“My art is always an attempt to go beyond the borders of reality and give free rein to my imagination engulfing my heart. It’s a wild life and an irrepressible voice of color. The sun lives here, flaming and burning, directing every movement of my brush".

Looking to outsource hybrid architects? Discover the benefits of hybrid teams in architecture and how it can streamline businesses!

During the opening of the 18th Venice Biennale of Architecture on 19th of May 2023, ITSLIQUID Group is proud to present 'Hybrid Futures - Artificial Intelligence and Architecture', a panel discussion dedicated to AI (artificial intelligence) and the impact on contemporary architecture, part of BORDERS ART FAIR 2023, an international exhibition of artists and architects, which will take place in Venice at Palazzo Albrizzi-Capello.

Download this Ai Generated Portrait of a futuristic robot. An artistic abstract cyberpunk fantasy. Concept of a modern robot image on Freepik

The term Big Data has been around since 2005, but 90 percent of the data currently available was created in just the last few years. It makes sense that as technology grows, and more data is about to be captured and stored, the more we have to compile and analyze. In fact, people generate 2.5 quintillion bytes of data on a daily basis. Wading through this significantly large amount of data, when you’re feeling the pressure to make key business decisions for your organization, means you need analytics fast.

The Asia-Pacific region presents lucrative opportunities for data center service providers, fueled by the rapid expansion of digital infrastructure, data center modernization, and the adoption of cloud-native applications.

Advanced Intelligent Systems for Sustainable Development (Ai2sd'2019): Volume 4 - Advanced Intelligent Systems for Applied Computing Sciences

Tom Meltzer: Advances in technology have long been recognised as a threat to manual labour. Now highly skilled, knowledge-based jobs that were once regarded as safe could be at risk. How will these professions adapt to the digital age?

Le futur se dessine chez Kartell… Pionnier et visionnaire, le célèbre designer Philippe Starck continue à innover : il crée la première chaise conçue par une intelligence artificielle. Pour la dessiner, Starck a fait appel à Autodesk, une entreprise américaine spécialisée dans les logiciels de conception 3D. Pour concevoir la chaise A.I (Artificial Intelligence), Philippe Starck, Autodesk et Kartell ont posé une seule question cruciale à Dreamcatcher (le logiciel de conception générative développé par Autodesk) : ‘’Saurais-tu reposer notre corps en utilisant un minimum de matière et d’énergie ? Sans culture, sans souvenirs, sans influence, le logiciel a répondu seulement avec l’intelligence, son intelligence ‘’artificielle.’’. A.I est la première chaise conçue hors de notre cerveau, hors de nos habitudes à penser. La pensée créative humaine et l’intelligence artificielle ont donc fusionné pour concevoir une chaise totalement élaborée par un algorithme qui respecte les exigences d’origine : créer avec le moins de matière et d’énergie possible, un siège confortable, solide et conforme aux canons esthétiques des collections Kartell. La chaise A.I est le fruit d’un travail commun entre intelligence artificielle et intelligence humaine, définie par Starck lui-même comme ‘’Intelligence naturelle’’. Autodesk a déclaré que le processus de conception avec le logiciel d'intelligence artificielle rappelait ‘’l'apprentissage d'un enfant’’. Starck décrit ce processus comme très semblable à une conversation entre deux personnes humaines. ‘’Pourquoi les chaises se ressemblent-elles toutes ? Comment créer une chaise structurellement différente ? A un moment, j’ai eu besoin d’ouvrir une nouvelle porte…’’ explique Philippe Starck à propos de la genèse du projet A.I chair. Cette collaboration entre l’intelligence humaine et artificielle a démontré qu’une intelligence artificielle peut épauler le travail d’un designer en calculant quelles sont les réponses optimales à une idée de forme. La chaise A .I est un symbole de l’approche expérimentale du design et l’implication écologique du designer Philippe Starck. ‘’ Venu d’ailleurs, un nouveau monde s’ouvre désormais à nous. Un monde illimité… ‘’ a conclu Starck. Nul doute que la chaise A.I marque un tournant pour le futur du design. Mark Davis, directeur Design Futures d’Autodesk, déclare : ‘’Travailler avec Starck et Kartell a été une véritable source d’inspiration. L’opportunité de comprendre leur philosophie du design et leur approche de résolution des problèmes est inestimable pour notre équipe, dans le cadre de notre réflexion sur l’avenir de la fabrication. Il s’agit d’une collaboration de recherche à l’apogée du design industriel, qui a permis d’obtenir l’un des résultats les plus créatifs que nous ayons tenté d’atteindre avec le design génératif. Grâce à une relation de collaboration avec l'intelligence artificielle, les humains verront leur expertise en matière de conception et d'ingénierie être amplifiée, ce qui augmentera leur capacité d'innovation et de productivité.’’ Poursuivant sa recherche technologique, Kartell continue d’innover pour sélectionner de nouveaux matériaux toujours plus performants et respectueux de l’environnement. Le fauteuil A.I est fabriqué en technopolymère thermoplastique recyclé à 100 %, provenant de déchets industriels. Avec ce matériau, Kartell trace un nouveau chemin dans la durabilité environnementale, en utilisant des matériaux recyclés, tout en débarrassant l’environnement des déchets qui redeviennent matière première, activant ainsi un cercle vertueux d’économie circulaire.

