MSYS20 Notes.pdf

MODULE 1: Introduction To Information Systems Systems Thinking involves INTRODUCTION TO 1) Recognizing Interconnections INFORMATION SYSTEMS - What are the key...

MODULE 1: Introduction To Information Systems Systems Thinking involves INTRODUCTION TO 1) Recognizing Interconnections INFORMATION SYSTEMS - What are the key connections between 1) Systems Thinking system parts? 2) Information Systems Definition What is a SYSTEM? An orderly grouping of interdependent components linked together according to a plan to achieve a specific objective. Defining a System Characteristics: 1) Organization 2) Interaction 3) Interdependence 2) Identifying and Understanding 4) Integration Feedback 5) Central Objective - What are the feedback Elements of a System: loops that affect system 1) Outputs and Inputs behavior? 2) Processors 3) Feedback 4) Environment 5) Boundaries and Interfaces Types of System: 1) Physical or Abstract Systems 2) Open or Closed Systems 3) Man-made Information Systems 3) Understanding Systems Structure - How are the system elements organized? 4) Identifying and Understanding Dynamic Behavior 5) Reducing Complexity by - What subject matter Modeling Systems expertise is needed to Conceptually understand the nature of - What is my view of the the system? system? What lens can I - How does the system use to create a work? What are the representation of system? sequences of activities? What are the flows (inputs and outputs)? - What are the resources used? When are they used? What levels of these resources are expected (as inputs? as outputs?) How does the system behaves given variations in these resources?  6) Understanding Systems at Different Scales - What’s the big picture? - What are the subsystems that make up the system? What is an INFORMATION SYSTEM? Systems Thinking Everything can be considered a system Definition ○ Composed of interrelated components Information system (IS) is the ○ Designed to meet a study of complementary certain goal networks of hardware and ○ Relates with other software that people and “systems” organizations use to collect, ○ Each component can be filter, process, create and considered “subsystem” distribute data Seeing interrelationships Information systems are combinations of hardware, software and telecommunications networks that people build and use to collect, create and distribute useful data, typically in organizational settings Information systems are interrelated components working together to collect, process, store and disseminate  information to support decision Data and Information making, coordination, control, analysis, and visualization in an Systems organization. The goal of an Information System is to transform data into information in Information System order to generate knowledge that can be used for decision making A set of interrelated components that collect, manipulate, store and disseminate data and information and provide a feedback mechanism to meet an objective Accessible - must be easily accessible by users to meet their Information Systems in needs in the right format at the right time. Access should be Action secure and prevent Here are some examples of IS that you unauthorized access. may or may not have noticed in your Consistent - contains no everyday life discrepancies, and the same Project Noah - Disaster and Risk measurements or structure are Management used regardless of the data FASSSTER - Public Health and source. Epidemiology Complete - Contains all AISIS - Education information and is not missing anything Timely - Delivered when needed Accurate - Free of errors and bias, and can be verified or validated Cost - Balance the value of information to the cost of producing it Relevant - Important to decision makers  Clear - Not overly complex, easy Educational background on to understand business is relevant Chief Privacy Officer Components of an Handles privacy issues and helps shape policy about Information System sensitive and confidential data Chief Security Officer Ensures that confidential information is protected from hackers, disasters, accidents and rogue employees Systems Administrator Installs, manages and updates servers Enterprise Systems Officer 1) People Develops, installs, maintains, People can be the most oversees the organization’s important element in most mission-critical software computer-based information applications systems. They make the Data Center Operations difference between success and Maintains environmentally failure for most organizations. controlled areas in which servers, mainframes and communications equipment are located Helpdesk Provides service to internal and external customers on technology issues 2) Technology Chief Information Officer Sometimes called “VP of Information Systems” Reports directly to CEOs or other VPs (usually under finance or administration) Great leadership abilities, communication skills, knowledgeable on ICT  3) Process Information system... A business process is a set of is a set of interrelated hardware, activities designed to achieve a software, networks, data, processes task, and organizations and people, working together to implement information systems collect, manipulate, store and support, streamline and disseminate data and information, and sometimes eliminate business provide a feedback mechanism processes to meet an objective. 4) Data Raw ingredient for every information system Converted into digital format, integrated and shared across systems Stored in databases SUMMARY Systems Thinking involves… 1) Recognizing Interconnections 2) Identifying and Understanding Feedback 3) Understanding Systems Structure 4) Identifying and Understanding Dynamic Behavior 5) Reducing Complexity by Modeling Systems Conceptually 6) Understanding Systems at Different Scales Module 2: Information Laptop computer Mobile phones Technology Tablet computer Server Infrastructure Storage devices Input and Output devices A. Hardware and Software Digital Devices in everyday Technology objects Automobiles/cars Application of scientific Refrigerators knowledge for practical Washing Machines purposes Smart Watches Technological components of information system: hardware, software, data, network Digital devices process electronic signals into discrete values Computers use digital electric signals, expressing all information using only ‘0’ and ‘1’ Hardware Computing device and its parts Tangible part of the IT Infrastructure that handles Digital Data ○ Collects Data ○ Processes Data ○ Stores Data ○ Produces User Output Digital Devices Desktop computer  Storage Permanent Storage - Does not lose