM Auditing: IBM audits refer to the process by which IBM (International Business Machines Corporation), a major multinational technology and consulting company, as- sesses and verifies the compliance of its software licenses and contracts with its customers. These audits are also known as IBM Software License Reviews or IBM License Compliance Verification (LCV). 2 What is the Purpose of IBM Audits? The primary purpose of IBM audits is to ensure that customers are using IBM software products in compliance with the terms and conditions outlined in their software licenses and contracts. This includes verifying that the customer has not exceeded the number of licenses purchased, is using the software for the intended purposes, and is not engaged in any unauthorized or unlicensed usage. It’s essential for organizations using IBM software to be aware of their con- tractual obligations and maintain compliance to avoid unexpected costs and penalties associated with non-compliance during an IBM audit. Engaging with IBM in a cooperative and transparent manner during the audit process can help facilitate a smoother resolution if compliance issues are identified. 3 Types of Auditing: 3.1 Compliance Auditing: Organizations may audit their AI systems to ensure they are in compliance with relevant regulations, industry standards, and internal policies. This is especially important in sectors like healthcare, finance, and legal services where strict compliance is necessary. 3.2 Performance Auditing: Auditing can involve assessing the performance of AI systems to ensure they meet expected accuracy, reliability, and efficiency standards. This may include 1 tracking metrics, evaluating model performance, and optimizing algorithms. 3.3 Bias and Fairness Auditing: To address concerns about bias and fairness in AI systems, organizations may conduct audits to identify and mitigate biases in data and algorithms. This helps ensure that AI systems provide equitable results for all user groups. 3.4 Security Auditing: Auditing AI systems for security vulnerabilities and threats is essential to pro- tect against data breaches and cyberattacks. This involves assessing the security measures in place to protect AI models and data. 3.5 Data Privacy Auditing: AI systems often process sensitive data. Auditing for data privacy compliance ensures that AI applications handle data in a manner consistent with privacy regulations (e.g., GDPR(General Data Protection Regulation), CCPA(Central Consumer Protection Authority). 3.6 Usage and Cost Auditing: For AI deployed in cloud environments or as part of subscription services, orga- nizations may audit usage and costs to ensure cost-effective utilization and to prevent unexpected expenses. 3.7 Ethical Auditing: Some organizations are increasingly focusing on ethical auditing of AI systems to ensure that the technology aligns with ethical principles and societal values. The specific tools and processes used for auditing AI systems, including those built with IBM Watson, can vary depending on the organization’s needs and objectives. These audits are often performed by a combination of data scientists, compliance officers, security experts, and domain specialists. 4 Top 5 Speech Recognition Softwares: Speech recognition, also known as Automatic Speech Recognition (ASR) or voice recognition, is a technology that allows a computer system or software to convert spoken language into written text. It enables computers to understand and interpret human speech, making it a valuable tool for various applications. ASR technology is used to convert spoken language or audio speech into written text. It is commonly used in various applications, including transcription services, voice assistants, and more, where spoken words are transcribed into text for further processing or analysis. 2 4.1 Dragon NaturallySpeaking (Dragon Professional): Developed by Nuance Communications, Dragon NaturallySpeaking has a long history and is recognized for its accuracy and robust features in speech recognition. It is popular among professionals and individuals for tasks like transcription and voice-controlled computer operations. 4.2 Microsoft Speech Recognition: Microsoft’s built-in speech recognition software is widely known because it comes pre-installed with Windows operating systems. Users can use it for dictation, controlling their computer, and interacting with applications. 4.3 Google Speech Recognition: Google’s speech recognition technology is integrated into various products and services, making it widely recognized. It’s commonly used in Google Docs for voice typing and for voice commands through Google Assistant. 4.4 Amazon Transcribe : Part of Amazon Web Services (AWS), Amazon Transcribe is a recognized and reliable solution for converting audio into text. It’s used in various applications, including transcription services, content indexing, and analytics. 4.5 Apple Dictation: Apple’s built-in dictation and speech recognition features on macOS and iOS devices are well-known among Apple users. They are used for voice-to-text input and controlling Apple devices through voice commands. Conclusion: These software options are known for their accessibility, func- tionality, and integration with popular operating systems and services. Keep in mind that the landscape of speech recognition technology can change over time, so it’s a good idea to check for updates and reviews to ensure you’re using the most suitable and up-to-date software for your needs. 3  Examples of Chatbots Ms.Prakriti, SJCC July 2024 1 Examples Of Chatbots: 1.1 Facebook bots: A Facebook Messenger bot refers to a chatbot embedded in Facebook Messenger. When set up correctly, Facebook Messenger chatbots can chat with millions of customers and help you scale your Facebook strategy. Bots can carry on conversations like real customer service agents. Facebook chatbots are primarily used for customer service and marketing. They operate 24/7 and reply instantly. It means that businesses can save the time and money they would normally spend on sending messages to customers via Facebook messenger live chat. 1.2 McDonald’s chatbot: McDonald’s designed a marketing campaign that incorporated a board game, discount codes, and Facebook Messenger bots. After scanning the board, you could chat with McBot and play a virtual game. 1.3 Disney Chatbot: At some point, Disney released a whole range of different interactive characters powered by artificial intelligence that you could chat with. They were available via Messenger chatbots, Kik, or you could even try SMS texting. Some of the most popular were Miss Piggy from The Muppets and Judy Hopps from Zootopia. Customers exchanged millions of messages with those two characters alone. Obviously, it is difficult to directly measure how these interactions translate into sales. However, some solutions offer advanced tools for measuring your bot performance. 