This open access book explores cutting-edge solutions and best practices for big data and data-driven AI applications for the data-driven economy. It provides the reader with a basis for understanding how technical issues can be overcome to offer real-world solutions to major industrial areas. The book starts with an introductory chapter that provides an overview of the book by positioning the following chapters in terms of their contributions to technology frameworks which are key elements of the Big Data Value Public-Private Partnership and the upcoming Partnership on AI, Data and Robotics. The remainder of the book is then arranged in two parts. The first part \"Technologies and Methods\" contains horizontal contributions of technologies and methods that enable data value chains to be applied in any sector. The second part \"Processes and Applications\" details experience reports and lessons from using big data and data-driven approaches in processes and applications. Its chapters are co-authored with industry experts and cover domains including health, law, finance, retail, manufacturing, mobility, and smart cities. Contributions emanate from the Big Data Value Public-Private Partnership and the Big Data Value Association, which have acted as the European data community's nucleus to bring together businesses with leading researchers to harness the value of data to benefit society, business, science, and industry. The book is of interest to two primary audiences, first, undergraduate and postgraduate students and researchers in various fields, including big data, data science, data engineering, and machine learning and AI. Second, practitioners and industry experts engaged in data-driven systems, software design and deployment projects who are interested in employing these advanced methods to address real-world problems.

This book constitutes the refereed proceedings of six International Workshops held as parallel events of the 17th IFIP WG 12.5 International Conference on Artificial Intelligence Applications and Innovations, AIAI 2021, virtually and in Hersonissos, Crete, Greece, in June 2021: the 6th Workshop on 5G-Putting Intelligence to the Network Edge, 5G-PINE 2021; Artificial Intelligence in Biomedical Engineering and Informatics Workshop, AI-BIO 2021; Workshop on Defense Applications of AI, DAAI 2021; Distributed AI for Resource-Constrained Platforms Workshop, DARE 2021; Energy Efficiency and Artificial Intelligence Workshop, EEAI 2021; and the 10th Mining Humanistic Data Workshop, MHDW 2021. The 24 full papers and 16 short papers presented at these workshops were carefully reviewed and selected from 72 submissions. The papers presented at 5G-PINE focus on the latest AI applications in the telecommunication industry and AI in modern 5G-oriented telecommunications infrastructures. Thepapers chosen for AI-BIO 2021 present research on the subject of AI, in its broadest sense, in biomedical engineering and health informatics. The DAAI 2021 papers aim at presenting recent evolutions in artificial intelligence applicable to defense and security applications. The papers selected for DARE 2021 address a variety of pertinent and challenging topics within the scope of distributed AI for resource-constrained platforms. The papers presented at EEAI 2021 aim to bring together interdisciplinary approaches that focus on the application of AI-driven solutions for increasing and improving energy efficiency of residential and tertiary buildings and of occupant behavior. The MHDW papers focus on topics such as recommendation systems, sentiment analysis, pattern recognition, data mining, and time series.