its data when unpowered Hard Disk Drives Used for long term data storage; non-volatile storage Solid State Drives Instead of spinning disks, it uses flash memory using EEPROM chips, which is much faster Removable Media Floppy disks -> CD-ROM -> USB Computer System Concept Non-permanent Storage - Loses its data when unpowered - Faster data access compared to permanent storage Random-Access Memory Stores all the data and instructions that needs to be processed Faster than a hard disk Can be thought of as the computer’s short-term memory Central Processing Unit CPU Cache “Brain” of the device Reserved high speed access Carries commands sent to it by area within RAM the software and returns results For memory blocks that are to be acted upon most frequently needed by the Hertz - speed of a CPU; 1 hertz is CPU one cycle per second CPU chips can contain multiple Input and Output Devices processors e.g. dual-core, quad-core Input Intel Core i7 - up to 20 cores Keyboard Intel Core i9 - up to 16 cores Mouse Scanner Microphone Camera/Webcams  Touch screens Output Display Monitor Printer Speaker Network Connection Network Interface Cards (NIC) - Ethernet Network Port Speed of your computer Compo Speed Units Descri nent measur ption Wireless Adapters ed by CPU Clock GHz Hertz speed (billions indicat of es the cycles) time it takes to comple How do we put them all te a cycle. together? Mother Bus MHz The MOTHERBOARD - a piece of board speed speed hardware that connects all the at components (CPU, video cards, which sound cards, RAM, hard devices data etc.) together can move across the bus RAM Data Mb/s The transfer (million time it  Application software - rate s of takes bytes for data performs specific tasks and per to be functions (productivity, second) transfe programming, enterprise rred system, etc.) from memor y to the system measur ed in Megab ytes. Hard Access ms The Disk Time (millise time it cond) takes for the drive to locate the Operating System data to 1) Integrated system of programs be accesse that… d. a) Manages the operations of the CPU Data MBit/s The b) Controls the input/output, transfer time it storage resources, and rate takes for data activities of the computer to be system transfer c) Provides support services red as the computer executes from application programs disk to 2) The operating system must be system loaded and activated before other tasks can be Software accomplished The set of instructions that tells the hardware what to do Operating System - Manage the hardware and create an interface between the hardware and user  Programming Software - environment where developers can write codes and convert it into programs or software that run on a computer Applications for the Enterprise Productivity Software Tools for managing company operations and business decision Tools for the workplace making Enterprise Resource Planning ○ System that manages and bring together an entire organization’s operations Utility Software and Customer Relationship Management Programming Software ○ Manages a company’s Utility software - programs that allow customers, marketing you to fix or modify your computer, and sales activities e.g., anti-virus, anti-malware Supply Chain Management ○ Handles the interconnection between the different stages of product development and delivery Supplier Relationship Management ○ Manages a company’s suppliers, negotiations and supplier contracts  Human Capital Management reproduce when ○ Manages a company’s destroyed employee data and HR ○ Not consumed when operations used ○ Can be stolen without being gone and can be used by many people at the same time ○ Unique to the organization Information Processed data possess context, relevance and purpose Involves manipulation of raw B. Data and Networks data to obtain insights, trends, patterns to be used in decision Data making Information that facilitates Information Requirements action is Knowledge in Decision Making Levels Combining knowledge and experience to produce a deeper understanding of a topic is Wisdom. Data, Information, Knowledge, Wisdom Data and Information Data Represents facts whether digital or written Digital Data is an asset with unique properties ○ Not tangible but durable ○ Easy to copy and transport but difficult to Organizing Business Data relational design. It could be a tree, a graph, a network, or a and Information key-value pairing Transactions and Decision Hierarchical Databases Making Oldest and most rigid database model, used in early mainframe DBMS Data is organized into a tree-like structure with mandatory parent/child relationships Each parent can have many children, but each child has only one parent Databases Any collection of stored data, regardless of structure or content An organized collection of related data with the purpose of putting data into context and providing tools for aggregation and analysis Common challenges for not using a database: Relational Databases ○ No control of redundant Commonly used in business data organizations with a few ○ Violation of data integrity exceptions ○ Relying on human Organized in tables, where memory to store and to tables in the database are sets search needed data of relations with identical structure Types of Databases Each table has a seat of fields which define the structure of Non-Relational Databases the data Stores data as simple strings or Row-oriented, where a row is a complete files record or an instance of a set of The primary differentiating fields in a table factor is the storage structure A field may be identified as a itself, where the data structure is primary key, which uniquely no longer bound to a tabular identifies a record in a table  Networking and Communication Components of a Simple Computer Network Database Management Network Operating System - System (DBMS) routes and manages communications on the Application to create and network resources; it can also manage a database, change a reside on every computer database’s structure or design, Switch - a network device that create queries, develop reports connects multiple hosts and do analysis together and forwards packets Other uses of a DBMS based on their destination ○ Data Security: within the local area network create/manage user (LAN) access to the database Router - a device that receives ○ Data Backups: ensure and analyzes packets and then availability of database by routes them towards their creating/managing destination database backups Packet - the fundamental unit of data transmitted over a Understanding the Data network Subsystem Computer Systems - servers, clients (PCs, mobile devices, digital gadgets) that contain, sends and receives the data and applications that need to be shared Network Processors ○ Switch - a network device that connect multiple hosts together and forwards packets based on their destination within the local area network (LAN) ○ Router - a device that receives and analyzes packets and then routes  them towards their into radio waves and these radio destination waves can be picked up or up to Network Media - wired or approx. 