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- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
Innovative Engineering with AI Applications
Innovative Engineering with AI Applications demonstrates how we can innovate in different engineering domains as well as how to make most business problems simpler by applying AI to them.
Engineering advancements combined with artificial intelligence (AI), have resulted in a hyper-connected society in which smart devices are not only used to exchange data but also have increased capabilities. These devices are becoming more context-aware and smarter by the day. This timely book shows how organizations, who want to innovate and adapt, can enter new markets using expertise in various emerging technologies (e.g. data, AI, system architecture, blockchain), and can build technology-based business models, a culture of innovation, and high-performing networks. The book specifies an approach that anyone can use to better architect, design, and more effectively build things that are technically novel, useful, and valuable, and to do so efficiently, on-time, and repeatable.
Audience
The book is essential to AI product developers, business leaders in all industries and organizational domains. Researchers, academicians, and students in the AI field will also benefit from reading this book.
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Veröffentlichungsjahr: 2023
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Table of Contents
Cover
Series Page
Title Page
Copyright Page
Preface
1 Introduction of AI in Innovative Engineering
1.1 Introduction to Innovation Engineering
1.2 Flow for Innovation Engineering
1.3 Guiding Principles for Innovation Engineering
1.4 Introduction to Artificial Intelligence
1.5 Types of Learning
1.6 Categories of AI
1.7 Branches of Artificial Intelligence
1.8 Conclusion
References
2 An Analytical Review of Deep Learning Algorithms for Stress Prediction in Teaching Professionals
2.1 Introduction
2.2 Literature Review
2.3 Dataset Pre-Processing
2.4 Machine Learning Techniques Used
2.5 Performance Parameter
2.6 Proposed Methodology
2.7 Result and Experiment
2.8 Comparison of Six Different Approaches For Stress Detection
2.9 Conclusions
2.10 Future Scope
References
3 Deep Learning: Tools and Models
3.1 Introduction
3.2 Deep Learning Models
3.3 Research Perspective of Deep Learning
3.4 Conclusion
References
4 Web Service Composition Using an AI Planning Technique
4.1 Introduction
4.2 Background
4.3 Proposed Methodology for AI Planning-Based Composition of Web Services
4.4 Implementation Details
4.5 Conclusions and Future Directions
References
5 Artificial Intelligence in Agricultural Engineering
5.1 Introduction
5.2 Artificial Intelligence in Agriculture
5.3 Scope of Artificial Intelligence in Agriculture
5.4 Applications of Artificial Intelligence in Agriculture
5.5 Advantages of AI in Agriculture
5.6 Disadvantages of AI in Agriculture
5.7 Conclusion
References
6 The Potential of Artificial Intelligence in the Healthcare System
6.1 Introduction
6.2 Machine Learning
6.3 Neural Networks
6.4 Expert Systems
6.5 Robots
6.6 Fuzzy Logic
6.7 Natural Language Processing
6.8 Sensor Network Technology in Artificial Intelligence
6.9 Sensory Devices in Healthcare
6.10 Neural Interface for Sensors
6.11 Artificial Intelligence in Healthcare
6.12 Why Artificial Intelligence in Healthcare
6.13 Advancements of Artificial Intelligence in Healthcare
6.14 Future Challenges
6.15 Discussion
6.16 Conclusion
References
7 Improvement of Computer Vision-Based Elephant Intrusion Detection System (EIDS) with Deep Learning Models
7.1 Introduction
7.2 Elephant Intrusion Detection System (EIDS)
7.3 Theoretical Framework
7.4 Experimental Results
7.5 Conclusion
References
8 A Study of WSN Privacy Through AI Technique
8.1 Introduction
8.2 Review of Literature
8.3 ML in WSNs
8.4 Conclusion
References
9 Introduction to AI Technique and Analysis of Time Series Data Using Facebook Prophet Model
9.1 Introduction
9.2 What is AI?
