In the article below, you can check out twelve examples of AI being present in our everyday lives. Artificial intelligence (AI) is growing in popularity, and it's not hard to see why. AI has the potential to be applied in many different ways, from cooking to healthcare. Though artificial intelligence may be a buzzword today, tomorrow, it might just become a standard part of our everyday lives. They work and continue to advance by using lots of sensor data, learning how to handle traffic and making real-time decisions.
We in pharma are all about improving our customer experience and customer engagement. A lot is being done in this area. However, if you really want to put your customer experience and customer engagement on steroids, you need to consider using AI in the process. For the past decade, pharma companies have employed one-to-many communications using social media in their customer engagement efforts. There is a great opportunity for companies to use conversational AI for ongoing customer engagement rather than just a one off transactional point in time. I mean conversations that use natural language with a conversational flow, not'Push 1 to discuss X, Push 2 to discuss Y' prompts.
In the article below, you can check out twelve examples of AI being present in our everyday lives. Artificial intelligence (opens in new tab) (AI) is growing in popularity, and it's not hard to see why. AI has the potential to be applied in many different ways, from cooking to healthcare. Though artificial intelligence may be a buzzword today, tomorrow, it might just become a standard part of our everyday lives. They work and continue to advance by using lots of sensor data, learning how to handle traffic and making real-time decisions.
Artificial intelligence, AI for short, is a newcomer in the digital marketing arena. And yet it has the power to create a true revolution in the field if businesses choose to embrace it. In this article we will examine how artificial intelligence could be applied to digital marketing, and why it's best to start adopting it sooner rather than later. Search engines are always evolving and adapting, and AI is the next frontier for the likes of Google, Bing, Yahoo and more. The old SEO game is dead.
When it comes to the mobile app industry, businesses of all sizes and specialisations confront strong competition. This position compels them to keep up with all developing digital developments in order to maintain their worth. Recognizing the huge influence of artificial intelligence on business, top firms such as Amazon, eBay, and Tinder make extensive use of AI in their applications to generate tailored mobile user experiences and improve profitability. Start-ups also raise more investment for AI integrations, propelling them to high marketability and competitiveness. Annually, more AI apps go viral, bringing greater exposure and revenues to their owners.
Artificial intelligence (AI) has witnessed a substantial breakthrough in a variety of Internet of Things (IoT) applications and services, spanning from recommendation systems to robotics control and military surveillance. This is driven by the easier access to sensory data and the enormous scale of pervasive/ubiquitous devices that generate zettabytes (ZB) of real-time data streams. Designing accurate models using such data streams, to predict future insights and revolutionize the decision-taking process, inaugurates pervasive systems as a worthy paradigm for a better quality-of-life. The confluence of pervasive computing and artificial intelligence, Pervasive AI, expanded the role of ubiquitous IoT systems from mainly data collection to executing distributed computations with a promising alternative to centralized learning, presenting various challenges. In this context, a wise cooperation and resource scheduling should be envisaged among IoT devices (e.g., smartphones, smart vehicles) and infrastructure (e.g. edge nodes, and base stations) to avoid communication and computation overheads and ensure maximum performance. In this paper, we conduct a comprehensive survey of the recent techniques developed to overcome these resource challenges in pervasive AI systems. Specifically, we first present an overview of the pervasive computing, its architecture, and its intersection with artificial intelligence. We then review the background, applications and performance metrics of AI, particularly Deep Learning (DL) and online learning, running in a ubiquitous system. Next, we provide a deep literature review of communication-efficient techniques, from both algorithmic and system perspectives, of distributed inference, training and online learning tasks across the combination of IoT devices, edge devices and cloud servers. Finally, we discuss our future vision and research challenges.
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.
The global IoT healthcare market is expected to grow at a CAGR of 29.9% to reach $322.2 billion by 2025. This can be attributed to the fact that this technology has tremendous potential to transform healthcare operations and efficiencies, including patient care and operational workflows. While it is still at a developing stage, and may have its fair share of obstacles, it is revolutionizing the entire healthcare industry in many ways. Virtual assistants are commonplace in our everyday life with Siri, Alexa, Google Home being the forerunners. The same technology i.e., conversation-based assistance using AI is also used in the healthcare sector for increasing patient engagement.
What is the Internet of Things (IoT)? In simple terms, IoT refers to connected devices that work over the internet. But it's increasingly being known for devices that we interact with like Google Home and Amazon Echo (Alexa). IoT is disrupting existing markets and creating new ones by increasing efficiency and reducing costs. IoT is nothing new but the number of devices and people using them has grown tremendously in recent years.
The world, in both a business and personal setting, continues to be disrupted by digital trends that are driving innovation across industries and sectors. Digital trends continue to create richer, more personal customer relationships, while enhancing operations and processes across the board. These trends are ultimately helping organisations reimagine their capabilities and stave off elimination in the era of disruption. Information Age asked ten experts for their views on what are some of the key current digital trends, and how they will shape future endeavours. Chris Dixon, technical consultant at Axians UK, explains the benefits that 5G will bring in an increasingly connected era.