1  Figure 1: Facebook Chatbot Figure 2: McDonald’s Chatbot Figure 3: Disney Chatbot 2 Chatbot Definition, Types, Pros Cons, Examples Ms.Prakriti, SJCC July 2024 1 What Is a Chatbot? A chatbot is a computer program that simulates human conversation through voice commands or text chats or both. Chatbot, short for chatterbot, is an Artificial Intelligence (AI) feature that can be embedded and used through any major messaging application. A chatbot is an automated program that interacts with customers as a hu- man would and costs little to nothing to engage with. Chatbots attend to cus- tomers at all times of the day and week and are not limited by time or a physical location. This makes its implementation appealing to a lot of businesses that may not have the manpower or financial resources to keep employees working around the clock. Cool Fact:According to industry research, the COVID-19 pandemic greatly accelerated the implementation and user adoption of chatbots around the globe. Some examples of chatbot technology are virtual assistants like Amazon’s Alexa and Google Assistant, and messaging apps, such as WeChat and Face- book’s Messenger. Chatbots, also called chatterbots, is a form of artificial intelligence (AI) used in messaging apps. This tool helps add convenience for customers—they are automated programs that interact with customers like a human would and cost little to nothing to engage with. Key examples are chatbots used by businesses in Facebook Messenger, or as virtual assistants, such as Amazon’s Alexa. Chatbots tend to operate in one of two ways—either via machine learning or with set guidelines. However, due to advancements in AI technology, chatbots using set guidelines are becoming a historical footnote. There are a number of synonyms for chatbot, including ”talkbot”, ”bot”, ”interactive agent” or ”artificial conversation entity.” 1 2 Types of Chatbots: 2.1 Set Guidelines Chatbot A chatbot that functions with a set of guidelines in place is limited in its con- versation. It can only respond to a set number of requests and vocabulary and is only as intelligent as its programming code. An example of a limited bot is an automated banking bot that asks the caller some questions to understand what the caller wants to do. The bot would make a command like “Please tell me what I can do for you by saying account balances, account transfer, or bill payment.” If the customer responds with ”credit card balance,” the bot would not understand the request and would proceed to either repeat the command or transfer the caller to a human assistant. Over time, chatbots have evolved with new AI advancements and are far more responsive to human interaction than chatbots based on set guidelines. 2.2 Machine Learning Chatbot A chatbot that functions through machine learning has an artificial Neural Net- work inspired by the neural nodes of the human brain. The bot is programmed to self-learn as it is introduced to new dialogues and words. In effect, as a chat- bot receives new voice or textual dialogues, the number of inquiries that it can reply to and the accuracy of each response it gives increases. Meta has a machine learning chatbot that creates a platform for compa- nies to interact with their user through the Messenger application. Using the Messenger bot, users can buy shoes from Spring, order a ride from Uber, and have conversations with The New York Timeson news issues of the day. If a user asked The New York Times through the app a question like “What’s new today?” or “What do the polls say?”the bot would reply to the request. Chatbots are used in a variety of sectors and built for different purposes. There are retail bots designed to pick and order groceries, weather bots that give you weather forecasts of the day or week, and simply friendly bots that just talk to people in need of a friend. The fintech sector also uses chatbots to make users’ inquiries and applica- tions for financial services easier. In 2016, a small business lender in Montreal, Thinking Capital, uses a virtual assistant to provide customers with 24/7 assis- tance through Facebook Messenger. A small business hoping to get a loan from the company needs only answer key qualification questions asked by the bot in order to be deemed eligible to receive up to $300,000 in financing. 3 Advantages and Disadvantages of Chatbots: Chatbots are convenient for providing customer service and support 24 hours a day, 7 days a week. They also free up phone lines and are far less expen- sive over the long run than hiring people to perform support. Using AI and 2 natural language processing, chatbots are becoming better at understanding what customers want and providing the help they need. Companies also like chatbots because they can collect data about customer queries, response times, satisfaction, and so on. Chatbots, however, are still limited. Even with natural language processing, they may not fully comprehend a customer’s input and may provide incoherent answers. Many chatbots are also limited in the scope of queries that they are able to respond to. This may lead to frustration with a lack of emotion, sympathy, and personalization given fairly generic feedback. In addition to customer dissatisfaction with not reaching a human being, chatbots can be expensive to implement and maintain, especially if they must be customized and updated often. Pros: 1. Lower cost than human workers 2. Online 24/7 3. Can be used as a sales and marketing tool Cons: 1. May not understand user queries 2. Lacks emotion and is not personalized 3. May be expensive/complicated to install and maintain 4 What Was the First Chatbot? The word ”chatbot” first appeared in 1992; however, the first chatbot is thought to be a software program called ELIZA, developed by MIT profes- sor Joseph Weizenbaum in the 1960s. ELIZA was able to recognize certain key phrases and respond with open-ended questions or comments. The intent at the time was that ELIZA could be used as sort of a therapist that could listen to peoples’ problems and respond in a way that made them think that the software understood and empathized with them. You can still interact with a version of ELIZA: https://web.njit.edu/ ronkowit/eliza.html 3  AI in Accounting and Auditing Ms. Prakriti Thapa. SJCC 2024 1 AI in Accounting: Introduction: AI in accounting refers to the incorporation of artificial intelli- gence (AI) technologies and techniques into the field of accounting. This integra- tion aims to streamline and enhance various accounting processes, increase ac- curacy, reduce human errors, and provide valuable insights for decision-making. 1.1 Automation and Efficiency: AI automates repetitive and time-consuming tasks such as data entry, catego- rization of expenses, and reconciliations. This frees up accountants to focus on more value-added activities, improving overall efficiency. 