65ft. By devices with wireless wireless adapter Network Protocols and Standards - sets rules and guidelines on the transmission technologies and data ○ TCP/IP ○ CDMA, GSM, LTE, 5G Other Scope of Business Corporate Network Networks Infrastructure Intranet - provides web-based Today’s corporate network resources from users within the infrastructure is a collection of organization; available only thru many networks from public LAN; e.g. employee portal switched telephone network, to Extranet - part of the company’s the internet, to corporate local network that can be made area network (LAN) linking available securely to those workgroups, departments, or outside the company; access office floors can be provide via virtual private LAN - designed to connect network (VPN) computers and digital devices within a 500m radius. Using ethernet cable or WiFi. Ethernet - a way of connecting computers of network devices in a physical space (wired network) Wi-Fi access point - takes a network signal and converts it  Domain Name System (DNS) Used to remember internet addresses easier Convert domain names to IP addresses Domain Name - English-like name that corresponds to an IP Address What is the Internet? The world’s most extensive public communication network Internet Service Providers (ISP) - commercial organization with a permanent connection with the internet providing temporary connection to subscribers (homes/ businesses) The internet addressing is based on TCP/IP networking protocol ○ TCP - Transmission Control Protocol handles the movement of data between computers ○ IP - Internet protocol is Internet is based on a responsible for the Client/ Server Technology delivery of packets; each computer on the internet Client: PCs or computing has an IP address, e..g. devices are networked and www.microsoft.com is connected to servers 207.46.250.119. Server: Provides client computers with variety of services and capabilities Web/Application Server: A web server will serve a web page to a client in response to a request for service. Application server software handles all application operations between a user and organization.  Understanding the Network Subsystem Computer Systems - servers clients (PCs, mobile devices, digital gadgets) that contain, sends and receive the data and applications that need to be shared Network Processors Other Communication ○ Switch - a network device that connects multiple Tools hosts together and VOIP (Voice over IP) allows forwards packets based analog signals to be converted on their destination to digital signals within the local area network (LAN) ○ Router - a device that receives and analyzed packets and then routes them towards their destination Network Media - wired or wireless Bluetooth - standard method of Software - system software wirelessly connecting nearby found in the computers and devices within approx. 300ft. network processors specialized for transmission of data; application software used by end-users in the network environment Type of network - LAN, WAN, Intranet, Extranet, Internet Network Protocols and Standards – sets rules and guidelines on the transmission technologies and data ○ TCP/IP ○ CDMA, GSM, LTE, 5G  c) Data Science d) Artificial Intelligence Global Use of Internet is growing all over the world C. Trends in Information Technology RECAP: Technological components of IS Social In 2024, there are 5.0B social media users worldwide This number is expected to jump to approximately 5.85 billion users by 2027 The most used social media platform in the world is Facebook, with 2.9 million Information Technology monthly active users across the Trends world 1) Global Digitalization The average person spends a) Internet about 145 minutes on social b) Web 2.0 media every day c) Social The most common way people d) Mobile access social media is a mobile 2) Technology Convergence device a) Cloud Computing b) Internet of Things  Web 2.0 Web 2.0 is a second-generation interactive internet-based Mobile service Websites became interactive, and user experience became customizable Defining features of Web 2.0 ○ Interactivity ○ Real-time user control ○ Social sharing ○ User generated content Technologies and services behind these features ○ Cloud Computing ○ Social Networks ○ Blogs ○ Apps Mobile Applications We are at the age of the Internet of Things Data Science Fourth Industrial Artificial Intelligence Revolution Characterized by Global Digitalization Cloud Computing “Cloud” refers to applications, services, and data storage located on the internet ○ Changed how we can access and use software and hardware resources ○ For businesses: changed acquisition, financing practices ○ Software-as-a-Service Microsoft 365 vs Microsoft Office ○ Platform-as-a-Service Hosted development via the Internet Technology Convergence ○ Infrastructure-as-a-Servi ce Understanding Emerging Subscribe vs buy Technologies and maintain Convergence of servers Information Technologies Cloud computing Internet of Things The Internet of things, IoT is a Big Data: Business network of interrelated devices that connect and exchange data with Intelligence, Analytics, other IoT devices and the cloud Data Science IoT devices are typically embedded with technology such as sensors and software and can include mechanical and digital machines and consumer objects  Business Intelligence - infrastructure (connected technologies) for data warehousing, integration, reporting and analyzing data Examples coming from the business environment Data Analytics - examining large data sets to identify trends, develop charts and create visual presentations to help business make more strategic decisions Data Science - design and constructing new processes for data modeling and production using prototypes, algorithms, predictive models and custom analysis to help solve complex problems  able to surpass the knowledge and capabilities of humans Reactive Machine AI: AI capable of responding to external stimuli in real time; unable to build memory or store information for future Limited Memory AI: AI that can store knowledge and use it to learn and train for future tasks Artificial Intelligence Theory