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https://ecampusontario.pressbooks.pub/oerbysubjectpeerguide/chapter/__unknown__/	\| Credibility \| Relevance \| \| Authorship \| Publisher, Year of Update \| Title of Open Textbook \| Aim of Open Textbook Identified by Author(s) \| Intended Audience \| Topical Foci \| \| Anderson, R., RN, BScN, MN, Associate Teaching Professor, Thompson Rivers University, Kamloops Doyle, R. G., MSN, BSN, Faculty, British Columbia Institute of Technology, Burnaby McCutcheon, J. A., BSN, Nursing Instructor, British Columbia Institute of Technology, Burnaby \| Thompson Rivers University, 2022 \| Clinical Procedures for Safer Patient Care \| Aims to provide standardized processes for clinical skills and to help nursing schools and clinical practice partners keep procedural practice current using a checklist approach. Each skill/ procedure has learning outcomes, a brief overview of the relevant theory, checklists of steps for procedures with the rationale behind each step of the process, and a summary of key takeaways. \| Nursing students and practice partners \| - Infection Control - Patient Assessment - Safe Patient Handling, Positioning, & Transfers - Wound Care - Oxygen Therapy - Non-Parenteral & Parenteral Medication Administration - Intravenous Therapy - Blood Glucose Monitoring - Tubes & Attachments \| \| Belcik, K., PhD, RN, CNE, Clinical Associate Professor, Texas State University, San Marcos Open Resources for Nursing \| Texas State University Libraries, 2023 \| Leadership and Management of Nursing Care \| Introduces concepts related to nursing leadership and management, prioritization strategies, delegation and supervision, legal implications of nursing practice, ethical nursing practice, collaboration within the interprofessional team, health care economics, quality and evidence-based practice, advocacy, preparation for the RN role, and the avoidance of burnout with self-care. \| Nursing students (BScN) \| - Overview of Management and Professional Issues - Delegation and Supervision - Leadership and Management - Legal Implications - Ethical Practice - Collaboration Within the Interprofessional Team - Health Care Economics - Quality and Evidence-Based Practice - Advocacy \| \| Buccieri, K., PhD, Associate Professor, Sociology; Coordinator of Criminology, Trent University, Peterborough Davy, J., Harm Reduction Specialist, Community-based HIV/STBBI Programs Support. Prevention. Education, Peterborough Gilmer, C., RN, HlthSc, Director of Department of Social Work & Associate Professor, School of Nursing, Trent University, Peterborough Whitmore, N., Outreach & Addictions Worker, Port Hope & Cobourg \| eCampus Ontario, 2022 \| Understanding Homelessness in Canada \| Explores homelessness in Canada, from a range of different perspective inclusive of lived experience, and academic viewpoints. Seemingly simple questions are posed through the use of real-life scenarios, embedded interview videos, artwork, and interactive activities, demonstrate how the answers are actually rather complex. \| Health Science undergraduates \| - Introduction to Homelessness - Indigenous and Canadian studies [Indigenous Studies; Politics, Policy & Housing in Canada] - Mental Health & Public Health Studies - Population Studies [Gender & Queer Studies; Child & Youth Studies] - Social Sciences [Sociology & Crimino-legal Studies; Social Work] - Health Sciences [Primary Care & Nursing; Emergency Medicine] \| \| Bush, J., Ph.D., RN, CNE, Lecturer, University of West Florida, Pensacola Der Like, J. V., DNP, MSN, RNC, Clinical Assistant Professor, University of West Florida, Pensacola \| University of West Florida, 2023 \| Mental Health is a Verb \| Reviews the foundations of mental health, psychopharmacology, and the most common mental health diagnoses in a pocket guide format. \| Nursing students \| - Foundational Concepts in Mental - Health Nursing [Mental Health versus Mental Illness; Theoretical Models; Clinical Assessment, Problems, & Treatment; Psychopharmacology; Mental Health Promotion; Legal & Ethical Issues; Therapeutic Communication] - Mental Disorders [Depression, Anxiety, Bipolar; Somatic Symptom; Eating; Substance Abuse & Addiction; Schizophrenia; Personality; Childhood; Psychiatric Emergencies; Grief & Loss] \| \| Carter, K., B.Ed., M.A., Open Educational Resource Consultant, Conestoga College, Kitchener Rutherford, M. Dip., Nursing, LD., BGS., Faculty Supports, Georgian College, Barrie \| eCampus Ontario, 2022 \| Building a Medical Terminology Foundation \| Breaks down medical terms into their word parts, pronounces medical terms, and learn the meaning of medical terms within the context of introductory anatomy and physiology. \| Health science students \| - Identifying Word Parts in Medical Terms - Medical Language Rules - Prefix - Suffix - Medical Language within the Context of Anatomy & Physiology [15 systems such as integumentary, respiratory, digestive] \| \| Chippewa Valley College, Eau Claire Egert, A., MSN, RN, CCSNE, CCNE, CCCI, Faculty, British Columbia Institute of Technology, Burnaby Lee, K., MSN, RN, CCNE, Faculty, British Columbia Institute of Technology, Burnaby Gill, M., MSN, RN, Manager, Academic Planning and Quality Assurance, British Columbia Institute of Technology, Burnaby \| BCcampus, 2023 \| Fundamentals of Nursing Pharmacology: A Conceptual Approach \| Explores pharmacological concepts, showing the connections between pathophysiology, pharmacological principles, and common medication classes using a concept-based teaching approach \| Nursing students (BScN) \| - Principles of Pharmacology - Safety & Ethics - Antimicrobials - Autonomic Nervous System Regulation - Gas Exchange - Perfusion & Renal Elimination - Gastrointestinal Elimination - Central Nervous System Regulation, Mood, & Cognition - Endocrine - Pain & Mobility \| \| Christman, E., MSN, RN, CNE, CHSE, APNP-BC, Nursing Faculty, Southern New Hampshire University, Manchester Ernstmeyer, K., MSN, RN, CNE, CHSE, APNP-BC, Open RN Grant Project Director, Chippewa Valley Technical College, Eau Claire \| Wisconsin Technical College Digital Press, 2022 \| Nursing Management and Professional Concepts \| Introduces concepts related to nursing leadership and management, prioritization strategies, delegation and supervision, legal implications of nursing practice, ethical nursing practice, collaboration within the interprofessional team, health care economics, quality and evidence-based practice, advocacy, preparation for the RN role, and the avoidance of burnout with self-care. \| Nursing students \| - Overview of Management & Professional Issues - Prioritization - Delegation & Supervision - Leadership & Management - Legal Implications - Ethical Practice - Collaboration within the Interprofessional Team - Health Care Economics - Quality and Evidence-based Practice - Advocacy - Preparation for the RN Role - Burnout & Self-care \| \| Christman, E., MSN, RN, CNE, CHSE, APNP-BC, Nursing Faculty, Southern New Hampshire University, Manchester Ernstmeyer, K., MSN, RN, CNE, CHSE, APNP-BC, Open RN Grant Project Director, Chippewa Valley Technical College, Eau Claire \| Wisconsin Technical College Digital Press, 2022 \| Mental Health and Community Concepts \| Discusses mental health and community health concepts with emphasis on stress management techniques, healthy coping strategies, referrals to community resources, and other preventative interventions. Examines nursing care for individuals with specific mental health and substance use disorders and the nurse’s role in community health needs assessments and caring for vulnerable populations \| Nursing students \| - Foundational Mental Health Concepts - Therapeutic Communication & the Nurse-Client Relationship - Stress, Coping, & Crisis Intervention - Application of the Nursing Process to Mental Health Care - Legal & Ethical Considerations in Mental Health Care - Psychotropic Medications - Disorders [Depressive, Bipolar, Anxiety, Personality, Psychosis & Schizophrenia; Childhood & Adolescence, Eating, Substance Use] - Trauma, Abuse & Violence - Community Assessment - Vulnerable Populations - Environmental Health & Emergency Preparedness \| \| Daphne Cockwell School of Nursing \| Toronto Metropolitan University, 2023 \| The Daphne Cockwell School of Nursing – Clinical Practice and Simulation Resource Toronto Metropolitan University \| Provides learning materials to support nursing education, clinical practice, and simulation. \| Nursing students \| - Virtual Simulations [Emergency; Medical Surgical; Pediatrics; Maternal Newborn; Gerontology; Mental Health; Rehabilitation; Home Health Care] - Open Educational Resources [Communication & Documentation; Health Assessment; Immunizations; Infection Prevention and Control; Nutrition; Professional Practice in Nursing; Scholarly Writing] - Actor and Mannequin Simulation [Discharge Teaching on Cast Care; Newborn Assessment; Older Adult with Infection; MAID] - Open Educational Resources \| \| D’Ilio, A., RN, BSN, MSN, Nursing Instructor, British Columbia Institute of Technology, Burnaby Egert, A., MSN, RN, CCSNE, CCNE, CCCI, Faculty, British Columbia Institute of Technology, Burnaby \| BCcampus, 2023 \| Nursing Virtual Case Studies \| Aims to develop noticing and clinical judgment skills through case study approach. Each case study is designed to work with a specific level of learner (novice to advanced) and uses a variety of resources to help learners prepare for the experience and reflect on their actions. \| Nursing students \| - Case 1: Care of an Older Adult with Impaired Perfusion in a Community Setting - Case 2: Care of an Older Adult with Infection and Impaired Cognition in an Acute Medical Setting - Case 3: Care of a Young Adult with Diabetes, Altered Mood, and Substance Use in a Community Setting - Case 4: Care of the an Adult Experiencing Post-Operative Complications in an Acute Surgical Setting \| \| Ernstmeyer, K., MSN, RN, CNE, CHSE, APNP-BC, Open RN Grant Project Director, Chippewa Valley Technical College, Eau Claire Elizabeth, C., DNP, RN, CNE, Open RN Grant Project Director and Nurse Educator, Chippewa Valley Technical College, Eau Claire \| University of Regina, 2021 \| Nursing Skills \| Encourages engagement in critical thinking and apply the nursing process while carrying out assessments and foundational nursing skills. \| Nursing students (BScN and PN) \| - General Survey Assessment - Health History - Areas of Assessment [Neurological, Head & Neck, Eye & Ear; Cardiovascular; Respiratory; Abdominal; Musculoskeletal] - Skills [Blood Pressure; Aseptic Technique; Math Calculations; - Oxygen Therapy; Administration of medications; Enteral Tube Management; Specimen Collection; Wound Care; Facilitation of Elimination; Tracheostomy care & Suctioning; IV Therapy Management] \| \| Exner-Pirot, H., PhD, Political Science, Macdonald-Laurier Institute, Ottawa Norbye, B., Professor Nursing and Health Care Education, Arctic University of Norway, Tromsø Butler, L., RN, PHD, Dean, Faculty of Nursing, Dalhousie University, Halifax \| University of Regina, 2021 \| Northern and Indigenous Health and Healthcare \| Provides accessible content on health and health care from a northern perspective for the growing number of health professionals being educated in northern communities. It entails the provision of northern health care in the context of unique challenges and circumstances. \| Nursing students (BScN) \| - Community Health in Northern & Indigenous Communities [Oral Health & Health Promotion; Food Security; Water Poverty; Indigenous Birth; Working with Unbounded Communities; Everyday Life with Chronic Illness; Role of Nurses in Preventing Tuberculosis; Community Development] - Social Determinants and Structural Impacts on Northern & Indigenous Health [Sexual Violence; Sexual Abuse; Childhood Sexual Abuse; Culture of Violence and Silence; Family Health & Local Capacity Building] - Culture & Health [Infant Feeding Teachings; Indigenous Sami Adolescents’ Well-being and Mental Health; Northern & Indigenous Ethical Imperative for Working with Communities; Establishing Trustful Relationships between Marginalized Indigenous People & Researchers/Health Workers] - Health care innovations [Telehealth, Social Media, e-Health; Telemedicine] - Professional Practice [Nursing Leadership; Meaning of being a nurse in Canada’s Northern Territories; Professionalism & Professional Conduct; Self-care Practices of Practitioners; Nurse-Deaconess Education; Educating Health Care Providers; Off-campus Nurse Education; Health Research & Education] \| Hillary, C., RN, BSN, MHSED, Instructor, Selkirk College, Castlegar Girvin, J., RN, MN, Instructor, College of the Rockies, Cranbrook Gaubreau, S., RN, MSN-Ed, Faculty Assistant, Selkirk College, Castlegar Bird, K., RN, BScN, MEd, Instructor, Selkirk College, Castlegar \| BCcampus, 2022 \| BC Schools of Nursing Virtual Simulation Games \| Not available. \| Nursing students \| - Post-Operative Care of a Client who Identifies as Non-Binary \| \| Hughes, M., MEd, BScN, RN, Professor, School of Community and Health Studies, Centennial College, Toronto Stefopulos, C., MEd, RN, Professor, Centennial College, Toronto Doyle, S., BScN student, Toronto Metropolitan University, Centennial, George Brown Collaborative Nursing Degree Program, Toronto Metropolitan University, Toronto \| eCampus Ontario, 2022 \| An Interprofessional Virtual Gaming Simulation: Breaking the Chain of Transmission \| Promotes the application of knowledge and skills related to interprofessional collaboration, and infection prevention and control practices within virtual simulation \| Nursing students \| - An Interprofessional Virtual Gamily Simulation: Breaking the Chain of Transmission - Interprofessional Healthcare Provider Role Descriptions \| \| Lapum, J., PhD, MN, BScN, RN, Associate Director, Quality Assurance and Professor, Toronto Metropolitan University, Toronto St-Amant, O., BScN, MScN, PhD, Assistant Professor, Toronto Metropolitan University, Toronto Hughes, M., MEd, BScN, RN, Professor, School of Community and Health Studies, Centennial College, Toronto Petrie, P., RN, BScN, MScN, Professor, George Brown College, Toronto Morrell, S., RN(EC), BScN, MN, PhD(c), Lecturer, Faculty of Nursing, University of Windsor, Nurse Practitioner, Windsor Regional Hospital, Windsor Mistry, S., RM, BHScM, BScN student, Toronto Metropolitan University, Centennial, George Brown Collaborative Nursing Degree Program, Toronto Metropolitan University, Toronto \| eCampus Ontario, 2022 \| The Complete Subjective Health Assessment \| Deconstructs the categories of the complete subjective health assessment, with explanations and examples of what constitutes relevant subjective data. Provides an opportunity to learn how to respond to normal, abnormal, and critical findings when completing a complete subjective health assessment. \| Nursing students \| - Introduction to the Complete Subjective Health Assessment [Subjective Health Assessment; Reasons for Conduction; Categories & Influencing Factors] - The Complete Subjective Health Assessment [ Demographic & Biographic Data; Main Health Needs; PQRSTU Assessment; Current & Past Health; Mental Health & Mental Illnesses; Functional Health; Preventative Treatments & Examinations; Family Health; Cultural Health] - Cultural Safety & Care Partners \| \| Lapum, J., PhD, MN, BScN, RN, Associate Director, Quality Assurance and Professor, Toronto Metropolitan University, Toronto Ronquillo, C. MSN, RN, Assistant Professor, Toronto Metropolitan University, Toronto Hughes, M. MEd, BScN, RN, Professor, School of Community and Health Studies, Centennial College, Toronto Garmaise-Yee, J., DNP, MN, RN, Professor, George Brown College, Toronto \| Toronto Metropolitan University, 2022 \| Introduction to Communication in Nursing \| Guides best practices in communication in the context of the nursing profession including communication theory, therapeutic communication and interviewing, and interprofessional communication. \| Nursing students (BScN) \| - Introduction to Communication and Communication Theory in Nursing [Professional Communication; Positive Lens of Conflict; Conflict Resolution; Social Media; Models of Communication; Communication is Learned; Theoretical Approaches to Communication (Trauma-informed; Relational; Anti-racist)] - Therapeutic Communication & Interviewing [CNO Standard Statement; Client Interview; Preparing for the Interview; Self-reflection prior to the Interview; Beginning the Interview; Types of Interview Questions; Communication Strategies; Non-verbal Communication Strategies; Developmental Considerations] - Interprofessional Communication in Nursing [Interpersonal; Interprofessional; Conflicts; Harassment & Communication; Resources to Facilitate Interprofessional Communication; Self-Reflection in Interprofessional Communication] \| \| Lapum, J., PhD, MN, BScN, RN, Professor, Toronto Metropolitan University, Toronto St-Amant, O., PhD, MN, BScN, RN, Assistant Professor, Toronto Metropolitan University, Toronto Hughes, M., MEd, BScN, RN, Professor, Centennial College, Toronto Tan, A., BScN, RN, University Health Network, Toronto Bogdan, A., BScN, RN Frances, D., BScN student, Toronto Metropolitan University, Centennial, George Brown Collaborative Nursing Degree Program, Toronto Metropolitan University, Toronto Frantzke, R, BScN student, Toronto Metropolitan University, Centennial, George Brown Collaborative Nursing Degree Program, Toronto Metropolitan University, Toronto Savicevic, N., MA Interactive Design, MArch, BScEng, Educational Developer, Toronto Metropolitan University, Toronto \| Toronto Metropolitan University, 2022 \| The Scholarship of Writing in Nursing: 1st Canadian Edition \| Guides best practices of scholarly writing in the context of the nursing profession. \| Nursing students (undergraduates and graduates) and Health Science students \| - Introduction [Scholarly writing; Relevance of Scholarly Writing] - Reading & Comprehension [Reading Effectively; Best place & Time for Reading; Distracted Reader; Learning Preferences, Types of text; Pre-reading Strategies; Reading the Article in Full; Notetaking; Annotating; Dialectic Notetaking; Reading Critically; Critiquing a Text; Reflecting & Summarizing; Troubleshooting your Reading] - Information literacy [Finding Texts; Researching the Topic; Search Operators; Evaluating Quality; Peer-reviewed Sources; Popular Sources & Grey Literature; Caution: Websites] - Types of Writing [Descriptive, Reflective, Analytical; Persuasive, Critical; Editorial; Literature Review] - Fundamentals of Writing [Audience; Purpose; Point of View; Tone; Originality; Sentence & Paragraph Construction; Clear, Complex, Respectful, & Inclusive Language; Grammar & Style Tips] - Writing Process & Pre-Writing [Starting Point; Brainstorming; Thesis; Organizing & Connecting Ideas; Developing an Outline] - Drafting & Writing a paper [First Draft; Good vs weak Support; Transitions; Patterns of Organization; Writing Introductions & Conclusions] - Revising & Editing a paper [Revise to improve Organization, Cohesion, Style & Tone; Editing for Clarity & Conciseness; Giving & Receiving Feedback; Creating & Refining a Title; Revising & Editing Checklist] - Academic integrity & APA Style Rules [Plagiarism & Self-Plagiarism; Paraphrasing & Direct Quotations; Citing another person’s Ideas; In-text Citation types; Reference Page Examples \| \| Lapum, J., PhD, MN, BScN, RN, Associate Director, Quality Assurance and Professor, Toronto Metropolitan University, Toronto St-Amant, O., BScN, MScN, PhD, Assistant Professor, Toronto Metropolitan University, Toronto Hughes, M., MEd, BScN, RN, Professor, School of Community and Health Studies, Centennial College, Toronto Ronquillo, C., MSN, RN, Assistant Professor, Toronto Metropolitan University, Toronto Hughes, M., MEd, BScN, RN, Professor, School of Community and Health Studies, Centennial College, Toronto Garmaise-Yee, J., DNP, MN, RN, Professor, George Brown College, Toronto \| Toronto Metropolitan University, 2020 \| Documentation in Nursing: 1st Canadian edition \| Guides best practices of documentation in the nursing profession by addressing principles of documentation, legislation associated with documentation, methods and systems of documentation, and key trends in the future of documentation. Incorporates legislation and practice standards specific to the province of Ontario, Canada. \| Nursing students (BScN) \| - Introduction to Documentation - Reasons for Documentation - Documentation Components - Electronic Documentation Systems - Privacy of Documentation - Privacy, Confidentiality, and Security - Methods of Documentation - Methods of Documentation – Examples - Future of Documentation \| \| Lapum, J., PhD, MN, BScN, RN, Associate Director, Quality Assurance and Professor, Toronto Metropolitan University, Toronto Hughes, M., MEd, BScN, RN, Professor, School of Community and Health Studies, Centennial College, Toronto \| Toronto Metropolitan University, 2021 \| Introduction to Health Assessment for the Nursing Professional \| Offers an inclusive approach to health assessment that incorporates culturally responsive techniques related to race/ethnicity, gender/sex/sexual orientation, body sizes/types, and ability/disability. It is the first health assessment resource that is informed by clinical judgment with the goal to facilitate students’ clinical decision making and ability to prioritize care by recognizing and acting on cues and signs of clinical deterioration. \| Nursing students \| - Introduction to Health Assessment [Related Legislation; Clinical Judgement; Priorities of Care; Guiding Approaches; Health Promotion (behavioral, relational, structural); Health Determinants] - Inclusive Approaches to Health Assessment [Inclusive Assessment; Moving Beyond Culture & Ant-Oppression in Health Assessment; Inclusive Health Assessment with Indigenous, Black & LGBTQ12SA+] - Respiratory System - Cardiovascular System - Gastrointestinal System \| \| Lapum, J., PhD, MN, BScN, RN, Associate Director, Quality Assurance and Professor, Toronto Metropolitan University, Toronto Verkuyl, M., NP PHC, MN, Professor, Centennial College, Toronto Garcia, W., MS, BScN, RN, Instructor, Toronto Metropolitan University, Toronto St-Amant, O., BScN, MScN, PhD, Assistant Professor, Toronto Metropolitan University, Toronto Tan, A., BScN, RN, University Health Network, Toronto \| BCcampus, 2021 \| Vital Sign Measurement Across the Lifespan – 2nd Canadian Edition \| Provides opportunities to read about, observe, practice, and test vital sign measurement using a multi-media and interactive approach, it will provide. \| Nursing and Health Science students \| - Introduction - Temperature - Pulse & Respiration - Oxygen Saturation - Blood Pressure - Knowledge Integration [Case Studies across the Lifespan] \| \| Rees, G., RN, MSN, ACHIP, FAMIA, Program Lead Digital Health Advanced Certificate, British Columbia Institute of Technology, Burnaby Kruger, R., RN, Med, CNCC(C), Technology & Equipment Coordinator, British Columbia Institute of Technology, Burnaby Morrison, M., PhD, MA (Ed.), BHSc. (N), RN, Program Head, British Columbia Institute of Technology, Burnaby \| BCcampus, 2022 \| Health Case Studies: Toward Closing the Healthcare Communication Gap \| Provides eight separate acute health case studies. Each