The 2023 J.U.C. Media, Research & Writing Awards | Submitted Category: RESEARCH
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The 2023 J.U.C. Media, Research & Writing Awards 
Submitted Category: RESEARCH 
Submission Code: R4 
Student Grade: 12 | Word count: 1306

Research Theme: Is Artificial Intelligence Angel or Demon
Research Title: Artificial Intelligence: The Shining Star of Modern Technology

ARTIFICIAL INTELLIGENCE IS AN ANGEL

[The 2023 J.U.C. LMGlobal.Org]
Artificial Intelligence: The Shining Star of Modern Technology Artificial Intelligence as a Creativity Tool in Product Design is an Angel to all of us. It is the science and engineering of making intelligent machines, especially intelligent computer programs. It is related to the similar task of using computers to understand human intelligence, but AI does not have to confine itself to methods that are biologically observable. Intelligence is the computational part of the ability to achieve goals in the world. Varying kinds and degrees of intelligence occur in people, many animals and some machines. Siri, Google Now and Cortana are making the use of the internet much easier than before. Management information systems allows business executive to control a multibillion-dollar corporation while sitting in his own office. Artificial Intelligence definitively has the potential to create lots of money while still providing considerable value for society

Definition of Artificial Intelligence:
Artificial refers to something which is made by humans or a non-natural thing and Intelligence means the ability to understand or think.
There can be so many definitions of AI, one definition can be “It is the study of how to train the computers so that computers can do things which at present humans can do better.”

Types of Artificial Intelligence:
1.Human-like

2. Rational-like:
   • Thought processes
   • Behavior

Strategy 1: User-centric ideas

When we develop a new product, we must always consider who will use it and how they will use it.This process needs to be included when brainstorming for conceptualization happens.User-centric product design consists of four primary stages, two of which must be completed parallelly. Analysis, conception, design, assessment, and optimization are among them

Strategy 2: Research and Ideation
• It is critical to understand that Design Thinking is not a linear process
• During the ideation phase, one will explore and generate as many concepts as possible. Some of these ideas will become potential answers to your design challenge, others will be dismissed.

Strategy 3: Function over facet
• Today, designers focus on aesthetics and unique beauty elements more than the function of the product.This element ignores the factor of a product’s functionality a lot of times.
• The functionality, preferences, goals, conflicts, and targets essential for a human-centric product should be meticulously curated and classified by designers.

Strategy 4: Ergonomics
• For usability and ergonomics testing don’t listen to users, rather observe what they work on.It is a scientific and academic field that studies the relationship between employees and their environments.Ergonomic design personalizes a product to specific user requirements.When evaluating the ergonomics of a particular product, various variables such as safety, convenience, comfort, ease of use, and enjoyment are essential parts of it

Strategy 5: Extensive Prototype Testing

•   Before we confidently pass the designed product over to the engineering and marketing team, you need to make sure to seek some feedback and test your prototype with users

AI in New and Innovative Product Design Methods is used to: Find Inspiration
Generate Ideas
Testing and Simulation Design Optimization

Finding Inspiration

Generating Ideas

•   AI is used to create concept designs for new products, either by using data and algorithms to generate designs based on certain criteria or; By working in collaboration with designers to create concepts that combine human creativity with AI-powered analysis and optimization.

Testing and Simulation:
Designers can use AI to simulate how a product will behave in real-world conditions, such as how it will respond to different loads and stresses, or how it will interact with other products or systems.This can help designers create products that are more robust and reliable, and that is less likely to fail in the field

Design Optimization
AI can help designers optimize their products for certain criteria, such as cost, weight, or performance. A designer could use AI to search for the optimal combination of materials and manufacturing processes for a new product or to find the most efficient design for a new component.This can help designers in creating products that are more cost-effective, lighter, or more powerful, depending on the specific goals of the design.