1.2 Error Reduction: AI’s accuracy and consistency help reduce human errors in data entry and cal- culations, leading to more accurate financial records and reports. 1.3 Real-time Financial Insights: AI can process large volumes of data quickly, allowing businesses to access real- time financial insights and make informed decisions promptly. 1.4 Predictive Analytics: AI’s ability to analyze historical data enables businesses to predict future trends and scenarios, aiding in better financial planning and decision-making. 1.5 Enhanced Decision-making: AI-generated insights provide a deeper understanding of financial patterns and trends, enabling better strategic decisions for the organization’s growth. 1 1.6 Fraud Detection and Security: AI algorithms can detect anomalies and patterns associated with fraudulent activities, enhancing fraud detection and preventing financial losses. 1.7 Cost Savings: By automating tasks and reducing errors, AI contributes to cost savings asso- ciated with manual labor and potential financial mistakes. 1.8 Auditing Efficiency: AI-powered auditing tools can review vast amounts of financial data, ensuring thorough and efficient audits while reducing the time required for the process. 2 AI in Auditing Introduction: Auditing is the process of evaluating and examining financial records, transactions, and other relevant information to ensure accuracy, com- pliance with regulations, and the overall reliability of financial statements and reports. The incorporation of AI into auditing brings about various advance- ments and improvements in the auditing process. Here are some ways AI is used in auditing: 2.1 Automated Data Analysis: AI can quickly process and analyze vast amounts of financial data, identifying anomalies, trends, and potential errors that might be missed through manual methods. This improves the accuracy of audits and reduces the risk of over- looking important information. 2.2 Continuous Monitoring: AI allows for real-time or near-real-time monitoring of financial data. This is particularly valuable for businesses with high transaction volumes, as it enables auditors to have an ongoing understanding of financial activities rather than relying solely on periodic audits. 2.3 Risk Assessment: AI can assess the risk associated with various financial transactions and activ- ities. This helps auditors focus their efforts on areas with higher risk, making audits more targeted and efficient. 2 2.4 Standard Compliance: AI can compare financial data against relevant accounting standards and regu- lations to ensure compliance. This reduces the chances of errors or discrepancies that might arise due to misunderstandings of complex accounting rules. 2.5 Audit Planning and Strategy: AI can assist auditors in developing audit plans and strategies based on historical data, risk assessment, and areas of focus, leading to more effective and targeted audits 3  Differences and Examples: AI, ML & DL Ms. Prakriti Thapa, SJCC July 2024 1 Introduction: The difference between AI, ML and DL: 1.1 Artificial Intelligence: AI involves machines that can perform tasks that are characteristic of human intelligence. That is, machines doing what humans can do. While this is rather general, it includes things like planning, understanding language, recognising objects and sounds, learning, and problem-solving. 1.1.1 Categories of AI: General AI would have all of the characteristics of human intelligence, includ- ing the capacities mentioned above. Narrow AI exhibits some facet(s) of human intelligence, and can do that facet extremely well, but is lacking in other areas. A machine that’s great at recognizing images, but nothing else, would be an example of narrow AI. 1.2 Machine Learning: Machine Learning is simply a way of achieving AI. Machine learning is a way of “training” an algorithm so that it can learn how. “Training” involves feeding huge amounts of data to the algorithm and allowing the algorithm to adjust itself and improve. Dynamic AI systems that can adapt and learn continuously from new data without human intervention. For example, the humans might tag pictures that have a cat in them versus those that do not. Then, the algorithm tries to build a model that can accurately tag a picture as containing a cat or not as well as a human. Once the accuracy level is high enough, the machine has now “learned” what a cat looks like. 1.3 Deep Learning: Deep Learning is one of the approaches to Machine Learning. Deep learning was inspired by the structure and function of the brain, namely the intercon- 1 necting of many neurons. Artificial Neural Networks (ANNs) are algorithms that mimic the biological structure of the brain. In ANNs, there are “neurons” which have discrete layers and connections to other “neurons”. Each layer picks out a specific feature to learn, such as curves/edges in image recognition. It’s this layering that gives deep learning its name, depth is created by using multiple layers as opposed to a single layer. 1.4 What are neural networks? Neural Networks is the old name for Deep Learning. Neural networks are a means of doing machine learning, in which a computer learns to perform some task by analyzing training examples. Usually, the examples have been hand- labeled in advance. An object recognition system, for instance, might be fed thousands of labeled images of cars, houses, coffee cups, and so on, and it would find visual patterns in the images that consistently correlate with particular la- bels. Modelled loosely on the human brain, a neural net consists of thousands of simple processing nodes that are densely interconnected. Most of today’s neu- ral nets are organized into layers of nodes, and they’re “feed-forward,” meaning that data moves through them in only one direction. An individual node might be connected to several nodes in the layer beneath it, from which it receives data, and several nodes in the layer above it, to which it sends data. To each of its incoming connections, a node will assign a number known as a “weight.” When the network is active, the node receives a different data item — a different number — over each of its connections and multiplies it by the asso- ciated weight. It then adds the resulting products together, yielding a single number. If that number is below a threshold value, the node passes no data to the next layer. If the number exceeds the threshold value, the node “fires,” which in today’s neural nets generally means sending the number — the sum of the weighted inputs — along all its outgoing connections. When a neural net is being trained, all of its weights and thresholds are initially set to random val- ues. Training data is fed to the bottom layer — the input layer — and it passes through the succeeding layers, getting multiplied and added together in complex ways, until it finally arrives, radically transformed, at the output layer. During training, the weights and thresholds are continually adjusted until training data with the same labels consistently yield similar outputs. 