Simply AI : facts made fast / Hilary Lamb, consultant; Claire Quigley, contributor
Series Simply | Simply (DK Publishing, Inc.)Editor: New York, NY : DK Publishing, a division of Penguin Random House LLC, 2024Fecha de copyright: ♭2024Edición: Second edition; American editionDescripción: 160 pages : color illustrations ; 22 cmTipo de contenido:- text
- still image
- unmediated
- volume
- 9780593847053
- 0593847059
- AI
- Artificial intelligence
- 006.3 23
- Q335 .S56 2024
| Imagen de cubierta | Tipo de ítem | Biblioteca actual | Biblioteca de origen | Colección | Ubicación en estantería | Signatura topográfica | Materiales especificados | Info Vol | URL | Copia número | Estado | Notas | Fecha de vencimiento | Código de barras | Reserva de ítems | Prioridad de la cola de reserva de ejemplar | Reservas para cursos | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Libro mb | Biblioteca de Mayagüez Colección General mb | Q335 .S56 2024 (Navegar estantería(Abre debajo)) | c. 1 | Disponible | 52000003299198 | |||||||||||||
| Libro | Biblioteca Encarnación Valdés Colección General bev | 006.3 S6124 2024 (Navegar estantería(Abre debajo)) | Disponible | 80000002664988 |
Descripciones mejoradas de Syndetics:
Combining clear labeled imagery with easy-to-understand text, this new edition of Simply Artificial Intelligence is the perfect introduction to the latest developments in AI, including ChatGPT and the Internet of Things.
Covering a broad range of fields within AI - from computing and mathematics to politics and philosophy - entries demystify what artificial intelligence is and how it works, how it has dramatically changed how we live, and how it might evolve in the future. Everyone is talking about AI, but this book helps to explain each individual aspect of AI more clearly than ever before.
This AI book:
-Outlines the key building blocks and technological milestones in its history, profiles its most important practical applications - both current and predicted
-Explores the ethical debates around AI and its increasing influence on culture and society.
-Provides an accessible, reader-friendly guide with detailed imagery and annotated illustrations, expert insights, and jargon-free text
-Studies and experiments such as The Turing Test and The Chinese Room experiment
This book is part of the Simply Series, which includes books such as Simply Astronomy, Simply Psychology, Simply Maths , and more. Whether you are studying science or AI-related subjects at school or college or simply want a jargon-free overview of this increasingly important subject, this essential guide is packed with everything you need to understand the basics quickly and easily.
Includes index.
"Previously printed as Simply Artificial Intelligence"--Cover.
Introduction -- History of artificial intelligence. An imitation of life -- Defining intelligence -- Thinking=computing -- Zeros and ones -- Ste by step -- Algorithms in action -- Instructing computers -- The first mechanical computers -- A mechanical computers -- A theoretical computer -- An electric brain -- Artificial neurons -- A programmable computer -- A theoretical program -- A computing blueprint -- Two kinds of AI -- AI in action -- Trial and error -- Mimicking the brain -- AI models -- Computing power -- Raw information -- Everything, everywhere, all of the time -- Classical artificial intelligence. Representing data. Following the rules -- What, when, why, and how? -- Presenting knowledge -- If this, then that -- The shortest route -- Imperfect solutions -- Performing a task -- Dealing with uncertainty -- Automated advice -- Handling 'messy' data -- Neats vs. scruffies -- Statistical artificial intelligence. Teaching AIs to think -- Gaining insight from data -- Teaching materials -- Giving data meaning -- Looking for patterns -- Yes or no? -- Types of data -- The line of best fit -- Grouping data -- The odd one out -- The most likely outcome? -- Machine learning with 'labeled' data -- Learning from feedback -- Working together -- The AI brain -- Network structure -- Assigning importance -- Goals and thresholds -- Measuring success -- Improving performance -- Refining the model -- A one-way network -- Fine-tuning data -- Structured data -- Building a brain -- AI vs. AI -- Processing visual data -- Using artificial intelligence. Using artificial intelligence. Uses of AI -- Ranking -- Recommending -- Detecting threats -- Online attacks -- Detecting fraud -- AI in finance -- Unraveling proteins -- Searching for planets -- Digital doctors -- Monitoring health -- Internet of things -- Smart devices -- Monitoring systems -- 'Smart farming -- Sensory AI -- Processing sound -- Mimicking sight -- Facial recognition -- Understanding words -- AI interpreters -- Talking with AI -- AI writers -- AI helpers -- AI artists -- Intelligent robots -- AI companions -- Movement and mobility -- Manual dexterity -- Driverless cars -- AI and warfare -- Philosophy of artificial intelligence. Humanlike AI -- The point of no return -- Where is consciousness? -- Do submarines swim? -- The imitation game -- Intelligence metrics -- Machines and understanding -- Philosophical zombies -- A new kind of person -- Replicating the mind -- Transparent thinking -- Living with artificial intelligence. Myth or reality? -- Powered by exploitation? -- Garbage in, garbage out -- Prejudiced outcomes -- Making assumptions -- Transparent processing -- An AI workforce -- The AI balance -- An echo chamber -- The limits of control -- Right vs. wrong -- Built-in ethics -- Who is to blame? -- What should we allow? -- Existential risks -- Unlimited rewards.
Covering a broad range of fields within AI - from computing and mathematics to politics and philosophy - entries demystify what artificial intelligence is and how it works, how it has dramatically changed how we live, and how it might evolve in the future. Everyone is talking about AI, but this book helps to explain each individual aspect of AI more clearly than ever before.
Tabla de contenidos provista por Syndetics
- Introduction(7)
- History of Artificial Intelligence
- An Imitation of Life Automata(10)
- Defining Intelligence Multiple Intelligences(11)
- Thinking = Computing Computationalism(12)
- Zeros and Ones Binary code(13)
- Step by Step Algorithms(14)
- Algorithms in Action Computation(15)
- Instructing Computers Programs(16)
- The First Mechanical Computers Babbage's machines(17)
- A Theoretical Computer Turing's universal machine(18)
- An Electric Brain Neurons and computation(20)
- Artificial Neurons Threshold logic units(21)
- A Programmable Computer ENIAC(22)
- A Theoretical Program Turochamp(23)
- A Computing Blueprint Von Neumann architecture(24)
- Two Kinds of AI Weak and strong AI(26)
- AI in Action Intelligent agents(27)
- Trial and Error Learning to learn(28)
- Mimicking the Brain Connectionism(29)
- AI Models Classical vs. statistical AI(30)
- Computing Power Moore's law(31)
- Raw Information Types of data(32)
- Everything, Everywhere, All of the Time Big data(33)
- Classical Artificial Intelligence
- Representing Data Symbols in AI(36)
- Following the Rules Computer Logic(37)
- What, When, Why, and How? Kinds of knowledge(38)
- Presenting Knowledge Knowledge representation(39)
- If This, Then That Rules(40)
- The Shortest Route Pathfinding(42)
- Imperfect Solutions Heuristics(43)
- Performing a Task Planning and AI(44)
- Dealing with Uncertainty Probability and AI(46)
- Modeling Changes The Markov chain(48)
- Modeling Uncertainty Stochastic models(49)
- Automated Advice Expert systems(50)
- Handling "Messy" Data Messiness(52)
- Neats vs. Scruffies Two fields of AI research(54)
- Statisitcal Artificial Intelligence
- Teaching AIs to Think Machine learning(58)
