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It explains AI at its core as computer systems performing tasks that typically require human intelligence, tracing how definitions changed from early symbolic, rule-based approaches to machine learning and deep learning. The chapter also highlights the influence of John Hopfield and Geoffrey Hinton, discusses modern AI capabilities, and outlines upcoming sections covering AI, ML, deep learning, models, algorithms, training methods, inference types, and data types.",{"@graph":69,"@context":123},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":20,"@type":76,"position":81},"https://docshare.wps.com/document/exam/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/chapter-1-basic-ai-concepts-and-terminology/409940/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/chapter-1-basic-ai-concepts-and-terminology/409940.png","ImageObject",300,407,{"name":92,"@type":93},"acoobca","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-09","2026-09-28",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",14,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"What is artificial intelligence, and what tasks does it cover?","Question",{"text":113,"@type":114},"Artificial intelligence refers to computer systems capable of performing tasks that typically require human intelligence, such as visual perception, speech recognition, decision-making, and language translation.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"How did the definition and approach to AI evolve since the term was coined in 1956?",{"text":118,"@type":114},"AI evolved from early symbolic, rule-based methods (GOFAI) toward machine learning approaches where systems learn from data, and later toward deep learning using neural networks.",{"name":120,"@type":111,"acceptedAnswer":121},"Which training and inference concepts does this chapter introduce next?",{"text":122,"@type":114},"The chapter sets up follow-on topics including how models are trained (supervised, unsupervised, reinforcement learning) and the types of inferencing (batch, real-time, and async), along with data types used in AI models.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},409940,1791330405,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":19,"category_name":20,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":140,"language":141,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":142,"faqs":143,"seo_title":144,"seo_description":67,"update_tm":145,"read_time":146,"preview_page_count":4},3985747870733,"https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd","Chapter  \n1  \nBasic AI Conceptsand Terminology  \nTHE AWS CERTIFIED AI PRACTITIONER EXAM OBJECTIVES COVERED IN THIS CHAPTER MAY INCLUDE:  \n✔ Domain 1: Fundamentals ofAI and ML  \n▪ Task Statement 1.1: Explain basic AI concepts and terminology.  \n4 Chapter 1 ▪ Basic AI Concepts andTerminology  \nArtificial intelligence (AI) has become one of the most transformative technologies of our time, revolutionizing industries, reshaping our daily lives, and challenging our understanding of human cognition. But what exactly is AI, and how has our understanding of it evolved over the decades?  \nAt its core, artificial intelligence refers to the development of computer systems capable of performing tasks that typically require human intelligence. These tasks include visual perception, speech recognition, decision-making, and language translation, among others. However, the definition of AI has been far from static, evolving significantly since the term was first coined in 1956 at the Dartmouth Conference. AI has come a long way since then—atthe time of writing this book, the 2024 Nobel Prize in Physics has been awarded to John Hopfield and Geoffrey Hinton for their fundamental discoveries in Machine Learning, which have directly or indirectly lead to all the advanced AI that we see around us today.  \n\n| The First Nobel Prize for Work in AI |\n| --- |\n| The 2024 Nobel Prize in Physics shines a spotlight on the fascinating intersection of artificial intelligence and fundamental physics. John J. Hopfield and Geoffrey E. Hinton, two visionaries in the field, have been honored for their groundbreaking contributions that have reshaped our understanding of both machine and human intelligence.\u003Cbr>Hopfield’s ingenious creation, the Hopfield network, emerged in the 1980s as a revolutionary approach to mimicking the human brain’s ability to store and recall information. This clever system uses binary patterns to represent memories, much like the on-off firing of neurons in our brains. What makes Hopfield’s work particularly intriguing is its ability to retrieve complete memories from partial or noisy inputs—imagine recognizing a friend’s face in a blurry photo, and you’ll get the idea of what these networks can do.\u003Cbr>Geoffrey Hinton, affectionately dubbed the “godfather of deep learning,” has been instrumental in bringing neural networks to life. His co-invention of the backpropagation algorithm in the 1980s was nothing short of revolutionary. This mathematical technique allows neural networks to learn from their mistakes, gradually refining their performance through a process of trial and error. It’s akin to how humans learn—by recognizing our errors and adjusting our approach. |\n\nBasic AI Concepts andTerminology 5  \nThe impact of Hopfield and Hinton’s work extends far beyond the realm of computer science. Their theories have paved the way for AI to tackle complex problems in fields as diverse as healthcare, autonomous driving, and even artistic creation. Imagine AI systems that can detect diseases from medical images with uncanny accuracy, or virtual assistants that can engage in natural conversations—these are direct descendants of the foundational work done by our Nobel laureates.  \nAs we look to the future, the theories proposed by Hopfield and Hinton continue to  \nfuel innovation in AI. Their work has opened doors to understanding not just artificial intelligence, but human intelligence as well. Who knows?The next breakthrough in understanding consciousness or cognition might very well stem from the seeds planted by these pioneering minds.  \nIn its early days, AI was primarily focused on mimicking human reasoning through symbolic logic and rule-based systems. This approach, known as “Good Old-Fashioned AI”(GOFAI), aimed to create machines that could solve complex problems by manipulating symbols according to predefined rules. The goal was to build systems that could reason about the world in a way similar to humans.  \nAs research progressed, it be","cbCaikVI1lzqYUgA","https://ap.wps.com/l/cbCaikVI1lzqYUgA","pdf",613949,32,"English","# Basic AI Concepts and Terminology\n## Fundamentals of AI and ML\n## What Is Artificial Intelligence\n## Evolution: From GOFAI to Machine Learning\n## Deep Learning and Modern AI\n## Chapter Scope: Key Terms and Core Components","[{\"question\":\"What is artificial intelligence, and what tasks does it cover?\",\"answer\":\"Artificial intelligence refers to computer systems capable of performing tasks that typically require human intelligence, such as visual perception, speech recognition, decision-making, and language translation.\"},{\"question\":\"How did the definition and approach to AI evolve since the term was coined in 1956?\",\"answer\":\"AI evolved from early symbolic, rule-based methods (GOFAI) toward machine learning approaches where systems learn from data, and later toward deep learning using neural networks.\"},{\"question\":\"Which training and inference concepts does this chapter introduce next?\",\"answer\":\"The chapter sets up follow-on topics including how models are trained (supervised, unsupervised, reinforcement learning) and the types of inferencing (batch, real-time, and async), along with data types used in AI models.\"}]","Chapter 1 - Basic AI Concepts and Terminology | PDF",1790583736,81]