2 Examples: AI, ML, DL: 1.Speech recognition: Speech recognition systems use deep learning algo- rithms to recognize and classify images and speech. These systems are used in a variety of applications, such as self-driving cars, security systems, and medical imaging. 2. Personalized recommendations:E-commerce sites and streaming ser- vices like Amazon and Netflix use AI algorithms to analyze users’ browsing and 2 viewing history to recommend products and content that they are likely to be interested in. 3. Predictive maintenance: AI-powered predictive maintenance systems analyze data from sensors and other sources to predict when equipment is likely to fail, helping to reduce downtime and maintenance costs. 4. Autonomous vehicles: Self-driving cars and other autonomous vehicles use AI algorithms and sensors to analyze their environment and make decisions about speed, direction, and other factors. 5. Virtual Personal Assistants (VPA) like Siri or Alexa: these use natural language processing to understand and respond to user requests, such as playing music, setting reminders, and answering questions. 6. Autonomous vehicles: Self-driving cars use AI to analyze sensor data, such as cameras and lidar, to make decisions about navigation, obstacle avoidance, and route planning. 7. Fraud detection: Financial institutions use AI to analyze transac- tions and detect patterns that are indicative of fraud, such as unusual spending patterns or transactions from unfamiliar locations. 8. Natural language processing: AI is used in chatbots and language translation systems to understand and generate human-like text. 9. Predictive analytics: AI is used in industries such as healthcare and marketing to analyze large amounts of data and make predictions about future events, such as disease outbreaks or consumer behavior. 10. Game-playing AI: AI algorithms have been developed to play games such as chess, Go, and poker at a superhuman level, by analyzing game data and making predictions about the outcomes of moves. 11. Natural language processing (NLP): Machine learning algorithms are used in NLP systems to understand and generate human language. These systems are used in chatbots, virtual assistants, and other applications that involve natural language interactions. 12. Sentiment analysis: Machine learning algorithms are used in senti- ment analysis systems to classify the sentiment of text or speech as positive, negative, or neutral. These systems are used in social media monitoring and other applications.(chat GPT) 13. Spam filters in email: ML algorithms analyze email content and metadata to identify and flag messages that are likely to be spam. 14. Predictive maintenance: ML algorithms are used in manufacturing to predict when machinery is likely to fail, allowing for proactive maintenance and reducing downtime. 15. Credit risk assessment: ML algorithms are used by financial insti- tutions to assess the credit risk of loan applicants, by analyzing data such as their income, employment history, and credit score. 16. Customer segmentation: ML algorithms are used in marketing to segment customers into different groups based on their characteristics and be- havior, allowing for targeted advertising and promotions. 17. Fraud detection: ML algorithms are used in financial transactions to detect patterns of behavior that are indicative of fraud, such as unusual 3 spending patterns or transactions from unfamiliar locations. 18. Game-playing AI: Deep Learning algorithms have been used to de- velop game-playing AI that can compete at a superhuman level, such as the AlphaGo AI that defeated the world champion in the game of Go. 19.Time series forecasting: Deep Learning algorithms are used to fore- cast future values in time series data, such as stock prices, energy consumption, and weather patterns. 20. Medical diagnosis: AI-powered medical diagnosis systems analyze medical images and other patient data to help doctors make more accurate diagnoses and treatment plans. 4  Differences and Examples: AI, ML & DL Ms. Prakriti Thapa, SJCC July 2024 1 Introduction: The difference between AI, ML and DL: 1.1 Artificial Intelligence: AI involves machines that can perform tasks that are characteristic of human intelligence. That is, machines doing what humans can do. While this is rather general, it includes things like planning, understanding language, recognising objects and sounds, learning, and problem-solving. 1.1.1 Categories of AI: General AI would have all of the characteristics of human intelligence, includ- ing the capacities mentioned above. Narrow AI exhibits some facet(s) of human intelligence, and can do that facet extremely well, but is lacking in other areas. A machine that’s great at recognizing images, but nothing else, would be an example of narrow AI. 1.2 Machine Learning: Machine Learning is simply a way of achieving AI. Machine learning is a way of “training” an algorithm so that it can learn how. “Training” involves feeding huge amounts of data to the algorithm and allowing the algorithm to adjust itself and improve. Dynamic AI systems that can adapt and learn continuously from new data without human intervention. For example, the humans might tag pictures that have a cat in them versus those that do not. Then, the algorithm tries to build a model that can accurately tag a picture as containing a cat or not as well as a human. Once the accuracy level is high enough, the machine has now “learned” what a cat looks like. 1.3 Deep Learning: Deep Learning is one of the approaches to Machine Learning. Deep learning was inspired by the structure and function of the brain, namely the intercon- 1 necting of many neurons. Artificial Neural Networks (ANNs) are algorithms that mimic the biological structure of the brain. In ANNs, there are “neurons” which have discrete layers and connections to other “neurons”. Each layer picks out a specific feature to learn, such as curves/edges in image recognition. It’s this layering that gives deep learning its name, depth is created by using multiple layers as opposed to a single layer. 