We are pleased to welcome you to the “Conference Announcement on Artificial Intelligence, IoT and Robotics “ after the successful completion of the series of Artificial Intelligence Congress. The International conference is scheduled on November 19-20, 2021 Paris time zone. This Artificial Intelligence 2021 conference will provide you with an exemplary research experience and huge ideas. The perspective of the Artificial Intelligence 2021 conference is to set up technology research to help people understand how Technology techniques have advanced and how the field has developed in recent years. Machine learning is an application of Artificial Intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it learn for themselves. Deep learning is an artificial intelligence (AI) function that imitates the workings of the human brain in processing data and creating patterns for use in decision making. Deep learning is a subset of machine learning in Artificial Intelligence that has networks capable of learning unsupervised from data that is unstructured or unlabelled. Robotics is an interdisciplinary research area at the interface of computer science and engineering. Robotics involves design, construction, operation, and use of robots. The goal of robotics is to design intelligent machines that can help and assist humans in their day-to-day lives and keep everyone safe. The Internet of Things (IoT) describes the network of physical objects—“things”—that are embedded with sensors, software, and other technologies for the purpose of connecting and exchanging data with other devices and systems over the internet. These devices range from ordinary household objects to sophisticated industrial tools. Natural language processing (NLP) is a subfield of linguistics, computer science, and artificial intelligence concerned with the interactions between computers and human language, in particular how to program computers to process and analyse large amounts of natural language data. In Artificial Intelligence, an expert system is a computer system that emulates the decision-making ability of a human expert. Expert systems are designed to solve complex problems by reasoning through bodies of knowledge, represented mainly as if–then rules rather than through conventional procedural code.

Abstract: The workflow of artificial intelligence projects begins with cleaning and labeling data. It proceeds over feature engineering, model selection, and hyper-parameter tuning to finally get to the training of the model. Once the model is ready, it can be used at inference to solve the original task. Each one of these major steps in the workflow can be assisted by tools that make that step either easier, faster or better than the, largely manual, process of today. Data labeling is augmented by Semi-automatic labeling methods. Feature engineering can be automated by creating synthetic features on the fly. Model selection and hyper-parameter tuning are essentially search problems that can also be automated. These possibilities will be illustrated and the common denominator discussed: All of these tools assume that the training of the model is fast. That can be accomplished by training the model on many computers simultaneously – distributed training. We illustrate the saving of time and the improvement of model accuracy in various domains such as natural-language-processing and image classification and segmentation Biography: Patrick is the Vice-President of Artificial Intelligence at Samsung SDSA where he heads the AI Engineering and AI Sciences teams. He is responsible for Brightics AI Accelerator, a distributed ML training and automated ML product that is also included in the Brightics AI platform. He is responsible for X.insights, a data center intelligence platform. Among his other responsibilities is to act as a visionary for the future of AI at Samsung. Before joining Samsung, Patrick spent 15 years as CEO at Algorithmica Technologies, a machine learning Software Company serving the chemicals and oil and gas industries. Prior to that, he was assistant professor of applied mathematics at Jacobs University in Germany, as well as a researcher at Los Alamos National Laboratory and NASA’s Jet Propulsion Laboratory. Patrick obtained his machine learning PhD in mathematics and his Masters in theoretical physics from University College London. A German native, Patrick grew up in Malaysia and the Philippines, and later lived in the UK, Austria, Nepal and USA. He has done business in many countries and believes that AI must serve humanity beyond mere automation of routine tasks. An avid reader of books, Patrick lives in the San Francisco Bay Area with his wife and two children. realization of Sigma Delta Radio over Fiber System. Dr Hadi is a Member IEEE, IEEE Photonics Society and IEEE Communications Society. He serves as a reviewer for IET Optoelectronics, IEEE Communications Letters and ASTES. Publication of speakers: 1. Bangert, Patrick. (2012). Optimization for Industrial Problems. 10.1007/978-3-642-24974-7. 2. Bangert, Patrick. (2012). Statistical Analysis in Solution Space. 10.1007/978-3-642-24974-7_2. 3. Bangert, Patrick. (2012). Overview of Heuristic Optimization. 10.1007/978-3-642-24974-7_1. 4. Bangert, Patrick & Jörg-A, C.. (2011). Increase of overall combined-heat-and-power efficiency through mathematical modelling. 91. 55-57.

The laser tube bending process is affected by many factors including the metal properties which consider as a crucial parameter. Moreover, the absorption and diffusion of the laser energy inside the metal bulk depend on the thermal and mechanical properties of the material. Numerous studies have been conducted to evaluate each parameter of the material properties factor. In this paper, an analytical examination has been employed to study the impact of different material specifications by using the Matlab package. The thermal expansion coefficient is directly proportional to the bending angle while the specific heat and the density are inversely proportional to the bending angle. furthermore, Aluminium had the highest bending angle in most tested conditions, the Stainless Steel 304 came next.

In this speech, high-speed optical wireless communication (OWC) technologies in C band (1550 nm) will be briefly addressed. Firstly, two proposed OWC schemes will be implemented and demonstrated. The transmission structures are multiple wavelengths, bi-directional transmission with 10Gb/s modulation for each channel. Compared results including back-to-back or uni-directional transmission will be provided. In free space transmission, some important issues such as temperature gradient, laser misalignment, laser beam divergent and cloudy/rainy condition to system performance will be analyzed. The experimental results of bit error rate (BER) performance will be addressed. Then, we evaluate the system performances between optical fiber transmission and optical wireless transmission in an outdoor bridge before and after it is broken. We find that negligible power penalty is induced for OWC case. A summary will be given to conclude that OWC is a good backup candidate for fiber-based communication.

What are the biggest differences between countries and individuals that we talk about? How could AI development influence those differences in the future? What is National AI Capital as a concept? Proposal how to measure NAIC. Is it important and what experts think about NAIC, and how much time do we have to react? Discussion about priority areas of AI investments and different stakeholders.