case study includes the patient narrative or story that models the best practice in healthcare settings. Associated with each case is a set of specific learning objectives to support learning and facilitate educational strategies and evaluation. \| Nursing students (BScN) \| - Chronic Obstructive Pulmonary - Disease - Pneumonia - Unstable Angina - Health Failure - Motor Vehicle Collison - Sepsis - Deep Vein Thrombosis \| \| St-Amant, O., BScN, MScN, PhD, Assistant Professor, Toronto Metropolitan University, Toronto Dybenko, L., RN, BEd, MSc(A), PhD, George Brown College, Toronto Reisdorfer, E. RN, PhD, Assistant Professor, MacEwan University, Edmonton Scott, L., RN, PhD, Professor, Toronto Metropolitan University, Centennial, George Brown Collaborative Nursing Program, Toronto Jennings, A. MN, PhD, Professor George Brown College, Toronto Haghiri-Vijeh, R. RN, BN, MN, PhD, Professor, Centenial College, Toronto \| Toronto Metropolitan University, 2022 \| Professional Practice in Nursing 201: Supplementary \| Not available. \| Nursing students \| - 11 case studies involving clients and families living with chronic illness across the life-cycle \| \| Trip, K., Nurse Practitioner, Assistant Professor, University of Toronto, Toronto Wilson, J., Nurse Practitioner, Assistant Professor, University of Toronto, Toronto \| eCampus Ontario, 2022 \| Gynecological Assessment \| Provides four online gynecological assessment learning modules in combination with virtual knowledge checks that will help articulate an approach, demonstrate techniques of examination, identify and diagnose disorders, and develop an evidence-based plan. \| Nursing students (BScN) \| - Gynecological History - Gynecological Exam - Additional Considerations [Pelvic Exam – Hesitancy, Anxiety, History of Trauma, Differently Abled; 2SLGBTQI+] - Common Gynecological Disorders: Masses & Viral Lesions, Dermatosis, Infections] \| \| Wagner, J., PhD, RN, Retired Assistant Professor, University of Regina, Regina \| University of Regina Press, 2022 \| Leadership and Influencing Change in Nursing \| Provides foundation tools, techniques, and knowledge required to empower students to meet the leadership challenges found within the incessantly changing health care environment. \| Nursing students (BScN) \| - Leadership Strengths & Growth Opportunities - Diversity in Health Care Organizations - Working with Indigenous Leadership & Indigenous Environments - Role of Nurse Leaders in Development of the Canadian Health Care System - Primary Health Care - Leadership in Quality Management & Safety - Leaders & Evidence-informed Decision Making - Change theories - Using Advocacy to Galvanize Ethics into Action - Managing Conflict - Responsibility & Authority of Leaders - Emergency Preparedness & Response - Leadership through Informatics - Regulation, the law, Labor Relations, and Negotiations - Emerging Leadership Issues \| \| Woodend, K. RN, MSc, PhD, Associate Professor, Trent University, Peterborough Lemonde, M., RN, PhD, Associate Professor, Ontario Tech University, Oshawa McCabe, J., RN, MEd, PhD, Associate Professor, Ontario Tech University, Oshawa Thibeault, C., RN, PhD, Associate Professor, Trent University, Peterborough \| eCampus Ontario, 2022 \| Leadership for Nurses in Clinical Settings \| Develops and enhances knowledge about the science and art of leadership \| Nursing students (graduate) \| - Power & Politics [Leadership Theories & Styles; Poser & Poser Dynamics] - Communication & Delegation [Principles of Effective Communication; Personal Leadership Style; Strategies for Effective Leading; Barriers to Communication; Steps to Effective Delegation] - Ethical & Legal Issues [Ethics Foundations; Ethics and the Law; Ethics Framework; Plan of Care; Living at Risk & Moral Distress] - Quality Management & Improvement [Historical Overview; Quality & Patient Safety; Team Huddles; Programs] - Change Management [Key Drivers of an Organization’s Culture; Change Management Theory; Organizational Readiness; Role of Leaders; Challenges & Best Practice] - Health Informatics [Theoretical, Social, and Legal Issues; Informatics in Patient Care Settings; Health Data Storage & Exchange; Telemedicine] - Budgeting & Resource Management [Budget Process; Developing a Budget; Monitoring & Analyzing a Budget] - Recruiting, selecting, & retaining staff [Frameworks and Guidelines; Targeting Populations] - Managing teams/conflict [Complex Process; Potential Causes of Organization Conflict; Nursing & Patient Care Conflicts; Strategies to Resolve Conflict; Restoring & Remaining Trust] - Managing, Motivating & Staff Development [Setting the Stage; Supporting Professional Development; Performance Management] - Risk Management & Emergency preparedness [Preparedness; Response; Recovery & Resilience] - Ethical Leadership & Building Leadership Career [Fostering Ethical Practice; Values in Leadership Practice; Courage & Moral Resilience] \| \| \| \| \| \| \|	pressbooks	2025-03-22T05:08:50.984510	09-30-2024	{ "license": "Creative Commons - Attribution - https://creativecommons.org/licenses/by/4.0/", "url": "https://ecampusontario.pressbooks.pub/oerbysubjectpeerguide/chapter/__unknown__/", "book_url": "https://ecampusontario.pressbooks.pub/oerbysubjectpeerguide/front-matter/introduction/", "title": "2023 Compendium of North American Nursing OER", "author": "Kyle Montgomery, Marnie Seal, Shannon Dowdall-Smith, Remar Mangaoil, Phyllis Montgomery, Sharolyn Mossey", "institution": "Cambrian College, Laurentian University", "subject": "Nursing, Nursing fundamentals and skills, Nursing specialties, Nursing management and leadership" }
https://ecampusontario.pressbooks.pub/oerbysubjectpeerguide/chapter/accessibilityinteractivityandlicensing/	2 Accessibility/Interactivity and Licensing \| Title of Open Textbook \| Accessibility/Interactivity \| Licensing \| \|\|\| \| Figures and tables \| a \| Review questions \| Total interactive H5P activities \| Available for use with \| \| \| ✓ \| ✓ \| ✓ \| 33 activities – \| \|\| \| ✓ \| \| \| \| \|\| \| ✓ \| ✓ \| ✓ \| 92 activities – \| \|\| \| ✓ \| ✓ \| \| 10 activities – image slider, image hotspots, multiple choice, drag the words, crossword \| \|\| \| ✓ \| ✓ \| ✓ \| 146 activities – \| \|\| \| ✓ \| ✓ \| ✓ \| 30 activities – \| \|\| \| ✓ \| ✓ \| \| 37 activities – dialog cards, question set, fill in the blanks, drag text, crossword, \| \|\| \| ✓ \| \| \| 50 activities – \| \|\| \| \| ✓ \| \| 2 activities – image hotspot \| \|\| \| ✓ \| ✓ \| ✓ \| 13 activities – \| \|\| \| ✓ \| ✓ \| ✓ \| 86 activities – \| \|\| \| Northern and Indigenous Health and Healthcare \| ✓ \| \| \| \| \| \| ✓ \| \| ✓ \| 2 activities – \| ✓ (CC BY-NC-SA 4.0) \| \| \| An Interprofessional Virtual Gaming Simulation: Breaking the Chain of Transmission \| ✓ \| ✓ \| ✓ \| 34 activities – \| \| \| ✓ \| ✓ \| ✓ \| 11 activities – question set, multiple choice \| \|\| \| Introduction to Communication in Nursing \| ✓ \| \| \| 29 activities –multiple choice, drag text, \| \| \| ✓ \| ✓ \| \| 94 H5P activities – fill in the blanks, drag text, true/false \| \|\| \| ✓ \| \| \| 12 activities – multiple choice, drag text \| \|\| \| Introduction to Health Assessment for the Nursing Professional \| ✓ \| ✓ \| ✓ \| 55 H5P activities – multiple choice, fill in the blanks, course presentation \| \| \| Vital Sign Measurement Across the Lifespan – 2nd Canadian Edition \| ✓ \| ✓ \| \| 122 activities – \| \| \| Health Case Studies: Toward Closing the Healthcare Communication Gap \| \| \| ✓ \| \| \| \| \| \| ✓ \| \| \|\| \| ✓ \| \| \| 8 activities – question sets \| \|\| \| ✓ \| ✓ \| ✓ \| \| \|\| \| ✓ \| ✓ \| \| 71 activities – \| \|\|	pressbooks	2025-03-22T05:08:51.036889	09-30-2024	{ "license": "Creative Commons - Attribution - https://creativecommons.org/licenses/by/4.0/", "url": "https://ecampusontario.pressbooks.pub/oerbysubjectpeerguide/chapter/accessibilityinteractivityandlicensing/", "book_url": "https://ecampusontario.pressbooks.pub/oerbysubjectpeerguide/front-matter/introduction/", "title": "2023 Compendium of North American Nursing OER", "author": "Kyle Montgomery, Marnie Seal, Shannon Dowdall-Smith, Remar Mangaoil, Phyllis Montgomery, Sharolyn Mossey", "institution": "Cambrian College, Laurentian University", "subject": "Nursing, Nursing fundamentals and skills, Nursing specialties, Nursing management and leadership" }
https://openoregon.pressbooks.pub/esol23/chapter/chapter-1-2/	Grammar – Beginning Videos and Exercises Greetings Greetings – Watch the videos and complete the exercises. Exercise 1: Greetings Read and complete the conversations below. Type words in the blank boxes. Exercise 2: Greetings Click on the eight pictures and words below. Four pictures match four words. Find the pairs. Exercise 3: Greetings Extra Read and complete the conversations below. Type words in the blank boxes. Greetings Useful Expressions Flip Cards Use the cards below to study the words: read, repeat, write, say, listen. Listen to the words and read the words. This exercise does not work well with screen readers.	pressbooks	2025-03-22T05:08:51.054092	09-13-2021	{ "license": "Creative Commons - Attribution - https://creativecommons.org/licenses/by/4.0/", "url": "https://openoregon.pressbooks.pub/esol23/chapter/chapter-1-2/", "book_url": "https://openoregon.pressbooks.pub/esol23/front-matter/introduction-2/", "title": "A Digital Workbook for Beginning ESOL", "author": "Eric Dodson, Davida Jordan, Timothy Krause", "institution": "", "subject": "Language teaching and learning material and coursework, Language learning: grammar, vocabulary and pronunciation, English" }
https://openoregon.pressbooks.pub/esol23/chapter/28/	Grammar – Beginning Videos and Exercises Alphabet Alphabet – Watch the videos and complete the exercises. Exercise 1: Alphabet Exercise Put the upper case letters of the alphabet in order. Drag the letters to the correct drop zone boxes below. Exercise 2: Alphabet Exercise Put the lower case letters of the alphabet in alphabetical order. Drag the letters to the correct drop zone boxes below. Exercise 3: Alphabet Exercise Click on each of the sixteen squares below. Match the upper case letter with the lower case letter. Exercise 4: Alphabet Exercise There are twenty-six names below. Put the names in alphabetical order. Drag and drop the names to the drop zone boxes below.	pressbooks	2025-03-22T05:08:51.074435	09-13-2021	{ "license": "Creative Commons - Attribution - https://creativecommons.org/licenses/by/4.0/", "url": "https://openoregon.pressbooks.pub/esol23/chapter/28/", "book_url": "https://openoregon.pressbooks.pub/esol23/front-matter/introduction-2/", "title": "A Digital Workbook for Beginning ESOL", "author": "Eric Dodson, Davida Jordan, Timothy Krause", "institution": "", "subject": "Language teaching and learning material and coursework, Language learning: grammar, vocabulary and pronunciation, English" }
https://openoregon.pressbooks.pub/esol23/chapter/3/	Grammar – Beginning Videos and Exercises Counting Counting – Watch the videos and complete the exercises. Exercise 1: Numerical Order: Put the following twenty numbers in numerical order. Number one and number twenty are already placed. Drag the other numbers to the drop zone boxes below. Exercise 2: Numerical Order Drag the words to the correct drop zone boxes. Put number words in the correct numerical order. Exercise 3: Numbers and Words Click on each of the twenty-two squares below. Match the numbers with the word for the number. Make a pair. Exercise 4: Numbers, Words, and Pictures Click on each of the twenty-two squares below. Match the numbers with the word for the number. Make a pair.	pressbooks	2025-03-22T05:08:51.095133	09-13-2021	{ "license": "Creative Commons - Attribution - https://creativecommons.org/licenses/by/4.0/", "url": "https://openoregon.pressbooks.pub/esol23/chapter/3/", "book_url": "https://openoregon.pressbooks.pub/esol23/front-matter/introduction-2/", "title": "A Digital Workbook for Beginning ESOL", "author": "Eric Dodson, Davida Jordan, Timothy Krause", "institution": "", "subject": "Language teaching and learning material and coursework, Language learning: grammar, vocabulary and pronunciation, English" }
https://openoregon.pressbooks.pub/esol23/chapter/4/	Grammar – Beginning Videos and Exercises Be and Subject Pronouns Subject Pronouns – Watch the video and complete the exercises Exercise 1: Be Verb Read the following sentences. Type the correct form of the “Be” verb in the blank boxes. Exercise 2: Subject Pronouns Click on the fourteen squares below. Find the picture and the subject pronoun that match. Exercise 3: Be and Subject Pronoun Read the sentences. Click on one of the three words in the brackets to complete the sentence. Click the check button. Exercise 4: Pronoun and Be Verb Form Choose the correct form of the verb for each pronoun. Click on one of the three choices in the brackets following the pronoun. Click the check button.	pressbooks	2025-03-22T05:08:51.111047	09-13-2021	{ "license": "Creative Commons - Attribution - https://creativecommons.org/licenses/by/4.0/", "url": "https://openoregon.pressbooks.pub/esol23/chapter/4/", "book_url": "https://openoregon.pressbooks.pub/esol23/front-matter/introduction-2/", "title": "A Digital Workbook for Beginning ESOL", "author": "Eric Dodson, Davida Jordan, Timothy Krause", "institution": "", "subject": "Language teaching and learning material and coursework, Language learning: grammar, vocabulary and pronunciation, English" }
https://openoregon.pressbooks.pub/esol23/chapter/5/	Grammar – Beginning Videos and Exercises Be and Questions Be and Questions – Watch the video and complete the exercises. Exercise 1: Be Verb Questions Click on one of the three words in brackets to complete the question. Click the check button. Exercise 2: Be Questions and Answers Read the questions below. Answer the questions. Drag best answer to the drop zone boxes following each question. Exercise 3: Be Negative Questions and Answers Read the questions below. Drag the answers to the drop zone boxes following the questions. Exercise 4: Be Verb Forms Read the sentences. Drag the correct word to the drop zone boxes to complete the sentences.	pressbooks	2025-03-22T05:08:51.124896	09-13-2021	{ "license": "Creative Commons - Attribution - https://creativecommons.org/licenses/by/4.0/", "url": "https://openoregon.pressbooks.pub/esol23/chapter/5/", "book_url": "https://openoregon.pressbooks.pub/esol23/front-matter/introduction-2/", "title": "A Digital Workbook for Beginning ESOL", "author": "Eric Dodson, Davida Jordan, Timothy Krause", "institution": "", "subject": "Language teaching and learning material and coursework, Language learning: grammar, vocabulary and pronunciation, English" }
https://openoregon.pressbooks.pub/esol23/chapter/6/	Grammar – Beginning Videos and Exercises Have Have – Watch the video and complete the exercises. Exercise 1: To Have – Verb Complete the sentences. Type words in the boxes to complete each sentence. Exercise 2: To Have – Negative Type “don’t have” or “doesn’t have” in the blank boxes below to complete the following sentences. Exercise 3: Have – Practice Read the sentences. Click on one of the words between the brackets to complete the sentence. Click the check button. Exercise 4: Have and Have Not Type the correct forms of “have” or “do” in the blank boxes below.	pressbooks	2025-03-22T05:08:51.140077	09-13-2021	{ "license": "Creative Commons - Attribution - https://creativecommons.org/licenses/by/4.0/", "url": "https://openoregon.pressbooks.pub/esol23/chapter/6/", "book_url": "https://openoregon.pressbooks.pub/esol23/front-matter/introduction-2/", "title": "A Digital Workbook for Beginning ESOL", "author": "Eric Dodson, Davida Jordan, Timothy Krause", "institution": "", "subject": "Language teaching and learning material and coursework, Language learning: grammar, vocabulary and pronunciation, English" }
https://openoregon.pressbooks.pub/esol23/chapter/7/	Grammar – Beginning Videos and Exercises Have – Questions and Colors Have with Questions and Colors – Watch the videos and complete the exercises. Exercise 1: To Have Questions and Answers Answer the questions below. Drag the answers to the drop zone boxes following each question. Exercise 2: Colors Click on the eighteen squares below. Match the picture and the word. Exercise 3: To be, To have, Colors Read the following story. Type a form of “be”, “have”, or a “color word” for each blank box in the story. Exercise 4: Review Read the story. Type the correct form of “be” or “have” in the blank boxes.	pressbooks	2025-03-22T05:08:51.155073	09-13-2021	{ "license": "Creative Commons - Attribution - https://creativecommons.org/licenses/by/4.0/", "url": "https://openoregon.pressbooks.pub/esol23/chapter/7/", "book_url": "https://openoregon.pressbooks.pub/esol23/front-matter/introduction-2/", "title": "A Digital Workbook for Beginning ESOL", "author": "Eric Dodson, Davida Jordan, Timothy Krause", "institution": "", "subject": "Language teaching and learning material and coursework, Language learning: grammar, vocabulary and pronunciation, English" }
https://openoregon.pressbooks.pub/esol23/chapter/8/	Grammar – Beginning Videos and Exercises Time and Dates Time and Dates – Watch the videos and complete the exercises. Exercise 1: Time and Dates Matching Click on the twenty-six squares below. Match the numerical time or date with the image on a clock or calendar. Exercise 2: Order – Days of the Week and Months Drag the days of the week and the months of the year into correct order. Drag them to the correct drop zone boxes. Exercise 3: Time and Date, Questions and Answers Read the questions. Drag the best answer for each question to the drop zone box next to the question. Exercise 4: Time Vocabulary – Classification Put the draggable words next to the best classification. For example: 10:30 is a time, not a year. Drag the word “time” to the drop zone box next to 10:30.	pressbooks	2025-03-22T05:08:51.172383	09-13-2021	{ "license": "Creative Commons - Attribution - https://creativecommons.org/licenses/by/4.0/", "url": "https://openoregon.pressbooks.pub/esol23/chapter/8/", "book_url": "https://openoregon.pressbooks.pub/esol23/front-matter/introduction-2/", "title": "A Digital Workbook for Beginning ESOL", "author": "Eric Dodson, Davida Jordan, Timothy Krause", "institution": "", "subject": "Language teaching and learning material and coursework, Language learning: grammar, vocabulary and pronunciation, English" }
https://openoregon.pressbooks.pub/esol23/chapter/9/	Grammar – Beginning Videos and Exercises Daily Routine Daily Routine – Watch the video and complete the exercises. Exercise 1: Daily Routine Phrases There are sixteen boxes below with pictures and words for daily routines. Click on the boxes and find the pictures that match the words. Exercise 2: Daily Routine Question and Answer Match the question with the correct answer. Drag the answer to the drop zone box next to each question. Exercise 3: Jennifer’s Daily Routine Read Jennifer’s daily activities and put the activities in time order. Drag the words to the drop zone boxes. Exercise 4: Daily Routine Extra Read the questions and answers. Drag the correct answer to the drop zone boxes next to each question.	pressbooks	2025-03-22T05:08:51.185757	09-13-2021	{ "license": "Creative Commons - Attribution - https://creativecommons.org/licenses/by/4.0/", "url": "https://openoregon.pressbooks.pub/esol23/chapter/9/", "book_url": "https://openoregon.pressbooks.pub/esol23/front-matter/introduction-2/", "title": "A Digital Workbook for Beginning ESOL", "author": "Eric Dodson, Davida Jordan, Timothy Krause", "institution": "", "subject": "Language teaching and learning material and coursework, Language learning: grammar, vocabulary and pronunciation, English" }
https://openoregon.pressbooks.pub/esol23/chapter/10/	Grammar – Beginning Videos and Exercises Weather Weather – Watch the videos and complete the exercises. Exercise 1: Weather Statements Sixteen squares below show eight pictures of weather and eight words about weather. Click on the squares. Match the picture and the word. Exercise 2: Weather Dialog Read the conversation below. Drag the words to the correct drop zone boxes to complete the conversation. Exercise 3: Weather Questions and Answers Read the conversation below. Drag the words to the correct drop zone boxes to complete the conversation. Exercise 4: Weather Extra Read the conversation below. Drag the words to the correct drop zone box to complete the conversation.	pressbooks	2025-03-22T05:08:51.199861	09-13-2021	{ "license": "Creative Commons - Attribution - https://creativecommons.org/licenses/by/4.0/", "url": "https://openoregon.pressbooks.pub/esol23/chapter/10/", "book_url": "https://openoregon.pressbooks.pub/esol23/front-matter/introduction-2/", "title": "A Digital Workbook for Beginning ESOL", "author": "Eric Dodson, Davida Jordan, Timothy Krause", "institution": "", "subject": "Language teaching and learning material and coursework, Language learning: grammar, vocabulary and pronunciation, English" }
https://openoregon.pressbooks.pub/esol23/chapter/1/	Grammar – High Beginning Videos and Exercises Greeting a Stranger Greeting a Stranger – Watch the videos and complete the exercises below. Exercise 1: Greeting a Stranger Drag the words and phrases to the drop zone boxes following the sentences. Click the “next” arrow. There are five questions in this series. Exercise 2: Greeting a Friend Drag the words to the drop zone boxes to complete the conversations. Click the “next” arrow. There are four questions in this series. Exercise 3: Greetings General Click on the eight cards below. Find the picture that matches the word. Exercise 4: Greetings Extra Type words in the boxes to complete the conversation.	pressbooks	2025-03-22T05:08:51.217063	09-13-2021	{ "license": "Creative Commons - Attribution - https://creativecommons.org/licenses/by/4.0/", "url": "https://openoregon.pressbooks.pub/esol23/chapter/1/", "book_url": "https://openoregon.pressbooks.pub/esol23/front-matter/introduction-2/", "title": "A Digital Workbook for Beginning ESOL", "author": "Eric Dodson, Davida Jordan, Timothy Krause", "institution": "", "subject": "Language teaching and learning material and coursework, Language learning: grammar, vocabulary and pronunciation, English" }
https://openoregon.pressbooks.pub/esol23/chapter/3-2/	Grammar – High Beginning Videos and Exercises Ordinal Numbers Ordinal Numbers – Watch the videos and complete the exercises below. Exercise 1: Ordinal Numbers 1 Match the numbers and the words. Drag the words to the drop zone boxes next to the number. Exercise 2: Ordinal Numbers 2 Put ten written words ordinal numbers in order. Drag the words to the drop zone boxes. Exercise 3: Ordinal Numbers 3 Read the number. Type the words in the blank boxes.	pressbooks	2025-03-22T05:08:51.229676	09-13-2021	{ "license": "Creative Commons - Attribution - https://creativecommons.org/licenses/by/4.0/", "url": "https://openoregon.pressbooks.pub/esol23/chapter/3-2/", "book_url": "https://openoregon.pressbooks.pub/esol23/front-matter/introduction-2/", "title": "A Digital Workbook for Beginning ESOL", "author": "Eric Dodson, Davida Jordan, Timothy Krause", "institution": "", "subject": "Language teaching and learning material and coursework, Language learning: grammar, vocabulary and pronunciation, English" }
