Artificial intelligence, machine learning, and deep learning technologies as catalysts for industry 4.0, 5.0, and society 5.0

Industry 4.0 brought with it by the next-gen Industry 5.0 and Society 5.0 paradigms, catalysed by Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) technologies. These advances have the benefit of encouraging sustainability, improving output, and updating manufacturing. By enabling self-decision, continuous monitoring, and predictive maintenance with the processing of large data, AI is dramatically reducing downtime and associated costs of system downtime. As a result, ML algorithms, in light of their applicability for continuous learning and adaptation, have contributed to enriching product quality, streamlining supply networks and okaying personalized customer experiences. Neural networks are also being leveraged to improve computer vision and speech capabilities, for applications such as smart automation and human-robot cooperation in challenging industrial contexts. Industry 5.0 truly puts humans back at the centre of innovation. It is aimed to create an evolved society in which AI, ML, and DL are fused with the digital and physical world Society 5.0. The integration aims to address a plethora of societal challenges: environmental sustainability, health and ageing population, among others. It is a convergence of these said technologies that lead to a paradigm shift towards more resilient, adaptive, and sustainable industrial ecosystems. This paper aims to address these questions in a systematic way to offer a comprehensive view of what the future industrial landscape could look like leveraging the promise of Industry 4.0 and Industry 5.0 thus, and more opportunities to embrace intelligent and sustainable industries of tomorrow.

Keywords: Artificial intelligence, Machine learning, Deep learning, Internet of things, Industry 4.0, Industry 5.0, Society 5.0

Citation: Rane, N. L., Kaya, O., & Rane, J. (2024). Artificial intelligence, machine learning, and deep learning technologies as catalysts for industry 4.0, 5.0, and society 5.0. In Artificial Intelligence, Machine Learning, and Deep Learning for Sustainable Industry 5.0 (pp. 1-27). Deep Science Publishing. https://doi.org/10.70593/978-81-981271-8-1_1

 

1.1 Introduction

Industry 5.0 and Society 5.0 represent the next phase of modernization in the way technologies have incorporated into industrial processes and societal framework on Industry 4.0, the fourth industrial revolution during which digital systems are integrated with the physical systems, and the associated common realities of increased automation, data exchange, and real-time capabilities enabled by contemporary industrial technologies such as; cloud computing, cyber-physical systems, and the Internet of Things (IoT) (Paschek et al., 2022; Raja Santhi, & Muthuswamy, 2023, Paramesha et al., 2024a). The people and machine collaboration are the main intention for Industry 5.0 to make a profession in which workers and machines can easily work together (Paschek et al., 2022; Rane et al., 2024a). Industry 5.0 represents the combined method among Industry 4.0 and worker-friendly on-line applications using this usage or want of labour, which who will be playing a contemporary interface among humans and the newest technology (Mourtzis et al., 2022; Rane et al., 2024b). It uses the power of the human creativity to solve problems and is complemented by the precision and efficiency of intelligent systems. In Society 5.0, a future society in which both the public and private sectors can craft solutions to many of the structural societal challenges by leveraging sophisticated societal-technical approaches, these ideas are explored in greater detail (Huang et al., 2022). These transformational changes can be achieved by deep learning (DL), machine learning (ML) and artificial intelligence (AI) which are the three basic building blocks (Adel, 2023; Paramesha et al., 2024b; Rane et al., 2024c). They are analytical and computational abilities to help us process and analyse large volume of data, find patterns and make self-decisions. Being an AI enabled organization to build Industry 4.0 through specifically focusing on AI and ML and a bit of what DL enables for predictive maintenance, in product quality. They are the ones which allow Industry 5.0 to bring that personalized and human-cantered touch, where automation is now a catalyst to human skills and not a replacement. Society 5.0 realizes a kind of utopia, the beneficiaries of that being, sharing and creating all kinds of information resources in this world and better than its predecessors, being blessed with the benefits of solving the complex societal problems with AI powered solutions on the environment, health, safety or even urban planning. The application of AI, ML and DL combined with the principles of Industry 4.0, 5.0 and Society 5.0 appear to take us to a new realm of intelligent and flexible systems (Kasinathan et al., 2022; Paschek et al., 2022; Paramesha et al., 2024c). Such intersectionality leads to a better, more balanced society and it also makes people perform and think better. All of these emerging technologies are being assimilated, in progress, into newer healthcare systems, autonomous vehicles, and smart cities. Challenges of Implementation Despite the exciting possibilities of AI, ML, and DL in complex industrial and societal systems, the actual implementation is a challenging task to achieve (Rane, 2023a). Factors include the need for strong regulatory frameworks, issues of ethics, and challenges around data privacy. In addition, the worker must continually cultivate new skills and adapt to changing technology, underscoring the importance of education and retraining.

