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Artificial Intelligence - use of computer systems to Machine Learning Categories simulate human mental processes such as interpreting and generating language 1. Supervised Learning - creating models that can learn fr...

Artificial Intelligence - use of computer systems to Machine Learning Categories simulate human mental processes such as interpreting and generating language 1. Supervised Learning - creating models that can learn from marked datasets to make John McCarthy - coined AI in 1956, where he predictions defined it as “the science and engineering of making intelligent machines” Eg. Spam Filtering and Face Recognition Automation - setting up robots to follow a set of 2. Unsupervised Learning - given an unlabeled predefined rules dataset, the algorithm must find some way to learn without the guidance of humans Artificial Intelligence - setting up robots to make their own decisions based on human input Eg. Recommendation engines and anomaly detection 5 Components of Intelligence 3. Reinforcement Learning - uses feedback 1. Learning data to improve the performance of a model 2. Reasoning 3. Problem-solving Eg. games like Chess and Go, using 4. Perception autonomous vehicle 5. Language Deep Learning How Artificial Intelligence Works: - subset of machine learning that uses Combines massive amounts of data with quick, complex algorithms and artificial neural iterative processing and intelligent algorithms networks (ANN) - used many-layered neural networks to build 1. Data Input algorithms that find the best way to perform 2. Data Processing tasks on their own based on vast sets of 3. Outcome data 4. Assessment - Model human learning by digesting massive 5. Adjustments amounts of information aka training data - Performs tasks with data, improves in Artificial Intelligence accuracy each time - science devoted to making machines think Structure of Artificial Intelligence and act like humans - umbrella term for machines capable of perception, logic, and learning Machine Learning - Programming machines to think and act like humans - Aims to enable computers to learn automatically - focuses on enabling computers to perform tasks without explicit programming - employs algorithms that learn from data to Deep Learning - made up of a neural network with make decisions or predictions through three or more layers: pattern recognition - improves when exposed to more data 1. Input layer: Data enters through the input layer. 2. Hidden layers: Hidden layers process and transport data to other layers. 3. Output layer: The final result or prediction is made in the output layer. Predictive Analytics - Statistics based method used 2. Atlas and Spot (Boston Dynamics)- one of to make assumptions and forecast outcomes the most technologically advanced humanoids and one has high motor skills Natural Language Processing - Enables computers 3. OceanOne (Stanford Robotics Lab)- to recognize, understand and generate text and humanoid robot that dives, built for speech underwater exploration 4. Eve (1X)- first case of humanoid robot Speech AI - Enables computers to understand, deployed in industrial application interpret, and generate human speech 5. Smart Home AI Agent (LG Electronics)- can Two Components of Speech AI: follow users with its wheel to assist in daily tasks 1. Speech Recognition - spoken words to text; speech to text (STT) Computer Vision Tasks Examples: 2. Speech Synthesis - text into spoken words, 1. Image Classification - seeing and text to speech (TTS) classifying image Expert Systems - simulate the judgment of a 2. Object Detection - identifying class of image human with expertise and experience ; relies on then tabulate appearance in image having a good knowledge 3. Object Tracking - follows or tracks an object 4. Content-based Image Retrieval - uses Three Components of an Expert System computer vision to browse search and retrieve images 1. Knowledge Base - storage of data 2. Inference Engine - rule-based system that maps known information to make decisions 3. User Interface - part that end users interact with Examples of Expert Systems 1. CaDet (Cancer Decision Support Tool) - identify cancer in its early stages 2. Dendral - helps chemists identify unknown organic molecules 3. DXplain - clinical support system that diagnoses various diseases Turing Test - created by Alan Turing (1950) 4. MYCIN - identifies bacteremia and John McCarthy - founding fathers of AI together meningitis and recommends antibiotics with Alan Turing, Marvin Minsky, Allen Newell and 5. PXDES - determines type and severity of Herbert A. Simon lung cancer 6. R1 /XCON - manufacturing expert system Eliza - created by Joseph Weizenbaum that automatically selects and orders (1965), MIT; considered to be the first computer components based on