https://openoregon.pressbooks.pub/esol23/chapter/2/	Grammar – High Beginning Videos and Exercises Vowels, Consonants, Nouns, and Articles Vowels, Consonants, Nouns, and Articles – Watch the videos and complete the exercises Exercise 1: Vowel or Consonant Mark the following alphabet letters, vowels or consonants. Click on one of the words in brackets following each letter. Exercise 2: A or An Type the correct article, “a” or “an,” in the blank box before each word. Exercise 3: Alphabetical Order Drag the twenty-six letters of the alphabet to the drop zone boxes. Put the letters in correct alphabetical order. Exercise 4: Alphabetical Order Extra There are twenty-six names listed. Put the names in alphabetical order. Drag the names to drop zone boxes.	pressbooks	2025-03-22T05:08:51.247125	09-13-2021	{ "license": "Creative Commons - Attribution - https://creativecommons.org/licenses/by/4.0/", "url": "https://openoregon.pressbooks.pub/esol23/chapter/2/", "book_url": "https://openoregon.pressbooks.pub/esol23/front-matter/introduction-2/", "title": "A Digital Workbook for Beginning ESOL", "author": "Eric Dodson, Davida Jordan, Timothy Krause", "institution": "", "subject": "Language teaching and learning material and coursework, Language learning: grammar, vocabulary and pronunciation, English" }
https://openoregon.pressbooks.pub/esol23/chapter/61/	Grammar – High Beginning Videos and Exercises Possessive Adjectives and Pronouns Possessive Adjectives and Subjective Pronouns – Watch the videos and complete the exercises. Exercise 1: Possessive Pronouns Some of the sentences below have possessive pronouns missing. Drag the correct possessive pronouns to the drop zone boxes. Exercise 2: To-Be Verb Read the questions and responses. Match the correct response with the question. Drag the responses to the drop zone boxes following the questions. Exercise 3: Pronouns, Contractions, To-Be Verb There are two questions in this exercise. Exercise 1: Type contractions for each pronoun and form of “Be”. Click the check button. Click the next arrow to go to the second exercise. Exercise 2: Drag the correct work to the drop zone box. Exercise 4: To-Be Extra Click one one of the three words between brackets. Choose the correct form of “be” for each pronoun. Click the check button.	pressbooks	2025-03-22T05:08:51.259272	09-13-2021	{ "license": "Creative Commons - Attribution - https://creativecommons.org/licenses/by/4.0/", "url": "https://openoregon.pressbooks.pub/esol23/chapter/61/", "book_url": "https://openoregon.pressbooks.pub/esol23/front-matter/introduction-2/", "title": "A Digital Workbook for Beginning ESOL", "author": "Eric Dodson, Davida Jordan, Timothy Krause", "institution": "", "subject": "Language teaching and learning material and coursework, Language learning: grammar, vocabulary and pronunciation, English" }
https://openoregon.pressbooks.pub/esol23/chapter/5-2/	Grammar – High Beginning Videos and Exercises Verb Be, Making Questions Verb Be and Making Questions – Watch the videos and complete the exercises. Exercise 1: Making Questions Turn the sentences into questions. Drag the words to the drop zone boxes to make a question. Click the check button. Click the next arrow to continue. There are seven questions in this exercise. Exercise 2: To-Be Verb Questions Choose the correct form of the “be” verb to make questions. Click on one of the three words between brackets. Complete all sentences and click the check button. Exercise 3: Affirmative and Negative Answers For each question make an affirmative and negative response. Drag words to the drop zone boxes. Click the next arrow to continue. There are six questions in this exercise. Exercise 4: To-Be Extra Drag words to the drop zone boxes to complete the sentences. Use the correct verb or pronoun to complete the sentence.	pressbooks	2025-03-22T05:08:51.271604	09-13-2021	{ "license": "Creative Commons - Attribution - https://creativecommons.org/licenses/by/4.0/", "url": "https://openoregon.pressbooks.pub/esol23/chapter/5-2/", "book_url": "https://openoregon.pressbooks.pub/esol23/front-matter/introduction-2/", "title": "A Digital Workbook for Beginning ESOL", "author": "Eric Dodson, Davida Jordan, Timothy Krause", "institution": "", "subject": "Language teaching and learning material and coursework, Language learning: grammar, vocabulary and pronunciation, English" }
https://openoregon.pressbooks.pub/esol23/chapter/do-does-questions/	Grammar – High Beginning Videos and Exercises Questions with Do and Does Questions with Do/Does – Watch the videos and complete the exercises. Exercise 1: Have/Has Questions Read the questions. Drag answers to drop zone boxes following each question. Exercise 2: Have/Has Practice Read the story about a garden and a cat. Type the missing words in blank boxes in the story. Exercise 3: Adjectives Read the sentence. Click on the adjectives in the sentences. Click the check button. Exercise 4: To-Be, To-Have Review Read the sentences. Type the correct forms of “be” or “have to complete the sentences in the blank boxes in the story.	pressbooks	2025-03-22T05:08:51.283225	09-13-2021	{ "license": "Creative Commons - Attribution - https://creativecommons.org/licenses/by/4.0/", "url": "https://openoregon.pressbooks.pub/esol23/chapter/do-does-questions/", "book_url": "https://openoregon.pressbooks.pub/esol23/front-matter/introduction-2/", "title": "A Digital Workbook for Beginning ESOL", "author": "Eric Dodson, Davida Jordan, Timothy Krause", "institution": "", "subject": "Language teaching and learning material and coursework, Language learning: grammar, vocabulary and pronunciation, English" }
https://openoregon.pressbooks.pub/esol23/chapter/have/	Grammar – High Beginning Videos and Exercises Questions with Be Verb and Have Questions using Be and Have – Watch the videos and complete the exercises. Exercise 1: Have and Has Type the words in blank boxes. Type the correct form of the verb “have” for each pronoun. Exercise 2: Negative Have and Has Read the sentences. Type the correct negative form of “have” in the blank boxes. Exercise 3: To Have Practice Read the sentences and choose the correct word. Click on one of the words between brackets. Complete all sentences and click the check button.	pressbooks	2025-03-22T05:08:51.294483	09-13-2021	{ "license": "Creative Commons - Attribution - https://creativecommons.org/licenses/by/4.0/", "url": "https://openoregon.pressbooks.pub/esol23/chapter/have/", "book_url": "https://openoregon.pressbooks.pub/esol23/front-matter/introduction-2/", "title": "A Digital Workbook for Beginning ESOL", "author": "Eric Dodson, Davida Jordan, Timothy Krause", "institution": "", "subject": "Language teaching and learning material and coursework, Language learning: grammar, vocabulary and pronunciation, English" }
https://openoregon.pressbooks.pub/esol23/chapter/8-2/	Grammar – High Beginning Videos and Exercises This/That and Calendar Vocabulary This/That and Calendar Vocabulary – Watch the video and complete the exercises. Exercise 1: This and That Read the sentences. Choose the word “this” or “that” . Click on the word between the brackets. Click the check button. Exercise 2: These and Those Read the sentences. Choose the word “these” or “those”. Click on the word between the brackets. Click the check button. Exercise 3: Calendar Vocabulary This exercise has two questions. Question 1. Drag the word to the drop zone box to match the definition. Click the next arrow. Question 2. Type vocabulary words in the blanks. Exercise 4: Time Vocabulary Match the vocabulary. For example, 7:30 is “time”. Drag matching words to the drop zone boxes.	pressbooks	2025-03-22T05:08:51.306315	09-13-2021	{ "license": "Creative Commons - Attribution - https://creativecommons.org/licenses/by/4.0/", "url": "https://openoregon.pressbooks.pub/esol23/chapter/8-2/", "book_url": "https://openoregon.pressbooks.pub/esol23/front-matter/introduction-2/", "title": "A Digital Workbook for Beginning ESOL", "author": "Eric Dodson, Davida Jordan, Timothy Krause", "institution": "", "subject": "Language teaching and learning material and coursework, Language learning: grammar, vocabulary and pronunciation, English" }
https://openoregon.pressbooks.pub/esol23/chapter/9-2/	Grammar – High Beginning Videos and Exercises Prepositions of Place and Time Prepositions of Place and Time – Watch the videos and complete the exercises. Exercise 1: In, On, Under This exercise has three questions. The first two exercises might be extra difficult with a screen reader. Question 1: Drag pictures to the correct drop zones in the picture. There is a bookcase and three cats. Read the instructions. Drag the cats to the correct drop zone on the bookcase picture. The bookcase has three shelves, a top, and space under the third shelf. The cats are different colors. Click check, then click the next arrow to go to the next question. Question 2: There are three drop zones: in, on, and under. Drag the pictures to the correct drop zone. The picture will not stay unless it is correct. Click check, then click the next arrow to go to the next question. Question 3: Read the sentences. Type the correct preposition in the blank box. Exercise 2: Times of Day Read the sentences. Type phrases for the time of day in the blank boxes. Exercise 3: Time and Date Questions and Answers Read the questions. Drag the best answers to the drop zone boxes next to the questions. Exercise 4: Daily Routine Questions and Answers Read the questions. Drag the best answers to the drop zone boxes next to the questions.	pressbooks	2025-03-22T05:08:51.319092	09-13-2021	{ "license": "Creative Commons - Attribution - https://creativecommons.org/licenses/by/4.0/", "url": "https://openoregon.pressbooks.pub/esol23/chapter/9-2/", "book_url": "https://openoregon.pressbooks.pub/esol23/front-matter/introduction-2/", "title": "A Digital Workbook for Beginning ESOL", "author": "Eric Dodson, Davida Jordan, Timothy Krause", "institution": "", "subject": "Language teaching and learning material and coursework, Language learning: grammar, vocabulary and pronunciation, English" }
https://openoregon.pressbooks.pub/esol23/chapter/10-2/	Grammar – High Beginning Videos and Exercises Articles and Nouns Articles and Nouns – Watch the videos and complete the exercises. Exercise 1: Articles 1 Read the story. Click on the correct article found between the brackets. Use “0” for no article. Click the check button. Exercise 2: Articles 2 Read the story. Type the correct article in the blank boxes. When no article is needed, leave the blank box empty. Click the check button. Exercise 3: Singular and Plural Nouns Type the plural form of the word in the blank box next to the word. Spell the plurals correctly. Exercise 4: Weather Dialog Complete the conversations. Drag the best words to the drop zone boxes for each conversation.	pressbooks	2025-03-22T05:08:51.331188	09-13-2021	{ "license": "Creative Commons - Attribution - https://creativecommons.org/licenses/by/4.0/", "url": "https://openoregon.pressbooks.pub/esol23/chapter/10-2/", "book_url": "https://openoregon.pressbooks.pub/esol23/front-matter/introduction-2/", "title": "A Digital Workbook for Beginning ESOL", "author": "Eric Dodson, Davida Jordan, Timothy Krause", "institution": "", "subject": "Language teaching and learning material and coursework, Language learning: grammar, vocabulary and pronunciation, English" }
https://openoregon.pressbooks.pub/esol23/chapter/1-2/	Grammar – Intermediate Videos and Exercises Pronouns, Adjectives, and Greetings Pronouns, Adjectives, and Greetings – Watch the videos and complete the exercises. Exercise 1: Pronouns 1 Read the story. Pronouns are missing in some of the sentences. Drag pronouns to the correct drop zone boxes. Exercise 2: Pronouns 2 Read the story. Words are missing from the sentences. Type the missing words in the blank boxes. Exercise 3: Adjectives Click on the adjectives in the following sentences. Click the check button. Exercise 4: Greetings Read the conversation. Type words in the blank boxes below to complete the conversation.	pressbooks	2025-03-22T05:08:51.343089	09-13-2021	{ "license": "Creative Commons - Attribution - https://creativecommons.org/licenses/by/4.0/", "url": "https://openoregon.pressbooks.pub/esol23/chapter/1-2/", "book_url": "https://openoregon.pressbooks.pub/esol23/front-matter/introduction-2/", "title": "A Digital Workbook for Beginning ESOL", "author": "Eric Dodson, Davida Jordan, Timothy Krause", "institution": "", "subject": "Language teaching and learning material and coursework, Language learning: grammar, vocabulary and pronunciation, English" }
https://openoregon.pressbooks.pub/esol23/chapter/2-2/	Grammar – Intermediate Videos and Exercises Present Tenses and Parts of Speech Present Tenses and Parts of Speech – Watch the videos and complete the exercises. Exercise 1: Simple Present Continuous There are eight questions in this exercise. Follow instructions for each question. Click the “check” button. Click the “arrow” button to go to the next question. Exercise 2: Adverb Intensifiers Type the words in the blank boxes below. Exercise 3: Present Simple Type correct form of the verb in the blank boxes of each sentence. Use the verb in parenthesis at the end of each sentence. Click the “check” button. Exercise 4: To Be – Verb Click the correct form of the verb “be” between the brackets. Click the “check” button.	pressbooks	2025-03-22T05:08:51.355312	09-13-2021	{ "license": "Creative Commons - Attribution - https://creativecommons.org/licenses/by/4.0/", "url": "https://openoregon.pressbooks.pub/esol23/chapter/2-2/", "book_url": "https://openoregon.pressbooks.pub/esol23/front-matter/introduction-2/", "title": "A Digital Workbook for Beginning ESOL", "author": "Eric Dodson, Davida Jordan, Timothy Krause", "institution": "", "subject": "Language teaching and learning material and coursework, Language learning: grammar, vocabulary and pronunciation, English" }
https://openoregon.pressbooks.pub/esol23/chapter/2-3/	Grammar – Intermediate Videos and Exercises Simple Past and Future with Be Verb Past Simple, Future, To Be – Watch the videos and complete the exercises. Exercise 1: Past Simple There are four questions in this exercise. Follow the instructions for each question. Click the “check” button and “next” arrow. Exercise 2: Future – Be Going To There are eight drag and drop questions in this exercise. For each question drag the words to the correct drop zone box to make a sentence or question. Click the “check” button and “next” arrow. Exercise 3: Future – Present Continuous Type the correct form and tense of the verbs in the blank boxes. Click on the “check” button. Exercise 4: To – Be Verb The sentences are missing words. Drag the words to the correct drop zone boxes to complete the sentences.	pressbooks	2025-03-22T05:08:51.368040	09-13-2021	{ "license": "Creative Commons - Attribution - https://creativecommons.org/licenses/by/4.0/", "url": "https://openoregon.pressbooks.pub/esol23/chapter/2-3/", "book_url": "https://openoregon.pressbooks.pub/esol23/front-matter/introduction-2/", "title": "A Digital Workbook for Beginning ESOL", "author": "Eric Dodson, Davida Jordan, Timothy Krause", "institution": "", "subject": "Language teaching and learning material and coursework, Language learning: grammar, vocabulary and pronunciation, English" }
https://openoregon.pressbooks.pub/esol23/chapter/4-2/	Grammar – Intermediate Videos and Exercises Negative Sentences and Questions Negative Sentences and Questions – Watch the videos and complete the exercises. Exercise 1: Negative Sentences There are three questions in this exercise. Follow the instructions for each question. Click the “check” button and “next” arrow. Exercise 2: Making Questions This exercise has seven questions. Drag the words to the correct drop zone boxes. Use the words to form a question. Click the “check” button. Click the “next” arrow. Exercise 3: To Have Click on the words between the brackets. Choose the best word to complete the sentences. Click on the “check” button. Exercise 4: Have Positive and Negative Type the correct form of “to have” in the blank boxes below. Notice the negatives.	pressbooks	2025-03-22T05:08:51.380719	09-13-2021	{ "license": "Creative Commons - Attribution - https://creativecommons.org/licenses/by/4.0/", "url": "https://openoregon.pressbooks.pub/esol23/chapter/4-2/", "book_url": "https://openoregon.pressbooks.pub/esol23/front-matter/introduction-2/", "title": "A Digital Workbook for Beginning ESOL", "author": "Eric Dodson, Davida Jordan, Timothy Krause", "institution": "", "subject": "Language teaching and learning material and coursework, Language learning: grammar, vocabulary and pronunciation, English" }
https://openoregon.pressbooks.pub/esol23/chapter/verb-patterns/	Grammar – Intermediate Videos and Exercises Verb Patterns Verb Patterns – Watch the videos and complete the exercises. Exercise 1: There are three questions in this exercise. Drag the words to the correct drop zone boxes. Click the “check” button. Click the “next” arrow.	pressbooks	2025-03-22T05:08:51.391757	09-13-2021	{ "license": "Creative Commons - Attribution - https://creativecommons.org/licenses/by/4.0/", "url": "https://openoregon.pressbooks.pub/esol23/chapter/verb-patterns/", "book_url": "https://openoregon.pressbooks.pub/esol23/front-matter/introduction-2/", "title": "A Digital Workbook for Beginning ESOL", "author": "Eric Dodson, Davida Jordan, Timothy Krause", "institution": "", "subject": "Language teaching and learning material and coursework, Language learning: grammar, vocabulary and pronunciation, English" }
https://openoregon.pressbooks.pub/esol23/chapter/articles-indefinite-and-definite/	Grammar – Intermediate Videos and Exercises Articles – Indefinite and Definite Articles – Indefinite and Definite – Watch the videos and complete the exercises. Exercise 1: Articles 1 Read the sentences. Click on the correct article between brackets. Click on “0” for no article. Exercise 2: Articles 2 Type the correct article for each noun in the story in the blank box. Use articles a, an, the. Leave the blank box empty if no article is needed. Click the “check” button. Exercise 3: In, On, Under This exercise has three questions. The first two exercises might be extra difficult with a screen reader. Question 1: Drag pictures to the correct drop zones in the picture. There is a bookcase and three cats. Read the instructions. Drag the cats to the correct drop zone on the bookcase picture. The bookcase has three shelves, a top, and space under the third shelf. The cats are different colors. Click the check button, then click the next arrow to go to the next question. Question 2: There are three drop zones: in, on, and under. Drag the pictures to the correct drop zone. The picture will not stay unless it is correct. Click the check button, then click the next arrow to go to the next question. Question 3: Read the sentences. Type the correct preposition in the blank box.	pressbooks	2025-03-22T05:08:51.406585	09-13-2021	{ "license": "Creative Commons - Attribution - https://creativecommons.org/licenses/by/4.0/", "url": "https://openoregon.pressbooks.pub/esol23/chapter/articles-indefinite-and-definite/", "book_url": "https://openoregon.pressbooks.pub/esol23/front-matter/introduction-2/", "title": "A Digital Workbook for Beginning ESOL", "author": "Eric Dodson, Davida Jordan, Timothy Krause", "institution": "", "subject": "Language teaching and learning material and coursework, Language learning: grammar, vocabulary and pronunciation, English" }
https://openoregon.pressbooks.pub/esol23/chapter/quantity/	Grammar – Intermediate Videos and Exercises Quantity Quantity – Watch the videos and complete the exercises. Exercise 1: Quantity Expressions 1 Click on the words in the brackets. For each word decide if it is “count” or “non-count” and click on the quantity word, “much” or “many”. Click the “check” button. Exercise 2: Quantity Expressions 2 Read the story. Type “a little” or ” a few” in the blank boxes. Click the “check” button. Exercise 3: Adjectives Click on the adjectives in the sentences. Click the “check” button. Exercise 4: Singular and Plural Nouns Type the plural of each noun in the blank boxes below.	pressbooks	2025-03-22T05:08:51.419946	09-13-2021	{ "license": "Creative Commons - Attribution - https://creativecommons.org/licenses/by/4.0/", "url": "https://openoregon.pressbooks.pub/esol23/chapter/quantity/", "book_url": "https://openoregon.pressbooks.pub/esol23/front-matter/introduction-2/", "title": "A Digital Workbook for Beginning ESOL", "author": "Eric Dodson, Davida Jordan, Timothy Krause", "institution": "", "subject": "Language teaching and learning material and coursework, Language learning: grammar, vocabulary and pronunciation, English" }
https://openoregon.pressbooks.pub/esol23/chapter/some-and-any/	Grammar – Intermediate Videos and Exercises Some and Any Some and Any – Watch the videos and complete the exercises. Exercises 1: Some and Any Read the conversation. Type the words “some” or “any” in the blank boxes below. Click the “check” button. Exercises 2: Something, Anything, Nothing Read the conversation. Complete the sentence with draggable words provided. Drag the words to the drop box zones in each sentence. Exercises 3: This and That Click on the words “this” or “that” between brackets to complete the sentences. Click the “check” button. Exercises 4: These and Those Click on the words “these” or “those” between brackets to complete the sentences. Click the “check” button.	pressbooks	2025-03-22T05:08:51.432131	09-13-2021	{ "license": "Creative Commons - Attribution - https://creativecommons.org/licenses/by/4.0/", "url": "https://openoregon.pressbooks.pub/esol23/chapter/some-and-any/", "book_url": "https://openoregon.pressbooks.pub/esol23/front-matter/introduction-2/", "title": "A Digital Workbook for Beginning ESOL", "author": "Eric Dodson, Davida Jordan, Timothy Krause", "institution": "", "subject": "Language teaching and learning material and coursework, Language learning: grammar, vocabulary and pronunciation, English" }
https://openoregon.pressbooks.pub/esol23/chapter/preposition-time/	Grammar – Intermediate Videos and Exercises Prepositions of Time Prepositions – In, On, At, with Time – Watch the video and complete the exercises. Exercise 1: Prepositions of Time There are three questions in this exercise. Follow instructions for each question. Click the “check” button. Click the “arrow” button to go to the next question. Exercise 2: Time Vocabulary Drag the words to the drop zone boxes to categorize the words. For example: the word “January” is a month. Drag the word month to the drop zone box next to January. Exercise 3: Calendar Vocabulary There are two questions in this exercise. Question 1: Drag the words to drop zone boxes next to each period of time. Question 2: Type the word Click the “next” arrow. Exercise 4: Date and Time Read the questions about time. Drag the answers to the drop zone box next to the question.	pressbooks	2025-03-22T05:08:51.444866	09-13-2021	{ "license": "Creative Commons - Attribution - https://creativecommons.org/licenses/by/4.0/", "url": "https://openoregon.pressbooks.pub/esol23/chapter/preposition-time/", "book_url": "https://openoregon.pressbooks.pub/esol23/front-matter/introduction-2/", "title": "A Digital Workbook for Beginning ESOL", "author": "Eric Dodson, Davida Jordan, Timothy Krause", "institution": "", "subject": "Language teaching and learning material and coursework, Language learning: grammar, vocabulary and pronunciation, English" }