9.3 Main Frameworks of Artificial Intelligence
9.4 Techniques of AI
9.5 Application of AI in Various Fields
9.6 Time Series Analysis Using Facebook Prophet Model
9.7 Feature Scope of AI
9.8 Conclusion
References
10 A Comparative Intelligent Environmental Analysis of Air-Pollution in COVID: Application of IoT and AI Using ML in a Study Conducted at the North Indian Zone
10. 1 Introduction
10.2 Related Previous Work
10.3 Methodology Adopted in Research
10.4 Results and Discussion
10.5 Novelties in the Work
10.6 Future Research Directions
10.7 Limitations
10.8 Conclusions
Acknowledgements
Key Terms and Definitions
Additional Readings
References
11 Eye-Based Cursor Control and Eye Coding Using Hog Algorithm and Neural Network
11.1 Introduction
11.2 Related Work
11.3 Methodology
11.4 Experimental Analysis
11.5 Observation and Results
11.6 Conclusion
11.7 Future Scope
References
12 Role of Artificial Intelligence in the Agricultural System
12.1 Introduction
12.2 Artificial Intelligence Effect on Farming
12.3 Applications of Artificial Intelligence in Agriculture
12.4 Robots in Agriculture
12.5 Drones for Agriculture
12.6 Advantage of AI Implementation in Farming
12.7 Research, Challenges, and Scope for the Future
12.8 Conclusion
References
13 Improving Wireless Sensor Networks Effectiveness with Artificial Intelligence
13.1 Introduction
13.2 Wireless Sensor Network (WSNs)
13.3 AI and Multi-Agent Systems
13.4 WSN and AI
13.5 Multi-Agent Constructed Simulation
13.6 Multi-Agent Model Plan
13.7 Simulation Models on Behalf of Wireless Sensor Network
13.8 Model Plan
13.9 Conclusion
References
Index
End User License Agreement
List of Tables
Chapter 1
Table 1.1 History of artificial intelligence.
Chapter 2
Table 2.1 Data collection from dataset.
Table 2.2 Comparison of six different approaches for stress detection.
Chapter 7
Table 7.1 Parameter comparison of DL algorithms.
Chapter 8
Table 8.1 Literature review.
Chapter 9
Table 9.1 Similarity between brain and neural network.
Chapter 10
Table 10.1 Sample dataset of air pollutant.
Table 10.2 EPA table.
Table 10.3 Model evaluation.
List of Illustrations
Chapter 1
Figure 1.1 Innovation engineering.
Figure 1.2 A process flow for innovation engineering.
Figure 1.3 Artificial intelligence equation.
Figure 1.4 Elements of intelligence.
Figure 1.5 Types of learning.
Figure 1.6 Branches of artificial intelligence.
Figure 1.7 Machine learning layout.
Figure 1.8 Deep learning layout.
Figure 1.9 Natural language processing layout.
Chapter 2
Figure 2.1 Process of data extraction from dataset.
Chapter 3
Figure 3.1 Major components of Keras.
Figure 3.2 Deep learning models.
Figure 3.3 Layers in the deep belief network.
Figure 3.4 Layers in RNN.
Figure 3.5 Layers in CNN.
Figure 3.6 Layers in GAN.
Chapter 4
Figure 4.1 Categorization of web service composition techniques.
Figure 4.2 Artificial intelligence system.
Figure 4.3 Overview of the proposed solution approach.
Figure 4.4 Flowchart.
Figure 4.5 Experimental results of Blackbox planner with respect to various me...
Chapter 5
Figure 5.1 Approaches to artificial intelligence.
Figure 5.2 Components of artificial intelligence.
Figure 5.3 Agricultural technologies.
Figure 5.4 Soil/moisture/temperature analysis in smart agriculture.
Figure 5.5 Inspection using drones
Figure 5.6 See and spray model.
Figure 5.7 Chatbot.
Figure 5.8 Types of artificial intelligence.
Figure 5.9 Field monitoring.
Chapter 6
Figure 6.1 Tuning and replication.
Figure 6.2 Application of artificial intelligence technology.
Figure 6.3 AI technologies.
Figure 6.4 Process architecture of machine learning.
Figure 6.5 Artificial neural network.
Figure 6.6 Working process of expert systems.
Figure 6.7 Artificial intelligence with robotic system.
Figure 6.8 Fuzzy logic system.
Figure 6.9 Language processing system.
Figure 6.10 Sensor network system.
Figure 6.11 Sensor network system.
Figure 6.12 Sensor-based healthcare devices.
Figure 6.13 Neural interface system.
Chapter 7
Figure 7.1 Relation between AI, ML, and DL.
Figure 7.2 Faster RCNN.