1.4 What are neural networks? Neural Networks is the old name for Deep Learning. Neural networks are a means of doing machine learning, in which a computer learns to perform some task by analyzing training examples. Usually, the examples have been hand- labeled in advance. An object recognition system, for instance, might be fed thousands of labeled images of cars, houses, coffee cups, and so on, and it would find visual patterns in the images that consistently correlate with particular la- bels. Modelled loosely on the human brain, a neural net consists of thousands of simple processing nodes that are densely interconnected. Most of today’s neu- ral nets are organized into layers of nodes, and they’re “feed-forward,” meaning that data moves through them in only one direction. An individual node might be connected to several nodes in the layer beneath it, from which it receives data, and several nodes in the layer above it, to which it sends data. To each of its incoming connections, a node will assign a number known as a “weight.” When the network is active, the node receives a different data item — a different number — over each of its connections and multiplies it by the asso- ciated weight. It then adds the resulting products together, yielding a single number. If that number is below a threshold value, the node passes no data to the next layer. If the number exceeds the threshold value, the node “fires,” which in today’s neural nets generally means sending the number — the sum of the weighted inputs — along all its outgoing connections. When a neural net is being trained, all of its weights and thresholds are initially set to random val- ues. Training data is fed to the bottom layer — the input layer — and it passes through the succeeding layers, getting multiplied and added together in complex ways, until it finally arrives, radically transformed, at the output layer. During training, the weights and thresholds are continually adjusted until training data with the same labels consistently yield similar outputs. 2 Examples: AI, ML, DL: 1.Speech recognition: Speech recognition systems use deep learning algo- rithms to recognize and classify images and speech. These systems are used in a variety of applications, such as self-driving cars, security systems, and medical imaging. 2. Personalized recommendations:E-commerce sites and streaming ser- vices like Amazon and Netflix use AI algorithms to analyze users’ browsing and 2 viewing history to recommend products and content that they are likely to be interested in. 3. Predictive maintenance: AI-powered predictive maintenance systems analyze data from sensors and other sources to predict when equipment is likely to fail, helping to reduce downtime and maintenance costs. 4. Autonomous vehicles: Self-driving cars and other autonomous vehicles use AI algorithms and sensors to analyze their environment and make decisions about speed, direction, and other factors. 5. Virtual Personal Assistants (VPA) like Siri or Alexa: these use natural language processing to understand and respond to user requests, such as playing music, setting reminders, and answering questions. 6. Autonomous vehicles: Self-driving cars use AI to analyze sensor data, such as cameras and lidar, to make decisions about navigation, obstacle avoidance, and route planning. 7. Fraud detection: Financial institutions use AI to analyze transac- tions and detect patterns that are indicative of fraud, such as unusual spending patterns or transactions from unfamiliar locations. 8. Natural language processing: AI is used in chatbots and language translation systems to understand and generate human-like text. 9. Predictive analytics: AI is used in industries such as healthcare and marketing to analyze large amounts of data and make predictions about future events, such as disease outbreaks or consumer behavior. 10. Game-playing AI: AI algorithms have been developed to play games such as chess, Go, and poker at a superhuman level, by analyzing game data and making predictions about the outcomes of moves. 11. Natural language processing (NLP): Machine learning algorithms are used in NLP systems to understand and generate human language. These systems are used in chatbots, virtual assistants, and other applications that involve natural language interactions. 12. Sentiment analysis: Machine learning algorithms are used in senti- ment analysis systems to classify the sentiment of text or speech as positive, negative, or neutral. These systems are used in social media monitoring and other applications.(chat GPT) 13. Spam filters in email: ML algorithms analyze email content and metadata to identify and flag messages that are likely to be spam. 14. Predictive maintenance: ML algorithms are used in manufacturing to predict when machinery is likely to fail, allowing for proactive maintenance and reducing downtime. 15. Credit risk assessment: ML algorithms are used by financial insti- tutions to assess the credit risk of loan applicants, by analyzing data such as their income, employment history, and credit score. 16. Customer segmentation: ML algorithms are used in marketing to segment customers into different groups based on their characteristics and be- havior, allowing for targeted advertising and promotions. 17. Fraud detection: ML algorithms are used in financial transactions to detect patterns of behavior that are indicative of fraud, such as unusual 3 spending patterns or transactions from unfamiliar locations. 18. Game-playing AI: Deep Learning algorithms have been used to de- velop game-playing AI that can compete at a superhuman level, such as the AlphaGo AI that defeated the world champion in the game of Go. 19.Time series forecasting: Deep Learning algorithms are used to fore- cast future values in time series data, such as stock prices, energy consumption, and weather patterns. 20. Medical diagnosis: AI-powered medical diagnosis systems analyze medical images and other patient data to help doctors make more accurate diagnoses and treatment plans. 4  Evolution of AI Ms. Prakriti Thapa, Sjcc July 2024 1 Introduction: Everything about the evolution of AI Artificial Intelligence has grown into a formidable tool in recent years allowing robots to think and act like humans. Furthermore, it has attracted the atten- tion of IT firms all around the world and is seen as the next major technological revolution following the growth of mobile and cloud platforms. It’s even been dubbed the “4th industrial revolution” by some. Researchers have devel- oped software that uses Darwinian evolution ideas, such as “survival of the fittest,” to construct AI algorithms that improve generation to generation with no need for human intervention. The computer was able to recreate decades of AI research in only a few days, and its creators believe that one day it will be able to find new AI techniques. In this article, we will learn about how AI is evolving day by day. 1.1 History of Artificial Intelligence: Despite artificial intelligence has been present for millennia, it was not until the 1950s that its real potential was investigated. A generation of scientists, physicists, and intellectuals had the idea of AI, but it wasn’t until Alan Turing, a British polymath, proposed that people solve problems and make decisions using available information and also a reason. The difficulty of computers was the major stumbling block to expansion. They needed to adapt fundamentally before they could expand any further. Machines could execute orders but not store them. Until 1974, financing was also a problem. By 1974, computers had become extremely popular. They were now quicker, less expensive, and capable of storing more data. 1.2 AI Research Today: AI research is ongoing and expanding in