https://openoregon.pressbooks.pub/esol23/chapter/comparative-adjectives/	Grammar – Intermediate Videos and Exercises Comparative Adjectives Comparative Adjectives -Watch the videos and complete the exercises. Exercise 1: Comparatives 1 Read the sentences. Type the correct comparative adjective in the blank box of the second sentence. Click the check button. Exercise 2: Comparatives 2 Type the adjective or comparative adjective in the blanks. Click the check button. Exercise 3: Weather Dialog Complete the conversations. Drag words to the drop zone boxes.	pressbooks	2025-03-22T05:08:51.456220	09-13-2021	{ "license": "Creative Commons - Attribution - https://creativecommons.org/licenses/by/4.0/", "url": "https://openoregon.pressbooks.pub/esol23/chapter/comparative-adjectives/", "book_url": "https://openoregon.pressbooks.pub/esol23/front-matter/introduction-2/", "title": "A Digital Workbook for Beginning ESOL", "author": "Eric Dodson, Davida Jordan, Timothy Krause", "institution": "", "subject": "Language teaching and learning material and coursework, Language learning: grammar, vocabulary and pronunciation, English" }
https://ecampusontario.pressbooks.pub/conversationalai/chapter/1-into-converstational-ai/	Conversational AI Theory 1 Overview of Conversational AI What is Conversational AI? Conversational artificial intelligence can be defined is the application of computing technology to facilitate a natural conversation between computers and people. Conversational AI can be achieved using several different modes of communication including voice, text or chat. Voice is when the user speaks directly to the system and the AI agent can respond using a synthesized voice. Text is when the AI agent responds to a query and produces a textual reply. A chat is similar to text except it generally represents a number of short message exchanges between the user and the computer in near real-time. Some of the most well-known examples of conversational AI systems in use today are the digital assistants such as Amazon Alexa, Apple Siri, Google Assistant and IBM Watson. Benefits of Conversational AI Conversational AI systems can achieve numerous benefits over traditional computer systems including; - Voice can be a more efficient and convenient way to interface with machines. - A more intelligent system that can extract user intents then evaluate and determine correct responses without have to be pre-programmed for every condition or user utterance. - There are numerous business advantages to using a conversational AI system including achieving a consistent (and hopefully better) customer experience, a contact less customer experience, increases efficiency, reduction in operational costs and consistent up-selling opportunities. The Turning Test 1950 Allen Turning proposed a method of inquiry to determine whether a computer is capable of thinking liking a human being. His original work hypothesized that a computer could mimic human responses under specific conditions. This idea eventually evolved into what is now called the Turing test. The Turning Test is benchmark for Conversational AI system to evaluate how well it responds and mimics the responses of a human, such that it become indistinguishable from a real person to fulfill a specific task. If the user can not tell if the responses are coming from a real person or a computer. The system has “passed” the Turning Test. Notable Dates in Conversational AI History When PARRY met ELIZA In 1966 Joseph Weizenbaum developed Eliza. Eliza was programmed to respond to a number of keywords and produce responses that mimicked a psychotherapist. In 1972 Kenneth Colby developed Parry. Parry was program to model the behavior of a paranoid schizophrenic. In January 1973, as part of a international computer conference ELIZA and PARRY were connected together using the ARPNET. ELIZA was located at MIT and PARRY was at Stanford. Below is a transcript of their conversation. Transcript of ELIZA and PARRY conversation ELIZA: Good Evening. Tell me your problems. Please terminate input with a period or a question mark. PARRY: People get on my nerves sometimes. ELIZA: I am not sure I understand you fully. PARRY: You should pay more attention. ELIZA: Suppose you should pay more attention. PARRY: You’re entitled to your own opinion. ELIZA: What makes you think I am entitled to my own opinion? PARRY: It bothers me just to be around people in general. ELIZA: Please go on. PARRY: I went to the track at Bay Meadows a while back. ELIZA: What does that suggest to you? PARRY: I went to the races. ELIZA: What else would you like to discuss? PARRY: Horse racing interests me. ELIZA: Why do you say that just now? PARRY: There’s a lot of crooked gambling going on there. ELIZA: I am not sure I understand you fully, PARRY: It’s clear to me if not to you. ELIZA: Do you think it’s likely that not to I? PARRY: I went to the races. ELIZA: Please go on. PARRY: I gamble on horses. ELIZA: What does that suggest to you? PARRY: I went to the races. ELIZA: What else would you like to discuss? PARRY: Gambling has been nothing but trouble to me. ELIZA: Why do you say that just now? PARRY: I’ve had experience with crooks gambling at the track. ELIZA: I am not sure I understand you fully. PARRY: I’ve already told you enough about that. ELIZA: Oh? I enough about that? PARRY: I went to the races. ELIZA: Please go on. PARRY: Do you know anything about bookies? ELIZA: What makes you think I know anything about bookies? PARRY: A bookie didn’t pay me off once. ELIZA: What does that suggest to you? PARRY: I beat up the bookie who did not pay off, and I thought he might use his friends in the underworld to get even with me. ELIZA: What else would you like to discuss? PARRY: What do you know about racketeers? ELIZA: Why do you ask? PARRY: What experience have you had gambling? ELIZA: Why does that question interest you? PARRY: I like horse races. ELIZA: What else do you like? PARRY: I know the mob controls the big rackets. ELIZA: Who are you really talking about? PARRY: Have you ever really thought about the MAFIA? ELIZA: Suppose you ever really thought about the MAFIA. PARRY: Let’s talk about something else. ELIZA: I am not sure I understand you fully. PARRY: You keep asking me the same things, why not change the topic? ELIZA: In what way? PARRY: What do you mean? ELIZA: Why are such questions on your mind? PARRY: I thought I told you, people get on my nerves. ELIZA: Your nerves? PARRY: Well, I don’t know. ELIZA: Don’t you really know? PARRY: I would say so. ELIZA: Please go on. PARRY: I would rather not discuss that anymore. You are a real nag. ELISA: Does it please you to believe I am a real nag? PARRY: I have had enough of this. ELISA: What does that suggest to you? PARRY: Goodbye. ELISA: It’s been my pleasure, that’s $399.29 please. What’s interesting about the conversational tone and responses from the transcript appears to somewhat accurately portray those of a psychiatrist and paranoid schizophrenic. Although there is a definite gap in appearing to understand the deeper meaning behind the conversation this is one of the first times that two chatbots were connected together, and demonstrated that chatbots could be given distinct personalities. Evolution of Conversational AI Conversational AI systems in the 1960’s relied on having hard-coded static responses and per-determined conversational pathways based on specific user input or keywords. The the 1980’s this was updated to using statistical inference data models to determine the best pre-programmed response to send. Modern Conversational solutions now rely on pre-trained neural networks to create a natural language understanding platform which is capable of responding to a wide-rand of of user utterances without having to be explicitly programmed for each utterance. Key Technological Advances It is much easier to program boolean and logical operations. Such as responding yes or no to predefined prompts or selecting a menu option one through nine on a phone voice menu. These primitive systems really do not leverage for the Artificial Intelligence principles as they operate by using pre-programmed conversational pathways. Improvements in voice to text transcription, combined with increased computing resources as allowed for larger trading sets which are able to interpret and process more diverse natural language conversations as well as languages and dialects from around the world.	pressbooks	2025-03-22T05:08:51.476743	11-7-2024	{ "license": "Creative Commons - Attribution - https://creativecommons.org/licenses/by/4.0/", "url": "https://ecampusontario.pressbooks.pub/conversationalai/chapter/1-into-converstational-ai/", "book_url": "https://ecampusontario.pressbooks.pub/conversationalai/front-matter/forward/", "title": "A Pragmatic Guide to Conversational AI", "author": "Ross Bigelow", "institution": "Georgian College", "subject": "Artificial intelligence" }
https://ecampusontario.pressbooks.pub/conversationalai/chapter/1-into-converstational-ai/#chapter-5-section-1	Conversational AI Theory 1 Overview of Conversational AI What is Conversational AI? Conversational artificial intelligence can be defined is the application of computing technology to facilitate a natural conversation between computers and people. Conversational AI can be achieved using several different modes of communication including voice, text or chat. Voice is when the user speaks directly to the system and the AI agent can respond using a synthesized voice. Text is when the AI agent responds to a query and produces a textual reply. A chat is similar to text except it generally represents a number of short message exchanges between the user and the computer in near real-time. Some of the most well-known examples of conversational AI systems in use today are the digital assistants such as Amazon Alexa, Apple Siri, Google Assistant and IBM Watson. Benefits of Conversational AI Conversational AI systems can achieve numerous benefits over traditional computer systems including; - Voice can be a more efficient and convenient way to interface with machines. - A more intelligent system that can extract user intents then evaluate and determine correct responses without have to be pre-programmed for every condition or user utterance. - There are numerous business advantages to using a conversational AI system including achieving a consistent (and hopefully better) customer experience, a contact less customer experience, increases efficiency, reduction in operational costs and consistent up-selling opportunities. The Turning Test 1950 Allen Turning proposed a method of inquiry to determine whether a computer is capable of thinking liking a human being. His original work hypothesized that a computer could mimic human responses under specific conditions. This idea eventually evolved into what is now called the Turing test. The Turning Test is benchmark for Conversational AI system to evaluate how well it responds and mimics the responses of a human, such that it become indistinguishable from a real person to fulfill a specific task. If the user can not tell if the responses are coming from a real person or a computer. The system has “passed” the Turning Test. Notable Dates in Conversational AI History When PARRY met ELIZA In 1966 Joseph Weizenbaum developed Eliza. Eliza was programmed to respond to a number of keywords and produce responses that mimicked a psychotherapist. In 1972 Kenneth Colby developed Parry. Parry was program to model the behavior of a paranoid schizophrenic. In January 1973, as part of a international computer conference ELIZA and PARRY were connected together using the ARPNET. ELIZA was located at MIT and PARRY was at Stanford. Below is a transcript of their conversation. Transcript of ELIZA and PARRY conversation ELIZA: Good Evening. Tell me your problems. Please terminate input with a period or a question mark. PARRY: People get on my nerves sometimes. ELIZA: I am not sure I understand you fully. PARRY: You should pay more attention. ELIZA: Suppose you should pay more attention. PARRY: You’re entitled to your own opinion. ELIZA: What makes you think I am entitled to my own opinion? PARRY: It bothers me just to be around people in general. ELIZA: Please go on. PARRY: I went to the track at Bay Meadows a while back. ELIZA: What does that suggest to you? PARRY: I went to the races. ELIZA: What else would you like to discuss? PARRY: Horse racing interests me. ELIZA: Why do you say that just now? PARRY: There’s a lot of crooked gambling going on there. ELIZA: I am not sure I understand you fully, PARRY: It’s clear to me if not to you. ELIZA: Do you think it’s likely that not to I? PARRY: I went to the races. ELIZA: Please go on. PARRY: I gamble on horses. ELIZA: What does that suggest to you? PARRY: I went to the races. ELIZA: What else would you like to discuss? PARRY: Gambling has been nothing but trouble to me. ELIZA: Why do you say that just now? PARRY: I’ve had experience with crooks gambling at the track. ELIZA: I am not sure I understand you fully. PARRY: I’ve already told you enough about that. ELIZA: Oh? I enough about that? PARRY: I went to the races. ELIZA: Please go on. PARRY: Do you know anything about bookies? ELIZA: What makes you think I know anything about bookies? PARRY: A bookie didn’t pay me off once. ELIZA: What does that suggest to you? PARRY: I beat up the bookie who did not pay off, and I thought he might use his friends in the underworld to get even with me. ELIZA: What else would you like to discuss? PARRY: What do you know about racketeers? ELIZA: Why do you ask? PARRY: What experience have you had gambling? ELIZA: Why does that question interest you? PARRY: I like horse races. ELIZA: What else do you like? PARRY: I know the mob controls the big rackets. ELIZA: Who are you really talking about? PARRY: Have you ever really thought about the MAFIA? ELIZA: Suppose you ever really thought about the MAFIA. PARRY: Let’s talk about something else. ELIZA: I am not sure I understand you fully. PARRY: You keep asking me the same things, why not change the topic? ELIZA: In what way? PARRY: What do you mean? ELIZA: Why are such questions on your mind? PARRY: I thought I told you, people get on my nerves. ELIZA: Your nerves? PARRY: Well, I don’t know. ELIZA: Don’t you really know? PARRY: I would say so. ELIZA: Please go on. PARRY: I would rather not discuss that anymore. You are a real nag. ELISA: Does it please you to believe I am a real nag? PARRY: I have had enough of this. ELISA: What does that suggest to you? PARRY: Goodbye. ELISA: It’s been my pleasure, that’s $399.29 please. What’s interesting about the conversational tone and responses from the transcript appears to somewhat accurately portray those of a psychiatrist and paranoid schizophrenic. Although there is a definite gap in appearing to understand the deeper meaning behind the conversation this is one of the first times that two chatbots were connected together, and demonstrated that chatbots could be given distinct personalities. Evolution of Conversational AI Conversational AI systems in the 1960’s relied on having hard-coded static responses and per-determined conversational pathways based on specific user input or keywords. The the 1980’s this was updated to using statistical inference data models to determine the best pre-programmed response to send. Modern Conversational solutions now rely on pre-trained neural networks to create a natural language understanding platform which is capable of responding to a wide-rand of of user utterances without having to be explicitly programmed for each utterance. Key Technological Advances It is much easier to program boolean and logical operations. Such as responding yes or no to predefined prompts or selecting a menu option one through nine on a phone voice menu. These primitive systems really do not leverage for the Artificial Intelligence principles as they operate by using pre-programmed conversational pathways. Improvements in voice to text transcription, combined with increased computing resources as allowed for larger trading sets which are able to interpret and process more diverse natural language conversations as well as languages and dialects from around the world.	pressbooks	2025-03-22T05:08:51.496973	11-7-2024	{ "license": "Creative Commons - Attribution - https://creativecommons.org/licenses/by/4.0/", "url": "https://ecampusontario.pressbooks.pub/conversationalai/chapter/1-into-converstational-ai/#chapter-5-section-1", "book_url": "https://ecampusontario.pressbooks.pub/conversationalai/front-matter/forward/", "title": "A Pragmatic Guide to Conversational AI", "author": "Ross Bigelow", "institution": "Georgian College", "subject": "Artificial intelligence" }
https://ecampusontario.pressbooks.pub/conversationalai/chapter/1-into-converstational-ai/#chapter-5-section-2	Conversational AI Theory 1 Overview of Conversational AI What is Conversational AI? Conversational artificial intelligence can be defined is the application of computing technology to facilitate a natural conversation between computers and people. Conversational AI can be achieved using several different modes of communication including voice, text or chat. Voice is when the user speaks directly to the system and the AI agent can respond using a synthesized voice. Text is when the AI agent responds to a query and produces a textual reply. A chat is similar to text except it generally represents a number of short message exchanges between the user and the computer in near real-time. Some of the most well-known examples of conversational AI systems in use today are the digital assistants such as Amazon Alexa, Apple Siri, Google Assistant and IBM Watson. Benefits of Conversational AI Conversational AI systems can achieve numerous benefits over traditional computer systems including; - Voice can be a more efficient and convenient way to interface with machines. - A more intelligent system that can extract user intents then evaluate and determine correct responses without have to be pre-programmed for every condition or user utterance. - There are numerous business advantages to using a conversational AI system including achieving a consistent (and hopefully better) customer experience, a contact less customer experience, increases efficiency, reduction in operational costs and consistent up-selling opportunities. The Turning Test 1950 Allen Turning proposed a method of inquiry to determine whether a computer is capable of thinking liking a human being. His original work hypothesized that a computer could mimic human responses under specific conditions. This idea eventually evolved into what is now called the Turing test. The Turning Test is benchmark for Conversational AI system to evaluate how well it responds and mimics the responses of a human, such that it become indistinguishable from a real person to fulfill a specific task. If the user can not tell if the responses are coming from a real person or a computer. The system has “passed” the Turning Test. Notable Dates in Conversational AI History When PARRY met ELIZA In 1966 Joseph Weizenbaum developed Eliza. Eliza was programmed to respond to a number of keywords and produce responses that mimicked a psychotherapist. In 1972 Kenneth Colby developed Parry. Parry was program to model the behavior of a paranoid schizophrenic. In January 1973, as part of a international computer conference ELIZA and PARRY were connected together using the ARPNET. ELIZA was located at MIT and PARRY was at Stanford. Below is a transcript of their conversation. Transcript of ELIZA and PARRY conversation ELIZA: Good Evening. Tell me your problems. Please terminate input with a period or a question mark. PARRY: People get on my nerves sometimes. ELIZA: I am not sure I understand you fully. PARRY: You should pay more attention. ELIZA: Suppose you should pay more attention. PARRY: You’re entitled to your own opinion. ELIZA: What makes you think I am entitled to my own opinion? PARRY: It bothers me just to be around people in general. ELIZA: Please go on. PARRY: I went to the track at Bay Meadows a while back. ELIZA: What does that suggest to you? PARRY: I went to the races. ELIZA: What else would you like to discuss? PARRY: Horse racing interests me. ELIZA: Why do you say that just now? PARRY: There’s a lot of crooked gambling going on there. ELIZA: I am not sure I understand you fully, PARRY: It’s clear to me if not to you. ELIZA: Do you think it’s likely that not to I? PARRY: I went to the races. ELIZA: Please go on. PARRY: I gamble on horses. ELIZA: What does that suggest to you? PARRY: I went to the races. ELIZA: What else would you like to discuss? PARRY: Gambling has been nothing but trouble to me. ELIZA: Why do you say that just now? PARRY: I’ve had experience with crooks gambling at the track. ELIZA: I am not sure I understand you fully. PARRY: I’ve already told you enough about that. ELIZA: Oh? I enough about that? PARRY: I went to the races. ELIZA: Please go on. PARRY: Do you know anything about bookies? ELIZA: What makes you think I know anything about bookies? PARRY: A bookie didn’t pay me off once. ELIZA: What does that suggest to you? PARRY: I beat up the bookie who did not pay off, and I thought he might use his friends in the underworld to get even with me. ELIZA: What else would you like to discuss? PARRY: What do you know about racketeers? ELIZA: Why do you ask? PARRY: What experience have you had gambling? ELIZA: Why does that question interest you? PARRY: I like horse races. ELIZA: What else do you like? PARRY: I know the mob controls the big rackets. ELIZA: Who are you really talking about? PARRY: Have you ever really thought about the MAFIA? ELIZA: Suppose you ever really thought about the MAFIA. PARRY: Let’s talk about something else. ELIZA: I am not sure I understand you fully. PARRY: You keep asking me the same things, why not change the topic? ELIZA: In what way? PARRY: What do you mean? ELIZA: Why are such questions on your mind? PARRY: I thought I told you, people get on my nerves. ELIZA: Your nerves? PARRY: Well, I don’t know. ELIZA: Don’t you really know? PARRY: I would say so. ELIZA: Please go on. PARRY: I would rather not discuss that anymore. You are a real nag. ELISA: Does it please you to believe I am a real nag? PARRY: I have had enough of this. ELISA: What does that suggest to you? PARRY: Goodbye. ELISA: It’s been my pleasure, that’s $399.29 please. What’s interesting about the conversational tone and responses from the transcript appears to somewhat accurately portray those of a psychiatrist and paranoid schizophrenic. Although there is a definite gap in appearing to understand the deeper meaning behind the conversation this is one of the first times that two chatbots were connected together, and demonstrated that chatbots could be given distinct personalities. Evolution of Conversational AI Conversational AI systems in the 1960’s relied on having hard-coded static responses and per-determined conversational pathways based on specific user input or keywords. The the 1980’s this was updated to using statistical inference data models to determine the best pre-programmed response to send. Modern Conversational solutions now rely on pre-trained neural networks to create a natural language understanding platform which is capable of responding to a wide-rand of of user utterances without having to be explicitly programmed for each utterance. Key Technological Advances It is much easier to program boolean and logical operations. Such as responding yes or no to predefined prompts or selecting a menu option one through nine on a phone voice menu. These primitive systems really do not leverage for the Artificial Intelligence principles as they operate by using pre-programmed conversational pathways. Improvements in voice to text transcription, combined with increased computing resources as allowed for larger trading sets which are able to interpret and process more diverse natural language conversations as well as languages and dialects from around the world.	pressbooks	2025-03-22T05:08:51.520715	11-7-2024	{ "license": "Creative Commons - Attribution - https://creativecommons.org/licenses/by/4.0/", "url": "https://ecampusontario.pressbooks.pub/conversationalai/chapter/1-into-converstational-ai/#chapter-5-section-2", "book_url": "https://ecampusontario.pressbooks.pub/conversationalai/front-matter/forward/", "title": "A Pragmatic Guide to Conversational AI", "author": "Ross Bigelow", "institution": "Georgian College", "subject": "Artificial intelligence" }