Contributions of the research work:

• The literature review provides a comprehensive analysis of current trends, challenges, and potential future developments in this field.
• This study provides a snapshot of the current research field by extracting the most frequent keywords and topical clusters that have mostly been discussed in the relevant articles in recent years.

 1.2 Methodology

In this study by applying a systematic approach, an attempt is made to understand the interplay of AI, ML, DL technologies in Industry 4.0, Industry 5.0, and Society 5.0 based on literature review. The methodology started with a systematic literature review for extracting significant research articles, conference papers, and review papers for different kinds of areas, from a range of different academic databases (e.g., IEEE Xplore, ScienceDirect, SpringerLink, and Google Scholar). This search was conducted with targeted keywords of "Industry 4.0", "Industry 5.0", "Society 5.0", "AI", "ML", and "DL" with the intent to cover the past ten years of work to provide a comprehensive view of recent advancements and trends. The literature was then screened based on title, abstract, followed by full-text review of selected papers to extract key information and insights. This thematic arrangement led to a large perspective of the part of AI, ML, and DL based technologies in enhancing Industry 4.0, Industry 5.0, and Society 5.0, and allowed us to recognize research spaces and potential area of focus for future investigation. Next, a keywords analysis was conducted to update on research trend, focus area within the literature in the corpus of the paper selected. Extracted keywords were analyzed to find the most popular terms in the identified papers, which would also give an idea on the focal areas and emerging themes of concern among the academic bandwagons. A word cloud with the former to intuitively display the main topics and the latter to quantify the dominance and significance of those topics respectively.

To observe the relationships between different themes in research, we have carried out a co-occurrence analysis of the keywords utilized in the included studies. The research analysed how often two keywords appeared together in the papers. We used the co-occurrence data to build a network map in order to show the connections and interactions between different research topics. This network map assisted in identifying the clusters of co-occurring themes and central topics that help to bridge different bodies of research. In order to reveal deeper insights into the structure and organization of the research landscape, we performed a cluster analysis of the co-occurrence data. We implemented clustering algorithms to partition the network of co-occurring keywords into clusters. Every cluster was a clear lump of topics that were often titled together in the literature. Clusters were analyzed considering themes and sub-themes throughout the research field. The results of the cluster analysis provided a more explicit knowledge of the multifaceted aspect of AI, ML, and DL technologies with respect to Industry 4.0, Industry 5.0, and Society 5.0

 1.3 Results and discussions

Co-occurrence and cluster analysis of the keywords

The network diagram (Fig. 1.1) shows the cluster analysis along with keyword co-occurrence. The network illustrates these areas of concern and how they are inter-related by showing the occurrence and connections of important terms on Industry 4.0, 5.0 and Society 5.0. Centrally on the diagram is "Industry 4.0" placed and thus also the underlying technologies of interest are highlighted. The concept of Industry 4.0 is the fourth generation of industrial transformation that includes cyber-physical systems, the internet of things, and the internet of systems. Another buzzword that entered into relevance is the "artificial intelligence” suggesting the urgency in guiding innovation and efficiency in numerous fields. Since the nodes are larger it implies, they are frequent and quite critical in the literate. On only two hubs, clusters "machine learning" and "deep learning". Machine learning is the aspect of AI science and includes the building of algorithms that allow a computer to learn from data and make accurate predictions. Deep learning falls under machine learning, which many machine learning algorithms are based off of used large layered neural networks to interpret and learn from complex data patterns. This is because these nodes are very close to each other and they have strong connections with each other, thus depending upon each other, cumulating to Industry 4.0 footprint.