customer chatterbot specifications Shakey - created by Stanford Research Institute; first electronic person Robotics - branch of engineering that includes Stanford Cart - created by Stanford AI design, construction and operation of machines Laboratory; built to simulate a remote that can perform tasks without human involvement; controlled moon rover, successfully crossed AI gives robots computer vision a room in 1979 Robotics Examples: Deep Blue- created by International Business Machines (IBM); beat the world 1. Figure 01 (Figure)- humanoid robot chess champion in May 11, 1997 equipped with OpenAI’s Language Model Roomba - created by Irobot (2002); mobile robot for vacuuming floors  Siri - spinoff from a project by SRI, uses armed Race, Loss of Human Connection, voice queries Misinformation, and Manipulation, Unintended Alexa - created by Amazon Inc. (2014); Consequences, Existential Risks virtual assistant technology; first used in dot echo, amazon echo Big Data Analytics Tesla Autopilot - features Traffic aware Data the new oil because of its worth and cruise control and Autosteer importance Sophia - made by Hanson Robotics; the Data fingerprint of creation, new gold, humanoid robot, granted citizenship in factual information in digital form Saudi Arabia Analytics queen of sciences, machinery that Chatgpt - Chat Generative Pretrained mines, molds and mints Transformer Analytics processes, technologies and Types of Artificial Intelligence - Based on algorithms to extract insights from data Capabilities, Based on Functionalities Data Analytics: Goals AI based on Capabilities- ANI, AGI, ASI To predict something Artificial Narrow Intelligence (ANI)- Weak To find patterns in the data AI, trained with specific task, only type successfully realized Finding relationships in the data Artificial General Intelligence (AGI)- Strong AI or Deep AI, can understand intellectual Types of Analytics. task, allows machine to apply knowledge or skills Descriptive Analytics. what happened Artificial Super Intelligence (ASI)- Diagnostic Analytics - why it happened Hypothetical AI that surpasses human knowledge, making judgments, decisions on Predictive Analytics - what's likely to its own happen AI Based on Functionalities - Reactive Machine, Prescriptive Analytics - decide what to do Limited Memory, Theory of Mind, Self Awareness next Reactive Machine - reactionary, no memory, can’t use past memories to inform current Cognitive Analytics - combines intelligent decisions, example: Deep Blue technologies to apply human brain like intelligence Limited Memory - imitates the way our brain to perform tasks neurons work together, gets smarter as it receives more data, trains from past data, Big Data - Collection of large datasets improve example: Self driving cars and chatbots operations require use of new tech architecture to Theory of Mind- have the potential to derive insights understand the world, example: Sophia Self Awareness- AIs point of singularity, 64.6% global population internet users, 5.2 understanding of their existence, billion people. 65 out of 100 people hypothetical only total amount of data, 120 zettabytes Applications of Artificial Intelligence - Media globally, projected to grow to 181 streaming, Chatbots, Smart assistants, Ecommerce, zettabytes Search engines, Social media feeds, Auto industry, 10% unique and 90% replicated data Navigation apps, Facial recognition, Text editors 15 Biggest Risks of AI - Lack of Transparency, Bias Big Data Analytics - Refers to the value of data as and discrimination, Privacy Concerns, Ethical the "new oil" due to its worth and importance in dilemmas, Security Risks, Concentration of Power, modern operations. Dependence on AI, Job Displacement, Economic Inequality, Legal and Regulatory Challenges, AI-  1 yottabyte - 1024 zettabytes Data - The "fingerprint of creation," often called the 1 brontobyte - 1024 yottabytes new gold, representing factual information in digital form. Data Analytics - It is a broad term that encompasses the processes, technologies, Analytics - Known as the "queen of sciences," it is frameworks, and algorithms to extract meaningful the process or machinery that mines, molds, and insights from data. mints insights from data. Data Analytics - It is the process of extracting and Analytics Processes - Involves technologies and creating information from raw data by filtering, algorithms used to extract meaningful insights processing, categorizing, condensing, and from data. contextualizing the data. Data Analytics Goals - To predict outcomes, identify Data Analytics - It is also about the provisioning of patterns, and discover relationships in data data, information, and insights to drive digitalized processes in an intelligent way. Big Data - A collection of large datasets requiring advanced technologies and architectures to derive Structured Data - Data containing a defined data insights and improve operations. type, format, and structure (that is, transaction data, online analytical processing [OLAP] data cubes, Global Internet Usage - 64.6% of the global traditional RDBMS, CSV files, and even simple population (5.2 billion people), meaning 65 out of spreadsheets). 