Figure 7.3 Anchor boxes.
Figure 7.4 SSD architecture.
Figure 7.5 YOLO architecture.
Figure 7.6 IOU.
Figure 7.7 YOLO model.
Figure 7.8 Specification of raspberry-Pi 3.
Figure 7.9 Specification of camera module.
Figure 7.10 Specification of PIR sensor.
Figure 7.11 Specification of GSM module.
Figure 7.12 Sample database of CSV file.
Figure 7.13 Cow (3) image.
Figure 7.14 Elephant (11) image.
Figure 7.15 Various frames of elephant video.
Figure 7.16 SMS indication to forest ranger.
Figure 7.17 Comparison of DL algorithms.
Figure 7.18 Accuracy of DL algorithms.
Figure 7.19 Output with smaller object from Faster RCNN.
Figure 7.20 Output with smaller object from YOLO.
Figure 7.21 Output with smaller object from SSD 300.
Figure 7.22 Classification loss.
Figure 7.23 Localization loss.
Figure 7.24 Total loss.
Chapter 8
Figure 8.1 (a) Architecture of wireless sensor network (WSN). (b) Scheme of th...
Figure 8.2 The NN architectute.
Figure 8.3 Machine learning (Source - Google).
Figure 8.4 Supervised learning.
Figure 8.5 Unsupervised learning (Source - Google).
Figure 8.6 Reinforcement learning (Source – Google).
Figure 8.7 Example of self-organizing map (SOM).
Chapter 9
Figure 9.1 The four categorized definitions of AI.
Figure 9.2 Cognitive model.
Figure 9.3 Classification of AI.
Figure 9.4 Stages of features engineering.
Figure 9.5 Architecture of deep learning.
Figure 9.6 Various stages in processing image.
Figure 9.7 Machine learning model.
Figure 9.8 Machine learning model with distinct data.
Figure 9.9 Process of avoiding overfitting.
Figure 9.10 Types of machine learning.
Figure 9.11 Supervised learning model.
Figure 9.12 Unsupervised learning model.
Figure 9.13 NLP pyramid.
Figure 9.14 Machine vision standards.
Figure 9.15 Data description.
Figure 9.16 Visualization of predicted value.
Figure 9.17 Overall trend of the vitamin D.
Figure 9.18 Weekly trend of vitamin D.
Figure 9.19 Yearly trend of vitamin D.
Figure 9.20 Prediction (ds column indicates the year).
Figure 9.21 Predicted trend (yhat column indicates predicted trend).
Chapter 10
Figure 10.1 The equation to calculate AQI.
Figure 10.2 Heat map of our dataset.
Figure 10.3 Heat map after removing null rows.
Figure 10.4 Filling missing values by mean.
Figure 10.5 Correlation in dataset.
Figure 10.6 Model for training the dataset.
Figure 10.7 Average of AQI year wise.
Figure 10.8 Average of AQI monthwise.
Figure 10.9 Average of AQI according to month.
Figure 10.10 Accuracy of k-nearest neighbor.
Figure 10.11 Comparison between actual values and predicted values.
Chapter 11
Figure 11.1 Eye-based cursor and coding control.
Figure 11.2 Facial landmarks.
Figure 11.3 Two-stage processing of HOG algorithm.
Figure 11.4 Landmark on human eye.
Figure 11.5 Image captured for processing.
Figure 11.6 Virtual keypad.
Figure 11.7 Virtual keyboard.
Figure 11.8 Keypad for left gaze.
Figure 11.9 Keypad for the right gaze.
Figure 11.10 Keypad for the bottom gaze.
Figure 11.11 Left eye blink.
Figure 11.12 Right eye blink.
Figure 11.13 Both eye blink detection.
Figure 11.14 Right gaze.
Figure 11.15 Up gaze.
Figure 11.16 Left gaze.
Figure 11.17 Down gaze.
Figure 11.18 Simulation of eye coding.
Chapter 12
Figure 12.1 Lifecycle of agriculture.
Figure 12.2 AI in agriculture system diagram [10].
Figure 12.3 Forecasting weather details [11].
Figure 12.4 Crop and soil quality surveillance [11].
Figure 12.5 Sprinkle method [11].
Figure 12.6 Robot in agriculture [17].
Figure 12.7 Drone for agriculture [17].
