Market Research Report
Artificial Intelligence Solutions and Market Opportunities: AI and Cognitive Computing Technologies, Infrastructure, Capabilities, Leading Apps and Services 2019 - 2024
|Published by||Mind Commerce||Product code||917252|
|Published||Content info||1,974 Pages
Delivery time: 1-2 business days
|Artificial Intelligence Solutions and Market Opportunities: AI and Cognitive Computing Technologies, Infrastructure, Capabilities, Leading Apps and Services 2019 - 2024|
|Published: November 26, 2019||Content info: 1,974 Pages||
This is the most comprehensive research available covering artificial intelligence in telecommunications, media, and digital technology as a whole. For example, it covers AI in everything from consumer devices to communications networks as well as AI in key markets and technologies such as AI in supply chain management and AI-bases smart machines used in various industry verticals. It also covers the convergence of AI with IoT (AIoT), which is also known as the Artificial Intelligence of Things. It also provides a look ahead towards general purpose intelligence, which represents the evolution of AI towards a utility function in which cognitive capabilities are leveraged within virtually every product, service, application, and solution.
One of the fastest growing areas for artificial intelligence is the AI chipset marketplace, which is poised to transform the entire embedded system ecosystem with a multitude of AI capabilities such as deep machine learning, image detection, and many others. This will also be transformational for existing critical business functions such as Identity management, authentication, and cybersecurity. Multi-processor AI chipsets learn from the environment, users, and machines to uncover hidden pattern among data, predict actionable insight, and perform actions based on specific situations. AI chipsets will become an integral part of both AI software/systems as well as critical support of any data-intensive operation as they drastically improve processing for various functions as well as enhance overall computing performance.
For example AI enabled chatbots are taking Customer Relationship Management (CRM) to a new level as business-to-business, business-to-consumer, and consumer-to-business communications is both automated and improved by way of push and pull of the right information at the right time. Chatbots also provide benefits to customers as both existing clients and prospects enjoy the freedom to interact on their own terms. Our research indicates that over 50% of customer queries may be managed today via AI-based chatbots. As the interface between humans and computers evolves from an "operational" interface (Websites and traditional Apps) to an increasingly more "conversational" interface expectations about how humans communicate, consume content, use apps, and engage in commerce will change dramatically. This transformation is poised to impact virtually every aspect of marketing and sales operations for every industry vertical. For example, AI enabled voice chat, also known as conversational AI, provides a completely human-like experience and will completely replace human-based CRM in some industries.
Smart machines collectively represent intelligent devices, machinery, equipment, and embedded automation software that perform repetitive tasks and solve complex problem autonomously. Along with AI, IoT connectivity, and M2M communications, smart machines are a key component of smart systems, which include many emerging technologies such as smart dust, neuro-computing, and advanced robotics. Smart machines will also benefit significantly from advancements in AIoT. The drivers for enterprise and industrial adoption of smart machines include improvements in the smart workplace, smart data discovery, cognitive automation, and more. Currently conceived smart machine products include autonomous robots (such as service robots), self-driving vehicles, expert systems (such as medical decision support systems), medical robots, intelligent assistants (such as automated online assistants), virtual private assistants (Siri, Google Assistant, Amazon Alexa, etc.), embedded software systems (such as machine monitoring and control systems), neurocomputers (such as purpose-built intelligent machines), and smart wearable devices.
Computing at the edge of IT and communications networks will require a different kind of intelligence. AI will be required for both security (data and infrastructure) as well as to optimize the flow of information in the form of streaming data and the ability to optimize decision-making via real-time data analytics. Edge networks will be the “point of the spear” so to speak when it comes to data handling, meaning that streaming data will be available for processing and decision-making. While advanced data analytics software solutions can be very effective for this purpose, there will be opportunities to enhance real-time data analytics by way of leveraging AI to automate decision making and to engage machine learning for ongoing efficiency and effectiveness improvements.
Cognitive informatics is poised to become an important aspect of every major vertical. The cognitive informatics market relies upon those technologies that improve human information processing. Technologies included within this interdisciplinary domain always include some degree of Artificial Intelligence and cognitive computing, but are increasingly involving Internet of Things (IoT) enabled devices, networks and systems. In fact, this multidisciplinary combination of cognition and information sciences includes the convergence of AI and IoT, which is also referred to as the AIoT market. As human beings have cognitive limitations (such as attention, comprehension, decision-making, learning, memory, learning, and visualization), the cognitive informatics market seeks to provide human cognition augmentation and enhancement. Advancements in the understanding human behavioral science, neuroscience, and psychology are combined with innovation in AI such as improved Natural Language Processing (NLP) mechanisms and linguistics processes. Machine learning improvements to areas such as “what is said vs. what is meant” and context-based AI are leading to an overall improvement in man-machine interfaces critical to successful cognitive informatics market implementation.
