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.
Self-driving cars, lifelike robots, and autonomous delivery drones are the sexy, headline-grabbing face of the digital transformation that we see all around us today. None of these would be possible, though, without data – the oil of the fourth industrial revolution – and the analytic technology we've built to allow us to interpret and understand it. Big Data is a term that's come to be used to describe the technology and practice of working with data that's not only large in volume but also fast and comes in many different forms. For every Elon Musk with a self-driving car to sell, or Jeff Bezos with a cashier-less convenience store, there is a sophisticated Big Data operation and an army of clever data scientists who've turned a vision into reality. The term Big Data itself may not be as ubiquitous as it was a few years ago, and that's purely because many of the concepts it embodies have been thoroughly embedded into the world around us.
We're all familiar with Siri, Echo, Google Maps and Translate – as well as IBM – using artificial intelligence, or AI, to answer questions, find locations, help us make sense of foreign languages, and predict the weather respectively. But AI is getting smarter, which of course, is exactly what it's designed to do. "Deep learning" means the software gets better at guessing as time goes by. If you haven't taken a ride in a recent model by Tesla you might be a tad surprised. A large "dashboard screen" shows any and all relevant journey information and the entire car is connected to your phone ecosystem.
Back in 2018, Elon Musk's SpaceX put the first car in space. A flurry of press releases suggest that the (surely simpler?) race to put cars -- and carmakers -- into the cloud is far from over. Last week, Volkswagen Group jumped into Microsoft's Azure cloud to "accelerate the development of automated driving." But the company is also a fan of Amazon Web Services (AWS) and previously announced a relationship with Azure back in 2018. Earlier this month, Ford picked Google as its preferred (but not only) cloud.
Uber acquired Drizly, an alcohol e-commerce platform, for $1.1 billion in cash and stock last week. It's just the latest brand Uber has added to its portfolio as the company seeks to satisfy consumer appetites. Uber is no longer synonymous with ride-hailing. These days, it has more to offer. Uber acquired a majority stake in Cornershop, its answer to Instacart, in 2019.
Now that Uber owns Postmates, it's apparently ready to cut workers it believes are superfluous. Uber has confirmed to the New York Times that it's laying off about 185 people at Postmates, or about 15 percent of the delivery company's staff. Most of the executives are leaving, including founder and CEO Bastian Lehmann. There may be more departures in the months ahead as contracts expire and others are asked to leave. The layoffs come as Uber melds its Eats platform with Postmates.
The edge of a network, as you may know, is the furthest extent of its reach. A cloud platform is a kind of network overlay that makes multiple network locations part of a single network domain. It should therefore stand to reason that an edge cloud is a single addressable, logical network at the furthest extent of a physical network. And an edge cloud on a global scale should be a way to make multiple, remote data centers accessible as a single pool of resources -- of processors, storage, and bandwidth. The combination of 5G and edge computing will unleash new capabilities from real-time analytics to automation to self-driving cars and trucks.
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.
Artificial Intelligence is quickly becoming one of the quintessential industries today and reshaping the world as we know it. It has proven its worth in different areas of work with an undeniable impact on the market. Siri to Alexa and self-driving cars to manufacturing-robots are just a few examples of what AI companies have achieved. Tech giants like Amazon, Google, Microsoft and Apple are putting their resources in Artificial Intelligence and are running the race of becoming the biggest artificial intelligence companies in the world. Organisations like NASA are now using artificial intelligence to make themselves even more efficient, a report says. Technical edge is the key to most of the businesses today. As big players are putting everything they have got to get that technical edge, small players may find it overwhelming, if not unfair, to compete with them. Apart from these giants, there are several AI companies which have shown the potential of changing the world and solve the possible disparity some companies may experience due to their technical prowess.
When most people hear the term Artificial Intelligence, the first thing they usually think of is robots or some famous science fiction movie like the Terminator depicting the rise of AI against humanity. Artificial intelligence refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions. The term may also be applied to any machine that exhibits traits associated with a human mind such as learning, analyzing, comprehending, and problem-solving. The applications of artificial intelligence in the real-world are perhaps more than what many people know. The ideal characteristic of artificial intelligence is its ability to rationalize and take actions that have the best chance of achieving a specific goal or defined operations. With the advancements of the human mind and their deep research into the field, AI is no longer just a few machines doing basic calculations.