Chapter 13
Figure 13.1 Artificial intelligence (Sources: aitimejournal.com).
Figure 13.2 Sensor nodes are connected.
Figure 13.3 AI and multi-agent systems.
Figure 13.4 AI and WSN in daily life of the elderly.
Figure 13.5 The overview of the multi-agent simulation system.
Figure 13.6 (a) Hardware layer. (b) Application layer. (c) All layers.
Guide
Cover Page
Series Page
Title Page
Copyright Page
Preface
Table of Contents
Begin Reading
Index
WILEY END USER LICENSE AGREEMENT
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Scrivener Publishing100 Cummings Center, Suite 541JBeverly, MA 01915-6106
Publishers at ScrivenerMartin Scrivener ([email protected])Phillip Carmical ([email protected])
Innovative Engineering with AI Applications
Edited by
Anamika AhirwarPiyush Kumar ShuklaManish ShrivastavaPriti Maheshwary
and
Bhupesh Gour
This edition first published 2023 by John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, USA and Scrivener Publishing LLC, 100 Cummings Center, Suite 541J, Beverly, MA 01915, USA© 2023 Scrivener Publishing LLCFor more information about Scrivener publications please visit www.scrivenerpublishing.com.
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Library of Congress Cataloging-in-Publication Data
ISBN 978-1-119-79163-8
Cover image: Pixabay.ComCover design by Russell Richardson
Preface
This book presents a study on current developments, trends, and the future usage of artificial intelligence. The impending research on AI applications—like improvements in agricultural systems, security systems, web services, etc.—has shown the usefulness of AI in engineering, as well as in deep learning tools and models.
Engineering advancements, along with innovative applications of AI, span the gap between the physical and virtual world. The result is a hyper-connected society in which smart devices are not only used to exchange data but also have increased capabilities. These devices are becoming more context-aware and smarter by the day.
This book thoroughly explores the immense scope of AI in the above areas for productivity and the betterment of society and all of humankind. Rather than looking at the field from only a theoretical or practical perspective, this book unifies both aspects to give a holistic understanding of the AI paradigm. It focuses on timely topics related to the field of AI applications and combines new and high-quality research works that promote critical advances. Readers will appreciate the overviews of the state-of-the-art developments in emerging AI research.
This book takes foundational steps toward analyzing stress among teaching professionals using deep learning algorithms. Various deep learning models are discussed, with a practical approach that employs the MNIST dataset. These models help to solve various complex problems in the domains of computer vision and natural language processing.
Also introduced are some emerging and interdisciplinary domains that are associated with deep learning and AI. The book touches upon the core concept of deep learning technology, which is fruitful for a beginner in this area. Furthermore, it compiles the various applications of AI in agriculture, such as irrigation, weeding, spraying with sensors, and other means that are facilitated by robots and drones. This book covers most of the popular and important deep-learning neural network models.
The advanced fields of AI, data analytics, deep learning algorithms, etc., can help in the fight against environmental pollution. Additionally, this book discusses how people with severe disabilities can use their laptops, personal computer, or smart phones to communicate with greater reach. Low-cost systems such as HOG or machine learning models will help disabled people to attain improved typing skills, or even perform coding.
Artificial agricultural intelligence not only lets farmers automate their agriculture, it provides them with accurate predictions for greater crop yield and results in better quality, all while using fewer resources. AI and ML techniques help farmers to analyze land, soil, health of crops, etc. This saves time and money, and allows farmers to grow the right crop per season, ensuring optimal yield.
AI has potential to be used for planning and resource allocation by health and social care services. It can be beneficial across all health care sectors due to its different technologies. As such, AI should be viewed as an exciting new addition to the healthcare sector, not as a threat to professionals.
The cross-disciplinary field of AI research attempts to understand, model, and replicate intelligence and cognitive processes by invoking computational, mathematical, logical, mechanical, and biological principles. AI plays a major role in many sectors. With continual enhancement, it will improve the quality of our lives.
The editors wish to thank Scrivener Publishing and their team for the opportunity to publish this volume.