The Internet-of-Things (IoT) is another widely used term that highlights the necessity in the same domain, its basic functionality to connect, share data, and automation. The term of IoT cluster, a liaised term with "cyber-physical system" or "embedded system" or "5G mobile communication system", provides the technological framework in terms of which devices and systems can connect to each other. Within the context of Industry 4.0, the areas of "smart manufacturing" and "decision making" are the most important domains. Smart manufacturing is the application of cutting-edge technology to factories, with the goal of using machine learning and artificial intelligence to produce the best results for a company. This association is indicative of how AI-driving technologies are transforming the industrial sector towards data and automation. The Fig. 1.1, another key concept that further addresses the importance of "sustainability" and "sustainable development"; that keeps with the current and growing trend of environmentally friendly practices within the industry. This class is represented by the themes of energy efficiency and energy use, indicating the need to reduce resource use and energy in order to meet sustainability targets.

Fig. 1.1 Co-occurrence analysis of the keywords in literature

Blockchain and cloud computing are basic sectors of the networking industry and they are supposed to dominate the future for the safe, decentralised processing of data and allowing for a parallel free use of computational power. These technologies combined form an integral part of how to automates industries could be applied to advance visibility, protectiveness, profitability over industries. The security of a computer network refers to one of the broadest areas defining the needs in secure interconnections of any networked devices against cybercrime or another form of cyber-incidents of potential unauthorized parcels being made available by these networks. Considering these packages have tight connections with AI and related projects like IoT, it becomes a rather necessary point for the heavy usage of robust security controls in an era of more connected and more automated industrial environments. The use of AI and ML techniques in predictive maintenance allow for the prediction of possible equipment breakdowns and at the same time, enables one to select carefully the schedule of maintenance, of different activities. This classification falls under "quality assurance," "error detection," and "forecasting analysis" which, again, is proof to how valuable data-driven information can be in regard to achieving higher TI and reducing downtime.

Within the network, the "digital twin" and "augmented reality" apportion a part of the emerging technology pie in the industry 4.0 context. The digital twin is the sophisticated virtual/visual depiction of the physical system that enables real-time inspection and real-time evaluation. Augmented reality, on the other hand, displays virtual information on top of the real world to enhance user interaction and decision making. On another side there is education, training and innovation and how it is helping to develop skills and human capital and these clusters are closely related with new technologies such as AI, and IoT.

Evolution of industry 4.0, 5.0, and society 5.0

Fig. 1.2. Shows the industrial revolutions. The Industrial Revolution was one of the most tremendous transformations throughout human history. It began in late 18th-century England and was a colossal shift in production methods from manual labour to mechanized factories. Mechanization and the introduction of steam power generally influenced agricultural output and productivity in manufacturing. This era reshaped the structure of societies, economies, and people's style of life and laid the foundation for industrialized society. Today, it still symbolizes the technological-industrial impetus of humankind.

Fig. 1.3 describes the fundamentals of Industry 5.0 and Society 5.0, emphasizing their shared pillar: human-centricity. Both Industry 5.0 and Society 5.0 aim to create a sustainable, resilient, and human-centric future, leveraging advanced technologies to enhance both industrial processes and overall quality of life.

Industry 5.0 has three basic focuses:

1. Sustainable: Actively leading in sustainability and respect for planetary boundaries.
2. Resilient: The value of being agile and resilient lies in the flexibility and adaptability of technology.
3. Human-Centric: Talent promotion, diversity, and empowerment in all aspects, with an overarching commitment to human-centric design and operation.