100 people are internet users. Semistructured Data - Textual data files with a Global Data Volume - Currently 120 zettabytes, discernable pattern that enables parsing (such as projected to grow to 181 zettabytes in the future. XML files). Unique vs. Replicated Data - 10% of data is unique, Quasi-structured Data - Textual data with erratic while 90% is replicated. data formats that can be formatted with effort, tools, and time. Data - Factual information (such as measurements or statistics) used as a basis for reasoning, Unstructured Data - Data that has no inherent discussion, or calculation. structure, which may include text documents, PDFs, images, and video. Data - Information in digital form that can be transmitted or processed. Volume - Refers to the amount of data that exists. It is the base of big data, representing the initial size Data - Information output by a sensing device or and amount of data collected. If it is large enough, organ that includes both useful and irrelevant or it can be considered big data. redundant information and must be processed to be meaningful. Velocity - Refers to how quickly data is generated and how fast it moves. This aspect is critical for Data - (In computing) Information that has been organizations that require data to flow quickly, translated into a form that is efficient for movement ensuring it is available at the right times to make or processing. BKMGTPEZYB the best business decisions possible. 1 byte - 8 bits Variety - Refers to the diversity of data types. 1 kilobyte - 1024 bytes Organizations might obtain data from several data 1 megabyte - 1024 kilobytes sources, which can vary in value and format. 1 gigabyte - 1024 megabytes 1 terabyte - 1024 gigabytes Veracity - Refers to the quality, accuracy, integrity, 1 petabyte - 1024 terabytes and credibility of data. Gathered data may have 1 exabyte - 1024 petabytes missing pieces, inaccuracies, or issues that affect 1 zettabyte - 1024 exabytes its ability to provide real, valuable insights. Value - Refers to the benefits that big data can Advantages provide. It relates directly to what organizations can achieve using the collected data. - Growing Interest - Interactivity Characteristics of Big Data (5 Vs) - Volume, - Cost efficient velocity, Variety, veracity, value. - Paperless - Better teaching and learning methods How Big Data Analytics Works - Data Collection > Data Processing > Data Cleaning / Data Wrangling Disadvantages > Data Analysis. - Not accessible by all - Equipment are expensive Benefits of Big Data Analytics - Improvement of - Hardware can be unreliable decision-making, more effective marketing, - Plagiarism improvement of customer service, increased - Students dependent on ICT efficiency of operations. ICT in ENTERTAINMENT Challenges of Big Data Analytics - Data quality, synchronization of data sources, organizational - To hold the audience’s interest and attention resistance, accessibility to big data, maintaining while also bringing them pleasure, quality data, keeping data security, finding the right enjoyment, and relaxation. tools and platforms, lack of talent. HOW ICT TRANSFORM THE ENTERTAINMENT ICT - diverse set of technological tools and LANDSCAPE: resources used to transmit, store, create, share or exchange information 1. Music 2. Television and Movies ICT in Education - mode of education that uses ICT 3. Arts to support, enhance and optimize information 4. Games delivery HOW DO WE USE IT: Objectives of ICT in Education 1. Content Creation - Enhancing Learning experiences and 2. Content Distribution outcomes 3. Consumption - Facilitating access to education 4. Marketing and Promotion - Fostering collaboration and communication 5. Monetization - Increasing efficiency and productivity 6. Virtual and Augmented Reality - Promoting innovation and creativity 7. Analytics How do we use it? E-COMMERCE 1. Online learning platforms - The trading of goods and services on the 2. Virtual classrooms internet 3. Digital content creation 4. Online Resources and Libraries TYPES OF E-COMMERCE: 5. Educational Apps and Games 6. Computer-based Simulations 1. B2C - Businesses sell to the individual 7. Assessment and Feedback Tools consumers 8. Collaborative tools and platforms 2. B2B - Business to business 9. Learning Management Systems 3. C2B - Consumers to business 10. Adaptive Learning Systems 4. C2C - Consumer to consumer 5. B2G and G2B - online transactions between government and businesses 6. C2G - enables consumers to request information or give feedback about public sectors.  