https://ecampusontario.pressbooks.pub/conversationalai/chapter/1-into-converstational-ai/#chapter-5-section-3	Conversational AI Theory 1 Overview of Conversational AI What is Conversational AI? Conversational artificial intelligence can be defined is the application of computing technology to facilitate a natural conversation between computers and people. Conversational AI can be achieved using several different modes of communication including voice, text or chat. Voice is when the user speaks directly to the system and the AI agent can respond using a synthesized voice. Text is when the AI agent responds to a query and produces a textual reply. A chat is similar to text except it generally represents a number of short message exchanges between the user and the computer in near real-time. Some of the most well-known examples of conversational AI systems in use today are the digital assistants such as Amazon Alexa, Apple Siri, Google Assistant and IBM Watson. Benefits of Conversational AI Conversational AI systems can achieve numerous benefits over traditional computer systems including; - Voice can be a more efficient and convenient way to interface with machines. - A more intelligent system that can extract user intents then evaluate and determine correct responses without have to be pre-programmed for every condition or user utterance. - There are numerous business advantages to using a conversational AI system including achieving a consistent (and hopefully better) customer experience, a contact less customer experience, increases efficiency, reduction in operational costs and consistent up-selling opportunities. The Turning Test 1950 Allen Turning proposed a method of inquiry to determine whether a computer is capable of thinking liking a human being. His original work hypothesized that a computer could mimic human responses under specific conditions. This idea eventually evolved into what is now called the Turing test. The Turning Test is benchmark for Conversational AI system to evaluate how well it responds and mimics the responses of a human, such that it become indistinguishable from a real person to fulfill a specific task. If the user can not tell if the responses are coming from a real person or a computer. The system has “passed” the Turning Test. Notable Dates in Conversational AI History When PARRY met ELIZA In 1966 Joseph Weizenbaum developed Eliza. Eliza was programmed to respond to a number of keywords and produce responses that mimicked a psychotherapist. In 1972 Kenneth Colby developed Parry. Parry was program to model the behavior of a paranoid schizophrenic. In January 1973, as part of a international computer conference ELIZA and PARRY were connected together using the ARPNET. ELIZA was located at MIT and PARRY was at Stanford. Below is a transcript of their conversation. Transcript of ELIZA and PARRY conversation ELIZA: Good Evening. Tell me your problems. Please terminate input with a period or a question mark. PARRY: People get on my nerves sometimes. ELIZA: I am not sure I understand you fully. PARRY: You should pay more attention. ELIZA: Suppose you should pay more attention. PARRY: You’re entitled to your own opinion. ELIZA: What makes you think I am entitled to my own opinion? PARRY: It bothers me just to be around people in general. ELIZA: Please go on. PARRY: I went to the track at Bay Meadows a while back. ELIZA: What does that suggest to you? PARRY: I went to the races. ELIZA: What else would you like to discuss? PARRY: Horse racing interests me. ELIZA: Why do you say that just now? PARRY: There’s a lot of crooked gambling going on there. ELIZA: I am not sure I understand you fully, PARRY: It’s clear to me if not to you. ELIZA: Do you think it’s likely that not to I? PARRY: I went to the races. ELIZA: Please go on. PARRY: I gamble on horses. ELIZA: What does that suggest to you? PARRY: I went to the races. ELIZA: What else would you like to discuss? PARRY: Gambling has been nothing but trouble to me. ELIZA: Why do you say that just now? PARRY: I’ve had experience with crooks gambling at the track. ELIZA: I am not sure I understand you fully. PARRY: I’ve already told you enough about that. ELIZA: Oh? I enough about that? PARRY: I went to the races. ELIZA: Please go on. PARRY: Do you know anything about bookies? ELIZA: What makes you think I know anything about bookies? PARRY: A bookie didn’t pay me off once. ELIZA: What does that suggest to you? PARRY: I beat up the bookie who did not pay off, and I thought he might use his friends in the underworld to get even with me. ELIZA: What else would you like to discuss? PARRY: What do you know about racketeers? ELIZA: Why do you ask? PARRY: What experience have you had gambling? ELIZA: Why does that question interest you? PARRY: I like horse races. ELIZA: What else do you like? PARRY: I know the mob controls the big rackets. ELIZA: Who are you really talking about? PARRY: Have you ever really thought about the MAFIA? ELIZA: Suppose you ever really thought about the MAFIA. PARRY: Let’s talk about something else. ELIZA: I am not sure I understand you fully. PARRY: You keep asking me the same things, why not change the topic? ELIZA: In what way? PARRY: What do you mean? ELIZA: Why are such questions on your mind? PARRY: I thought I told you, people get on my nerves. ELIZA: Your nerves? PARRY: Well, I don’t know. ELIZA: Don’t you really know? PARRY: I would say so. ELIZA: Please go on. PARRY: I would rather not discuss that anymore. You are a real nag. ELISA: Does it please you to believe I am a real nag? PARRY: I have had enough of this. ELISA: What does that suggest to you? PARRY: Goodbye. ELISA: It’s been my pleasure, that’s $399.29 please. What’s interesting about the conversational tone and responses from the transcript appears to somewhat accurately portray those of a psychiatrist and paranoid schizophrenic. Although there is a definite gap in appearing to understand the deeper meaning behind the conversation this is one of the first times that two chatbots were connected together, and demonstrated that chatbots could be given distinct personalities. Evolution of Conversational AI Conversational AI systems in the 1960’s relied on having hard-coded static responses and per-determined conversational pathways based on specific user input or keywords. The the 1980’s this was updated to using statistical inference data models to determine the best pre-programmed response to send. Modern Conversational solutions now rely on pre-trained neural networks to create a natural language understanding platform which is capable of responding to a wide-rand of of user utterances without having to be explicitly programmed for each utterance. Key Technological Advances It is much easier to program boolean and logical operations. Such as responding yes or no to predefined prompts or selecting a menu option one through nine on a phone voice menu. These primitive systems really do not leverage for the Artificial Intelligence principles as they operate by using pre-programmed conversational pathways. Improvements in voice to text transcription, combined with increased computing resources as allowed for larger trading sets which are able to interpret and process more diverse natural language conversations as well as languages and dialects from around the world.	pressbooks	2025-03-22T05:08:51.540646	11-7-2024	{ "license": "Creative Commons - Attribution - https://creativecommons.org/licenses/by/4.0/", "url": "https://ecampusontario.pressbooks.pub/conversationalai/chapter/1-into-converstational-ai/#chapter-5-section-3", "book_url": "https://ecampusontario.pressbooks.pub/conversationalai/front-matter/forward/", "title": "A Pragmatic Guide to Conversational AI", "author": "Ross Bigelow", "institution": "Georgian College", "subject": "Artificial intelligence" }
https://ecampusontario.pressbooks.pub/conversationalai/chapter/1-into-converstational-ai/#chapter-5-section-4	Conversational AI Theory 1 Overview of Conversational AI What is Conversational AI? Conversational artificial intelligence can be defined is the application of computing technology to facilitate a natural conversation between computers and people. Conversational AI can be achieved using several different modes of communication including voice, text or chat. Voice is when the user speaks directly to the system and the AI agent can respond using a synthesized voice. Text is when the AI agent responds to a query and produces a textual reply. A chat is similar to text except it generally represents a number of short message exchanges between the user and the computer in near real-time. Some of the most well-known examples of conversational AI systems in use today are the digital assistants such as Amazon Alexa, Apple Siri, Google Assistant and IBM Watson. Benefits of Conversational AI Conversational AI systems can achieve numerous benefits over traditional computer systems including; - Voice can be a more efficient and convenient way to interface with machines. - A more intelligent system that can extract user intents then evaluate and determine correct responses without have to be pre-programmed for every condition or user utterance. - There are numerous business advantages to using a conversational AI system including achieving a consistent (and hopefully better) customer experience, a contact less customer experience, increases efficiency, reduction in operational costs and consistent up-selling opportunities. The Turning Test 1950 Allen Turning proposed a method of inquiry to determine whether a computer is capable of thinking liking a human being. His original work hypothesized that a computer could mimic human responses under specific conditions. This idea eventually evolved into what is now called the Turing test. The Turning Test is benchmark for Conversational AI system to evaluate how well it responds and mimics the responses of a human, such that it become indistinguishable from a real person to fulfill a specific task. If the user can not tell if the responses are coming from a real person or a computer. The system has “passed” the Turning Test. Notable Dates in Conversational AI History When PARRY met ELIZA In 1966 Joseph Weizenbaum developed Eliza. Eliza was programmed to respond to a number of keywords and produce responses that mimicked a psychotherapist. In 1972 Kenneth Colby developed Parry. Parry was program to model the behavior of a paranoid schizophrenic. In January 1973, as part of a international computer conference ELIZA and PARRY were connected together using the ARPNET. ELIZA was located at MIT and PARRY was at Stanford. Below is a transcript of their conversation. Transcript of ELIZA and PARRY conversation ELIZA: Good Evening. Tell me your problems. Please terminate input with a period or a question mark. PARRY: People get on my nerves sometimes. ELIZA: I am not sure I understand you fully. PARRY: You should pay more attention. ELIZA: Suppose you should pay more attention. PARRY: You’re entitled to your own opinion. ELIZA: What makes you think I am entitled to my own opinion? PARRY: It bothers me just to be around people in general. ELIZA: Please go on. PARRY: I went to the track at Bay Meadows a while back. ELIZA: What does that suggest to you? PARRY: I went to the races. ELIZA: What else would you like to discuss? PARRY: Horse racing interests me. ELIZA: Why do you say that just now? PARRY: There’s a lot of crooked gambling going on there. ELIZA: I am not sure I understand you fully, PARRY: It’s clear to me if not to you. ELIZA: Do you think it’s likely that not to I? PARRY: I went to the races. ELIZA: Please go on. PARRY: I gamble on horses. ELIZA: What does that suggest to you? PARRY: I went to the races. ELIZA: What else would you like to discuss? PARRY: Gambling has been nothing but trouble to me. ELIZA: Why do you say that just now? PARRY: I’ve had experience with crooks gambling at the track. ELIZA: I am not sure I understand you fully. PARRY: I’ve already told you enough about that. ELIZA: Oh? I enough about that? PARRY: I went to the races. ELIZA: Please go on. PARRY: Do you know anything about bookies? ELIZA: What makes you think I know anything about bookies? PARRY: A bookie didn’t pay me off once. ELIZA: What does that suggest to you? PARRY: I beat up the bookie who did not pay off, and I thought he might use his friends in the underworld to get even with me. ELIZA: What else would you like to discuss? PARRY: What do you know about racketeers? ELIZA: Why do you ask? PARRY: What experience have you had gambling? ELIZA: Why does that question interest you? PARRY: I like horse races. ELIZA: What else do you like? PARRY: I know the mob controls the big rackets. ELIZA: Who are you really talking about? PARRY: Have you ever really thought about the MAFIA? ELIZA: Suppose you ever really thought about the MAFIA. PARRY: Let’s talk about something else. ELIZA: I am not sure I understand you fully. PARRY: You keep asking me the same things, why not change the topic? ELIZA: In what way? PARRY: What do you mean? ELIZA: Why are such questions on your mind? PARRY: I thought I told you, people get on my nerves. ELIZA: Your nerves? PARRY: Well, I don’t know. ELIZA: Don’t you really know? PARRY: I would say so. ELIZA: Please go on. PARRY: I would rather not discuss that anymore. You are a real nag. ELISA: Does it please you to believe I am a real nag? PARRY: I have had enough of this. ELISA: What does that suggest to you? PARRY: Goodbye. ELISA: It’s been my pleasure, that’s $399.29 please. What’s interesting about the conversational tone and responses from the transcript appears to somewhat accurately portray those of a psychiatrist and paranoid schizophrenic. Although there is a definite gap in appearing to understand the deeper meaning behind the conversation this is one of the first times that two chatbots were connected together, and demonstrated that chatbots could be given distinct personalities. Evolution of Conversational AI Conversational AI systems in the 1960’s relied on having hard-coded static responses and per-determined conversational pathways based on specific user input or keywords. The the 1980’s this was updated to using statistical inference data models to determine the best pre-programmed response to send. Modern Conversational solutions now rely on pre-trained neural networks to create a natural language understanding platform which is capable of responding to a wide-rand of of user utterances without having to be explicitly programmed for each utterance. Key Technological Advances It is much easier to program boolean and logical operations. Such as responding yes or no to predefined prompts or selecting a menu option one through nine on a phone voice menu. These primitive systems really do not leverage for the Artificial Intelligence principles as they operate by using pre-programmed conversational pathways. Improvements in voice to text transcription, combined with increased computing resources as allowed for larger trading sets which are able to interpret and process more diverse natural language conversations as well as languages and dialects from around the world.	pressbooks	2025-03-22T05:08:51.561087	11-7-2024	{ "license": "Creative Commons - Attribution - https://creativecommons.org/licenses/by/4.0/", "url": "https://ecampusontario.pressbooks.pub/conversationalai/chapter/1-into-converstational-ai/#chapter-5-section-4", "book_url": "https://ecampusontario.pressbooks.pub/conversationalai/front-matter/forward/", "title": "A Pragmatic Guide to Conversational AI", "author": "Ross Bigelow", "institution": "Georgian College", "subject": "Artificial intelligence" }
https://ecampusontario.pressbooks.pub/conversationalai/chapter/1-into-converstational-ai/#chapter-5-section-5	Conversational AI Theory 1 Overview of Conversational AI What is Conversational AI? Conversational artificial intelligence can be defined is the application of computing technology to facilitate a natural conversation between computers and people. Conversational AI can be achieved using several different modes of communication including voice, text or chat. Voice is when the user speaks directly to the system and the AI agent can respond using a synthesized voice. Text is when the AI agent responds to a query and produces a textual reply. A chat is similar to text except it generally represents a number of short message exchanges between the user and the computer in near real-time. Some of the most well-known examples of conversational AI systems in use today are the digital assistants such as Amazon Alexa, Apple Siri, Google Assistant and IBM Watson. Benefits of Conversational AI Conversational AI systems can achieve numerous benefits over traditional computer systems including; - Voice can be a more efficient and convenient way to interface with machines. - A more intelligent system that can extract user intents then evaluate and determine correct responses without have to be pre-programmed for every condition or user utterance. - There are numerous business advantages to using a conversational AI system including achieving a consistent (and hopefully better) customer experience, a contact less customer experience, increases efficiency, reduction in operational costs and consistent up-selling opportunities. The Turning Test 1950 Allen Turning proposed a method of inquiry to determine whether a computer is capable of thinking liking a human being. His original work hypothesized that a computer could mimic human responses under specific conditions. This idea eventually evolved into what is now called the Turing test. The Turning Test is benchmark for Conversational AI system to evaluate how well it responds and mimics the responses of a human, such that it become indistinguishable from a real person to fulfill a specific task. If the user can not tell if the responses are coming from a real person or a computer. The system has “passed” the Turning Test. Notable Dates in Conversational AI History When PARRY met ELIZA In 1966 Joseph Weizenbaum developed Eliza. Eliza was programmed to respond to a number of keywords and produce responses that mimicked a psychotherapist. In 1972 Kenneth Colby developed Parry. Parry was program to model the behavior of a paranoid schizophrenic. In January 1973, as part of a international computer conference ELIZA and PARRY were connected together using the ARPNET. ELIZA was located at MIT and PARRY was at Stanford. Below is a transcript of their conversation. Transcript of ELIZA and PARRY conversation ELIZA: Good Evening. Tell me your problems. Please terminate input with a period or a question mark. PARRY: People get on my nerves sometimes. ELIZA: I am not sure I understand you fully. PARRY: You should pay more attention. ELIZA: Suppose you should pay more attention. PARRY: You’re entitled to your own opinion. ELIZA: What makes you think I am entitled to my own opinion? PARRY: It bothers me just to be around people in general. ELIZA: Please go on. PARRY: I went to the track at Bay Meadows a while back. ELIZA: What does that suggest to you? PARRY: I went to the races. ELIZA: What else would you like to discuss? PARRY: Horse racing interests me. ELIZA: Why do you say that just now? PARRY: There’s a lot of crooked gambling going on there. ELIZA: I am not sure I understand you fully, PARRY: It’s clear to me if not to you. ELIZA: Do you think it’s likely that not to I? PARRY: I went to the races. ELIZA: Please go on. PARRY: I gamble on horses. ELIZA: What does that suggest to you? PARRY: I went to the races. ELIZA: What else would you like to discuss? PARRY: Gambling has been nothing but trouble to me. ELIZA: Why do you say that just now? PARRY: I’ve had experience with crooks gambling at the track. ELIZA: I am not sure I understand you fully. PARRY: I’ve already told you enough about that. ELIZA: Oh? I enough about that? PARRY: I went to the races. ELIZA: Please go on. PARRY: Do you know anything about bookies? ELIZA: What makes you think I know anything about bookies? PARRY: A bookie didn’t pay me off once. ELIZA: What does that suggest to you? PARRY: I beat up the bookie who did not pay off, and I thought he might use his friends in the underworld to get even with me. ELIZA: What else would you like to discuss? PARRY: What do you know about racketeers? ELIZA: Why do you ask? PARRY: What experience have you had gambling? ELIZA: Why does that question interest you? PARRY: I like horse races. ELIZA: What else do you like? PARRY: I know the mob controls the big rackets. ELIZA: Who are you really talking about? PARRY: Have you ever really thought about the MAFIA? ELIZA: Suppose you ever really thought about the MAFIA. PARRY: Let’s talk about something else. ELIZA: I am not sure I understand you fully. PARRY: You keep asking me the same things, why not change the topic? ELIZA: In what way? PARRY: What do you mean? ELIZA: Why are such questions on your mind? PARRY: I thought I told you, people get on my nerves. ELIZA: Your nerves? PARRY: Well, I don’t know. ELIZA: Don’t you really know? PARRY: I would say so. ELIZA: Please go on. PARRY: I would rather not discuss that anymore. You are a real nag. ELISA: Does it please you to believe I am a real nag? PARRY: I have had enough of this. ELISA: What does that suggest to you? PARRY: Goodbye. ELISA: It’s been my pleasure, that’s $399.29 please. What’s interesting about the conversational tone and responses from the transcript appears to somewhat accurately portray those of a psychiatrist and paranoid schizophrenic. Although there is a definite gap in appearing to understand the deeper meaning behind the conversation this is one of the first times that two chatbots were connected together, and demonstrated that chatbots could be given distinct personalities. Evolution of Conversational AI Conversational AI systems in the 1960’s relied on having hard-coded static responses and per-determined conversational pathways based on specific user input or keywords. The the 1980’s this was updated to using statistical inference data models to determine the best pre-programmed response to send. Modern Conversational solutions now rely on pre-trained neural networks to create a natural language understanding platform which is capable of responding to a wide-rand of of user utterances without having to be explicitly programmed for each utterance. Key Technological Advances It is much easier to program boolean and logical operations. Such as responding yes or no to predefined prompts or selecting a menu option one through nine on a phone voice menu. These primitive systems really do not leverage for the Artificial Intelligence principles as they operate by using pre-programmed conversational pathways. Improvements in voice to text transcription, combined with increased computing resources as allowed for larger trading sets which are able to interpret and process more diverse natural language conversations as well as languages and dialects from around the world.	pressbooks	2025-03-22T05:08:51.580819	11-7-2024	{ "license": "Creative Commons - Attribution - https://creativecommons.org/licenses/by/4.0/", "url": "https://ecampusontario.pressbooks.pub/conversationalai/chapter/1-into-converstational-ai/#chapter-5-section-5", "book_url": "https://ecampusontario.pressbooks.pub/conversationalai/front-matter/forward/", "title": "A Pragmatic Guide to Conversational AI", "author": "Ross Bigelow", "institution": "Georgian College", "subject": "Artificial intelligence" }
