As we enter the new year, several promising technologies are poised to lead the way by improving how businesses and consumers use and experience the digital world. Here are some of the most important technologies and the practical solutions they will provide in the year ahead. The fifth generation of the mobile internet is going to bring the kind of speed most people associate with Wi-Fi to uploading and downloading data from remote locations. This will lead to sharp improvements in the way applications can be written, deployed and interacted with by mobile users. This also includes the development of data-intensive applications and the Internet of Things (IOT) -- physical objects with sensors that connect to and share data with the internet, autonomous vehicles and similar projects.
Though challenges and headwinds exist, we believe that the intelligent edge is poised to transform the computing landscape, propelling the world's largest technology companies toward the next generation of connectivity and operational efficiency. By bringing powerful computing capabilities closer to where data originates and needs to be consumed, the intelligent edge unlocks the potential for faster, less expensive, and more secure operations in everything from autonomous vehicles to virtual reality to the Internet of Things (IoT)--helping to accelerate the Fourth Industrial Revolution.5 The intelligent edge is the combination of advanced connectivity, compact processing power, and artificial intelligence (AI) located near devices that use and generate data.6 It represents an evolution and convergence of trends in industrial monitoring, automated manufacturing, utility management, and telecommunications, amplified by cloud computing, data analytics, and AI. The intelligent edge puts these latter capabilities physically near where data needs rapid analysis and response, enabling that data to be acted on directly or filtered to push only the most important bits to the core. In particular, the intelligent edge's ability to bring cloud capabilities to remote operations could greatly amplify their performance.
Cisco Systems Inc. is pulling the plug on a flagship effort to help digitize the modern city, the latest example of a big tech company struggling to enter a new market. The setback comes as the pandemic has weighed on Cisco's core business of supplying networking equipment and has limited the ability of local governments to finance such projects. City planners and local governments for years have been preparing for a future where technology remakes the urban landscape, with features such as self-driving cars, smart lighting and connected alarms to help safeguard residents. Communications using 5G technology blanketing the city would allow widespread adoption of smart devices. For Cisco, best known for providing routers and other networking gear to corporate customers, that vision promised a budding new market.
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.
The Internet of Things (IoT) and edge computing applications aim to support a variety of societal needs, including the global pandemic situation that the entire world is currently experiencing and responses to natural disasters. The need for real-time interactive applications such as immersive video conferencing, augmented/virtual reality, and autonomous vehicles, in education, healthcare, disaster recovery and other domains, has never been higher. At the same time, there have been recent technological breakthroughs in highly relevant fields such as artificial intelligence (AI)/machine learning (ML), advanced communication systems (5G and beyond), privacy-preserving computations, and hardware accelerators. 5G mobile communication networks increase communication capacity, reduce transmission latency and error, and save energy -- capabilities that are essential for new applications. The envisioned future 6G technology will integrate many more technologies, including for example visible light communication, to support groundbreaking applications, such as holographic communications and high precision manufacturing. Many of these applications require computations and analytics close to application end-points: that is, at the edge of the network, rather than in a centralized cloud. AI techniques applied at the edge have tremendous potential both to power new applications and to need more efficient operation of edge infrastructure. However, it is critical to understand where to deploy AI systems within complex ecosystems consisting of advanced applications and the specific real-time requirements towards AI systems.
Edge AI is one of the most notable new sectors of artificial intelligence, and it aims to let people run AI processes without having to be concerned about privacy or slowdowns due to data transmission. Edge AI is enabling greater, more widespread use of AI, letting smart devices react quickly to inputs without access to a cloud. While that's a quick definition of Edge AI, let's take a moment to better understand Edge AI by exploring the technologies that make it possible and seeing some use cases for Edge AI. In order to truly understand Edge AI, we need to first understand Edge computing, and the best way to understand Edge computing is to contrast it with cloud computing. Cloud computing is the delivery of computing services over the internet.
Due to the advancements in cellular technologies and the dense deployment of cellular infrastructure, integrating unmanned aerial vehicles (UAVs) into the fifth-generation (5G) and beyond cellular networks is a promising solution to achieve safe UAV operation as well as enabling diversified applications with mission-specific payload data delivery. In particular, 5G networks need to support three typical usage scenarios, namely, enhanced mobile broadband (eMBB), ultra-reliable low-latency communications (URLLC), and massive machine-type communications (mMTC). On the one hand, UAVs can be leveraged as cost-effective aerial platforms to provide ground users with enhanced communication services by exploiting their high cruising altitude and controllable maneuverability in three-dimensional (3D) space. On the other hand, providing such communication services simultaneously for both UAV and ground users poses new challenges due to the need for ubiquitous 3D signal coverage as well as the strong air-ground network interference. Besides the requirement of high-performance wireless communications, the ability to support effective and efficient sensing as well as network intelligence is also essential for 5G-and-beyond 3D heterogeneous wireless networks with coexisting aerial and ground users. In this paper, we provide a comprehensive overview of the latest research efforts on integrating UAVs into cellular networks, with an emphasis on how to exploit advanced techniques (e.g., intelligent reflecting surface, short packet transmission, energy harvesting, joint communication and radar sensing, and edge intelligence) to meet the diversified service requirements of next-generation wireless systems. Moreover, we highlight important directions for further investigation in future work.
Humans are not perfect drivers; we are vulnerable to many physical and emotional factors influencing our driving behavior. A study suggests that many road accidents occur due to a lack of response time for drivers. In order to make informed judgments, drivers need a smart assistance system that can predict a possible event beforehand and prevent a fatal crash or serious injuries. V2X is an intelligent transport system comprising of Vehicle-to-vehicle (V2V), Vehicle-to-infrastructure (V2I), and Vehicle-to-Pedestrian (V2P) communications. Biometric seat technology; autonomously managed municipality; and highway system are also part of advanced IoT technologies.
With connected cars becoming more common, the industry has more standards and options when it comes to autonomous vehicle security. Adam Laurie, known in hacker circles as Major Malfunction, leads X-Force Red's automotive testing practice. He has seen firsthand how easy it can be to compromise an autonomous vehicle if strong security processes and controls are not in place. He recently found an opening in the keyless entry device of his own vehicle, then leveraged it to unlock every vehicle of the same model in a parking lot. The project was for research purposes as opposed to a real attack, but it did show how easy it could be for an attacker to purchase a vehicle, reverse engineer it to find flaws and then exploit those flaws to compromise every other vehicle of that same model.
It was reported that Venture Capital investments into AI related startups made a significant increase in 2018, jumping by 72% compared to 2017, with 466 startups funded from 533 in 2017. PWC moneytree report stated that that seed-stage deal activity in the US among AI-related companies rose to 28% in the fourth-quarter of 2018, compared to 24% in the three months prior, while expansion-stage deal activity jumped to 32%, from 23%. There will be an increasing international rivalry over the global leadership of AI. President Putin of Russia was quoted as saying that "the nation that leads in AI will be the ruler of the world". Billionaire Mark Cuban was reported in CNBC as stating that "the world's first trillionaire would be an AI entrepreneur".