today’s world. AI research has grown at a pace of 12.9 % percent annually over the last five years, as per Alice Bonasio, a technology journalist. China is expected to overtake the United States as the world’s leading source of AI technology in the next 4 years, having overtaken 1 the United States’ second position in 2004 and is rapidly closing in on Europe’s top rank. In the area of artificial intelligence development, Europe is the largest and most diverse continent, with significant levels of international collaboration. India is the 3rd largest country in AI research output, behind China and the USA. 1.3 AI in The Present: Artificial intelligence is being utilized for so many things and has so much promise that it’s difficult to imagine our future without it, related to busi- ness. Artificial intelligence technologies are boosting productivity like never seen before, from workflow management solutions to trend forecasts and even the way companies buy advertisements. Artificial Intelligence can gather and organize vast volumes of data in order to draw inferences and estimates that are outside of the human ability to comprehend manually. It also improves organi- zational efficiency while lowering the risk of a mistake, and it identifies unusual patterns, such as spam and frauds, instantaneously to alert organizations about suspicious behaviour, among other things. AI has grown in importance and sophistication to the point that a Japanese investment firm became the first to propose an AI Board Member for its ability to forecast market trends faster than humans. Artificial intelligence will indeed be and is already being used in many aspects of life, such as self-driving cars in the coming years, more precise weather forecasting, and earlier health diagnoses, to mention a few. 1.4 AI in The Future: It has been suggested that we are on the verge of the 4th Industrial Revolution, which will be unlike any of the previous three. From steam and water power through electricity and manufacturing process, computerization, and now, the question of what it is to be human is being challenged. Smarter technology in our factories and workplaces, as well as linked equipment that will communicate, view the entire production process, and make autonomous choices, are just a few of the methods the Industrial Revolution will lead to business improvements. One of the most significant benefits of the 4th Industrial Revolution is the ability to improve the world’s populace’s quality of life and increase income levels. As robots, humans, and smart devices work on improving supply chains and warehousing, our businesses and organizations are becoming “smarter” and more productive. 2  Evolution of AI PRAKRITI THAPA 2470047 July 2024 1 Introduction: Everything about the evolution of AI Artificial Intelligence has grown into a formidable tool in recent years allowing robots to think and act like humans. Furthermore, it has attracted the atten- tion of IT firms all around the world and is seen as the next major technological revolution following the growth of mobile and cloud platforms. It’s even been dubbed the “4th industrial revolution” by some. Researchers have devel- oped software that uses Darwinian evolution ideas, such as “survival of the fittest,” to construct AI algorithms that improve generation to generation with no need for human intervention. The computer was able to recreate decades of AI research in only a few days, and its creators believe that one day it will be able to find new AI techniques. In this article, we will learn about how AI is evolving day by day. 1.1 History of Artificial Intelligence: Despite artificial intelligence has been present for millennia, it was not until the 1950s that its real potential was investigated. A generation of scientists, physicists, and intellectuals had the idea of AI, but it wasn’t until Alan Turing, a British polymath, proposed that people solve problems and make decisions using available information and also a reason. The difficulty of computers was the major stumbling block to expansion. They needed to adapt fundamentally before they could expand any further. Machines could execute orders but not store them. Until 1974, financing was also a problem. By 1974, computers had become extremely popular. They were now quicker, less expensive, and capable of storing more data. 1.2 AI Research Today: AI research is ongoing and expanding in today’s world. AI research has grown at a pace of 12.9 % percent annually over the last five years, as per Alice Bonasio, a technology journalist. China is expected to overtake the United States as the world’s leading source of AI technology in the next 4 years, having overtaken 1 the United States’ second position in 2004 and is rapidly closing in on Europe’s top rank. In the area of artificial intelligence development, Europe is the largest and most diverse continent, with significant levels of international collaboration. India is the 3rd largest country in AI research output, behind China and the USA. 1.3 AI in The Present: Artificial intelligence is being utilized for so many things and has so much promise that it’s difficult to imagine our future without it, related to busi- ness. Artificial intelligence technologies are boosting productivity like never seen before, from workflow management solutions to trend forecasts and even the way companies buy advertisements. Artificial Intelligence can gather and organize vast volumes of data in order to draw inferences and estimates that are outside of the human ability to comprehend manually. It also improves organi- zational efficiency while lowering the risk of a mistake, and it identifies unusual patterns, such as spam and frauds, instantaneously to alert organizations about suspicious behaviour, among other things. AI has grown in importance and sophistication to the point that a Japanese investment firm became the first to propose an AI Board Member for its ability to forecast market trends faster than humans. Artificial intelligence will indeed be and is already being used in many aspects of life, such as self-driving cars in the coming years, more precise weather forecasting, and earlier health diagnoses, to mention a few. 1.4 AI in The Future: It has been suggested that we are on the verge of the 4th Industrial Revolution, which will be unlike any of the previous three. From steam and water power through electricity and manufacturing process, computerization, and now, the question of what it is to be human is being challenged. Smarter technology in our factories and workplaces, as well as linked equipment that will communicate, view the entire production process, and make autonomous choices, are just a few of the methods the Industrial Revolution will lead to business improvements. One of the most significant benefits of the 4th Industrial Revolution is the ability to improve the world’s populace’s quality of life and increase income levels. As robots, humans, and smart devices work on improving supply chains and warehousing, our businesses and organizations are becoming “smarter” and more productive. 