Achieve your business goals and build highly available, scalable, and secure cloud infrastructure by designing robust and cost-effective solutions as a Google Cloud Architect. Key Features: Gain hands-on experience in designing and managing high-performance cloud solutionsLeverage Google Cloud Platform to optimize technical and business processes using cutting-edge technologies and servicesUse Google Cloud Big Data, AI, and ML services to design scalable and intelligent data solutions Book Description: Google has been one of the top players in the public cloud domain thanks to its agility and performance capabilities. This book will help you design, develop, and manage robust, secure, and dynamic solutions to successfully meet your business needs. You'll learn how to plan and design network, compute, storage, and big data systems that incorporate security and compliance from the ground up. The chapters will cover simple to complex use cases for devising solutions to business problems, before focusing on how to leverage Google Cloud's Platform-as-a-Service (PaaS) and Software-as-a-Service (SaaS) capabilities for designing modern no-operations platforms. Throughout this book, you'll discover how to design for scalability, resiliency, and high availability. Later, you'll find out how to use Google Cloud to design modern applications using microservices architecture, automation, and Infrastructure-as-Code (IaC) practices. The concluding chapters then demonstrate how to apply machine learning and artificial intelligence (AI) to derive insights from your data. Finally, you will discover best practices for operating and monitoring your cloud solutions, as well as performing troubleshooting and quality assurance. By the end of this Google Cloud book, you'll be able to design robust enterprise-grade solutions using Google Cloud Platform. What You Will Learn: Get to grips with compute, storage, networking, data analytics, and pricingDiscover delivery models such as IaaS, PaaS, and SaaSExplore the underlying technologies and economics of cloud computingDesign for scalability, business continuity, observability, and resiliencySecure Google Cloud solutions and ensure complianceUnderstand operational best practices and learn how to architect a monitoring solutionGain insights into modern application design with Google CloudLeverage big data, machine learning, and AI with Google Cloud Who this book is for: This book is for cloud architects who are responsible for designing and managing cloud solutions with GCP. You'll also find the book useful if you're a system engineer or enterprise architect looking to learn how to design solutions with Google Cloud. Moreover, cloud architects who already have experience with other cloud providers and are now beginning to work with Google Cloud will benefit from the book. Although an intermediate-level understanding of cloud computing and distributed apps is required, prior experience of working in the public and hybrid cloud domain is not mandatory.

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Machine Learning and Models for Optimization in Cloud's main aim is to meet the user requirement with high quality of service, least time for computation and high reliability. With increase in services migrating over cloud providers, the load over the cloud increases resulting in fault and various security failure in the system results in decreasing reliability. To fulfill this requirement cloud system uses intelligent metaheuristic and prediction algorithm to provide resources to the user in an efficient manner to manage the performance of the system and plan for upcoming requests. Intelligent algorithm helps the system to predict and find a suitable resource for a cloud environment in real time with least computational complexity taking into mind the system performance in under loaded and over loaded condition.This book discusses the future improvements and possible intelligent optimization models using artificial intelligence, deep learning techniques and other hybrid models to improve the performance of cloud. Various methods to enhance the directivity of cloud services have been presented which would enable cloud to provide better services, performance and quality of service to user. It talks about the next generation intelligent optimization and fault model to improve security and reliability of cloud.Key Features- Comprehensive introduction to cloud architecture and its service models.- Vulnerability and issues in cloud SAAS, PAAS and IAAS- Fundamental issues related to optimizing the performance in Cloud Computing using meta-heuristic, AI and ML models- Detailed study of optimization techniques, and fault management techniques in multi layered cloud.- Methods to improve reliability and fault in cloud using nature inspired algorithms and artificial neural network.- Advanced study of algorithms using artificial intelligence for optimization in cloud- Method for power efficient virtual machine placement using neural network in cloud- Method for task scheduling using metaheuristic algorithms.- A study of machine learning and deep learning inspired resource allocation algorithm for cloud in fault aware environment.This book aims to create a research interest & motivation for graduates degree or post-graduates. It aims to present a study on optimization algorithms in cloud for researchers to provide them with a glimpse of future of cloud computing in the era of artificial intelligence.