The Editors
May 2023
1Introduction of AI in Innovative Engineering
Anamika Ahirwar
Compucom Institute of Information Technology and Management, Jaipur, Rajasthan, India
Abstract
The widespread use of Artificial Intelligence [1] technology and its ongoing development have created new opportunities for creative engineering. Our daily lives have been completely taken over by the revolutionary realm of Artificial Intelligence (AI). It is the unique fusion of brains and therefore of machines. Artificial intelligence has been growing steadily over the last few years, establishing roots in most industries. There have been recent developments and technologies that support AI. The uses of AI don’t seem to be limited to physical space; they can be found in everything from a secondary aspect to a novel development. A new society is being created by many technologies, devices, and even some brand-new inventions that have yet to be realized. Therefore, it offers a seamless route that leads to a promising future. This chapter intends to focus on an overview of innovative engineering in artificial intelligence and introduces the concepts of innovative engineering and artificial engineering with the aid of many innovation engineering guiding principles. This chapter covers the background, need for, and applications of artificial intelligence and also explains the various subfields of artificial intelligence [8]. This chapter covers the background, need for, and applications of artificial intelligence.
Keywords: Innovation Engineering, Artificial Intelligence (AI), Artificial Narrow Intelligence (ANI), Artificial General Intelligence (AGI), Artificial Super Intelligence (ASI)
1.1 Introduction to Innovation Engineering
Innovation Engineering is defined as a method for solving technology and business problems for organizations who want to innovate, adapt, and/or enter new markets using expertise in emerging technologies (e.g. data, AI, system architecture, blockchain), technology business models, innovation culture, and high-performing networks.
When Dave Kelly specified the IDEO process for design in 1971, he changed the predictability of design projects around the world and made each design project more likely to serve its users well. In a similar way, this Innovation Engineering process is intended to make innovation projects in engineering more successful. The process builds upon many best practices in innovation, but it also brings them into a domain of more technically sophisticated areas.
The concept of Innovation Engineering also integrates many years of observing our students who have engineered novel technologies and companies. The goal is to specify an approach that anyone can use to better architect, design, and more effectively build things that are technically novel, useful, and valuable. And further, the goal is to be able to do this efficiently, on-time, and repeatable.
At its core, Innovation Engineering is the result of using the approaches, processes, behaviors, and mindsets of entrepreneurs/innovators with the context of engineering projects. This is illustrated in the Figure 1.1.
One thing that we have observed is that innovative technical leaders employ similar behavioral patterns as entrepreneurs even in areas of engineering architecture, design, and implementation. And further, these behaviors can be amplified within a process.
Figure 1.1 Innovation engineering.
1.2 Flow for Innovation Engineering
A high-level process example is shown in Figure 1.2. It simply illustrates the concept of brainstorming a problem/solution, converting the problem/ solution into a ‘story’ called a low-tech demo, and then using agile sprints to develop the project.
This simple process flow can be extended to include business and/or organizational context. Figure 1.2 shows a process flow for Innovation Engineering with greater detail and broader context. The flow illustrates that effective projects start always with a story or narrative. This narrative is generally based on background of the team and an observation of changes in the world (e.g. market, technical, societal, or regulatory changes). When a project does not start with a story narrative, it is typically too narrowly defined and generally goes off target in our experience. Note, the “Low Tech Demo” in the example maps to the Technical Story in the lower diagram which is used to kick-off an Agile project leading to an Implementation.
The story narrative is used to collect initial stakeholders, resources, and obtain initial validation for the project. In our experience, there is no better way to attract resources than by testing a story and/or initial prototype.
From here, the story narrative can be broken into two sub-narratives, one for the technical story and another for the broader context or business story. Each story is the starting point of a learning path, and specifically not an execution path. The technical path is an agile process that leads to an implementation starting first from the user’s viewpoint. For example, in Data-X, we use the following components as part of the technical story which we call a “low tech demo”.
Figure 1.2 A process flow for innovation engineering.
Low tech demo outline, an example of a technical story:
What is it supposed to do – and ideally why
User’s perspective, top three user expectations
Key technical components with risk levels
An architecture, and
Short-term plan and assignments towards the simplest demonstration.
In contrast, the business learning path is intended to result in
An industry ecosystem of customers, partners, suppliers, etc. and
The discovery of a working business model or fulfillment of a mission in a government organization.
These learning paths converge when the business model/mission and the technology are all working and integrated. Only after this step can the innovation be scaled via execution and planning. Innovation Engineering tends to focus more on the technical path as required for successful implementation, but must include the broader process as described to be successful.