of Mind AI: AI that can Consists of computer-based sense and respond to human systems (both hardware and emotions, plus perform the software) that attempt to tasks of limited memory emulate human behavior machines Such systems would be able to Self-Aware AI: AI that can ○ Learn languages, recognize others’ emotions, plus accomplish physical tasks has a sense of self and (robotics) human-level intelligence; the ○ Use a perceptual final stage of AI. apparatus that informs Examples physical behavior and language ○ Emulate human expertise and decision making Some Types of AI Weak AI - also known as narrow AI, is designed and trained to complete a specific task Strong AI - also known as artificial intelligence (AGI), describes programming that can replicate the cognitive abilities of the human brain; uses fuzzy logic to apply knowledge from one domain to another and find a solution autonomously Super AI - also known as artificial superintelligence (ASI), AI Technology Malware: Malicious Components Software A computer virus is a rogue software program that attached itself to other software programs or datafiles to be executed, usually without user knowledge Worms are independent computer programs that copy themselves from one computer to other computers over a network Trojan horse is a software program that appears to be benign but then does something other than expected. The Trojan horse is not itself a virus because it does not replicate, but it is often a way for viruses or other malicious code to be introduced into a D. Information Security computer system Security Threats Ransomware tries to extort money from users by taking Security Challenges and control of their computers or Vulnerabilities displaying annoying pop-up messages All components of Information Spyware are small programs Systems (IS) are vulnerable to security that install themselves threats and challenges surreptitiously on computers to monitor user web-surfing activity and serve up advertising Keyloggers record every keystroke made on a computer to steal serial numbers for software, to launch Internet attacks, to gain access to email accounts, to obtain passwords to protected computer systems, or to pick up personal  information such as credit card In a Denial-of-service (DoS) or bank account numbers attack, hackers flood a network server or web server with many thousands of false communications or requests for services to crash the network. The network receives so many queries that it cannot keep up with them and is thus unavailable to service legitimate requests Identity Theft Identity theft is a crime in which an imposter obtains key pieces of personal information, such as social security numbers, driver’s license numbers, or credit card numbers, to impersonate someone else Hackers Phishing involves setting up Hacker is an individual who fake websites or sending email intends to gain unauthorized messages that look like those of access to a computer system legitimate businesses to ask Cracker is typically used to users for confidential personal denote a hacker with criminal data. The email message intent instructs recipients to update or Spoofing involves redirecting a confirm records by providing web link to an address different social security numbers, bank from the intended one, with the and credit card information, and site masquerading as the other confidential data either by intended destination responding to the email Sniffer is a type of message, by entering the eavesdropping program that information at bogus website or monitors information traveling by calling a telephone number over a network. They enable Click fraud occurs when an hackers to steal proprietary individual or computer program information form anywhere on a fraudulently clicks an online ad network, including e-mail without any intention of messages, company files and learning more about the confidential reports advertiser or making a purchase Internal Threat Types of Insider Threats Negligent - Insiders who pose an unintentional threat due to human error and lack of security awareness Malicious - Current or former employees who abused their access to steal intellectual property for personal gains Third-Party - Vendors who misuse their access and Authentication compromise the security of Ensures that the person critical data accessing the information is, Information Security indeed, who they represent themselves to be The Information Security Triad Confidentiality - restrict access to those who are allowed to see it; NTK (need to know) Integrity - information truly represents its intended meaning; it has not been altered Availability - information can be accessed and modified by Access Control authorized personnel in an Ensures that users can only appropriate timeframe access the information resources and perform system tasks that appropriate  ○ Offsite storage of backup data sets ○ Test of data restoration Encryption Process of encoding data upon its transmission or storage so that only authorized individuals can read it Physical Security Proper Datacenter Operation and Security Firewall and Intrusion Detection Systems Backups Firewall A comprehensive backup plan is essential for information A hardware firewall is a device security that is connected to the network ○ Full understanding of the and filters packets based on a organization’s set of rules information resources Software firewall runs on the ○ Regular backups for all operating systems and data  intercepts packets as they arrive to the computer IDS Provides the capability to Private Cloud identify if the network is being A private cloud is operated attacked solely for an organization. It may be managed by the organization or a third party and may be hosted either internally or externally. Companies that want flexible IT resources and a cloud service model while retaining control over their own IT infrastructure VPN use these private clouds. VPN is a secure, encrypted, private network that has been configured within a public network to take advantage of the economies of scale and management facilities of large networks, such as the Internet Provides your firm with secure, encrypted communications at a much lower cost Also provide a network infrastructure for combining voice and data networks Personal Information Security 1) Keep your software up to date 2) Install antivirus software and keep it up to date  3) Be smart about your Artificial intelligence (AI) refers to the connections ability of a machine to learn patterns 4) Backup your data and make predictions. AI does not 5) Secure accounts with two-factor replace human decisions; instead, AI authentication adds value to human judgment. 