Название: Artificial Intelligence and Machine Learning Foundations: Learning from experience, 2nd EditionАвтор: Steve Lawless, Andrew Lowe
Издательство: BCS, The Chartered Institute for IT
Год: 2024
Страниц: 312
Язык: английский
Формат: pdf, epub, mobi
Размер: 10.7 MB
Unlock the potential of Artificial Intelligence (AI) in your organization with this updated, must-read guide, and discover how to leverage AI to drive innovation, improve efficiency, and gain a competitive edge.
This comprehensive guide dives into the latest AI ethics legislation, explore advanced robotics and Machine Learning, and discover how AI enhances human capabilities. Gain powerful insights into today’s AI applications and learn how emerging trends could reshape industries while addressing critical ethical challenges.
Its aim is to document what Artificial Intelligence and Machine Learning are and what they are not, separate fact from fiction and educate those with an interest in AI and Machine Learning. We have included a number of topics that introduce the basics of Machine Learning, and ethics. We believe that this book is relatively unique in that it brings together information and concepts in one book that are, until now, spread across numerous other volumes. The book also aims to simplify (where possible) complex and confusing concepts, making the topic highly accessible to those without a high-level degree in the subjects covered.
Deep Learning and Generative AI represent significant milestones in the journey of narrow ML; a step change in how we interact with technology to create new realms of possibility in art and science – these technologies are reshaping our world. As research progresses, we can anticipate even more innovative applications and breakthroughs, making Deep Learning and Generative AI integral components of our future.
Deep Learning has the ability to model and understand complex patterns in data. They are built on the use of artificial NNs that date back nearly a hundred years. In essence, they are a simple mathematical model of the human brain. Deep Learning models consist of multiple layers, including an input layer, several hidden layers and an output layer. Each layer contains nodes (neurons) that process and transform data. The depth of the network (i.e. the number of layers) enables it to learn hierarchical representations of data, capturing intricate features at different levels of abstraction. The term ‘deep’ captures the large number of layers we see in the large model.
Notable applications include autonomous vehicles, where Deep Learning algorithms enable perception and decision-making, and healthcare, where Deep Learning assists in diagnosing diseases and predicting patient outcomes. Moreover, Deep Learning models power recommendation systems, enhancing user experiences on platforms such as Netflix and Amazon. In Natural Language Processing (NLP), recurrent neural networks (RNNs) and transformers have significantly improved machine translation, sentiment analysis and language generation.
Generative AI creates new content rather than analysing existing data. This is achieved through generative models, which learn the underlying distribution of data and can produce novel samples that resemble the training data. Two prominent types of generative models are generative adversarial networks (GANs) and variational autoencoders (VAEs). GANs have been instrumental in generating high-quality images, videos and even music. They have applications in art, fashion and content creation, allowing for the synthesis of new, creative works.
Perfect for beginners and strategists alike, this book is your launchpad to crafting a forward-thinking AI strategy that meets modern standards and keeps your organization on the cutting edge of responsible AI. The new edition aligns to the updated BCS Certification in AI Foundation and Essentials, which this book supports as essential reading.
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