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Artificial intelligence. Robotics. Simulation. Graphics --- Beeldformaten --- Beeldformaat

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Artificial intelligence. Robotics. Simulation. Graphics --- Inkjetprinter --- Graphics industry --- Graphic arts

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De zoekmachine van Google, de tijdslijn van Facebook en Alexa van Amazon Echo: veel van onze dagelijkse technologie bevat kunstmatige of artificiële intelligentie. Stofzuiger- en grasmaairobots helpen ons in en om het huis, robotchirurgen voeren precisieoperaties uit en online voeren chatbots zoals Woebot therapeutische gesprekken. We kunnen zelfs morele beslissingen aan algoritmen uitbesteden. De combinatie van robots en AI leidt, samen met big data, tot eindeloos veel mogelijkheden.Ondertussen praten media ons angsten aan. Van zelfbewuste en uit vrije wil handelende robots is nog lang geen sprake - en wie weet wel nooit - maar er zijn reële ethische problemen die we niet mogen negeren. Welke beslissingen delegeren we aan machines en welke liever niet? En van 'wie' leren zelflerende systemen eigenlijk?Ethische vragen over ontwerp moeten gesteld worden vóór en tijdens het ontwerpproces: elk product dat door mensen gemaakt is, is immers made with morality. Daarom dit pleidooi voor ethische regels voor robots en AI-systemen, maar ook voor ingenieurs en andere ontwerpers. Innovatie gaat immers niet alleen om technologische vooruitgang, maar ook om het (h)erkennen van de morele keuzes die de technologie in zich draagt en daar bewust mee omspringen. https://www.aspeditions.be

General ethics --- Artificial intelligence. Robotics. Simulation. Graphics --- Artificial intelligence --- Internet --- Artificiële intelligentie --- Technologische vooruitgang --- Ondernemingsethiek --- Ethiek --- Robotica --- Vraagstuk (maatschappij) --- Technologie --- Digitalisering --- Innovatie --- Ontwerpen

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An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives. Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.

Probability theory --- Information systems --- Artificial intelligence. Robotics. Simulation. Graphics --- Mathematical linguistics --- analyse (wiskunde) --- Machine learning. --- Artificiële intelligentie --- Machine learning --- Learning, Machine --- Artificial intelligence --- Machine theory --- למידה חשובית --- Apprentissage automatique --- Machine Learning --- Apprentissage automatique. --- Transfer Learning --- Learning, Transfer --- Machinaal leren --- 681.3*I2 --- 681.3*I2 Artificial intelligence. AI --- Artificial intelligence. AI --- deep learning --- machine learning --- artificiële intelligentie (AI) --- Informatique --- Intelligence artificielle

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Artificial intelligence. Robotics. Simulation. Graphics --- Photoshop (informatica) --- Handboeken --- Informatica --- Fotografie --- Beeldcommunicatie