Applications of Artificial Intelligence : Manufacturing
Although scope of AI is almost unlimited, current practices in product design are limited to manufacturing that are yielding very fruitful results such as :
1.AI for creating Digital Twins 2.Generative Design 3.Predictive Maintenance 4.Assembly Line Optimization 5.Quality Assurance 6.Inventory Management 7.Process Optimization

•   AI for creating Digital Twins

A digital twin is a virtual copy of a physical production system.
There are digital twins of specific machinery assets, entire machinery systems, or particular system components in the manufacturing area. The most common uses of digital twins are real-time diagnostic and evaluation of production process, prediction, and visualization of product performance, etc.To teach digital twin models to optimize the physical system, data science engineers utilize supervised, and unsupervised machine learning algorithms by processing historical and unlabeled data gathered from continuous real-time monitoring

•   Generative Design

Generative design is a process that involves a program generating some outputs to meet specified criteria.Designers or engineers input design goals and parameters such as materials, manufacturing methods, and cost constraints into generative design software to explore design alternatives.The solution utilizes machine learning techniques to learn from each iteration what works and what doesn’t.

•   Predictive Maintenance

Manufacturers leverage AI technology to identify potential downtime and accidents by analysing the sensor data. AI systems help manufacturers forecast when or if functional equipment will fail so its maintenance and repair can be scheduled before the failure occurs.Thanks to AI applications predictive maintenance, manufacturers can improve efficiency while reducing the cost of machine failure.

•   Assembly Line Optimization

The huge amount of data generated for condition monitoring and predictive maintenance can also be used for creating automation by layering Artificial Intelligence into the ecosystem.For example, when equipment operators are showing signs of fatigue, supervisors get notifications. When a piece of equipment breaks down, the system can automatically trigger contingency plans or other reorganization activities.
• Quality Assurance

Quality assurance has been a manual job for years, requiring a highly skilled engineer to ensure that components were being manufactured correctly. Today, image processing algorithms can automatically validate whether an item has been correctly produced. By installing cameras at critical points along the factory floor, this sorting can happen automatically and in real-time.

•   Inventory Management

Machine learning solutions can promote inventory planning activities as they are good at dealing with demand forecasting and supply planning. AI-powered demand forecasting tools provide more accurate results than traditional demand forecasting methods (ARIMA, exponential smoothing, etc.) engineers use in

manufacturing facilities. These tools enable businesses to manage inventory levels better so that cash-in-stock and out-of-stock scenarios are less likely to happen.

•   Process Optimization

AI-powered software can help organizations optimize processes to achieve sustainable production levels. Manufacturers can prefer AI-powered process mining tools to identify and eliminate bottlenecks in the organization’s operations.By using a process mining tool, manufacturers can compare the performance of other areas down to individual process steps, including duration, cost, and the person performing the step. These insights help streamline processes and identify where the bottlenecks are so that manufacturers can take action.

Conclusion


Hence it is concluded that Artificial intelligence is an angel to all

of us. It is certain that the use of AI in product design is changing the way we create and innovate. From generating concepts to simulating real-world behavior, AI is proving to be a valuable tool for product designers looking to create innovative and successful products. With its ability to quickly analyze vast amounts of data and generate new ideas, AI is helping designers push the boundaries of what is possible.As AI continues to advance, it will become an increasingly powerful tool for designers, enabling them to create products that are more innovative, more efficient, and more successful than ever before.

REFERENCES:

•   Algorithms and Artificial Intelligence (CQ Researcher)
•   Robotics and the Economy (CQ Researcher)
•   Robotic Warfare (CQ Researcher)
•   Artificial Intelligence (Times Topics)
•   Artificial Intelligence (National Public Radio)
•   Robots (National Public Radio)
•   Robotics (National Public Radio)
•   Computerization, Atomization, Crowd Sourcing and the New Economics of Employment (Journalist's Resource)

•   American Association for Artificial Intelligence (AAAI), Welcome to AI Topics, 2003, http://www.aaai.org/AITopics/ -- a Web-based library of introductory information about various areas of artificial intelligence; altogether, a resource with links to hundreds (thousands?) of sites, organized by an easy-to-use, interactive index.
•   George Luger, Artificial Intelligence: Structures and Strategies for Complex Problem Solving, Fourth Edition Addison-Wesley, 2002 -- a well-respected introduction to artificial intelligence, as witnessed by its being in its fourth edition.
•   Peter Norvig, AI on the Web, http://aima.cs.berkeley.edu/ai.html -- a list of over 800 links on various aspects of artificial intelligence.
•   Nils J. Nilsson, Artificial Intelligence: A New Synthesis, Morgan Kaufmann Publishers, 1998 -- another fine introductory textbook on artificial intelligence.
•   Stuart Russell and Peter Norvig, Artificial Intelligence: A Modern Approach, Second Edition, Prentice-Hall, 2003 -- the leading introductory textbook in the field.