Fig. 1.2. The industrial revolutions

Society 5.0 integrates the principles of Industry 5.0 into society, moving toward a super-smart society with:

1. Super Smart: Cyberspace will integrate perfectly with the physical space using technologies such as those associated with 5G, large sets of data, and artificial intelligence, amongst others.
2. Lean: Guaranteed supply of goods and services required with a focus on efficiency at required levels and times.
3. Human-Centric: Wrapping up a high-quality life with comfort and vitality, extending even further the human-centric approach of Industry 5.0.

Fig 1.3 Human-Centric Evolution: Industry 5.0 and Society 5.0

Table 1.1 compares the different technological perspectives between Industry 4.0 and Industry 5.0. Perspective one is centered on the continuation and incremental improvement of already existing digital technologies viewed as a part of a natural extension of Industry 4.0. Technologies emphasized by this perspective include additive manufacturing, big data analytics, blockchain, cloud computing, and the Internet of Things (IoT). The second perspective symbolizes a far-reaching digression from conventional digital technologies with a revolutionary change in technological advancement. It focuses on innovations like artificial general intelligence, bio-inspired technologies, brain-machine interfaces, and self-healing materials. Perspective Three bridges both Industry 4.0 and Industry 5.0 with a hybrid approach, focusing more on trans-formative new technologies. From this perspective, one finds adaptive robotics, cognitive cyber-physical systems, extended reality, and intelligent energy management systems. These three visions combine in an all-inclusive, stage-by-stage concept of the development of different directions in industry digital transformation.

Table 1.1 Comparison of Technological Perspectives in Industry 4.0 and Industry 5.0

Perspective One: Well, it's like the natural, incremental extension of Industry 4.0.

Perspective Two: Radical Departure from Industry 4.0

Perspective Three: Considerable transformation of Industry 4.0 technologies

Additive manufacturing: Boosting the power of 3D printing for custom production.

Artificial General Intelligence: A system with cognitive capabilities like humans.

Adaptive, cognitive robotics—robots which adapt and learn in non-stationary environments.

Big data means advanced analytics for improved decision-making.

Bio-inspired technologies: Imitation of biological processes for innovation.

Cognitive cyber-physical systems: An integrated AI basis of physical processes to Smarter Operations.

Blockchain: Protecting transactions and data integrity.

Biosensors: Real-time monitoring of biological conditions.

Cognitive/creative artificial intelligence: AI systems that do create and innovate.

Cloud computing: Scalable, flexible IT resources.

Brain-machine interfaces—direct communication between brains and machines.

Extended Reality: Augmented and Virtual Realities for Immersive Experiences.

Cybersecurity and Cryptography: The Security of Digital Infrastructures.

Causal artificial intelligence would be AI that understands cause-effect relations.

Human Recognition Technologies: High-Tech Biometric Systems for Security and Personalization.

Edge computing: Processing data at source.

Fiber computing technologies: High-speed data transmission and processing.

Industrial wearables: intelligent devices that empower workers' abilities and safety.

Embedded Systems: Specialized computing systems inside larger devices.

Genomics: advances in personalized medicine and genetic engineering.

Internet of Everything: All devices interconnected for the purpose of perfect communication.

Enterprise systems are integrated software for business processes.

Humanoid robots: These are robots that the resemblance and act like human beings.

Mobile autonomous robots are designed to move on their own and perform tasks independently.

Execution systems: Those systems in charge of production and operation management.

Internet of Medical Things: Connected Medical Devices for Healthcare.

The multiscale dynamic simulation will compute processes at various involved scales for optimization.

Industrial control systems: Manufacturing process electrification and automation.

Autoself-healing/repairing material: Materials that, in some way, automatically self-repair.

Smart energy management systems: Efficiency through better use of energy.

Industrial robots: These are production automatons.

Smart learning material: Educational material responding to learners' needs.

Smart product lifecycle management: Managing product data from creation to disposal.

Internet of Things: Interconnected devices sharing data.

Swarm Intelligence: A Collective Behavior in Decentralized Systems.

 

Machine Learning: Algorithms that improve from experience.

 

Networking Infrastructure: Robust, scalable networks.