While all of this is a very quick overview of the process, it does set the context for a set of important principles that are required for the process to be successful. Like with any other organizational activity, Innovation Engineering requires a set of shared beliefs and behaviors to be successful. These ‘Guiding Principles’ for Innovation Engineering are outlined in the section below are intended to be synergistic with the process flow explained in the Figure 1.2.
1.3 Guiding Principles for Innovation Engineering
Start with Story: Virtually all successful projects start with a story narrative. The story is the means of validation, consensus building, and collecting stakeholders. Any project that starts without a validated story likely jumps to an invalidated conclusion about the problem. Stories can vary in length and complexity,
i.e.
the problem of a user and its resolution, or the famous NABC story developed at SRI which stands for Needs, Approach, Benefit, and Competition. However, the key to a good story is that there is an insight that others have not seen and that there is substantial benefit of the solution to at least some segment or stakeholder.
Scale or Invent: Determine if the project is about creating something new (i.e. a new product, new service, new technology, new customer, etc.) then it’s a learning process, and in that case it requires a team with corresponding behaviors. If the project is about scaling something that already works (i.e. serving more customers, increasing the capacity of a system, etc.) then it’s an execution process best accomplished by someone who has done it or something like it before. In this later case, the team can jump immediately to the scaling phase at the end of the process.
User-first: The technical story must highlight a solution first from the user’s viewpoint. Note that entrepreneurial stories typically explain how a venture will both solve a problem and achieve a working business model, the technical story must explain the user’s viewpoint first and only then lead to the system architecture and the implementation.
Effectuation: Great technical innovators and entrepreneurs all use “Effectuation Principals” in a natural manner. It roughly means to start with what you have, and sometimes it means you must take inventory of what you have first. To illustrate, if you were to make a dinner, do you first choose an intended dish and then gather the ingredients (not effectuation), or would you look at what you already have in the kitchen and then invent a new recipe from the ingredients you already have (effectuation). This principle can be applied to technical and business projects in the same manner.
Break it down: Components, interfaces, and interconnections. Evaluate potential solutions by breaking the proposed system down into simple sub-systems with minimal inter-connections. Understand the interactions and causal relationships between subcomponents. And of course, if a sub-component already exists or can be easily obtained, then there is no need to build or redesign that subcomponent. For example, when Tesla created its battery, it created it from thousands of cells that were already being produced in mass scale, instead of designing a completely new battery architecture.
Look for Insight in the technical story: This is related to having insight about the location of value, the power, or “the magic” in the system design. What will make it effective or exciting? This principal is a technical parallel to the entrepreneurial behavior of understanding the user’s true needs or what they actually care about, or what they are willing to pay for.
Minimal Viable System Architecture: Get as quickly as possible to a 1.0 version. Distill the story as quickly as possible to the simplest possible implementation. From this, a more complex system can be evolved using an agile, iterative model to develop greater capability. This is parallel to the entrepreneurial approach of building a Minimum Viable Product (MVP) for testing product market fit, but in this case the focus is the system architecture for testing technical feasibility.
Agile increments: After developing a minimal demonstrable solution, use agile increments to prioritize further development.
Start with the simplest possible demonstration on the path to the best solution.
Use a technology strategy that allows easiest adaptation.
Be agile driven. We can’t predict final product in advance.
Keep it Simple: The focus of the project should be on keeping the design simple, easy to explain, easy to verify, and easy to debug. Technical architects and designers are often interested in technically brilliant and complex solutions, but true elegance lies in simplicity. As quoted from a historical Apple advertisement, “Simplicity is the ultimate sophistication.” You might think of this in parallel to timeless works of art, which are characterized by having exactly what is needed to convey the message, but never a single extra music notes or an extra paint stroke.
Reduce the Downside: Optimize to reduce downside risk and failure, not to maximize performance/cost. Always evaluate corner cases. This is the parallel of broad vs narrow thinking within engineering. The broad thinking version in business would be used to avoid business risk as well as a predict the expected outcome in the broadest terms.
Measurable Objectives: Develop measurable objectives to know when goals are being achieved because you cannot improve what you cannot measure. For example, in a data science algorithm, how will you know that the prediction is good enough? Having both a measure and a target allows you to estimate whether the marginal (extra) work to get a better result is worth the expense of doing that extra work. To understand this more, learn about the concept of “Value of Perfect Information”.