6) Make your passwords long, strong and unique In its simplest form, artificial 7) Be suspicious of strange links intelligence is a field that combines and attachments computer science and robust datasets to enable problem-solving. CISCO Introduction to What is the difference Artificial Intelligence between AI and augmented Artificial intelligence: intelligence? When learning about artificial Recommends merchandise you intelligence, you’ll come across the might like to buy on the internet term augmented intelligence. Both Alerts you if your smartwatch or terms share the same objective, but fitness band detects low oxygen have different approaches. in your bloodstream, Augmented intelligence has a inflammation, or an unhealthy modest goal of helping humans with spike in blood sugar tasks that are not practical to do. For Scans your social media posts to example, “reading” 1000 pages in an learn more about what you are hour. In contrast, artificial intelligence thinking has a lofty goal of mimicking human Helps banks invest money in thinking and processes. However, it’s your family’s bank accounts to important to note that AI today is not keep the economy around you mature enough to perform growing independent tasks such as diagnosing cancer. Module 1: What is Artificial Example Intelligence? Human Intelligence - operate vehicle What is AI? Artificial intelligence - self driving feature (replaces need of human) AI plays an often invisible role in Augmented intelligence - collision everyday life, powering search engines, detection; blindspot avoidance ( product recommendations, and machines and human both working speech recognition systems. together) eg. screen reader for blind Machines Humans Ingesting Generalizing Data Information Repetitive Creative Accurate Emotional So, what continues to drive the development of AI? As computing power and algorithms become more powerful and data volumes increase, companies will adopt new use cases for AI technologies. Companies will embed smart systems into their applications to drive innovation and efficiencies, enhance employee experience, automate tasks, decrease costs, and Example: Sorting Meat with very little improve revenue. time Artificial Intelligence What does AI do? Artificial intelligence machines (researchers call them “AI services”) don’t think. They calculate. They represent some of the newest, most sophisticated calculating machines in human history. Some can perform what’s called machine learning as they acquire new data. Others, using calculations arranged in ways inspired by neurons in the human brain, can even perform deep learning with multiple levels of calculations. Machine learning  Analysis - AI services can take in (or “ingest”) enormous amounts of data. They can apply mathematical calculations in order to analyze data, sorting and organizing it in ways that would have been considered impossible only a few years ago. Prediction - AI services can use their data analysis to make predictions. They can, in effect, say, “Based on this information, a certain thing will probably happen.” What predictions can AI make? Autocorrect spelling Deep learning Human Language ○ Online chatbots use natural language processing (NLP) to analyze poorly typed or spoken questions, then predict which answers to give on topics ranging from shipping or business hours to merchandise and sizes. Vision recognition ○ AI helps doctors identify serious diseases based on unusual symptoms and early-warning signs, and it reads speed limit and stop signs as it guides How do AI services calculate? And, cars through traffic. what do they do with those Fraud detection calculations? ○ AI analyzes patterns created when thousands of bank customers make credit card purchases,  then predicts which and data to train AI systems. For charges might be the example, you can buy a book result of identity theft. with a voice-based device. Narrow AI also enables robust Today’s AI has gone beyond creating applications, such as using Siri driving directions, vacuuming floors, or on an iPhone, the Amazon recommending new fashions. Now it recommendation engine, really can mimic the capabilities of the autonomous vehicles, and more. human mind. AI can learn from Narrow AI systems like Siri have examples and experience, recognize conversational capabilities, but objects, understand and respond to only if you stick to the script. language, and solve problems. Even more exciting are its future Broad AI possibilities. Broad AI is a midpoint between Narrow and General AI. How is AI evolving? Rather than being limited to a single task, Broad AI systems are Computer scientists have identified more versatile and can handle a three levels of AI based on predicted wider range of related tasks. growth in its ability to analyze data and Broad AI is focused on make predictions. They call these integrating AI within a specific levels: business process where companies need business- and Narrow AI enterprise-specific knowledge Broad AI and data to train this type of General AI system. Newer Broad AI systems predict global weather, trace pandemics, and help businesses predict future trends. General AI General AI refers to machines that can perform any Narrow AI intellectual task that a human Narrow AI is focused on can. addressing a single task such as Currently, AI does not have the predicting your next purchase ability to think abstractly, or planning your day. strategize, and use previous Narrow AI is scaling very quickly experiences to come up with in the consumer world, in which new, creative ideas as humans there are a lot of common tasks do, such as inventing a new  product or responding to people trigonometry. Had they built it, with appropriate emotions. And the difference engine might don't worry, AI is nowhere near have helped the English Navy this point. build tables of ocean tides and depth soundings that could There might be another level, known guide English sailors through as artificial superintelligence (ASI) rough waters. that could appear near the end of this By the early 1900s, companies century. Then machines might like IBM were using machines become self-aware! Even then, no to tabulate and analyze the levels of AI are expected to replace or census numbers for entire dominate you. national populations. They didn’t just count people. They Module 2: What are the found patterns and structure within the data—useful three eras of computing? meaning beyond mere The Era of Tabulation numbers. These machines uncovered ways that different Dark data - It’s information without a groups within the population structure: just a huge, unsorted mess moved and settled, earned a of facts. living, or experienced health problems—information that To sort out unstructured data, helped governments better humans have created many different understand and serve them. calculating machines. The word to remember across those Over 2000 years ago, tax twenty centuries is tabulate. Think of collectors for Emperor Qin tabulation as “slicing and dicing” data Shihuang used the abacus—a to give it a structure, so that people device with beads on wires—to can uncover patterns of useful break down tax receipts and information. You tabulate when you arrange them into categories. want to get a feel for what all those From this, they could determine columns and rows of data in a table how much the Emperor should really mean. spend on building extensions to Researchers call these centuries the the Great Wall of China. Era of Tabulation, a time when In England during the machines helped humans sort data mid-1800s, Charles Babbage into structures to reveal its secrets. and Ada Lovelace designed (but never finished) what they called The Era of Programming a “difference engine” designed to handle complex calculations Data analysis changed in the 1940s using logarithms and  During the turmoil of World War This question lead to discussions and II, a new approach to dark data the very beginning of many ideas emerged: the Era of about the possibilities involving Programming. Scientists began technology. building electronic computers, like the Electronic Numerical Since the advent of electronic Integrator and Computer computing, there are some important (ENIAC) at the University of events and milestones in the evolution Pennsylvania, that could run of artificial intelligence to know about. more than one kind of instruction (today we call those “programs”) in order to do more than one kind of calculation. ENIAC, for example, not only calculated artillery firing tables for the US Army, it worked in secret to study the feasibility of Turing Test - "can machines think?" ; if thermonuclear weapons. a computer can demonstrate the same This was a huge breakthrough. intelligence (or the results of the same Programmable computers intelligence) as a human. guided astronauts from Earth to the moon and were The Era of AI began one summer in reprogrammed during Apollo 1956 13’s troubled mission to bring its Early in the summer of 1956, a astronauts safely back to Earth. small group of researchers, led You’ve grown up during the Era of by John McCarthy and Marvin Programming. It even drives the phone Minsky, gathered at Dartmouth you hold in your hand. But the dark College in New Hampshire. data problem has also grown. Modern There, at one of the oldest businesses and technology generate colleges in the United States, so much data that even the finest they launched a revolution in programmable supercomputer can't scientific research and coined analyze it before the “heat-death” of the term “artificial intelligence”. the universe. Electronic computing is The researchers proposed that facing a crisis. “every aspect of learning or any other feature of intelligence can The Era of AI be so precisely described that a machine can be made to The history of artificial intelligence simulate it.” They called their dates back to philosophers thinking vision “artificial intelligence” and about the question, "What more can they raised millions of dollars to be done with the world we live in?"  achieve it within 20 years. entire floors of large university During the next two decades, and corporate buildings. It they accomplished tremendous seemed as if AI was booming things, creating machines that once again. could prove geometry theorems, speak simple English, But soon the needs of scientists, and even solve word problems businesses, and governments with algebra. outgrew even these new But then came winter systems. Again, funding for AI By the early 1970s, it became collapsed. clear that the problem was larger than researchers Then came another AI chill imagined. There were In the late 1980s, the boom in AI fundamental limits that no research cooled, in part, because amount of money and effort of the rise of personal could solve. computers. Machines from ○ Limited calculating power Apple and IBM, sitting on desks ○ Limited information in people’s homes, grew more storage powerful than the huge For a short time, AI was one of corporate systems purchased the most exciting fields in just a few years earlier. computer science. Businesses and governments As these issues became clear, stopped investing in large-scale the money dried up for The computing research, and First Winter of AI. funding dried up. Over 300 AI companies shut The weather was rough for half a down or went bankrupt during century The Second Winter of AI. It took about a decade for technology and AI theory to Now, the forecast is sunny catch up, primarily with new In the mid-1990s, almost half a forms of AI called “expert century after the Dartmouth systems”. These were limited to research project, the Second specific knowledge that could Winter of AI began to thaw. be manipulated with sets of Behind the scenes, computer rules. They worked well processing finally reached enough—for a while—and speeds fast enough for became popular in the 1980s. machines to solve complex Money poured in. Researchers problems. invested in tremendous At the same time, the public mainframe machines that cost began to see AI’s ability to play millions of dollars and occupied sophisticated games.  ○ In 1997, IBM’s Deep Blue Structured data is typically beat the world’s chess categorized as quantitative champion by processing data and is highly organized. over 200 million possible Structured data is information moves per second. that can be organized in rows ○ In 2005, a Stanford and columns. Perhaps you've University robot drove seen structured data in a itself down a 131-mile spreadsheet, like Google Sheets desert trail. or Microsoft Excel. Examples of ○ In 2011, IBM’s Watson structured data includes names, defeated two grand dates, addresses, credit card champions in the game numbers, stock information. of Jeopardy! Unstructured data, also known as dark data, is typically Today, AI has proven its ability in fields categorized as qualitative data. ranging from cancer research and big It cannot be processed and data analysis to defense systems and analyzed by conventional data energy production. Artificial tools and methods. intelligence has come of age. AI has Unstructured data lacks any