 

Key technologies in Industry 4.0, 5.0, and Society 5.0

Industry 4.0, Industry 5.0, and Society 5.0 are stages in technology development and integration into different aspects of human life (Carayannis et al., 2022; Paramesha et al., 2024d). Every phase leverage innovation from the phase before, as new technologies and paradigms are added, which escalates the overall societal efficiency, productivity, and prosperity of society (Kasinathan et al., 2022; Tyagi et al., 2023). The basic tenets of Industry 4.0 are the IoT, big data analytics, AI, cloud computing, and cyber-physical systems. Table 1.2 shows the key technologies in Industry 4.0, 5.0, and Society 5.0.

Industry 4.0: The fourth industrial revolution

IoT: IoT is the base of Industry 4.0 which connects physical devices to the internet and collecting and sharing the data (Dautaj, & Rossi, 2021). This interconnection allows real-time tracking, maintenance forecasts and automation (Roblek et al., 2021; Dautaj, & Rossi, 2021). Smart factories, for example, use the IoT to streamline production lines, minimize downtime and improve operational performance.

Big Data Analytics - The data collected by IoT devices is massive and big data analytics needs to be employed to find actionable insights from it (Trehan et al., 2022; Paramesha et al., 2024e). For manufacturers, big data analytics is all about analysing trends, making better decisions and reducing waste in the process. It assists in equipment failure predictions, improves quality of products and lowers operational costs.

AI: AI plays a fundamental role in converting data into insights, so it is essential to every organization (Huang et al., 2022; Carayannis, & Morawska-Jancelewicz, 2022). Cash flow forecasting, supply chain optimization and product design update can be improved with the use of machine learning algorithms. There are other ways in which AI could also be used to increase the accuracy and speed of manufacturing like in this instance, with AI-powered robots and automation systems, manufacturers can improve the level of coordination between these systems and carry out a large portion of tasks automatically, hence minimizing human error and operational costs.

Cloud Computing: A type of internet-based computing that provides shared processing resources and data to computers and other devices with all 24/7 on-demand Cloud computing systems are one of the most cost-effective ways to utilize AI (Paschek et al., 2022; Sharma et al., 2024). It facilitates collaboration between locations, promoting integration and organization of stages of production. Another important facility that cloud platforms provide are advanced security measures to secure sensitive data.

Cyber-Physical Systems (CPS): A system that connects the worlds of embedded systems by connecting the digital and real worlds through networks and allows information sharing and control in real time (Jazdi, 2014). CPS enables the realization of smart factories, where machines and entire systems are networked and able to communicate and act autonomously (Jazdi, 2014; Oks et al., 2022). This results in higher efficiency, lower wastage and better quality of the product.

Industry 5.0: Human-centric innovation

It is a phase focused on personalization, sustainability, and ethics-based on technologies like cobots (collaborative robots), AR (augmented reality), and advanced HMIs (human-machine interfaces) (Xu et al., 2021; Leng et al., 2022). Fig. 1.2 shows the sankey diagram on key technologies in Industry 4.0, 5.0, and Society 5.0.

Collaborative Robots (Cobots): Cobots are designed to collaborate with humans to increase efficiency and safety (Prassida, & Asfari, 2022; Liao et al., 2023). These machines are built with sensors and AI to be able to adapt human movements so that they can be near them without danger in the work area, something that industrial robots could not do until now. These are increasingly used for tasks requiring precision and flexibility such as assembly, quality inspection and packaging.

Augmented Reality (AR): AR superimposes digital information on the physical world, offering workers real-time help (Leng et al., 2022; Zafar et al., 2024). AR can also be used to help technicians in manufacturing navigate a complex assembly job, catch mistakes and improve training. It also opens up the possibility of remote collaboration, so experts in one location can provide immediate support to another no matter how many miles apart they are.

Human-Machine Interfaces (HMIs): facilitate human interaction with machines with ease (Adel, 2023; Panter et al., 2024). Intend interfaces use natural language processing (NLP), gesture recognition, and brain-computer interfaces (BCIs) in order to establish simple and effective communication pathways. The role of HMIs is essential in improving user experience and making technology cater to human demands.