Create a support ecosystem: Build a support ecosystem with the highest quality partners that you can both reach and trust. Many technical leaders are tempted to reach out to the lower quality contacts (as team members, suppliers, partners, and customers) who are easiest to contact, but it is better to push our comfort zones to find the best people and organizations that you can — as long as trust can still be generated.
1.4 Introduction to Artificial Intelligence
Artificial intelligence (AI) is the science and engineering of creating intelligent machines, with the goal of providing machines the ability to comprehend, reach, and outperform human intelligence. This chapter begins with an overview of AI’s fundamentals, then moves on to the birth, history, and future of AI in inventive engineering. Then we’ll look at the primary runnel in the field, as well as its evolution and uses in different aspects of our lives. The wrapper will cover the most important and contemporary AI research, such as reinforcement learning [2–7], robotics, computer vision, and symbolic logic.
To better perceive the term AI, we must always comprehend the term intelligence in an equation shown in Figure1.3. Intelligence is that the ability to find out and solve issues. The foremost common answer that one expects is “to build computers intelligent in order that they will act intelligently!”, however the question is what proportion intelligent? However will one decide the intelligence?
Intelligence is the ability to acquire and apply the knowledge.
Knowledge is the information acquired through experience.
Experience is the knowledge gained through exposure (training).
Summing the terms up, we tend to get artificial intelligence because the “copy of something natural (i.e., human beings) ‘WHO’ is capable of exploit and applying the data it’s gained through exposure.”
Figure 1.3 Artificial intelligence equation.
Artificial intelligence was first suggested by John Mc Carthy in 1956. According to the John McCarthy, father of Artificial Intelligence (AI): AI is “The science and engineering of developing intelligent machines using brilliant computer programs”. Artificial Intelligence is the way of developing computers, computer-controlled robots, intelligent thinking software’s, which is similar to humans think. AI have been developed an intelligent software’s and system based on the outcomes of how the human brain thinks, learn, decide, and work while trying to solve a problem. When developing the power of computer systems using AI, the anxiety of human lead him to wonder, “Can a machine think and behave as humans do?” In AI implementations start with producing common intelligence in machine, which see high regards of humans. AI is the branch of science that helps machines to find solutions of complex problems for different sectors such as humans, industries, researchers, etc.
So we can say that Artificial Intelligence (AI) [11] is the simulation of human intelligence by machines.
The ability to solve problems.
The ability to act rationally.
The ability to act like humans.
1.4.1 History of Artificial Intelligence
The history of Artificial Intelligence in 20th century is given in Table 1.1 [10].
1.4.2 Need for Artificial Intelligence
To create expert systems which exhibit intelligent behavior with the capability to learn, demonstrate, explain and advice its users.
Helping machines find solutions to complex problems like humans do and applying them as algorithms in a computer-friendly manner.
1.4.3 Applications of AI
AI has been leading in many domains like [15, 16]:
Astronomy: Artificial Intelligence can be very convenient to solve complex universe problems. AI mechanization can be helpful for recognize the universe such as how it works, origin, etc.
Table 1.1 History of artificial intelligence.
Year
Milestone/Innovation
1923
The word “robot” in English firstly introduced by Karel Capek using “Rossum’s Universal Robots” (RUR) in London.
1943
Foundations of Neural networks (Artificial Intelligence).
1945
The term “Robotics” was continued by Isaac Asimov, alumni of Columbia University.
1950
Turing Testing [
12
], the word Turing was introduced by Alan Turing for evaluate intelligence and also published Computing Machinery and Intelligence
.
Claude Shannon was published detailed Analysis of Chess Playing as a search.
1956
John McCarthy was introduced the term “Artificial Intelligence” demonstrated the first AI running program at Carnegie Mellon University.
1958
John McCarthy again invented LISP programming language for AI [
14
].
1964
Danny Bobrow’s presented in dissertation report the computers could recognize natural language well enough to solve algebra word problems efficiently at MIT.
1965
Joseph Weizenbaum developed ELIZA: an interactive problem that carries on a dialogue in English at MIT.
1969
The Scientists of Stanford Research Institute developed a robot equipped with locomotion, perception, and problem solving, which was named Shakey.