7. G2C - Governments or government agencies sell to consumers HOW DO WE USE IT? 1. M-commerce (Mobile) 2. Enterprise e-commerce 3. Social media e-commerce FORMS OF E-COMMERCE: 1. Retail 2. Wholesale 3. Drop Shipping 4. Crowdsourcing SOCIAL NETWORKS 5. Subscription 6. Physical products - Websites and apps that allows users and 7. Digital products org to connect, communicate, share info 8. Services and form relationships. “A social network focuses on connections and relationships between individuals while social media focuses on individual sharing with a large audience.” Objectives of ICT in SOCIAL NETWORKS: 1. Sharing ICT IN GOVERNANCE 2. Learning E-governance 3. Interacting 4. Marketing - The use of ict to deliver services to citizens that can transform relations with clients, HOW ICT TRANSFORMS SOCIAL NETWORKS: businesses and other arms of Gov't. 1. Increased accessibility - G2C, G2B, G2G, G2E 2. Real-time communication 3. Personalization 4. Social commerce 5. Global connectivity DIGITAL CITIZENSHIP - The responsible, ethical and safe use of technology, particularly the internet and digital devices. GOOD DIGITAL CITIZENSHIP - Engages and shows students how to connect and empathize with each other and create lasting relationships through digital tools. BAD DIGITAL CITIZENSHIP - Skills that help users utilize digital tools to the fullest through finding, creating, sharing - Entails cyberbullying, irresponsible social and evaluating information media usage, and a general lack of - Issue: Digital divide, misinformation, and knowledge about how to safely use the disinformation internet DIGITAL RIGHTS AND RESPONSIBILITIES - Freedom for all online users - The right and freedom to use all types of digital technology while using the technology in an acceptable and appropriate manner. - Risks: Data privacy and security DIGITAL LAW - Complying with online policies - Legal rights and restrictions governing technology usage DIGITAL ACCESS - Issue: Lack of digital awareness - The equitable distribution of technology DIGITAL HEALTH AND WELFARE - The ability to connect to the internet or the other digital networks - Promoting wellness while using technology - Issue: digital divide - Psycho-social and physical well-being in a technology world DIGITAL COMMERCE - Issue: Lack of psycho-social and physical well-being awareness - Buying and selling goods online - End-to-end process of selling goods and DIGITAL PRIVACY AND SECURITY services thru digital channels - Risks: Vast competition and date security - Online precautions to promote safety - Privacy - the right to control how your info is viewed and used - Security - need to prevent unauthorized DIGITAL COMMUNICATION access to data, often involving protection against hackers or cyber criminals. - understanding different technology mediums - Risks: Cybercrimes - the process of connecting with people CYBERSECURITY across online channels - The subset of information technology - Issue: User training security DIGITAL ETIQUETTE - Practice of protecting systems, networks, and programs from digital attacks, theft or - Encouraging responsible behavior online damage - A set of guidelines that are needed to create - “Focused primarily on the security of digital a safe environment for all digital users assets against attack vectors” - Risks: miscommunication and improper - To keep up with the changing security risks, behavior the National Institute of Standards and Technology (NIST) recommends adopting DIGITAL LITERACY continuous monitoring and real-time assessments as part of a risk assessment - Utilizing the many forms of technology framework to defend against known and unknown threats.  that resemble those from reputable or known sources are sent to steal sensitive TYPES OF CYBERSECURITY data 5. Distributed denial-of-service (DDoS)- 1. Infrastructure Security - the safeguarding multiple systems disrupt the traffic of a of utility services that power and operates targeted system technologies, data center, cloud, and 6. Man-in-the-middle (MitM) - eavesdropping networks attacks that involve an attacker intercepting 2. Network security- the protection of data as and relaying messages between two parties it is being transmitted between servers and who believe they are communicating with clients each other. 3. Information security (InfoSec) - the security of data that ensures that only authorized users, apps, and systems are able to access the required information. 