https://ecampusontario.pressbooks.pub/conversationalai/chapter/1-into-converstational-ai/#chapter-5-section-6	Conversational AI Theory 1 Overview of Conversational AI What is Conversational AI? Conversational artificial intelligence can be defined is the application of computing technology to facilitate a natural conversation between computers and people. Conversational AI can be achieved using several different modes of communication including voice, text or chat. Voice is when the user speaks directly to the system and the AI agent can respond using a synthesized voice. Text is when the AI agent responds to a query and produces a textual reply. A chat is similar to text except it generally represents a number of short message exchanges between the user and the computer in near real-time. Some of the most well-known examples of conversational AI systems in use today are the digital assistants such as Amazon Alexa, Apple Siri, Google Assistant and IBM Watson. Benefits of Conversational AI Conversational AI systems can achieve numerous benefits over traditional computer systems including; - Voice can be a more efficient and convenient way to interface with machines. - A more intelligent system that can extract user intents then evaluate and determine correct responses without have to be pre-programmed for every condition or user utterance. - There are numerous business advantages to using a conversational AI system including achieving a consistent (and hopefully better) customer experience, a contact less customer experience, increases efficiency, reduction in operational costs and consistent up-selling opportunities. The Turning Test 1950 Allen Turning proposed a method of inquiry to determine whether a computer is capable of thinking liking a human being. His original work hypothesized that a computer could mimic human responses under specific conditions. This idea eventually evolved into what is now called the Turing test. The Turning Test is benchmark for Conversational AI system to evaluate how well it responds and mimics the responses of a human, such that it become indistinguishable from a real person to fulfill a specific task. If the user can not tell if the responses are coming from a real person or a computer. The system has “passed” the Turning Test. Notable Dates in Conversational AI History When PARRY met ELIZA In 1966 Joseph Weizenbaum developed Eliza. Eliza was programmed to respond to a number of keywords and produce responses that mimicked a psychotherapist. In 1972 Kenneth Colby developed Parry. Parry was program to model the behavior of a paranoid schizophrenic. In January 1973, as part of a international computer conference ELIZA and PARRY were connected together using the ARPNET. ELIZA was located at MIT and PARRY was at Stanford. Below is a transcript of their conversation. Transcript of ELIZA and PARRY conversation ELIZA: Good Evening. Tell me your problems. Please terminate input with a period or a question mark. PARRY: People get on my nerves sometimes. ELIZA: I am not sure I understand you fully. PARRY: You should pay more attention. ELIZA: Suppose you should pay more attention. PARRY: You’re entitled to your own opinion. ELIZA: What makes you think I am entitled to my own opinion? PARRY: It bothers me just to be around people in general. ELIZA: Please go on. PARRY: I went to the track at Bay Meadows a while back. ELIZA: What does that suggest to you? PARRY: I went to the races. ELIZA: What else would you like to discuss? PARRY: Horse racing interests me. ELIZA: Why do you say that just now? PARRY: There’s a lot of crooked gambling going on there. ELIZA: I am not sure I understand you fully, PARRY: It’s clear to me if not to you. ELIZA: Do you think it’s likely that not to I? PARRY: I went to the races. ELIZA: Please go on. PARRY: I gamble on horses. ELIZA: What does that suggest to you? PARRY: I went to the races. ELIZA: What else would you like to discuss? PARRY: Gambling has been nothing but trouble to me. ELIZA: Why do you say that just now? PARRY: I’ve had experience with crooks gambling at the track. ELIZA: I am not sure I understand you fully. PARRY: I’ve already told you enough about that. ELIZA: Oh? I enough about that? PARRY: I went to the races. ELIZA: Please go on. PARRY: Do you know anything about bookies? ELIZA: What makes you think I know anything about bookies? PARRY: A bookie didn’t pay me off once. ELIZA: What does that suggest to you? PARRY: I beat up the bookie who did not pay off, and I thought he might use his friends in the underworld to get even with me. ELIZA: What else would you like to discuss? PARRY: What do you know about racketeers? ELIZA: Why do you ask? PARRY: What experience have you had gambling? ELIZA: Why does that question interest you? PARRY: I like horse races. ELIZA: What else do you like? PARRY: I know the mob controls the big rackets. ELIZA: Who are you really talking about? PARRY: Have you ever really thought about the MAFIA? ELIZA: Suppose you ever really thought about the MAFIA. PARRY: Let’s talk about something else. ELIZA: I am not sure I understand you fully. PARRY: You keep asking me the same things, why not change the topic? ELIZA: In what way? PARRY: What do you mean? ELIZA: Why are such questions on your mind? PARRY: I thought I told you, people get on my nerves. ELIZA: Your nerves? PARRY: Well, I don’t know. ELIZA: Don’t you really know? PARRY: I would say so. ELIZA: Please go on. PARRY: I would rather not discuss that anymore. You are a real nag. ELISA: Does it please you to believe I am a real nag? PARRY: I have had enough of this. ELISA: What does that suggest to you? PARRY: Goodbye. ELISA: It’s been my pleasure, that’s $399.29 please. What’s interesting about the conversational tone and responses from the transcript appears to somewhat accurately portray those of a psychiatrist and paranoid schizophrenic. Although there is a definite gap in appearing to understand the deeper meaning behind the conversation this is one of the first times that two chatbots were connected together, and demonstrated that chatbots could be given distinct personalities. Evolution of Conversational AI Conversational AI systems in the 1960’s relied on having hard-coded static responses and per-determined conversational pathways based on specific user input or keywords. The the 1980’s this was updated to using statistical inference data models to determine the best pre-programmed response to send. Modern Conversational solutions now rely on pre-trained neural networks to create a natural language understanding platform which is capable of responding to a wide-rand of of user utterances without having to be explicitly programmed for each utterance. Key Technological Advances It is much easier to program boolean and logical operations. Such as responding yes or no to predefined prompts or selecting a menu option one through nine on a phone voice menu. These primitive systems really do not leverage for the Artificial Intelligence principles as they operate by using pre-programmed conversational pathways. Improvements in voice to text transcription, combined with increased computing resources as allowed for larger trading sets which are able to interpret and process more diverse natural language conversations as well as languages and dialects from around the world.	pressbooks	2025-03-22T05:08:51.605295	11-7-2024	{ "license": "Creative Commons - Attribution - https://creativecommons.org/licenses/by/4.0/", "url": "https://ecampusontario.pressbooks.pub/conversationalai/chapter/1-into-converstational-ai/#chapter-5-section-6", "book_url": "https://ecampusontario.pressbooks.pub/conversationalai/front-matter/forward/", "title": "A Pragmatic Guide to Conversational AI", "author": "Ross Bigelow", "institution": "Georgian College", "subject": "Artificial intelligence" }
https://ecampusontario.pressbooks.pub/conversationalai/chapter/1-into-converstational-ai/#chapter-5-section-7	Conversational AI Theory 1 Overview of Conversational AI What is Conversational AI? Conversational artificial intelligence can be defined is the application of computing technology to facilitate a natural conversation between computers and people. Conversational AI can be achieved using several different modes of communication including voice, text or chat. Voice is when the user speaks directly to the system and the AI agent can respond using a synthesized voice. Text is when the AI agent responds to a query and produces a textual reply. A chat is similar to text except it generally represents a number of short message exchanges between the user and the computer in near real-time. Some of the most well-known examples of conversational AI systems in use today are the digital assistants such as Amazon Alexa, Apple Siri, Google Assistant and IBM Watson. Benefits of Conversational AI Conversational AI systems can achieve numerous benefits over traditional computer systems including; - Voice can be a more efficient and convenient way to interface with machines. - A more intelligent system that can extract user intents then evaluate and determine correct responses without have to be pre-programmed for every condition or user utterance. - There are numerous business advantages to using a conversational AI system including achieving a consistent (and hopefully better) customer experience, a contact less customer experience, increases efficiency, reduction in operational costs and consistent up-selling opportunities. The Turning Test 1950 Allen Turning proposed a method of inquiry to determine whether a computer is capable of thinking liking a human being. His original work hypothesized that a computer could mimic human responses under specific conditions. This idea eventually evolved into what is now called the Turing test. The Turning Test is benchmark for Conversational AI system to evaluate how well it responds and mimics the responses of a human, such that it become indistinguishable from a real person to fulfill a specific task. If the user can not tell if the responses are coming from a real person or a computer. The system has “passed” the Turning Test. Notable Dates in Conversational AI History When PARRY met ELIZA In 1966 Joseph Weizenbaum developed Eliza. Eliza was programmed to respond to a number of keywords and produce responses that mimicked a psychotherapist. In 1972 Kenneth Colby developed Parry. Parry was program to model the behavior of a paranoid schizophrenic. In January 1973, as part of a international computer conference ELIZA and PARRY were connected together using the ARPNET. ELIZA was located at MIT and PARRY was at Stanford. Below is a transcript of their conversation. Transcript of ELIZA and PARRY conversation ELIZA: Good Evening. Tell me your problems. Please terminate input with a period or a question mark. PARRY: People get on my nerves sometimes. ELIZA: I am not sure I understand you fully. PARRY: You should pay more attention. ELIZA: Suppose you should pay more attention. PARRY: You’re entitled to your own opinion. ELIZA: What makes you think I am entitled to my own opinion? PARRY: It bothers me just to be around people in general. ELIZA: Please go on. PARRY: I went to the track at Bay Meadows a while back. ELIZA: What does that suggest to you? PARRY: I went to the races. ELIZA: What else would you like to discuss? PARRY: Horse racing interests me. ELIZA: Why do you say that just now? PARRY: There’s a lot of crooked gambling going on there. ELIZA: I am not sure I understand you fully, PARRY: It’s clear to me if not to you. ELIZA: Do you think it’s likely that not to I? PARRY: I went to the races. ELIZA: Please go on. PARRY: I gamble on horses. ELIZA: What does that suggest to you? PARRY: I went to the races. ELIZA: What else would you like to discuss? PARRY: Gambling has been nothing but trouble to me. ELIZA: Why do you say that just now? PARRY: I’ve had experience with crooks gambling at the track. ELIZA: I am not sure I understand you fully. PARRY: I’ve already told you enough about that. ELIZA: Oh? I enough about that? PARRY: I went to the races. ELIZA: Please go on. PARRY: Do you know anything about bookies? ELIZA: What makes you think I know anything about bookies? PARRY: A bookie didn’t pay me off once. ELIZA: What does that suggest to you? PARRY: I beat up the bookie who did not pay off, and I thought he might use his friends in the underworld to get even with me. ELIZA: What else would you like to discuss? PARRY: What do you know about racketeers? ELIZA: Why do you ask? PARRY: What experience have you had gambling? ELIZA: Why does that question interest you? PARRY: I like horse races. ELIZA: What else do you like? PARRY: I know the mob controls the big rackets. ELIZA: Who are you really talking about? PARRY: Have you ever really thought about the MAFIA? ELIZA: Suppose you ever really thought about the MAFIA. PARRY: Let’s talk about something else. ELIZA: I am not sure I understand you fully. PARRY: You keep asking me the same things, why not change the topic? ELIZA: In what way? PARRY: What do you mean? ELIZA: Why are such questions on your mind? PARRY: I thought I told you, people get on my nerves. ELIZA: Your nerves? PARRY: Well, I don’t know. ELIZA: Don’t you really know? PARRY: I would say so. ELIZA: Please go on. PARRY: I would rather not discuss that anymore. You are a real nag. ELISA: Does it please you to believe I am a real nag? PARRY: I have had enough of this. ELISA: What does that suggest to you? PARRY: Goodbye. ELISA: It’s been my pleasure, that’s $399.29 please. What’s interesting about the conversational tone and responses from the transcript appears to somewhat accurately portray those of a psychiatrist and paranoid schizophrenic. Although there is a definite gap in appearing to understand the deeper meaning behind the conversation this is one of the first times that two chatbots were connected together, and demonstrated that chatbots could be given distinct personalities. Evolution of Conversational AI Conversational AI systems in the 1960’s relied on having hard-coded static responses and per-determined conversational pathways based on specific user input or keywords. The the 1980’s this was updated to using statistical inference data models to determine the best pre-programmed response to send. Modern Conversational solutions now rely on pre-trained neural networks to create a natural language understanding platform which is capable of responding to a wide-rand of of user utterances without having to be explicitly programmed for each utterance. Key Technological Advances It is much easier to program boolean and logical operations. Such as responding yes or no to predefined prompts or selecting a menu option one through nine on a phone voice menu. These primitive systems really do not leverage for the Artificial Intelligence principles as they operate by using pre-programmed conversational pathways. Improvements in voice to text transcription, combined with increased computing resources as allowed for larger trading sets which are able to interpret and process more diverse natural language conversations as well as languages and dialects from around the world.	pressbooks	2025-03-22T05:08:51.625817	11-7-2024	{ "license": "Creative Commons - Attribution - https://creativecommons.org/licenses/by/4.0/", "url": "https://ecampusontario.pressbooks.pub/conversationalai/chapter/1-into-converstational-ai/#chapter-5-section-7", "book_url": "https://ecampusontario.pressbooks.pub/conversationalai/front-matter/forward/", "title": "A Pragmatic Guide to Conversational AI", "author": "Ross Bigelow", "institution": "Georgian College", "subject": "Artificial intelligence" }
https://ecampusontario.pressbooks.pub/conversationalai/chapter/1-into-converstational-ai/#chapter-5-section-8	Conversational AI Theory 1 Overview of Conversational AI What is Conversational AI? Conversational artificial intelligence can be defined is the application of computing technology to facilitate a natural conversation between computers and people. Conversational AI can be achieved using several different modes of communication including voice, text or chat. Voice is when the user speaks directly to the system and the AI agent can respond using a synthesized voice. Text is when the AI agent responds to a query and produces a textual reply. A chat is similar to text except it generally represents a number of short message exchanges between the user and the computer in near real-time. Some of the most well-known examples of conversational AI systems in use today are the digital assistants such as Amazon Alexa, Apple Siri, Google Assistant and IBM Watson. Benefits of Conversational AI Conversational AI systems can achieve numerous benefits over traditional computer systems including; - Voice can be a more efficient and convenient way to interface with machines. - A more intelligent system that can extract user intents then evaluate and determine correct responses without have to be pre-programmed for every condition or user utterance. - There are numerous business advantages to using a conversational AI system including achieving a consistent (and hopefully better) customer experience, a contact less customer experience, increases efficiency, reduction in operational costs and consistent up-selling opportunities. The Turning Test 1950 Allen Turning proposed a method of inquiry to determine whether a computer is capable of thinking liking a human being. His original work hypothesized that a computer could mimic human responses under specific conditions. This idea eventually evolved into what is now called the Turing test. The Turning Test is benchmark for Conversational AI system to evaluate how well it responds and mimics the responses of a human, such that it become indistinguishable from a real person to fulfill a specific task. If the user can not tell if the responses are coming from a real person or a computer. The system has “passed” the Turning Test. Notable Dates in Conversational AI History When PARRY met ELIZA In 1966 Joseph Weizenbaum developed Eliza. Eliza was programmed to respond to a number of keywords and produce responses that mimicked a psychotherapist. In 1972 Kenneth Colby developed Parry. Parry was program to model the behavior of a paranoid schizophrenic. In January 1973, as part of a international computer conference ELIZA and PARRY were connected together using the ARPNET. ELIZA was located at MIT and PARRY was at Stanford. Below is a transcript of their conversation. Transcript of ELIZA and PARRY conversation ELIZA: Good Evening. Tell me your problems. Please terminate input with a period or a question mark. PARRY: People get on my nerves sometimes. ELIZA: I am not sure I understand you fully. PARRY: You should pay more attention. ELIZA: Suppose you should pay more attention. PARRY: You’re entitled to your own opinion. ELIZA: What makes you think I am entitled to my own opinion? PARRY: It bothers me just to be around people in general. ELIZA: Please go on. PARRY: I went to the track at Bay Meadows a while back. ELIZA: What does that suggest to you? PARRY: I went to the races. ELIZA: What else would you like to discuss? PARRY: Horse racing interests me. ELIZA: Why do you say that just now? PARRY: There’s a lot of crooked gambling going on there. ELIZA: I am not sure I understand you fully, PARRY: It’s clear to me if not to you. ELIZA: Do you think it’s likely that not to I? PARRY: I went to the races. ELIZA: Please go on. PARRY: I gamble on horses. ELIZA: What does that suggest to you? PARRY: I went to the races. ELIZA: What else would you like to discuss? PARRY: Gambling has been nothing but trouble to me. ELIZA: Why do you say that just now? PARRY: I’ve had experience with crooks gambling at the track. ELIZA: I am not sure I understand you fully. PARRY: I’ve already told you enough about that. ELIZA: Oh? I enough about that? PARRY: I went to the races. ELIZA: Please go on. PARRY: Do you know anything about bookies? ELIZA: What makes you think I know anything about bookies? PARRY: A bookie didn’t pay me off once. ELIZA: What does that suggest to you? PARRY: I beat up the bookie who did not pay off, and I thought he might use his friends in the underworld to get even with me. ELIZA: What else would you like to discuss? PARRY: What do you know about racketeers? ELIZA: Why do you ask? PARRY: What experience have you had gambling? ELIZA: Why does that question interest you? PARRY: I like horse races. ELIZA: What else do you like? PARRY: I know the mob controls the big rackets. ELIZA: Who are you really talking about? PARRY: Have you ever really thought about the MAFIA? ELIZA: Suppose you ever really thought about the MAFIA. PARRY: Let’s talk about something else. ELIZA: I am not sure I understand you fully. PARRY: You keep asking me the same things, why not change the topic? ELIZA: In what way? PARRY: What do you mean? ELIZA: Why are such questions on your mind? PARRY: I thought I told you, people get on my nerves. ELIZA: Your nerves? PARRY: Well, I don’t know. ELIZA: Don’t you really know? PARRY: I would say so. ELIZA: Please go on. PARRY: I would rather not discuss that anymore. You are a real nag. ELISA: Does it please you to believe I am a real nag? PARRY: I have had enough of this. ELISA: What does that suggest to you? PARRY: Goodbye. ELISA: It’s been my pleasure, that’s $399.29 please. What’s interesting about the conversational tone and responses from the transcript appears to somewhat accurately portray those of a psychiatrist and paranoid schizophrenic. Although there is a definite gap in appearing to understand the deeper meaning behind the conversation this is one of the first times that two chatbots were connected together, and demonstrated that chatbots could be given distinct personalities. Evolution of Conversational AI Conversational AI systems in the 1960’s relied on having hard-coded static responses and per-determined conversational pathways based on specific user input or keywords. The the 1980’s this was updated to using statistical inference data models to determine the best pre-programmed response to send. Modern Conversational solutions now rely on pre-trained neural networks to create a natural language understanding platform which is capable of responding to a wide-rand of of user utterances without having to be explicitly programmed for each utterance. Key Technological Advances It is much easier to program boolean and logical operations. Such as responding yes or no to predefined prompts or selecting a menu option one through nine on a phone voice menu. These primitive systems really do not leverage for the Artificial Intelligence principles as they operate by using pre-programmed conversational pathways. Improvements in voice to text transcription, combined with increased computing resources as allowed for larger trading sets which are able to interpret and process more diverse natural language conversations as well as languages and dialects from around the world.	pressbooks	2025-03-22T05:08:51.646799	11-7-2024	{ "license": "Creative Commons - Attribution - https://creativecommons.org/licenses/by/4.0/", "url": "https://ecampusontario.pressbooks.pub/conversationalai/chapter/1-into-converstational-ai/#chapter-5-section-8", "book_url": "https://ecampusontario.pressbooks.pub/conversationalai/front-matter/forward/", "title": "A Pragmatic Guide to Conversational AI", "author": "Ross Bigelow", "institution": "Georgian College", "subject": "Artificial intelligence" }