The role and importance of AI in 5G ranges from optimizing resource allocation to data security and protection of network and enterprise assets. However, the concept of using AI in networking is a relatively new area that will ultimately require a more unified approach to fully realize its great potential. In addition, AI will assist 5G network slicing, which represents the ability to dynamically allocate bandwidth, and enforce associated service level agreements, and a per-customer and per-application basis. AI will automate the process of assigning network slices, including informing enterprise customers when the slices they are requesting are not in their best interest based on anticipated network conditions.
The convergence of AI and Internet of Things (IoT) technologies and solutions (AIoT) is leading to “thinking” networks and systems that are becoming increasingly more capable of solving a wide range of problems across a diverse number of industry verticals. AI adds value to IoT through machine learning and improved decision making. IoT adds value to AI through connectivity, signaling, and data exchange. AIoT is just beginning to become part of the ICT lexicon as the possibilities for the former adding value to the latter are only limited by the imagination. 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 early AIoT solutions are rather monolithic, it is anticipated that AIoT integration within businesses and industries will ultimately lead to more sophisticated and valuable inter-business and cross-industry solutions. These solutions will focus primarily upon optimizing system and network operations as well as extracting value from industry data through dramatically improved analytics and decision-making processes.
IoT in consumer, enterprise, industrial, and government market segments has very unique needs in terms of infrastructure, devices, systems, and processes. One thing they all have in common is that they each produce massive amounts of data, most of which is of the unstructured variety, requiring big data technologies for management. AI algorithms enhance the ability for big data analytics and IoT platforms to provide value to each of these market segments. The author sees three different types of IoT Data: (1) Raw (untouched and unstructured) Data, (2) Meta (data about data), and (3) Transformed (valued-added data). AI will be useful in support of managing each of these data types in terms of identifying, categorizing, and decision making.
AI coupled with advanced big data analytics provides the ability to make raw data meaningful and useful as information for decision-making purposes. The use of AI for decision making in IoT and data analytics will be crucial for efficient and effective decision making, especially in the area of streaming data and real-time analytics associated with edge computing networks. Real-time data will be a key value proposition for all use cases, segments, and solutions. The ability to capture streaming data, determine valuable attributes, and make decisions in real-time will add an entirely new dimension to service logic. In many cases, the data itself, and actionable information will be the service.
Experiential networking is a relatively new concept with ETSI forming an Industry Specification Group (ISG) focused on “Experiential Network Intelligence” (ENI) and holding an initial ISG ENI meeting in 2017. These efforts define an “observe-orient-decide-act” control model with the intent that networks will become increasingly more adaptive, supporting intelligent service operations by way of cognitive network management. Accordingly, core to the experiential networking market is the use of AI and cognitive computing. More specifically, ENI wll leverage data and contextual information (such as AI-based decision making) to take actions based on device and system-related events. Responses to events, related processes, and machine learning, allows ENI to make automated decisions and provide recommendations for use by other systems such as management and orchestration platforms. This event-driven approach allows the experiential networking market to use various technologies to engage in intelligent analysis necessary for network and service policies and modeling.
Also known as Artificial General Intelligence (AGI), General Purpose Artificial Intelligence represents silicon-based AI that mimics human-like cognition to perform a wide variety of tasks that span beyond mere number crunching. Whereas most current AI solutions are limited in terms of the type and variety of problems that may be solved, AGI may be employed to solve many different problems including machine translation, natural language processing, logical reasoning, knowledge representation, social intelligence, and numerous others. Unlike many early AI solutions that were designed and implemented with a narrow focus, AGI will be leveraged to solve problems in many different domains and across many different industry verticals including 3D design, transforming customer service, securing enterprise data, securing public facility and personnel, financial trading, healthcare solution, highly personalized target marketing, detecting fraud, recommendation engines, autonomous vehicles and smart mobility, online search, and many other areas. AGI is rapidly evolving in many areas. However, scalability remains a challenge.
<>Modern supply chains represent complex systems of organizations, people, activities, information, and resources involved in moving a product or service from supplier to customer. Supply Chain Management (SCM) solutions are typically manifest in software architecture and systems that facilitate the flow of information among different functions within and between enterprise organizations. Leading SCM solutions catalyze information sharing across organizational units and geographical locations, enabling decision-makers to have an enterprise-wide view of the information needed in a timely, reliable and consistent fashion. Various forms of AI are being integrated into SCM solution to improve everything from process automation to providing greater visibility into static and real-time data as well as related management information systems. In addition to fully automated decision making, AI systems are also leveraging various forms of cognitive computing to optimize the combined efforts of artificial and human intelligence. For example, AI in SCM is enabling improved supply chain automation through the use of virtual assistants, which are used both internally (within a given enterprise) as well as between supply chain members (e.g. customer-supplier chains).