4. Cloud security - ensures that cloud systems meet the Service Level Agreement (SLA) and data stored in this infrastructure is secure against cyber threats 5. Organization Policy Framework- relates to everything, ranging from the choice of cybersecurity solutions, access controls and privileges assigned to end-users, disaster response, and preparation 6. End-user behavior - users are the first line of defense against cyber-attacks 1. What is Artificial Intelligence (AI)? a. The use of robots to follow a set of predefined rules b. The use of computer systems to simulate human mental processes such as interpreting and generating language c. The science of building hardware devices d. Programming computers to always require human input 2. Who coined the term "Artificial Intelligence" in 1956? a. Alan Turing CYBERSECURITY THREATS b. John McCarthy c. Marvin Minsky Cyberattack - a harmful and intentional attempt d. Joseph Weizenbaum made by a person or organization to access another person’s or organization’s info system 3. Which of the following is NOT one of the five TYPES OF CYBERSECURITY THREATS components of intelligence? a. Reasoning 1. Malware- malicious software; any file or b. Automation program that can be used to harm a c. Perception computer d. Problem-solving 2. Ransomware- involves an attacker locking the victim’s computer system files 4. What is the difference between automation and 3. Social Engineering - to gain sensitive info AI? that is protected a. Automation involves robots making their own 4. Phishing- a form of social engineering decisions, while AI follows predefined rules. where fraudulent email or text messages b. Automation involves predefined rules, while AI involves making decisions based on human input. 12. Natural Language Processing enables c. Automation uses human logic, while AI does not. computers to: d. Automation and AI are the same. a. Generate human-like speech and text b. Make real-time decisions 5. What is a key feature of how Artificial c. Filter spam emails Intelligence works? d. Play games like chess a. Reliance solely on human intervention b. Combines large amounts of data with intelligent 13. Speech AI includes: algorithms a. Recommendation engines and chatbots c. Ignores data patterns b. Speech recognition and speech synthesis d. Only uses structured data c. Image classification and object detection d. Data processing and cleaning 6. What is Machine Learning? a. The process of explicitly programming machines 14. What does the inference engine in an expert b. Enabling computers to learn automatically system do? without explicit programming a. Stores data c. Creating devices to physically interact with the b. Maps known information to make decisions environment c. Acts as a user interface d. Limiting AI to supervised tasks only d. Generates predictive analytics 7. What is an example of supervised learning in 15. Which expert system is designed to diagnose Machine Learning? diseases? a. Recommendation engines a. PXDES b. Spam filtering b. Dendral c. Anomaly detection c. DXplain d. Autonomous vehicle navigation d. R1/XCON 8. What is the key goal of unsupervised learning? 16. Which robot is designed for underwater a. To follow predefined rules exploration? b. To use human guidance for predictions a. Spot c. To find patterns in unlabeled data b. Atlas d. To mimic human experts c. OceanOne d. Eve 9. Deep Learning is a subset of Machine Learning that: 17. The Turing Test was designed to: a. Build the first chatbot a. Relies only on structured data b. Test a machine's ability to exhibit human-like b. Uses neural networks with three or more layers intelligence c. Requires explicit human rules for every task c. Diagnose diseases using AI d. Works only with small datasets d. Recognize human speech 10. What is the input layer in a neural network 18. Which AI capability category does Sophia fall responsible for? under? a. Transporting data to hidden layers a. Artificial Narrow Intelligence (ANI) b. Generating the final prediction b. Theory of Mind c. Receiving data for processing c. Limited Memory d. Providing feedback to algorithms d. Self-awareness 11. What does predictive analytics do? 19. What is an example of Reactive AI? a. Explains why an event happened a. Tesla Autopilot b. Recommends future actions b. Deep Blue c. Forecasts what is likely to happen c. ChatGPT d. Identifies current trends d. Sophia 20. What percentage of global data is unique? c. ASI a. 20% d. Reactive AI b. 10% c. 15% 29. Machine learning improves with: d. 90% a. Less data b. More exposure to data 21. What are the five Vs of Big Data? c. Human-guided rules only a. Volume, Variety, Velocity, Vision, Value d. Predefined algorithms b. Veracity, Volume, Variety, Velocity, Value c. Volume, Velocity, Vision, Veracity, Variety 30. The goal of prescriptive analytics is: d. Value, Volume, Velocity, Validation, Variety a. Recommending actions to take next b. Finding patterns in historical data 22. Structured data includes: c. Explaining why events happened a. Text documents and images d. Forecasting outcomes b. Transactional data and spreadsheets c. Erratic textual formats 31. What does the term “data wrangling” refer to in d. Data without inherent structure Big Data Analytics? a. Generating predictions from data 23. An advantage of ICT in education is: b. Cleaning and preparing data for analysis a. Expensive equipment c. Storing data in a knowledge base b. Growing interest in learning d. Visualizing data patterns c. Limited accessibility d. Over-reliance on technology 32. Which of the following is an example of descriptive analytics? 