become one of the hottest fields of built-in organization, or computer science. Its achievements structure. Examples of impact people every day and its unstructured data include abilities increase exponentially. The images, texts, customer Two Winters of AI have ended! comments, medical records, and even song lyrics. Module 3: Structured, Semi-structured data is the “bridge” between structured semi-structured or and unstructured data. It unstructured data: What doesn't have a predefined data are the differences? model. It combines features of both structured data and A look at the types of data unstructured data. It's more complex than structured data, Data is raw information. Data might yet easier to store than be facts, statistics, opinions, or any kind unstructured data. of content that is recorded in some Semi-structured data uses format. This could include voices, metadata to identify specific photos, names, and even dance moves! data characteristics and scale data into records and preset Data can be organized into the fields. Metadata ultimately following three types. enables semi-structured data to be better cataloged, searched,  and analyzed than unstructured data. An example If AI doesn’t rely on programming of semi-structured data is a instructions to work with unstructured video on a social media site. The data, how does AI do it? Machine video by itself is unstructured learning can analyze dark data far data, but a video typically has more quickly than a programmable text for the internet to easily computer can. To see why, consider categorize that information, the problem of finding a route through such as through a hashtag to big city traffic using a navigation identify a location. system. It’s a dark data problem because solving it requires working Experts estimate that about 80% of all with not only a complicated street the data in today’s world is map, but also with changing variables unstructured. It contains so many like weather, traffic jams, and variables and changes so quickly that accidents. Let’s look at how two no conventional computer program different systems might try to solve can learn much from it. this problem Analyzing Unstructured Data Programmable computer But AI can shed light on unstructured data! AI uses new kinds of computing—some modeled on the human brain—to rapidly give dark data structure, and from it, make new discoveries. AI can even learn things—by itself—from the data it manages and teach itself how to make better predictions over time. This is the Era of AI, and it changes everything! Module 4: Is machine learning the answer to the unstructured data problem? How does machine learning AI with machine learning approach a problem? Two ways to solve dark data problems  But machine learning has two more advantages that programmable computers lack: Machine learning can predict. You know this already. A machine can determine, “Based on traffic right now, this route is likely to be faster than that one.” It knows this because it compared routes as it built them. Machine learning learns! It can notice that your car was delayed by a temporary detour and adjust its recommendations to help other drivers. The machine learning process is entirely different Machine learning uses The machine learning process has probabilistic calculation advantages: There are two other ways to contrast It doesn’t need a database of all classical and machine learning the possible routes from one systems. One is deterministic and the place to another. It just needs to other is probabilistic. know where places are on the map. Deterministic It can respond to traffic there must be an enormous, problems quickly because it predetermined structure of doesn’t need to store alternative routes—a gigantic database of routes for every possible traffic possibilities from which the situation. It notes where machine can make its choice. If slowdowns are and finds a way a certain route leads to the around them through trial and destination, then the machine error. flags it as “YES”. If not, it flags it It can work very quickly. While as “NO”. This is basically binary trying single turns one at a time, thinking: on or off, yes or no. This it can work through millions of is the essence of a computer tiny calculations. program. The answer is either true or false, not a confidence value. Probabilistic  It never says “YES” or “NO” Machine learning is analog (like waves gradually going up and down) rather than binary (like arrows pointing upward and downward). Machine learning constructs every possible route to a destination and compares them in real time, including all the variables such as changing Machine learning enables a rich traffic. So, a machine learning partnership between technology and system doesn’t say, “This is the humans fastest route.” It says something like, “I am 84% confident that AI systems and humans excel at this route will get you there in different things. For example, you, as a the shortest time.” You might person, might excel at imagining have seen this yourself if you’ve possibilities, while AI excels at traveled in a car with an pinpointing patterns. up-to-date GPS navigation system that offers you two or Does common sense make sense? three choices with estimated It turns out that in fields, ranging from times. medicine and education to social studies and government, the best If machine learning offers only decisions are made using a balance of probabilities, who makes the final human and machine strengths. But decision? remember, there is another elusive but vital capability that must also be This can literally be a life-and-death considered: common sense. You question. Suppose you have a serious might know people with strong disease and your doctor offers you a common sense and understand its choice. Do you want your doctor to value. You also might have seen or prescribe your treatment, or do you read output from machines that want the treatment that a machine makes no sense. Yet, there’s a learning system determines is most contribution to be made from both likely to succeed? sides. Common sense draws on many complex generalizations mixed with compassion and abstractions. At this time, only humans can use common sense well. The problem is that with high accuracy. This is known as a common sense is often tainted with classification problem. bias that can distort your judgment. AI systems can balance this. As long as AI Unsupervised learning systems are provided and trained with unbiased data, they can make Unsupervised learning recommendations that are free of bias. a person feeds a machine a A