- Gaining Insight from Data Data mining(60)
- Teaching Materials Training data(61)
- Giving Data Meaning Features and labels(62)
- Looking for Patterns Pattern recognition(64)
- Yes or No? Decision trees(65)
- Types of Data Classification(66)
- The Line of Best Fit Regression(67)
- Grouping Data Clustering(68)
- The Odd One Out Anomaly detection(69)
- The Most Likely Outcome? Predictions(70)
- Machine Learning with "Labeled" Data Supervised learning(72)
- Machine Learning with "Raw" Data Unsupervised learning(73)
- Learning from Feedback Reinforcement learning(74)
- Working Together Ensemble learning(75)
- The AI Brain Artificial neural networks(76)
- Network Structure Layers(77)
- Assigning Importance Weighting(78)
- Goals and Thresholds Bias(79)
- Measuring Success Cost function(80)
- Improving Performance Gradient descent(81)
- Refining the Model The Delta rule(82)
- A One-Way Network Feedforward neural networks(83)
- Fine-Tuning Data Backpropagation(84)
- Structured Data Recurrent neural networks(85)
- Building a Brain Deep learning(86)
- AI vs. AI Generative adversarial networks(87)
- Processing Visual Data Convolutional neural networks(88)
- Using Artificial Intelligence
- Uses of AI Applications of AI(92)
- Ranking Data hierarchies(94)
- Recommending Tailored content(95)
- Detecting Threats Cybersecurity(96)
- Online Attacks Cyber warfare(97)
- Detecting Fraud Transaction monitoring(98)
- AI in Finance Algorithmic trading(99)
- Unraveling Proteins Medical research(100)
- Searching for Planet Astronomical research(101)
- Digital Doctors AI in medical diagnosis(102)
- Monitoring Health AI and healthcare(103)
- Internet of Things Connected devices(104)
- Smart Devices Embedded AI(105)
- Monitoring Systems AI and infrastructure(106)
- "Smart" Farming Precision agriculture(107)
- Sensory AI Machine perception(108)
- Processing Sound Machine hearing(109)
- Mimicking Sight Computer vision(110)
- Facial Recognition Feature mapping(111)
- Understanding Words Natural language processing(112)
- AI Interpreters Machine translation(113)
- Talking with AI Chatbots(114)
- AI Write Large language models(115)
- AI Helpers Virtual assistants(116)
- AI Artists Generative AI(117)
- Intelligent Robots Embodied AI(118)
- AI Companions Social robots(119)
- Movement and Mobility Physical interactions I(120)
- Manual Dexterity Physical interactions II(121)
- Driverless Cars Autonomous vehicles(122)
- AI and Warfare Autonomous weapons(123)
- Philosophy of Artificial Intelligence
- Humanlike AI Artificial general intelligence(126)
- The Point of No Return The technological singularity(127)
- Where is Consciousness? Leibniz's question(128)
- Do Submarines Swim? Functionalism(129)
- The Imitation Game The Turing test(130)
- Intelligence Metrics Intelligence tests(132)
- Machines and Understanding The Chinese Room experiment(133)
- Philosophical Zombies Human vs. machine intelligence(134)
- A New Kind of Person AI rights and responsibilities(135)
- Replicating the Mind Multiple readability(136)
- Transparent Thinking Opening the box(137)
- Living with Artificial Intelligence
- Myth or Reality? The truth about AI(140)
- Powered by Exploitation? Invisible labor(141)
- Garbage In, Garbage Out Data quality(142)
- Prejudiced Outcomes Hidden bias(143)
- Making Assumptions AI profiling(144)
- Transparent Processing White box AI(145)
- An AI Workforce Technological unemployment(146)
- The AI Balance AI and equality(147)
- An Echo Chamber Filter bubbles(148)
- The Limits of Control AI autonomy(149)
- Right vs. Wrong Ethical design(150)
- Built-In Ethics Asimov's three laws(151)
- Who is to Blame? AI and liability(152)
- What Should We Allow? AI and regulation(153)
- Existential Risks An AI dystopia(154)
- Unlimited Rewards An AI Utopia(155)
Notas de autor provistas por Syndetics
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