24. What is the disadvantage of ICT in education? a. Forecasting sales for the next quarter a. It promotes interactivity b. Determining why sales dropped last quarter b. Equipment is expensive c. Summarizing sales figures for the past year c. It improves teaching methods d. Recommending marketing strategies for the d. It encourages paperless classrooms future 25. Which AI application is used for facial 33. Which component of Big Data Analytics recognition? ensures the accuracy and credibility of data? a. Speech AI a. Velocity b. Robotics b. Variety c. Computer Vision c. Veracity d. Predictive Analytics d. Value 26. Which AI type applies human brain-like 34. What makes Big Data valuable to intelligence to tasks? organizations? a. Cognitive Analytics a. Its massive volume b. Prescriptive Analytics b. Its speed of generation c. Descriptive Analytics c. The actionable insights it provides d. Diagnostic Analytics d. Its diverse formats 27. The knowledge base in an expert system: 35. Which layer in Deep Learning processes data a. Interacts with the user for predictions? b. Stores data a. Input layer c. Makes decisions b. Hidden layers d. Generates predictions c. Output layer d. Feedback layer 28. Which AI type is hypothetical and surpasses human intelligence? 36. What type of AI is currently realized and used a. ANI in applications like voice assistants? b. AGI a. Artificial Super Intelligence (ASI) b. Artificial General Intelligence (AGI) c. Artificial Narrow Intelligence (ANI) b. It drives informed decision-making and insights d. Theory of Mind c. It focuses only on storing large datasets d. It reduces the need for data security 37. What are the primary components of Speech AI? 45. Which AI type is capable of limited memory a. Data wrangling and data analysis and learning from past data? b. Speech recognition and speech synthesis a. Reactive machines c. Predictive and prescriptive analytics b. Limited memory AI d. Text recognition and object detection c. Theory of Mind AI d. Self-aware AI 38. Which AI application is used in media streaming platforms to suggest content? 46. What is the primary challenge of implementing a. Unsupervised learning Big Data Analytics? b. Reinforcement learning a. Lack of data volume c. Expert systems b. Data quality and synchronization issues d. Predictive analytics c. Over Reliance on small datasets d. Limited computational power 39. Which of the following is NOT an application of computer vision? 47. Which robot by Boston Dynamics is known for a. Object tracking its advanced motor skills? b. Image classification a. Sophia c. Recommendation engines b. Spot d. Content-based image retrieval c. OceanOne d. Figure 40. What kind of data has a well-defined structure and format? 48. What does a user interface in an expert system a. Unstructured data do? b. Semi-structured data a. Stores data c. Structured data b. Simulates human decision-making d. Quasi-structured data c. Provides interaction for end users d. Processes natural language 41. Which term describes the use of large datasets to improve operations and derive insights? 49. What is the goal of reinforcement learning? a. Artificial Intelligence a. Learning from labeled datasets b. Predictive Analytics b. Finding patterns in unstructured data c. Big Data Analytics c. Improving model performance using feedback d. Cognitive Computing d. Generating predictions with historical data 42. What is the main purpose of diagnostic 50. Which analytics type combines intelligent analytics? technologies to mimic human intelligence? a. Recommending future actions a. Cognitive Analytics b. Summarizing historical data b. Diagnostic Analytics c. Determining why something happened c. Prescriptive Analytics d. Predicting future trends d. Descriptive Analytics 43. Which of the following is a risk of Artificial Intelligence? a. Enhanced decision-making b. Bias and discrimination c. Increased efficiency d. Improved customer service 44. What makes data analytics important in digital processes? a. It eliminates the need for human oversight  21. a 22. c 23. c 24. b 25. d 26. b 27. b 28. d 29. b 30. c 31. b 32. c 33. c 34. c 35. c 36. c 37. b 38. d 39. c 40. c 41. c 42. c 43. b 44. b 45. b 46. b 47. b 48. c 49. c 50. a Answer Keys: 1. a 2. b 3. d 4. b 5. a 6. d 7. b 8. c 9. a 10. b 11. b 12. c 13. b 14. a 15. b 16. c 17. d 18. c 19. b 20. d