Role of Artificial Intelligence and Machine Learning in Industry 4.0

What Is Industry 4.0?

Success is achieved in any discipline through practice, effort, and planning. Industrialization has achieved remarkable success all across the globe. The transformative effects are visible and we live in an interconnected world. Here is a breakdown of industrial development and remarkable changes in respective categories:

1. Industry 1.0: The dawn of the Industrial Age due to rapid advancements in science and mechanization, e.g. power mills, steam engines, and railways lines.

2. Industry 2.0: Symbolized by the revolutionary Ford company and automotive, e.g. the assembly line mass production of cars and introduction of electricity as energy.

3. Industry 3.0: Discovery of semiconductor properties and popularity of computers, e.g. large-scale automation in manufacturing, construction, steel, oil refineries, and IT.

4. Industry 4.0: A prescribed and predicted paradigm shift through robotics, e.g. exploring human-machine interaction, cyber-physical systems, space tourism, and driverless cars.

Creative Confluence

Science is all about accurate principles, while technology aims for success. Technical implementation has to be supported by business acumen. Above all, innovative creations should have effective applications. In recent years, there has been a lot of buzz around artificial intelligence (AI) and the Internet of Things (IoT).

IoT deals mainly with big data, predictive analytics, and cloud computing. The aim is to revolutionize digital services using frameworks, platforms, and connectivity architectures. The digitization of businesses and governments is expected to bring greater transparency and accountability. Future projections also incorporate smart cities, adaptive cruise control, and brain/computer interfaces.

This further leads into the exciting world of artificial intelligence, machine learning, cybernetics, neural networks, and deep learning. However, IoT does not stop with office automation and advanced communications. Smartness is also being extended to housing, transport, and industrial manufacturing. For example, a smart factory would have humans and cyber-physical systems interacting over the cloud. Remote monitoring of processes and decisions using big data analytics are also possible.  

What Is AI?

Artificial intelligence, as the name suggests, describes the ability of machines to imitate human mental prowess. However, this intelligence is not restricted to machines — it also applies to software systems; hence, the differentiation of demarcation between robotics and machine learning (or AI). The three major components are, therefore, machine or system, software, and internet connectivity (cloud and big data).

1. Machine: The most popular example is undoubtedly robots and robotics. Thanks to science fiction and movies, everyone knows about them. Mechanical engineering techniques are used to fabricate metal into cars and steely human bodies. They are fitted with electrical circuits and electronic chips for control and command.

2. Software: This is the part of AI that delivers and maintains software mechanisms. The machines are not programmed for deterministic functions alone. The software also has a feedback or loop design to facilitate learning.

3. IoT: In addition to sensory-motor functions, the system is also expected to interact. Hooking on the system to the cloud is very useful for AI researchers. They can perform refined data analytics, adaptive research, and real-time communications.

Note: Robotics is not the only area of application for artificial intelligence (AI) and machine learning. The game-changing Industry 4.0 standard recognizes the role of both humans and cyber-physical systems. Applications are envisioned for manufacturing, healthcare, space exploration, corporate sector, R&D, and governance. 

Machine Learning: A Subset of AI

Hardware, software, and control engineering have worked magic since the 1960s. The modern buzzwords are high-performance computing, big data, parallelism, distributed systems, and quantum computing. Storage and processing capacities, as well as algorithmic research, have made rapid strides. This has created a renewed interested in heuristics and learning techniques for future generations.

Machines can only imitate human faculties of reasoning and knowledge acquisition. Humans learn through various methods, as there is no "one-size-fits-all" solution. Learning is done by example (imitation), trial and error (heuristics), and repetition or memorization. Machine learning is often defined by experts as a subset of artificial intelligence. It is also rated highly by both pedagogical researchers and computer scientists.

The above-mentioned confluence of diverse technologies clearly indicates the value of ML. It is expected to simplify manufacturing and make urban transportation more effective. The medical, legal, and gubernatorial fraternities are also expected to draw immense profit. ML has the potential to enhance the quality of justice, drug dispensation, and mechanical production. Machine learning also liberates business intelligence and administrative decision-making into a higher realm. It achieves this through logical consistency, reliable predictions, and resourceful visualization.

So, What Is Machine Learning (ML)?

For a while, AI experts focused their research on pattern recognition and computational pedagogy. ML principles have been extracted out of these experiences and taken forward. Currently, machine learning pertains to algorithmic refinement and data modeling. Efficient algorithms are designed, built, and analyzed using computers. Big data is also collected from various sources and modeled for making more accurate predictions.

The strength of ML lies in inputs, data-driven analysis, and informed decision-making. Unlike ordinary computer instructions, machine learning specializes in predictions. Technically, the subject is closely related to computational statistics and probability. Hence, it plays a major role in predictive analytics for business solutions. Researchers also relate the subject to mathematical optimization and heuristic learning techniques.

Applications: Manufacturing Sector

Machine learning techniques are integral to image, face, and speech recognition. The smart agents also act as digital assistants, intelligent bots, and speech processors. The other notable uses are audio-visual analysis, automatic translation or transcription, and driverless cars. Industry 4.0 is expected to benefit from MI methodologies in the following manners:

• Smart factories will have a closely monitored, automated production process.

• Advanced digitized networks will be installed for data collection and transfer.

• Mechanical data comprises of energy, speed, power, weight, pressure, etc.

• Smart manufacturing is characterized by preventive actions and adaptive production.

• Machines, humans, software systems, and products interact over the internet.

• Inspections, supervision, modifications, and communication can be automated.

• This value-added manufacturing process is heterogeneous, decentralized, and flexible.

• New devices will be embedded to scale up the system and achieve complete automation.

Conclusion

Computerization and automated industrial procedures go hand-in-hand. Their confluence is achieved by the right combination of hardware and software. Artificial intelligence and machine learning techniques are also applied heuristically. The future of the industry is visualized as a combination of refined machines and adaptive software. Both these vital components are plugged into a grid or framework.

Big data analytics and cloud computing architecture ensure flexibility and scalability. Businesses can refine production processes by using the "learning by example" method. Predictive analytics help in sharpening business intelligence. Profits can be invested in the latest embedded infrastructure. Operations can be scaled up without compromising the quality of communications or machine feedback.