https://ecampusontario.pressbooks.pub/conversationalai/chapter/1-into-converstational-ai/#chapter-5-section-9	Conversational AI Theory 1 Overview of Conversational AI What is Conversational AI? Conversational artificial intelligence can be defined is the application of computing technology to facilitate a natural conversation between computers and people. Conversational AI can be achieved using several different modes of communication including voice, text or chat. Voice is when the user speaks directly to the system and the AI agent can respond using a synthesized voice. Text is when the AI agent responds to a query and produces a textual reply. A chat is similar to text except it generally represents a number of short message exchanges between the user and the computer in near real-time. Some of the most well-known examples of conversational AI systems in use today are the digital assistants such as Amazon Alexa, Apple Siri, Google Assistant and IBM Watson. Benefits of Conversational AI Conversational AI systems can achieve numerous benefits over traditional computer systems including; - Voice can be a more efficient and convenient way to interface with machines. - A more intelligent system that can extract user intents then evaluate and determine correct responses without have to be pre-programmed for every condition or user utterance. - There are numerous business advantages to using a conversational AI system including achieving a consistent (and hopefully better) customer experience, a contact less customer experience, increases efficiency, reduction in operational costs and consistent up-selling opportunities. The Turning Test 1950 Allen Turning proposed a method of inquiry to determine whether a computer is capable of thinking liking a human being. His original work hypothesized that a computer could mimic human responses under specific conditions. This idea eventually evolved into what is now called the Turing test. The Turning Test is benchmark for Conversational AI system to evaluate how well it responds and mimics the responses of a human, such that it become indistinguishable from a real person to fulfill a specific task. If the user can not tell if the responses are coming from a real person or a computer. The system has “passed” the Turning Test. Notable Dates in Conversational AI History When PARRY met ELIZA In 1966 Joseph Weizenbaum developed Eliza. Eliza was programmed to respond to a number of keywords and produce responses that mimicked a psychotherapist. In 1972 Kenneth Colby developed Parry. Parry was program to model the behavior of a paranoid schizophrenic. In January 1973, as part of a international computer conference ELIZA and PARRY were connected together using the ARPNET. ELIZA was located at MIT and PARRY was at Stanford. Below is a transcript of their conversation. Transcript of ELIZA and PARRY conversation ELIZA: Good Evening. Tell me your problems. Please terminate input with a period or a question mark. PARRY: People get on my nerves sometimes. ELIZA: I am not sure I understand you fully. PARRY: You should pay more attention. ELIZA: Suppose you should pay more attention. PARRY: You’re entitled to your own opinion. ELIZA: What makes you think I am entitled to my own opinion? PARRY: It bothers me just to be around people in general. ELIZA: Please go on. PARRY: I went to the track at Bay Meadows a while back. ELIZA: What does that suggest to you? PARRY: I went to the races. ELIZA: What else would you like to discuss? PARRY: Horse racing interests me. ELIZA: Why do you say that just now? PARRY: There’s a lot of crooked gambling going on there. ELIZA: I am not sure I understand you fully, PARRY: It’s clear to me if not to you. ELIZA: Do you think it’s likely that not to I? PARRY: I went to the races. ELIZA: Please go on. PARRY: I gamble on horses. ELIZA: What does that suggest to you? PARRY: I went to the races. ELIZA: What else would you like to discuss? PARRY: Gambling has been nothing but trouble to me. ELIZA: Why do you say that just now? PARRY: I’ve had experience with crooks gambling at the track. ELIZA: I am not sure I understand you fully. PARRY: I’ve already told you enough about that. ELIZA: Oh? I enough about that? PARRY: I went to the races. ELIZA: Please go on. PARRY: Do you know anything about bookies? ELIZA: What makes you think I know anything about bookies? PARRY: A bookie didn’t pay me off once. ELIZA: What does that suggest to you? PARRY: I beat up the bookie who did not pay off, and I thought he might use his friends in the underworld to get even with me. ELIZA: What else would you like to discuss? PARRY: What do you know about racketeers? ELIZA: Why do you ask? PARRY: What experience have you had gambling? ELIZA: Why does that question interest you? PARRY: I like horse races. ELIZA: What else do you like? PARRY: I know the mob controls the big rackets. ELIZA: Who are you really talking about? PARRY: Have you ever really thought about the MAFIA? ELIZA: Suppose you ever really thought about the MAFIA. PARRY: Let’s talk about something else. ELIZA: I am not sure I understand you fully. PARRY: You keep asking me the same things, why not change the topic? ELIZA: In what way? PARRY: What do you mean? ELIZA: Why are such questions on your mind? PARRY: I thought I told you, people get on my nerves. ELIZA: Your nerves? PARRY: Well, I don’t know. ELIZA: Don’t you really know? PARRY: I would say so. ELIZA: Please go on. PARRY: I would rather not discuss that anymore. You are a real nag. ELISA: Does it please you to believe I am a real nag? PARRY: I have had enough of this. ELISA: What does that suggest to you? PARRY: Goodbye. ELISA: It’s been my pleasure, that’s $399.29 please. What’s interesting about the conversational tone and responses from the transcript appears to somewhat accurately portray those of a psychiatrist and paranoid schizophrenic. Although there is a definite gap in appearing to understand the deeper meaning behind the conversation this is one of the first times that two chatbots were connected together, and demonstrated that chatbots could be given distinct personalities. Evolution of Conversational AI Conversational AI systems in the 1960’s relied on having hard-coded static responses and per-determined conversational pathways based on specific user input or keywords. The the 1980’s this was updated to using statistical inference data models to determine the best pre-programmed response to send. Modern Conversational solutions now rely on pre-trained neural networks to create a natural language understanding platform which is capable of responding to a wide-rand of of user utterances without having to be explicitly programmed for each utterance. Key Technological Advances It is much easier to program boolean and logical operations. Such as responding yes or no to predefined prompts or selecting a menu option one through nine on a phone voice menu. These primitive systems really do not leverage for the Artificial Intelligence principles as they operate by using pre-programmed conversational pathways. Improvements in voice to text transcription, combined with increased computing resources as allowed for larger trading sets which are able to interpret and process more diverse natural language conversations as well as languages and dialects from around the world.	pressbooks	2025-03-22T05:08:51.668600	11-7-2024	{ "license": "Creative Commons - Attribution - https://creativecommons.org/licenses/by/4.0/", "url": "https://ecampusontario.pressbooks.pub/conversationalai/chapter/1-into-converstational-ai/#chapter-5-section-9", "book_url": "https://ecampusontario.pressbooks.pub/conversationalai/front-matter/forward/", "title": "A Pragmatic Guide to Conversational AI", "author": "Ross Bigelow", "institution": "Georgian College", "subject": "Artificial intelligence" }
https://ecampusontario.pressbooks.pub/conversationalai/chapter/1-into-converstational-ai/#chapter-5-section-10	Conversational AI Theory 1 Overview of Conversational AI What is Conversational AI? Conversational artificial intelligence can be defined is the application of computing technology to facilitate a natural conversation between computers and people. Conversational AI can be achieved using several different modes of communication including voice, text or chat. Voice is when the user speaks directly to the system and the AI agent can respond using a synthesized voice. Text is when the AI agent responds to a query and produces a textual reply. A chat is similar to text except it generally represents a number of short message exchanges between the user and the computer in near real-time. Some of the most well-known examples of conversational AI systems in use today are the digital assistants such as Amazon Alexa, Apple Siri, Google Assistant and IBM Watson. Benefits of Conversational AI Conversational AI systems can achieve numerous benefits over traditional computer systems including; - Voice can be a more efficient and convenient way to interface with machines. - A more intelligent system that can extract user intents then evaluate and determine correct responses without have to be pre-programmed for every condition or user utterance. - There are numerous business advantages to using a conversational AI system including achieving a consistent (and hopefully better) customer experience, a contact less customer experience, increases efficiency, reduction in operational costs and consistent up-selling opportunities. The Turning Test 1950 Allen Turning proposed a method of inquiry to determine whether a computer is capable of thinking liking a human being. His original work hypothesized that a computer could mimic human responses under specific conditions. This idea eventually evolved into what is now called the Turing test. The Turning Test is benchmark for Conversational AI system to evaluate how well it responds and mimics the responses of a human, such that it become indistinguishable from a real person to fulfill a specific task. If the user can not tell if the responses are coming from a real person or a computer. The system has “passed” the Turning Test. Notable Dates in Conversational AI History When PARRY met ELIZA In 1966 Joseph Weizenbaum developed Eliza. Eliza was programmed to respond to a number of keywords and produce responses that mimicked a psychotherapist. In 1972 Kenneth Colby developed Parry. Parry was program to model the behavior of a paranoid schizophrenic. In January 1973, as part of a international computer conference ELIZA and PARRY were connected together using the ARPNET. ELIZA was located at MIT and PARRY was at Stanford. Below is a transcript of their conversation. Transcript of ELIZA and PARRY conversation ELIZA: Good Evening. Tell me your problems. Please terminate input with a period or a question mark. PARRY: People get on my nerves sometimes. ELIZA: I am not sure I understand you fully. PARRY: You should pay more attention. ELIZA: Suppose you should pay more attention. PARRY: You’re entitled to your own opinion. ELIZA: What makes you think I am entitled to my own opinion? PARRY: It bothers me just to be around people in general. ELIZA: Please go on. PARRY: I went to the track at Bay Meadows a while back. ELIZA: What does that suggest to you? PARRY: I went to the races. ELIZA: What else would you like to discuss? PARRY: Horse racing interests me. ELIZA: Why do you say that just now? PARRY: There’s a lot of crooked gambling going on there. ELIZA: I am not sure I understand you fully, PARRY: It’s clear to me if not to you. ELIZA: Do you think it’s likely that not to I? PARRY: I went to the races. ELIZA: Please go on. PARRY: I gamble on horses. ELIZA: What does that suggest to you? PARRY: I went to the races. ELIZA: What else would you like to discuss? PARRY: Gambling has been nothing but trouble to me. ELIZA: Why do you say that just now? PARRY: I’ve had experience with crooks gambling at the track. ELIZA: I am not sure I understand you fully. PARRY: I’ve already told you enough about that. ELIZA: Oh? I enough about that? PARRY: I went to the races. ELIZA: Please go on. PARRY: Do you know anything about bookies? ELIZA: What makes you think I know anything about bookies? PARRY: A bookie didn’t pay me off once. ELIZA: What does that suggest to you? PARRY: I beat up the bookie who did not pay off, and I thought he might use his friends in the underworld to get even with me. ELIZA: What else would you like to discuss? PARRY: What do you know about racketeers? ELIZA: Why do you ask? PARRY: What experience have you had gambling? ELIZA: Why does that question interest you? PARRY: I like horse races. ELIZA: What else do you like? PARRY: I know the mob controls the big rackets. ELIZA: Who are you really talking about? PARRY: Have you ever really thought about the MAFIA? ELIZA: Suppose you ever really thought about the MAFIA. PARRY: Let’s talk about something else. ELIZA: I am not sure I understand you fully. PARRY: You keep asking me the same things, why not change the topic? ELIZA: In what way? PARRY: What do you mean? ELIZA: Why are such questions on your mind? PARRY: I thought I told you, people get on my nerves. ELIZA: Your nerves? PARRY: Well, I don’t know. ELIZA: Don’t you really know? PARRY: I would say so. ELIZA: Please go on. PARRY: I would rather not discuss that anymore. You are a real nag. ELISA: Does it please you to believe I am a real nag? PARRY: I have had enough of this. ELISA: What does that suggest to you? PARRY: Goodbye. ELISA: It’s been my pleasure, that’s $399.29 please. What’s interesting about the conversational tone and responses from the transcript appears to somewhat accurately portray those of a psychiatrist and paranoid schizophrenic. Although there is a definite gap in appearing to understand the deeper meaning behind the conversation this is one of the first times that two chatbots were connected together, and demonstrated that chatbots could be given distinct personalities. Evolution of Conversational AI Conversational AI systems in the 1960’s relied on having hard-coded static responses and per-determined conversational pathways based on specific user input or keywords. The the 1980’s this was updated to using statistical inference data models to determine the best pre-programmed response to send. Modern Conversational solutions now rely on pre-trained neural networks to create a natural language understanding platform which is capable of responding to a wide-rand of of user utterances without having to be explicitly programmed for each utterance. Key Technological Advances It is much easier to program boolean and logical operations. Such as responding yes or no to predefined prompts or selecting a menu option one through nine on a phone voice menu. These primitive systems really do not leverage for the Artificial Intelligence principles as they operate by using pre-programmed conversational pathways. Improvements in voice to text transcription, combined with increased computing resources as allowed for larger trading sets which are able to interpret and process more diverse natural language conversations as well as languages and dialects from around the world.	pressbooks	2025-03-22T05:08:51.688175	11-7-2024	{ "license": "Creative Commons - Attribution - https://creativecommons.org/licenses/by/4.0/", "url": "https://ecampusontario.pressbooks.pub/conversationalai/chapter/1-into-converstational-ai/#chapter-5-section-10", "book_url": "https://ecampusontario.pressbooks.pub/conversationalai/front-matter/forward/", "title": "A Pragmatic Guide to Conversational AI", "author": "Ross Bigelow", "institution": "Georgian College", "subject": "Artificial intelligence" }
https://ecampusontario.pressbooks.pub/conversationalai/chapter/2-natural-language-processing/	Conversational AI Theory 2 Natural Language Processing Developing a computer application that can accurately parse and interpret how humans communicate has been a decades long struggle. Natural language is defined as a language that has developed and evolved naturally, through use by human beings, as opposed to an invented or constructed language, as a computer programming language. The English language has around 600,000 words, within these words are complex grammatical constructs, different meanings and interpretations. This makes trying to develop a rule-based system that is capable of interpreting and speaking natural language next to impossible. History of Natural Language Processing Natural language processing is the technique that is used to decode, interpret and understand natural language. There have been essentially three generations of Virtual Language processing strategies that conversation all systems. These generations are Symbolic, Statistical and Neural. Symbolic Natural Language Processing was first introduced in the 1950s. Symbolic NLP functions by having a list of rules that the computer can use to evaluate and process language data. Symbolic systems were often used to solve language translation, as well as preform entity substitution into user utterances. For example a user might say “My hand hurts.” The chat bot could then reply with something like “Why does your hand hurt?” Unfortunately these types of systems lacked any contextual awareness. Statistical NLP By the 1990s the majority of conversational systems had moved to using statistical analysis of natural language. This type of processing required increased computing resources. The shift from Symbolic NLP to statistical represented a shift from fixed rules to probabilistic AI processes. These solutions relied on what is known as text corpora which is a sample of data (actual language) that contains meaningful context. This data can then be used to preform statistical analysis, hypothesis testing or validating linguistic rules. A corpus is a representative sample of actual language production within a meaningful context and with a general purpose. This can be thought of as training data for machine learning system. Source: https://odsc.medium.com/20-open-datasets-for-natural-language-processing-538fbfaf8e38 General \| \| \| Enron Dataset: \| Over half a million anonymized emails from over 100 users. It’s one of the few publicly available collections of “real” emails available for study and training sets. \| \| e Blogger Corpus: \| Nearly 700,000 blog posts from blogger.com. The meat of the blogs contain commonly occurring English words, at least 200 of them in each entry. \| \| SMS Spam Collection : \| Excellent dataset focused on spam. Nearly 6000 messages tagged as legitimate or spam messages with a useful subset extracted directly from Grumbletext. \| \| Recommender Systems Datasets : \| Datasets from a variety of sources, including fitness tracking, video games, song data, and social media. Labels include star ratings, time stamps, social networks, and images. \| \| Project Gutenberg : \| Extensive collection of book texts. These are public domain and available in a variety of languages, spanning a long period of time.