Fig. 1.3 Sankey diagram on key technologies in Industry 4.0, 5.0, and Society 5.0

Personalization and Customization: Industry 5.0 instead is shift is towards making individualized products which are more in line with individual preferences. One example is the use of advanced AI algorithms that use the data of consumers for predicting trends and preferences, so that we can manufacture personalized-at-scale products. It improves customer satisfaction as the seller only produces goods when an order is placed.

Sustainability and ethical issues incorporating sustainability into Industry 5.0 is important the need for a sustainable economy that is, an economy which has the ability to continue to, over a long period of time, function well, support community and ecological vitality, and be flexible enough to adapt to change (Tyagi et al., 2024). AI and IoT are leveraged here for sustainable energy management and reduce pollution, emissions or circular economy. Industry 5.0 also encompasses ethical issues that cover data privacy and labour rights, among others to make sure that the adoption and advancement of technology benefits society at large.

Table 1.2 Key technologies in Industry 4.0, 5.0, and Society 5.0

References

Technology Category

Industry 4.0

Industry 5.0

Society 5.0

Coronado et al., (2022)

Automation and Robotics

Advanced robotics and automation

Human-robot collaboration

Social robots and human-centered robotics

Mourtzis et al., (2022); Uddin et al., (2023)

AI

AI and machine learning for process optimization

AI for personalized solutions and human enhancement

AI for societal well-being and sustainable development

Mourtzis et al., (2022); Adel, (2022); Saikia, (2023)

IoT

IoT for connected machines and devices

IoT for human-centric applications

IoT for enhancing quality of life and smart living

Mourtzis et al., (2022); Adel, (2022); Troisi et al., (2023)

 

Big Data and Analytics

Data analytics for operational efficiency

Data-driven decision making with human input

Data for societal insights and public services

Adel, (2023)

Cloud Computing

Cloud-based infrastructure and services

Hybrid cloud solutions for better human interaction

Cloud services for public welfare and smart cities

Sverko et al., (2022); Taj, & Zaman, (2022)

Cyber-Physical Systems (CPS)

Integration of physical and digital systems

Human-in-the-loop CPS

CPS for societal challenges and disaster management

Yao et al., (2024); Mourtzis et al., (2022) 

Additive Manufacturing

3D printing and rapid prototyping

Customization and personalization through 3D printing

Distributed manufacturing for local needs

Leng et al., (2023); Hemamalini et al., (2024)

Blockchain

Secure and transparent supply chains

Blockchain for trust and human rights

Blockchain for secure digital identities and democracy

Hassan et al., (2024)

Augmented Reality (AR) and Virtual Reality (VR)

AR/VR for training and maintenance

AR/VR for enhanced human-machine interaction

AR/VR for education, healthcare, and social inclusion

Mourtzis et al., (2022); Narkhede et a., (2023)

Sustainable Technologies

Green manufacturing and energy efficiency

Sustainable practices with human focus

Technologies for environmental sustainability

Adel, (2023); Sharma et al., (2024)

Edge Computing

Real-time data processing at the edge

Enhanced human-machine interaction at the edge

Edge computing for public safety and emergency response

Efe, 2023; Sharma et al., (2024)

Quantum Computing

Emerging quantum technologies for complex problems

Quantum computing for advanced human applications

Quantum computing for societal problem-solving

Alves (2022); Pereira et al., (2023)

Human Augmentation

Wearable technology and exoskeletons

Augmented reality interfaces and brain-computer interfaces

Human augmentation for disability support and enhancement

Lv, (2023); Wang et al., (2024); Fernández-Caramés, & Fraga-Lamas, (2024)

Digital Twin

Real-time digital replicas of physical assets

Digital twins for human-centric applications

Digital twins for societal infrastructure and services

 

Society 5.0: A Smart Society

AI: AI is the primary element in innovation across all sectors in Society 5.0 (Zamzami et al., 2022). Better patient outcomes through AI-guided diagnostics and tailored treatment plans in healthcare (Al Mamun et al., 2021; Zamzami et al., 2022). Autonomous vehicles and smart traffic management systems using AI can improve safety and reduce traffic congestion in transportation. AI also ensures proper education for students using personal learning experiences, along with advancement by providing the facility of acquiring knowledge.