partnership between humans and large amount of information, machines can lead to sensible asks a question, and then the decisions. machine is left to figure out how to answer the question by itself. Module 5: Three common Example methods of machine The machine might be fed many learning photos and articles about dogs. It will classify and cluster information about Supervised learning all of them. When shown a new photo of a dog, the machine can identify the Supervised learning photo as a dog, with reasonable providing AI with enough accuracy. examples to make accurate predictions. Unsupervised learning occurs when the algorithm is not given a specific All supervised learning algorithms “wrong” or “right” outcome. Instead, need labeled data. Labeled data is the algorithm is given unlabeled data. data that is grouped into samples that are tagged with one or more labels. In Unsupervised learning is helpful when other words, applying supervised you don't know how to classify data. learning requires you to tell your Example: imagine you work for a model: banking institution and you have a large set of customer financial data. What the key characteristics of You don't know what type of groups or a thing are, also called features categories to organize the data. Here, What the thing actually is an unsupervised learning algorithm could find natural groupings of similar Example customers in a database, and then you The information might be drawings could describe and label them. and photos of animals, some of which are dogs and are labeled “dog”. The This type of learning has the ability to machine will learn by identifying a discover similarities and differences in pattern for “dog”. When the machine information, which makes it an ideal sees a new dog photo and is asked, solution for exploratory data analysis, “What is this?”, it will respond, “dog”, cross-selling strategies, customer Over time, a machine’s predictions will segmentation, and image recognition. grow to be more accurate. It accomplishes this automatically Reinforcement learning based on feedback, rather than through human intervention. Reinforcement learning a machine learning model similar to supervised learning, Module 6: How will but the algorithm isn’t trained machine learning using sample data. This model transform human life? learns as it goes by using trial and error. A sequence of Take another look at the three successful outcomes is levels of artificial intelligence reinforced to develop the best recommendation for a given Think about this problem. The foundation of Perhaps, 25 years from now, reinforcement learning is General AI is expected to rewarding the “right” behavior emerge. AI researcher Nick and punishing the “wrong” Bostrom defines this behavior. superintelligence as, “an intellect that is much smarter Rewarding a machine means than the best human brains in that you give your agent positive practically every field, including reinforcement for performing scientific creativity, general the "right" thing and negative wisdom and social skills.” reinforcement for performing You’re likely to see General AI the "wrong" things. appear in your lifetime. General AI will enable supersmart bots As a machine learns through trial and and technologies to link AI with error, it tries a prediction, then the Internet of Things through compares it with data in its corpus. “embodied cognition”. This will give machines the ability to Each time the comparison is interact in human-like ways as positive, the machine receives they work alongside humans. positive numerical feedback, or a reward. What will interacting with general AI Each time the comparison is feel like to humans? negative, the machine receives AI everywhere negative numerical feedback, AI will move into all industries, from or a penalty. finance, to education, to healthcare. AI will increase productivity and enable new opportunities. Deeper insights repetitive tasks, while New technologies will sense, analyze, augmented intelligence allows and understand things never before humans to make final decisions possible. after analyzing data, reports, Engagement reimagined and other types of data. New forms of human-machine 3) The three levels of AI include: interaction and emerging Narrow AI, Broad AI, and General technologies, such as conversational AI. Narrow AI and Broad AI are bots, will transform how humans available today. In fact, most engage with each other and with enterprises use Broad AI. machines. General AI won’t come online Personalization until sometime in the future. Machine interactions will be 4) The history of AI has progressed personalized for you, with new levels of across the Era of Tabulation, detail and scale. Era of Programming, and Era of Instrumented planet AI. Billions of sensors generating exabytes 5) Data can be structured, of data will open new possibilities for unstructured, or improving Earth’s safety, sustainability, semi-structured. and security. a) Structured data is quantitative and highly What’s beyond these wonders? organized, such as a Humans, devices, and robots might spreadsheet of data. exist as a collective “digital brain” that b) Unstructured data is anticipates human needs, makes qualitative data that predictions, and provides solutions. doesn't have structure, Farther in the future, we might trust such as medical records. the digital brain to do things on our It's becoming increasing behalf across a broad spectrum of valuable to businesses. endeavors! c) And semi-structured data combines features Key points to remember of both structured data and unstructured data. It 1) Artificial intelligence refers to uses metadata. the ability of a machine to learn 6) About 80% of all the data in patterns and make predictions. today’s world is unstructured. AI does not replace human 7) Machine learning has decisions; instead, AI adds value advantages compared to to human judgment. programmable computers. 2) AI performs tasks without Machine learning can predict human intervention and and machine learning learns! completes mundane and 8) Machine learning uses three methods. a) Supervised learning requires enough examples to make accurate predictions b) Unsupervised learning requires large amounts of information so the machine can ask a question, and then figure out how to answer the question by itself. c) Reinforcement learning requires the process of trial and error. 9) With AI everywhere, AI will move into all industries, from finance, to education, to healthcare. 10) AI can increase productivity, create new opportunities, provide deeper insights, and enable personalization.