\ \| \| Sentiment 140 : \| 160,000 tweets scrubbed of emoticons. They’re arranged in six fields — polarity, tweet date, user, text, query, and ID. \| \| MultiDomain Sentiment Analysis Dataset : \| Includes a wide range of Amazon reviews. Dataset can be converted to binary labels based on star review, and some product categories have thousands of entries. \| \| Yelp Reviews : \| Restaurant rankings and reviews. It includes a variety of aspects including reviews for sentiment analysis plus a challenge with cash prizes for those working with Yelp’s datasets. \| \| Dictionaries for Movies and Finance : \| Specific dictionaries for sentiment analysis using a specific field for testing data. Entries are clean and arranged in positive or negative connotations. \| \| OpinRank Dataset : \| 300,000 reviews from Edmunds and TripAdvisor. They’re neatly arranged by car model or by travel destination and relevant to the hotel. \| Text \| \| \| 20 Newsgroups : \| 20,000 documents from over 20 different newsgroups. The content covers a variety of topics with some closely related for reference. There are three versions, one in its original form, one with dates removed, and one with duplicates removed. \| \| The WikiQA Corpus : \| Contains question and sentence pairs. It’s robust and compiled from Bing query logs. There are over 3000 questions and over 29,000 answer sentences with just under 1500 labeled as answer sentences. \| \| European Parliament Proceedings Parallel Corpus : \| Sentence pairs from Parliament proceedings. There are entries from 21 European languages including some less common entries for ML corpus. \| \| Jeopardy : \| Over 200,000 questions from the famed tv show. It includes category and value designations as well as other descriptors like question and answer fields and rounds. \| \| Legal Case Reports Dataset : \| Text summaries of legal cases. It contains wrapups of over 4000 legal cases and could be great for training for automatic text summarization. \| Speech \| \| \| LibriSpeech : \| Nearly 1000 hours of speech in English taken from audiobook clips. \| \| Spoken Wikipedia Corpora : \| Spoken articles from Wikipedia in three languages, English, German, and Dutch. It includes a diverse speaker set and range of topics. There are hundreds of hours available for training sets. \| \| LJ Speech Dataset : \| 13,100 clips of short passages from audiobooks. They vary in length but contain a single speaker and include a transcription of the audio, which has been verified by a human reader. \| \| M-AI Labs Speech Dataset : \| Nearly 1000 hours of audio plus transcriptions. It includes multiple languages arranged by male voices, female voices, and a mix of the two. \| Neural Natural Language processing utilize deep neural network machine learning that results in enhanced language modeling and parsing. The majority of natural language processing solutions developed in the last 10 years generally use Neural NLP. The use of Neural networks greatly improves the ability of the capability of a NLP system to model, learn and reason. While also greatly reducing the amount of human perpetration of these systems. Key Natural Language concepts NLP Modeling – The modeling of natural language can refer to a number of different aspects such as encoding and decoding a sentence, creating sequence of labels. Using modern neural network in NLP this type of modeling allows for the breakdown of language into millions of trainable nodes that can preform syntactical semantic and sentiment analysis, language translation, topic extraction, classification and next sentence prediction. NLP Reasoning – Also known as common-sense reasoning is ability to allow computer to better interact and understand human interaction by collecting assumptions and extracting meaning behind the text provided but refining the assumptions throughout the duration of the conversation. In many ways common sense is the application of pragmatics in a conversational. Being situational aware of the context of the conversation is extremely important to interpreting meaning. This area of NLP, focusing on trying to reduce the “absurd” mistakes that NLP systems can make when they “jump to the wrong conclusion” due to a lack of commonsense. Natural Language Generation – This is the process by which the the machine learning algorithm creates natural language in such a way that is indistinguishable from a human response. This relies on constructing proper sentences that are suitable for the target demographics of users, without sounding to rigid or robotic. The key is to properly format and present the required response back to the user there are many ways to achive this one of the most common was is using statistical responses based from a large corpus of human text. Overview of a modern Conversational AI system. Natural Language Processing is an important part of a conversational AI system. It is this processing that is used to review the user’s input and prepare a reply back. Often conversational systems use voice interactions. In these cases the NLP is surrounded by a speech-to-text and text-to-speech process that is used to interact directly using voice. These voice replies utilize a synthetic voice engine which is modeled after the human voice. The goal of the synthesized voice is to provide a clear and easy to understand voice along while also attempting to mimic human speech inflections. \| Speech to Text Used to decode sound waves translate this into a textual representation of exactly what has been said. \| Natural Language Processing Evaluates the provided input, determines the correct response and formats the reply into natural language. \| Text to Speech The computer will formulate a response and translate a written text string into and auditory signal.. \| Natural Language is a language that has developed and evolved naturally, through use by human beings, as opposed to an invented or constructed language, as a computer programming language.	pressbooks	2025-03-22T05:08:51.709520	11-7-2024	{ "license": "Creative Commons - Attribution - https://creativecommons.org/licenses/by/4.0/", "url": "https://ecampusontario.pressbooks.pub/conversationalai/chapter/2-natural-language-processing/", "book_url": "https://ecampusontario.pressbooks.pub/conversationalai/front-matter/forward/", "title": "A Pragmatic Guide to Conversational AI", "author": "Ross Bigelow", "institution": "Georgian College", "subject": "Artificial intelligence" }
https://ecampusontario.pressbooks.pub/conversationalai/chapter/2-natural-language-processing/#chapter-22-section-1	Conversational AI Theory 2 Natural Language Processing Developing a computer application that can accurately parse and interpret how humans communicate has been a decades long struggle. Natural language is defined as a language that has developed and evolved naturally, through use by human beings, as opposed to an invented or constructed language, as a computer programming language. The English language has around 600,000 words, within these words are complex grammatical constructs, different meanings and interpretations. This makes trying to develop a rule-based system that is capable of interpreting and speaking natural language next to impossible. History of Natural Language Processing Natural language processing is the technique that is used to decode, interpret and understand natural language. There have been essentially three generations of Virtual Language processing strategies that conversation all systems. These generations are Symbolic, Statistical and Neural. Symbolic Natural Language Processing was first introduced in the 1950s. Symbolic NLP functions by having a list of rules that the computer can use to evaluate and process language data. Symbolic systems were often used to solve language translation, as well as preform entity substitution into user utterances. For example a user might say “My hand hurts.” The chat bot could then reply with something like “Why does your hand hurt?” Unfortunately these types of systems lacked any contextual awareness. Statistical NLP By the 1990s the majority of conversational systems had moved to using statistical analysis of natural language. This type of processing required increased computing resources. The shift from Symbolic NLP to statistical represented a shift from fixed rules to probabilistic AI processes. These solutions relied on what is known as text corpora which is a sample of data (actual language) that contains meaningful context. This data can then be used to preform statistical analysis, hypothesis testing or validating linguistic rules. A corpus is a representative sample of actual language production within a meaningful context and with a general purpose. This can be thought of as training data for machine learning system. Source: https://odsc.medium.com/20-open-datasets-for-natural-language-processing-538fbfaf8e38 General \| \| \| Enron Dataset: \| Over half a million anonymized emails from over 100 users. It’s one of the few publicly available collections of “real” emails available for study and training sets. \| \| e Blogger Corpus: \| Nearly 700,000 blog posts from blogger.com. The meat of the blogs contain commonly occurring English words, at least 200 of them in each entry. \| \| SMS Spam Collection : \| Excellent dataset focused on spam. Nearly 6000 messages tagged as legitimate or spam messages with a useful subset extracted directly from Grumbletext. \| \| Recommender Systems Datasets : \| Datasets from a variety of sources, including fitness tracking, video games, song data, and social media. Labels include star ratings, time stamps, social networks, and images. \| \| Project Gutenberg : \| Extensive collection of book texts. These are public domain and available in a variety of languages, spanning a long period of time.\ \| \| Sentiment 140 : \| 160,000 tweets scrubbed of emoticons. They’re arranged in six fields — polarity, tweet date, user, text, query, and ID. \| \| MultiDomain Sentiment Analysis Dataset : \| Includes a wide range of Amazon reviews. Dataset can be converted to binary labels based on star review, and some product categories have thousands of entries. \| \| Yelp Reviews : \| Restaurant rankings and reviews. It includes a variety of aspects including reviews for sentiment analysis plus a challenge with cash prizes for those working with Yelp’s datasets. \| \| Dictionaries for Movies and Finance : \| Specific dictionaries for sentiment analysis using a specific field for testing data. Entries are clean and arranged in positive or negative connotations. \| \| OpinRank Dataset : \| 300,000 reviews from Edmunds and TripAdvisor. They’re neatly arranged by car model or by travel destination and relevant to the hotel. \| Text \| \| \| 20 Newsgroups : \| 20,000 documents from over 20 different newsgroups. The content covers a variety of topics with some closely related for reference. There are three versions, one in its original form, one with dates removed, and one with duplicates removed. \| \| The WikiQA Corpus : \| Contains question and sentence pairs. It’s robust and compiled from Bing query logs. There are over 3000 questions and over 29,000 answer sentences with just under 1500 labeled as answer sentences. \| \| European Parliament Proceedings Parallel Corpus : \| Sentence pairs from Parliament proceedings. There are entries from 21 European languages including some less common entries for ML corpus. \| \| Jeopardy : \| Over 200,000 questions from the famed tv show. It includes category and value designations as well as other descriptors like question and answer fields and rounds. \| \| Legal Case Reports Dataset : \| Text summaries of legal cases. It contains wrapups of over 4000 legal cases and could be great for training for automatic text summarization. \| Speech \| \| \| LibriSpeech : \| Nearly 1000 hours of speech in English taken from audiobook clips. \| \| Spoken Wikipedia Corpora : \| Spoken articles from Wikipedia in three languages, English, German, and Dutch. It includes a diverse speaker set and range of topics. There are hundreds of hours available for training sets. \| \| LJ Speech Dataset : \| 13,100 clips of short passages from audiobooks. They vary in length but contain a single speaker and include a transcription of the audio, which has been verified by a human reader. \| \| M-AI Labs Speech Dataset : \| Nearly 1000 hours of audio plus transcriptions. It includes multiple languages arranged by male voices, female voices, and a mix of the two. \| Neural Natural Language processing utilize deep neural network machine learning that results in enhanced language modeling and parsing. The majority of natural language processing solutions developed in the last 10 years generally use Neural NLP. The use of Neural networks greatly improves the ability of the capability of a NLP system to model, learn and reason. While also greatly reducing the amount of human perpetration of these systems. Key Natural Language concepts NLP Modeling – The modeling of natural language can refer to a number of different aspects such as encoding and decoding a sentence, creating sequence of labels. Using modern neural network in NLP this type of modeling allows for the breakdown of language into millions of trainable nodes that can preform syntactical semantic and sentiment analysis, language translation, topic extraction, classification and next sentence prediction. NLP Reasoning – Also known as common-sense reasoning is ability to allow computer to better interact and understand human interaction by collecting assumptions and extracting meaning behind the text provided but refining the assumptions throughout the duration of the conversation. In many ways common sense is the application of pragmatics in a conversational. Being situational aware of the context of the conversation is extremely important to interpreting meaning. This area of NLP, focusing on trying to reduce the “absurd” mistakes that NLP systems can make when they “jump to the wrong conclusion” due to a lack of commonsense. Natural Language Generation – This is the process by which the the machine learning algorithm creates natural language in such a way that is indistinguishable from a human response. This relies on constructing proper sentences that are suitable for the target demographics of users, without sounding to rigid or robotic. The key is to properly format and present the required response back to the user there are many ways to achive this one of the most common was is using statistical responses based from a large corpus of human text. Overview of a modern Conversational AI system. Natural Language Processing is an important part of a conversational AI system. It is this processing that is used to review the user’s input and prepare a reply back. Often conversational systems use voice interactions. In these cases the NLP is surrounded by a speech-to-text and text-to-speech process that is used to interact directly using voice. These voice replies utilize a synthetic voice engine which is modeled after the human voice. The goal of the synthesized voice is to provide a clear and easy to understand voice along while also attempting to mimic human speech inflections. \| Speech to Text Used to decode sound waves translate this into a textual representation of exactly what has been said. \| Natural Language Processing Evaluates the provided input, determines the correct response and formats the reply into natural language. \| Text to Speech The computer will formulate a response and translate a written text string into and auditory signal.. \| Natural Language is a language that has developed and evolved naturally, through use by human beings, as opposed to an invented or constructed language, as a computer programming language.	pressbooks	2025-03-22T05:08:51.730449	11-7-2024	{ "license": "Creative Commons - Attribution - https://creativecommons.org/licenses/by/4.0/", "url": "https://ecampusontario.pressbooks.pub/conversationalai/chapter/2-natural-language-processing/#chapter-22-section-1", "book_url": "https://ecampusontario.pressbooks.pub/conversationalai/front-matter/forward/", "title": "A Pragmatic Guide to Conversational AI", "author": "Ross Bigelow", "institution": "Georgian College", "subject": "Artificial intelligence" }
https://ecampusontario.pressbooks.pub/conversationalai/chapter/2-natural-language-processing/#chapter-22-section-2	Conversational AI Theory 2 Natural Language Processing Developing a computer application that can accurately parse and interpret how humans communicate has been a decades long struggle. Natural language is defined as a language that has developed and evolved naturally, through use by human beings, as opposed to an invented or constructed language, as a computer programming language. The English language has around 600,000 words, within these words are complex grammatical constructs, different meanings and interpretations. This makes trying to develop a rule-based system that is capable of interpreting and speaking natural language next to impossible. History of Natural Language Processing Natural language processing is the technique that is used to decode, interpret and understand natural language. There have been essentially three generations of Virtual Language processing strategies that conversation all systems. These generations are Symbolic, Statistical and Neural. Symbolic Natural Language Processing was first introduced in the 1950s. Symbolic NLP functions by having a list of rules that the computer can use to evaluate and process language data. Symbolic systems were often used to solve language translation, as well as preform entity substitution into user utterances. For example a user might say “My hand hurts.” The chat bot could then reply with something like “Why does your hand hurt?” Unfortunately these types of systems lacked any contextual awareness. Statistical NLP By the 1990s the majority of conversational systems had moved to using statistical analysis of natural language. This type of processing required increased computing resources. The shift from Symbolic NLP to statistical represented a shift from fixed rules to probabilistic AI processes. These solutions relied on what is known as text corpora which is a sample of data (actual language) that contains meaningful context. This data can then be used to preform statistical analysis, hypothesis testing or validating linguistic rules. A corpus is a representative sample of actual language production within a meaningful context and with a general purpose. This can be thought of as training data for machine learning system. Source: https://odsc.medium.com/20-open-datasets-for-natural-language-processing-538fbfaf8e38 General \| \| \| Enron Dataset: \| Over half a million anonymized emails from over 100 users. It’s one of the few publicly available collections of “real” emails available for study and training sets. \| \| e Blogger Corpus: \| Nearly 700,000 blog posts from blogger.com. The meat of the blogs contain commonly occurring English words, at least 200 of them in each entry. \| \| SMS Spam Collection : \| Excellent dataset focused on spam. Nearly 6000 messages tagged as legitimate or spam messages with a useful subset extracted directly from Grumbletext. \| \| Recommender Systems Datasets : \| Datasets from a variety of sources, including fitness tracking, video games, song data, and social media. Labels include star ratings, time stamps, social networks, and images. \| \| Project Gutenberg : \| Extensive collection of book texts. These are public domain and available in a variety of languages, spanning a long period of time.\ \| \| Sentiment 140 : \| 160,000 tweets scrubbed of emoticons. They’re arranged in six fields — polarity, tweet date, user, text, query, and ID. \| \| MultiDomain Sentiment Analysis Dataset : \| Includes a wide range of Amazon reviews. Dataset can be converted to binary labels based on star review, and some product categories have thousands of entries. \| \| Yelp Reviews : \| Restaurant rankings and reviews. It includes a variety of aspects including reviews for sentiment analysis plus a challenge with cash prizes for those working with Yelp’s datasets. \| \| Dictionaries for Movies and Finance : \| Specific dictionaries for sentiment analysis using a specific field for testing data. Entries are clean and arranged in positive or negative connotations. \| \| OpinRank Dataset : \| 300,000 reviews from Edmunds and TripAdvisor. They’re neatly arranged by car model or by travel destination and relevant to the hotel. \| Text \| \| \| 20 Newsgroups : \| 20,000 documents from over 20 different newsgroups. The content covers a variety of topics with some closely related for reference. There are three versions, one in its original form, one with dates removed, and one with duplicates removed. \| \| The WikiQA Corpus : \| Contains question and sentence pairs. It’s robust and compiled from Bing query logs. There are over 3000 questions and over 29,000 answer sentences with just under 1500 labeled as answer sentences. \| \| European Parliament Proceedings Parallel Corpus : \| Sentence pairs from Parliament proceedings. There are entries from 21 European languages including some less common entries for ML corpus. \| \| Jeopardy : \| Over 200,000 questions from the famed tv show. It includes category and value designations as well as other descriptors like question and answer fields and rounds. \| \| Legal Case Reports Dataset : \| Text summaries of legal cases. It contains wrapups of over 4000 legal cases and could be great for training for automatic text summarization. \| Speech \| \| \| LibriSpeech : \| Nearly 1000 hours of speech in English taken from audiobook clips. \| \| Spoken Wikipedia Corpora : \| Spoken articles from Wikipedia in three languages, English, German, and Dutch. It includes a diverse speaker set and range of topics. There are hundreds of hours available for training sets. \| \| LJ Speech Dataset : \| 13,100 clips of short passages from audiobooks. They vary in length but contain a single speaker and include a transcription of the audio, which has been verified by a human reader. \| \| M-AI Labs Speech Dataset : \| Nearly 1000 hours of audio plus transcriptions. It includes multiple languages arranged by male voices, female voices, and a mix of the two. \| Neural Natural Language processing utilize deep neural network machine learning that results in enhanced language modeling and parsing. The majority of natural language processing solutions developed in the last 10 years generally use Neural NLP. The use of Neural networks greatly improves the ability of the capability of a NLP system to model, learn and reason. While also greatly reducing the amount of human perpetration of these systems. Key Natural Language concepts NLP Modeling – The modeling of natural language can refer to a number of different aspects such as encoding and decoding a sentence, creating sequence of labels. Using modern neural network in NLP this type of modeling allows for the breakdown of language into millions of trainable nodes that can preform syntactical semantic and sentiment analysis, language translation, topic extraction, classification and next sentence prediction. NLP Reasoning – Also known as common-sense reasoning is ability to allow computer to better interact and understand human interaction by collecting assumptions and extracting meaning behind the text provided but refining the assumptions throughout the duration of the conversation. In many ways common sense is the application of pragmatics in a conversational. Being situational aware of the context of the conversation is extremely important to interpreting meaning. This area of NLP, focusing on trying to reduce the “absurd” mistakes that NLP systems can make when they “jump to the wrong conclusion” due to a lack of commonsense. Natural Language Generation – This is the process by which the the machine learning algorithm creates natural language in such a way that is indistinguishable from a human response. This relies on constructing proper sentences that are suitable for the target demographics of users, without sounding to rigid or robotic. The key is to properly format and present the required response back to the user there are many ways to achive this one of the most common was is using statistical responses based from a large corpus of human text. Overview of a modern Conversational AI system. Natural Language Processing is an important part of a conversational AI system. It is this processing that is used to review the user’s input and prepare a reply back. Often conversational systems use voice interactions. In these cases the NLP is surrounded by a speech-to-text and text-to-speech process that is used to interact directly using voice. These voice replies utilize a synthetic voice engine which is modeled after the human voice. The goal of the synthesized voice is to provide a clear and easy to understand voice along while also attempting to mimic human speech inflections. \| Speech to Text Used to decode sound waves translate this into a textual representation of exactly what has been said. \| Natural Language Processing Evaluates the provided input, determines the correct response and formats the reply into natural language. \| Text to Speech The computer will formulate a response and translate a written text string into and auditory signal.. \| Natural Language is a language that has developed and evolved naturally, through use by human beings, as opposed to an invented or constructed language, as a computer programming language.	pressbooks	2025-03-22T05:08:51.748705	11-7-2024	{ "license": "Creative Commons - Attribution - https://creativecommons.org/licenses/by/4.0/", "url": "https://ecampusontario.pressbooks.pub/conversationalai/chapter/2-natural-language-processing/#chapter-22-section-2", "book_url": "https://ecampusontario.pressbooks.pub/conversationalai/front-matter/forward/", "title": "A Pragmatic Guide to Conversational AI", "author": "Ross Bigelow", "institution": "Georgian College", "subject": "Artificial intelligence" }

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