2  Evolution of AI PRAKRITI THAPA 2470047 July 2024 1 Introduction: Everything about the evolution of AI Artificial Intelligence has grown into a formidable tool in recent years allowing robots to think and act like humans. Furthermore, it has attracted the atten- tion of IT firms all around the world and is seen as the next major technological revolution following the growth of mobile and cloud platforms. It’s even been dubbed the “4th industrial revolution” by some. Researchers have devel- oped software that uses Darwinian evolution ideas, such as “survival of the fittest,” to construct AI algorithms that improve generation to generation with no need for human intervention. The computer was able to recreate decades of AI research in only a few days, and its creators believe that one day it will be able to find new AI techniques. In this article, we will learn about how AI is evolving day by day. 1.1 History of Artificial Intelligence: Despite artificial intelligence has been present for millennia, it was not until the 1950s that its real potential was investigated. A generation of scientists, physicists, and intellectuals had the idea of AI, but it wasn’t until Alan Turing, a British polymath, proposed that people solve problems and make decisions using available information and also a reason. The difficulty of computers was the major stumbling block to expansion. They needed to adapt fundamentally before they could expand any further. Machines could execute orders but not store them. Until 1974, financing was also a problem. By 1974, computers had become extremely popular. They were now quicker, less expensive, and capable of storing more data. 1.2 AI Research Today: AI research is ongoing and expanding in today’s world. AI research has grown at a pace of 12.9 % percent annually over the last five years, as per Alice Bonasio, a technology journalist. China is expected to overtake the United States as the world’s leading source of AI technology in the next 4 years, having overtaken 1 the United States’ second position in 2004 and is rapidly closing in on Europe’s top rank. In the area of artificial intelligence development, Europe is the largest and most diverse continent, with significant levels of international collaboration. India is the 3rd largest country in AI research output, behind China and the USA. 1.3 AI in The Present: Artificial intelligence is being utilized for so many things and has so much promise that it’s difficult to imagine our future without it, related to busi- ness. Artificial intelligence technologies are boosting productivity like never seen before, from workflow management solutions to trend forecasts and even the way companies buy advertisements. Artificial Intelligence can gather and organize vast volumes of data in order to draw inferences and estimates that are outside of the human ability to comprehend manually. It also improves organi- zational efficiency while lowering the risk of a mistake, and it identifies unusual patterns, such as spam and frauds, instantaneously to alert organizations about suspicious behaviour, among other things. AI has grown in importance and sophistication to the point that a Japanese investment firm became the first to propose an AI Board Member for its ability to forecast market trends faster than humans. Artificial intelligence will indeed be and is already being used in many aspects of life, such as self-driving cars in the coming years, more precise weather forecasting, and earlier health diagnoses, to mention a few. 1.4 AI in The Future: It has been suggested that we are on the verge of the 4th Industrial Revolution, which will be unlike any of the previous three. From steam and water power through electricity and manufacturing process, computerization, and now, the question of what it is to be human is being challenged. Smarter technology in our factories and workplaces, as well as linked equipment that will communicate, view the entire production process, and make autonomous choices, are just a few of the methods the Industrial Revolution will lead to business improvements. One of the most significant benefits of the 4th Industrial Revolution is the ability to improve the world’s populace’s quality of life and increase income levels. As robots, humans, and smart devices work on improving supply chains and warehousing, our businesses and organizations are becoming “smarter” and more productive. 2 Introduction to Intelligence Artificial Intelligence Ms. Prakriti Thapa, SJCC July 2024, Session 1 1 Introduction: Intelligence Intelligence: All but the simplest human behaviour is ascribed to intelligence, while even the most complicated insert behaviour is usually not taken as an indication of intelligence. What is the difference? Example: Consider the context of the digger wasp’s behavior, when a fe- male wasp brings food to her burrow, she follows a specific sequence of actions: she places the food at the entrance, checks for any potential threats within her burrow, and only if she determines it’s safe, she moves the food inside. This instinctual behavior is revealed when the food is moved a short distance away from the entrance while she’s inside the burrow. When she exits and finds the food displaced, she consistently repeats the entire sequence of actions. The underlying message of this example is that the wasp’s behavior is fixed and automatic. It’s tied to specific triggers and responses without any flexibility or adaptation. This contrasts with human intelligence, which is characterized by the ability to adapt to new situations and circumstances. Psychologist generally characterize human intelligence not by just one trait but by the combination of many diverse abilities. Research in AI has fo- cused chiefly on the following components of intelligence: learning, reasoning, problem solving, perception and using language. 2 What is Artificial Intelligence? John McCarthy is considered as the father of Artificial Intelligence. J ohn McCarthy was an American computer scientist. The term ”Artificial Intelli- gence” was coined by him. He is one of the founder of artificial intelligence, together with Alan Turing, Marvin Minsky, Allen Newell, and Herbert A. The term Artificial intelligence was coined at the Dartmouth Conference in 1956. Artificial intelligence(AI), the ability of a digital computer or computer- controlled robot to perform tasks commonly associated with intelligent beings. 