The connectivity benefits of 5G are expected to make businesses more competitive and give consumers access to more information faster than ever before. Connected cars, smart communities, industrial IoT, healthcare, immersive education--they all will rely on unprecedented opportunities that 5G technology will create. The enterprise market opportunity is driving many telecoms operators' strategies for, and investments in, 5G. Companies are accelerating investment in core and emerging technologies such as cloud, internet of things, robotic process automation, artificial intelligence and machine learning. IoT (Internet of Things), as an example, improving connectivity and data sharing between devices, enabling biometric based transactions; with blockchain, enabling use cases, trade transactions, remittances, payments and investments; and with deep learning and artificial intelligence, utilization of advanced algorithms for high personalization.
As we make tremendous advances in machine learning and artificial intelligence technosciences, there is a renewed understanding in the AI community that we must ensure that humans being are at the center of our deliberations so that we don't end in technology-induced dystopias. As strongly argued by Green in his book Smart Enough City, the incorporation of technology in city environs does not automatically translate into prosperity, wellbeing, urban livability, or social justice. There is a great need to deliberate on the future of the cities worth living and designing. There are philosophical and ethical questions involved along with various challenges that relate to the security, safety, and interpretability of AI algorithms that will form the technological bedrock of future cities. Several research institutes on human centered AI have been established at top international universities. Globally there are calls for technology to be made more humane and human-compatible. For example, Stuart Russell has a book called Human Compatible AI. The Center for Humane Technology advocates for regulators and technology companies to avoid business models and product features that contribute to social problems such as extremism, polarization, misinformation, and Internet addiction. In this paper, we analyze and explore key challenges including security, robustness, interpretability, and ethical challenges to a successful deployment of AI or ML in human-centric applications, with a particular emphasis on the convergence of these challenges. We provide a detailed review of existing literature on these key challenges and analyze how one of these challenges may lead to others or help in solving other challenges. The paper also advises on the current limitations, pitfalls, and future directions of research in these domains, and how it can fill the current gaps and lead to better solutions.
Edge intelligence refers to a set of connected systems and devices for data collection, caching, processing, and analysis in locations close to where data is captured based on artificial intelligence. The aim of edge intelligence is to enhance the quality and speed of data processing and protect the privacy and security of the data. Although recently emerged, spanning the period from 2011 to now, this field of research has shown explosive growth over the past five years. In this paper, we present a thorough and comprehensive survey on the literature surrounding edge intelligence. We first identify four fundamental components of edge intelligence, namely edge caching, edge training, edge inference, and edge offloading, based on theoretical and practical results pertaining to proposed and deployed systems. We then aim for a systematic classification of the state of the solutions by examining research results and observations for each of the four components and present a taxonomy that includes practical problems, adopted techniques, and application goals. For each category, we elaborate, compare and analyse the literature from the perspectives of adopted techniques, objectives, performance, advantages and drawbacks, etc. This survey article provides a comprehensive introduction to edge intelligence and its application areas. In addition, we summarise the development of the emerging research field and the current state-of-the-art and discuss the important open issues and possible theoretical and technical solutions.
While conducting research for the recently released Industry 4.0 and Smart Manufacturing Market Report, IoT Analytics identified 300 leading Industry 4.0 companies that supply cutting edge products and services that are driving the fourth industrial revolution. The leading Industry 4.0 companies were selected based on a number of criteria (case studies, product offerings, estimated market share, etc.) and were categorized based on what type of Industry 4.0 product or service they supplied. Building on its long history of supporting industrial automation companies, Microsoft has emerged as the hosting partner of choice for many Industry 4.0 companies. Both end users (manufacturing facilities) and suppliers (OEMs, industrial automation companies, etc.) have partnered with Microsoft to develop and run mission-critical on-premise SCADA and MES applications for decades. Microsoft's deep domain knowledge and technical capabilities (especially with respect to hybrid cloud solutions) have helped it become a leading provider of hosting services for major manufacturing end users and suppliers such as Siemens, PTC, GE, and Emerson.
In a prior article, we talked about IoT being the connection of the physical and the digital worlds. That is, connecting those things that were physical in nature hitherto and now find a need to be connected to the digital world. This phenomenon about things/objects/entities is also influencing the enterprises in how they are transforming and shaping themselves to survive and thrive in the fast evolving world. The enterprises across consumer, commercial, public, and industrial sectors that were born in the pre-Internet era (Honeywell, ABB, GE, Philips, Siemens, and so on) are making moves to position themselves as digitally transformed companies. More subtle are the moves being made by the Internet era companies (Google, Amazon, etc.) to integrate themselves with the physical world.
Developing intelligent systems requires combining results from both industry and academia. In this report you find an overview of relevant research fields and industrially applicable technologies for building very large scale cyber physical systems. A concept architecture is used to illustrate how existing pieces may fit together, and the maturity of the subsystems is estimated. The goal is to structure the developments and the challenge of machine intelligence for Consumer and Industrial Internet technologists, cyber physical systems researchers and people interested in the convergence of data & Internet of Things. It can be used for planning developments of intelligent systems.