Dr. Neeraj Dahiyaet et al.International Journal of Recent Research Aspects ISSN: 2349-7688, Vol. 8, Issue 3 September 2021, pp. 18-26 © 2021 IJRAA All Rights Reserved page - 18- Artificial Intelligence of Things: AIoT in Various Markets of IoT Deployments Dr. Neeraj Dahiya 1 , Dr. Mahejabin Sayyad 2 1 Department of CSE, SRM University, Delhi-NCR, Sonipat, Haryana. 2 Dept. of Commerce, Agasti AC&DRS College, Akole, Maharashtra, India. Abstract: With AIoT, AI is embedded into infrastructure components, such as programs, chipsets and edge computing, all interconnected with IoT networks. APIs are then used to extend interoperability between components at the device level, software level and platform level. These units will focus primarily on optimizing system and network operations as well as extracting value from data. While the concept of AIoT is still relatively new, many possibilities exist to improve industry verticals, such as enterprise, industrial and consumer product and service sectors, and will continue to arise with its growth. AIoT could be a viable solution to solve existing operational problems, such as the expense associated with effective human capital management (HCM) or the complexity of supply chains and delivery models. Keywords: Artificial Intelligence, Artificial Intelligence of Things, Internet of Things, IoT Data as a Service I. INTRODUCTION The Artificial Intelligence of Things (AIoT) is the combination of artificial intelligence (AI) technologies with the Internet of Things (IoT) infrastructure to achieve more efficient IoT operations, improve human-machine interactions and enhance data management and analytics. AI can be used to transform IoT data into useful information for improved decision making processes, thus creating a foundation for newer technology such as IoT Data as a Service (IoTDaaS) [1]. AIoT is transformational and mutually beneficial for both types of technology as AI adds value to IoT through machine learning capabilities and IoT adds value to AI through connectivity, signaling and data exchange. As IoT networks spread throughout major industries, there will be an increasingly large amount of human-oriented and machine-generated unstructured data. AIoT can provide support for data analytics solutions that can create value out of this IoT-generated data [2]. Applications of AIoT Many AIoT applications are currently retail product oriented and often focus on the implementation of cognitive computing in consumer appliances. For example, smart home technology would be considered a part of AIoT as smart appliances learn through human interaction and response [3]. In terms of data analytics, AIoT technology combines machine learning with IoT networks and systems in order to create data "learning machines." This can then be applied to enterprise and industrial data use cases to harness IoT data, such as at the edge of networks, to automate tasks in a connected workplace. Real time data is a key value of all AIoT use cases and solutions [4]. In one specific use case example, AIoT solutions could also be integrated with social media and human resources-related platforms to create an AI Decision as a Service function for HR professionals [5]. Using AI to create thinking, learning things The next generation of internet of things platforms could be one that allows things to become thinking, learning objects. Imagine that your smartwatch could not only predict when you might be ripe for a heart attack, it could also sense when a hacker was trying to access your personal data. The way to augment things with a “brain” is to enhance them with artificial intelligence (AI). Let’s call this AIoT, the artificial intelligence of things [6]. This year has shown peak investment in AI, with startups in the U.S. alone having raised $1.5 billion, and I’m sure we will see the fruits of those investments in our daily lives very soon. To imagine where AI will play a role we need to understand what AI is — and what it is not. AI is an algorithm powered by statistical models allowing the AI to “learn” through feedback loops. So rather than deterministic models where an algorithm uses predefined rules upon which to base its decisions, other models are applied [7]. For example, Google makes use of a technique that’s called deep learning; much of the work in this area is inspired by how the human brain works. Those models are no longer deterministic and, as such, could mean that how an AI comes to a certain decision might become opaque. This could give rise to unforeseen situations; witness Microsoft’s AI chatbot that learned to be racist within hours through analyzing twitter feeds. Will AI become all-knowing? The current AI’s will certainly not, they are trained on specific domains and will not be able to apply that knowledge in other contexts. For example, a recent botnet attack crashed several high-profile websites by infiltrating things such as connected DVRs and cameras. Had they been augmented by AI, the things could have sensed a traffic overload and shut them down [8]. So where will AI augment IoT? The most likely area will be in manufacturing, an industry that is already spending heavily on IoT [85]. The use case that manufacturing is attacking with AI is predominantly predictive maintenance. The form of AI they are doing this with is called machine learning [9]. Manufacturers are chasing predictive maintenance because there are some real and tangible benefits; the low-hanging fruit is increased uptime