1 2.1 An Overview of Artificial Intelligence: AI is touching us in all aspects of our daily lives, most of the times unknowingly. Whenever we shop online, use our mobiles, drive to work daily, check our mail box or exercise, AI is coming into play and helping us, prodding us or controlling us. Since AI is already such an integral part of our lives, it makes sense to get more knowledge of this emerging technology. From chess-playing computers to self-driving vehicles, Artificial Intelligence (AI) is progressing rapidly and touching every aspect of our lives. Here, we will learn how machines can be made to learn from data and carry out human tasks. AI is techniques that help machines and computers mimic human behaviour. Or in simple words, AI is intelligence demonstrated by machines, as opposed to the natural intelligence displayed by humans or animals. 2.2 Examples of AI: 1. Typing using software: While typing reports using any word-processor, wrong spellings or incorrect grammar is highlighted. We also are exposed to auto-complete options of previously used words, or auto-suggest of commonly used words while typing an e-mail, a SMS message or a social-media post. These are all examples of AI in action. The underlying software is intelligently monitoring what is being typed. The word (complete or incomplete) is matched with an inbuilt database, and either suggestions or corrections are displayed for the user to choose from or ignore. 2. Shopping online: All of us are now used to shopping online. We are either ordering clothes or gadgets online, or using a streaming service (watching movies/shows online). Depending on the user profile, the system shows ads, products or suggests programs to watch. So, what a 65 year old male is shown is different from what a 16 year old girl will be shown, even though they maybe using the same service/portal. Here too, AI is in play. The software is constantly monitoring what we are watching or searching online. Previous history of browsing is also looked at. Shopping preferences are noted. Then, appropriate suggestions are displayed. All this is happening invisibly or unknown to us. 3. Chatbots: Chatbots are used universally today on many websites to interact with the human users that arrive on the specific sites. They try to provide them effective communication and explain to the users how the com- pany or industry works while providing detailed instructions and guides with spontaneous replies. Chatbots are usually used for quick responses to most commonly asked ques- tions on a particular website. They save time as well as reduce human labour and expenditure. 2 Introduction to Intelligence Artificial Intelligence Ms. Prakriti Thapa, SJCC July 2024, Session 1 1 Introduction: Intelligence Intelligence All but the simplest human behaviour is ascribed to intelligence, while even the most complicated insert behaviour is usually not taken as an indication of intelligence. What is the difference? Example: Consider the context of the digger wasp’s behavior, when a fe- male wasp brings food to her burrow, she follows a specific sequence of actions: she places the food at the entrance, checks for any potential threats within her burrow, and only if she determines it’s safe, she moves the food inside. This instinctual behavior is revealed when the food is moved a short distance away from the entrance while she’s inside the burrow. When she exits and finds the food displaced, she consistently repeats the entire sequence of actions. The underlying message of this example is that the wasp’s behavior is fixed and automatic. It’s tied to specific triggers and responses without any flexibility or adaptation. This contrasts with human intelligence, which is characterized by the ability to adapt to new situations and circumstances. Psychologist generally characterize human intelligence not by just one trait but by the combination of many diverse abilities. Research in AI has fo- cused chiefly on the following components of intelligence: learning, reasoning, problem solving, perception and using language. 2 What is Artificial Intelligence? John McCarthy is considered as the father of Artificial Intelligence. J ohn McCarthy was an American computer scientist. The term ”Artificial Intelli- gence” was coined by him. He is one of the founder of artificial intelligence, together with Alan Turing, Marvin Minsky, Allen Newell, and Herbert A. The term Artificial intelligence was coined at the Dartmouth Conference in 1956. Artificial intelligence(AI), the ability of a digital computer or computer- controlled robot to perform tasks commonly associated with intelligent beings. 1 2.1 An Overview of Artificial Intelligence: AI is touching us in all aspects of our daily lives, most of the times unknowingly. Whenever we shop online, use our mobiles, drive to work daily, check our mail box or exercise, AI is coming into play and helping us, prodding us or controlling us. Since AI is already such an integral part of our lives, it makes sense to get more knowledge of this emerging technology. From chess-playing computers to self-driving vehicles, Artificial Intelligence (AI) is progressing rapidly and touching every aspect of our lives. Here, we will learn how machines can be made to learn from data and carry out human tasks. AI is techniques that help machines and computers mimic human behaviour. Or in simple words, AI is intelligence demonstrated by machines, as opposed to the natural intelligence displayed by humans or animals. 2.2 Examples of AI: 1. Typing using software: While typing reports using any word-processor, wrong spellings or incorrect grammar is highlighted. We also are exposed to auto-complete options of previously used words, or auto-suggest of commonly used words while typing an e-mail, a SMS message or a social-media post. These are all examples of AI in action. The underlying software is intelligently monitoring what is being typed. The word (complete or incomplete) is matched with an inbuilt database, and either suggestions or corrections are displayed for the user to choose from or ignore. 2. Shopping online: All of us are now used to shopping online. We are either ordering clothes or gadgets online, or using a streaming service (watching movies/shows online). Depending on the user profile, the system shows ads, products or suggests programs to watch. So, what a 65 year old male is shown is different from what a 16 year old girl will be shown, even though they maybe using the same service/portal. Here too, AI is in play. The software is constantly monitoring what we are watching or searching online. Previous history of browsing is also looked at. Shopping preferences are noted. Then, appropriate suggestions are displayed. All this is happening invisibly or unknown to us. 3. Chatbots: Chatbots are used universally today on many websites to interact with the human users that arrive on the specific sites. They try to provide them effective communication and explain to the users how the com- pany or industry works while providing detailed instructions and guides with spontaneous replies. Chatbots are usually used for quick responses to most commonly asked ques- tions on a particular website. They save time as well as reduce human labour and expenditure. 2