IoT: As in the important stage of society, IoT play a key role in the fifth stage, connecting devices and systems to create smart cities, homes and industries (Nair et al., 2021; Mishra, & Pandey, 2023). Through IoT, smart city initiatives monitor and manage urban infrastructure such as waste management, energy distribution, and public safety. IoT devices in smart homes increase convenience and energy efficiency by automating and monitoring in real time.

Robotics: Society 5.0 is by definition not only an industrial robotics but also a robotics for healthcare robotics and for agriculture robotics for daily living (Calp, & Bütüner, 2022; Bissadu, et al., 2024). Inspired by this, there exist some caregiving robots that can help older persons maintain an active and independent lifestyle (Dautaj, & Rossi, 2021; Calp, & Bütüner, 2022). Farmers can use agricultural robots to improve farming methods, increasing output and reducing menial tasks. In daily life, robots are useful tools for cleaning and lower their burden work.

Data security, transparency and trust in blockchain technology: For finance, blockchain allows for transactions that are more secure and more efficient by preventing fraud and increasing trust (Beniiche et al., 2022; Tyagi, et al., 2023). Blockchain in supply chain management provides visibility and traceability, ensures the genuineness of products. This also provides support for decentralized identity and enablement of data privacy and security through blockchain.

5G Connectivity: Delivering the high-speed, low-latency connectivity necessary for seamless IoT device integration and high-volume, real-time data exchange (Ghosh et al., 2021). It also makes possible innovations such as telemedicine, autonomous driving and smart grid operation. The birth of Society 5.0 is a reality, but to make a comprehensive impact necessitates the realization of 5G, which separately supports the large-scale data transmission and communication burdens associated with a super-smart society (Ghosh et al., 2021; Thakur et al., 2022).

Quantum Computing: Quantum computing has the potential to address problems so complex that it is beyond the reach of classical computers (Griffin et al., 2021). Quantum computing can be powerful enough to reverse-engineer, and Society 5.0 fields like cryptography, materials science, and drug discovery, will forever alter (Griffin et al., 2021; Zamzami et al., 2022). When it comes to processing huge amounts of data extremely fast it could potentially revolutionise many industries and provide huge gains in optimisation and innovation.

This Sankey diagram (Fig. 1.3) reveals how advanced technologies flow and inter-relate across three major industrial and societal paradigms. The diagram predicts that in Industry 4.0, technologies such as IoT, Big data, AI, cyber-physical systems, cloud computing, robotics, additive manufacturing, and augmented reality will be increasingly applied towards smart manufacturing processes. This kind of technologies helps to automate things, real-time data analysis and run an efficient production process. In Industry 5.0, the focus moves to personalized manufacturing of goods, people will collaborate machine-human, replaces advanced robotics, AI, IoT, Big data, cyber-physical systems, cloud computing, and augmented reality etc. This approach highlights the collaboration between human intelligence and machine abilities to develop new manufacturing systems that are more flexible and effective. Society 5.0 goes further to apply these technologies to improve our quality of life as a society. The applications illustrate how AI, IoT, Big Data, robotics, augmented reality, virtual reality, blockchain, and cyber-physical systems are key to alleviating the challenges across the different aspects of daily life, including health, mobility and general societal welfare. The diagram also shows cross-industry technologies AI, IoT, Big data are crucial for all 3 paradigms. This is evidently the case for AI, which has proven itself to straddle various industries in its role in smart manufacturing, personalized manufacturing and health as an example of just three ways it is having such a profound impact on lives. IoT and Big data were among the areas that showed big influences that marked these two areas as critical to connecting devices, analysing large amounts of data and driving decision with insights. This holistic perspective highlights the interplay and versatility of technology to create innovation and productivity across various industrial and societal domains.

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