Artificial Intelligence (AI) is a way to train computers to carry out a range of complex tasks and to learn from these tasks. It uses a process called machine learning to identify patterns in images, texts, and other materials. This process can involve human input or be fully automated. Generative AI is a subset of artificial intelligence designed to create and produce new content using algorithms to generate images, text, and music. Generative AI models like ChatGPT replicate the structure and style using patterns from data they have been trained on to generate responses.
In order for the computer to generate new material, it needs to:
NYU Library' Machines and Society guide is a wonderful resource for learning more about Large Language Models as well as an overview of ChatGPT.
Artificial Intelligence by
Artificial Intelligenceis a compendium of articles and papers that were presented at ICAI '16, an international conference that serves researchers, scholars, professionals, students, and academicians.
Artificial Intelligence: Work, Machines and Human Interaction by
Today, many Americans are concerned about the impact that artificial intelligence and machine learning will have on jobs. This book examines the impact of these factors on the workforce, including issues related to worker displacement, retraining of the current workforce, and developing a skilled technical workforce of the future that can thrive in an economy in which AI increasingly plays a role.
Artificial Intelligence for Digitising Industry - Applications by
This book provides in-depth insights into use cases implementing artificial intelligence (AI) applications at the edge. It covers new ideas, concepts, research, and innovation to enable the development and deployment of AI, the industrial internet of things (IIoT), edge computing, and digital twin technologies in industrial environments. The work is based on the research results and activities of the AI4DI project, including an overview of industrial use cases, research, technological innovation, validation, and deployment. This book's sections build on the research, development, and innovative ideas elaborated for applications in five industries: automotive, semiconductor, industrial machinery, food and beverage, and transportation. The articles included under each of these five industrial sectors discuss AI-based methods, techniques, models, algorithms, and supporting technologies, such as IIoT, edge computing, digital twins, collaborative robots, silicon-born AI circuit concepts, neuromorphic architectures, and augmented intelligence, that are anticipating the development of Industry 5.0. Automotive applications cover use cases addressing AI-based solutions for inbound logistics and assembly process optimisation, autonomous reconfigurable battery systems, virtual AI training platforms for robot learning, autonomous mobile robotic agents, and predictive maintenance for machines on the level of a digital twin. AI-based technologies and applications in the semiconductor manufacturing industry address use cases related to AI-based failure modes and effects analysis assistants, neural networks for predicting critical 3D dimensions in MEMS inertial sensors, machine vision systems developed in the wafer inspection production line, semiconductor wafer fault classifications, automatic inspection of scanning electron microscope cross-section images for technology verification, anomaly detection on wire bond process trace data, and optical inspection. The use cases presented for machinery and industrial equipment industry applications cover topics related to wood machinery, with the perception of the surrounding environment and intelligent robot applications. AI, IIoT, and robotics solutions are highlighted for the food and beverage industry, presenting use cases addressing novel AI-based environmental monitoring; autonomous environment-aware, quality control systems for Champagne production; and production process optimisation and predictive maintenance for soybeans manufacturing. For the transportation sector, the use cases presented cover the mobility-as-a-service development of AI-based fleet management for supporting multimodal transport. This book highlights the significant technological challenges that AI application developments in industrial sectors are facing, presenting several research challenges and open issues that should guide future development for evolution towards an environment-friendly Industry 5.0. The challenges presented for AI-based applications in industrial environments include issues related to complexity, multidisciplinary and heterogeneity, convergence of AI with other technologies, energy consumption and efficiency, knowledge acquisition, reasoning with limited data, fusion of heterogeneous data, availability of reliable data sets, verification, validation, and testing for decision-making processes.
The Democratization of Artificial Intelligence by
After a long time of neglect, Artificial Intelligence is once again at the center of most of our political, economic, and socio-cultural debates. Recent advances in the field of Artifical Neural Networks have led to a renaissance of dystopian and utopian speculations on an AI-rendered future. Algorithmic technologies are deployed for identifying potential terrorists through vast surveillance networks, for producing sentencing guidelines and recidivism risk profiles in criminal justice systems, for demographic and psychographic targeting of bodies for advertising or propaganda, and more generally for automating the analysis of language, text, and images. Against this background, the aim of this book is to discuss the heterogenous conditions, implications, and effects of modern AI and Internet technologies in terms of their political dimension: What does it mean to critically investigate efforts of net politics in the age of machine learning algorithms? Rezension»Eine ungewöhnlich perspektivenreiche Publikation.Interessant [...] für Lehrende und Forschende, die nach neuen Denkanstößen suchen.«Alexander Godulla, Communicatio Socialis, 4 (2020)Besprochen in:https://bookskeeper.ru, 17.05.2021
Kant and Artificial Intelligence by
How are artificial intelligence (AI) and the strong claims made by their philosophical representatives to be understood and evaluated from a Kantian perspective? Conversely, what can we learn from AI and its functions about Kantian philosophy's claims to validity? This volume focuses on various aspects, such as the self, the spirit, self-consciousness, ethics, law, and aesthetics to answer these questions.
This 15 minute video from Google Cloud Tech provides a more thorough introduction to Large Language Models.
This three hour talk, with questions, by Stephen Wolfram (Founder of Wolfram/Mathematica) was originally streamed on Twitch on February 17, 2023. The talk begins starts at 9:53.
