Название: Understanding Artificial Intelligence: Fundamentals, Use Cases and Methods for a Corporate AI JourneyАвтор: Ralf T. Kreutzer
Издательство: Springer
Год: 2024
Страниц: 491
Язык: английский
Формат: pdf (true)
Размер: 19.7 MB
This book on Artificial Intelligence (AI) explores its transformative potential for individuals and businesses. It covers AI basics and its applications across various industries, presenting AI as a foundational technology that will impact all aspects of life and the economy. The author emphasizes the need for responsible AI usage and introduces the concept of the "AI Journey" for businesses to leverage AI's potential. The second edition is updated with recent developments, including large language models like Aleph Alpha and ChatGPT, Generative AI, affective computing, and ethical considerations. It also discusses open-source solutions, legal frameworks, and practical use cases. Recommended for leaders, decision-makers, students, professors, and anyone interested in understanding AI's future impact.
In Machine Learning, programs learn from existing data and apply this knowledge to new data or use it to predict data. In Machine Learning, the AI systems independently develop new learning algorithms and improve existing ones. These algorithms make it possible to analyze large amounts of complex data and handle various tasks. A special configuration of neural networks and a subset of Machine Learning is the so-called Deep Learning. The algorithms of Deep Learning have several layers of neural networks that process information on many levels. Before the development of Deep Learning, artificial neural networks often only had three layers. Deep Learning networks today often have ten or more layers. The “Deep” therefore refers to the large number of layers of the neural network. The biggest difference between Deep Learning and other Machine Learning techniques is that larger neural networks continuously improve their performance through larger amounts of data. Deep Learning achieves better performance especially when processing complex and often high-dimensional data such as images, speech and text than Machine Learning.
Convolutional Neural Networks (CNN) function similarly to ordinary neural networks. The only difference is that the connections between the neural layers resemble the part of the brain that processes images. These architectures are programmed to perceive each input as an image. Recurrent Neural Networks (RNN) differ from other neural networks in their architecture. The neurons are connected in such a way that they can send feedback signals to each other. As a result, the information loops from layer to layer.
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