1973
The Famous Scottish Robot called Freddy can use vision to locate and assemble models under the Assembly Robotics group at Edinburgh University.
1979
In Stanford Cart, the first computer controlled autonomous vehicle was developed.
1985
In Aaron, The drawing program was created and also demonstrated by Harold Cohen.
1990
Important improvements in all areas of AI
Implementations in machine learning
Case based reasoning
Multi agent planning
Arrangement
Data mining
Web Crawler
Natural languages understands and translations
Vision and Virtual Reality
Games
1997
The “World chess championship” named after beating the Deep Blue Chess Program by Garry Kasparov.
2000
The Interactive robot pets become available commercially.
“Kismet” the robot with expresses emotions displayed at MIT.
“Nomad” the robot explores remote regions of Antarctica and locates meteorites.
Healthcare: Healthcare Industries are soliciting AI to make a preferable and turbo diagnosis than humans.
Gaming: AI plays a vital role in tactical games such as poker, chess, tic-tac-toe, etc., In these games play on the basis of heuristic knowledge i.e. a machine can think of the huge number of possible positions.
Natural Language Processing: The interaction with computer which understands humans’ natural language are possible through NLP.
Finance: AI and investment production are the best fixture for each other. The investment production is enacting automation, chatbot adaptive intelligence, algorithm trading, and machine learning into action.
Expert Systems: The integrate machine, software and particular information which can impart reasoning and advising are provides expert system. The applications are facilitates, explanations and advice to the users as well.
Vision Systems: In this system recognize, apprehend and realize the visual input on computer. The examples are:
Figure out the spatial information or map of areas, the spying aeroplane is used for taking the photographs.
For diagnoses the patient, doctors are using a clinical expert system.
For recognizing the faces of criminals in forensic artist’s stored portrait, police use the computer software.
Speech Recognition: Generally, the knowledgeable systems can listen and understand the languages in form of the sentences with significances in human interact to it. Which can be managed using slang words, various pronunciations sound in the background, change in human’s noise due to cough and cold, etc.?
Handwriting Recognition: In handwriting detection software, reads text present in the piece of paper though a pen or on-screen by the stylus. Which also identify the outlines of letters and translate it into editable text.
Data Security: The reliability of data is climacteric for every venture and cyber-attacks are extending very swiftly in the multi-channel world.
Agriculture: In this also AI are starting setting up its field by agriculture robotics, solid and crop monitoring, predictive analysis.
E-Commerce: AI is helping all user/client to know about its associated products with recommended size, color, or even brand detail.
Education: AI can self-closing grading so that the teacher can have more time to teach.
Social Media: Social Media websites hold billions of customer profiles, which require be storing and managing in a very well-planned way. AI can organize and manage massive amounts of data.
Entertainment: AI help in entertainment sector by prime videos which are watched through the NET system.
Transport: AI is fetching extremely demanding for travel industries.
Automotive: Automotive fabrication is using AI to provide real world virtual assistant to their user for better staging.
Intelligent Robots: The Robots can be execute the jobs which given through a human. These devices to notice the substantial data from the actual world like heat, light, motion, bump, sound, temperature and pressure. In this they have well organized processors, several sensors and large amount of memory to display knowledge and intelligence. Also, they can understand from their blunders and adjust to the new environment.
Heuristic Classification: The Heuristic Classifications is one of the most realistic kinds of skilled system, which gives the current knowledge of AI for set information in stable set of categories using in forms of various information. The example of heuristic classification, it advising whether to accept purchase of proposed credit card or not, the proprietor of the credit card information are present, his status of payment, the purchasing item and its creation of buying items (whether there has been past credit card scams in this establishment).
1.4.4 Comprised Elements of Intelligence
The intelligence is insubstantial and contains (shown in Figure 1.4):
Reasoning
Learning
Problem Solving
Perception
Linguistic Intelligence
Reasoning: It is the collection of processes, which makes an ability to deliver on the basis for judgment, decisions making, and prediction. The followings are broad categories of reasoning:
Figure 1.4 Elements of intelligence.
Inductive Reasoning
The broad general statements are made using particular observations.
Inductive reasoning allows conclusions to be false, whether all the properties are true in the statements.
Example is: When Nita is a teacher and she is studious as well. Therefore, all teachers are intellectual.
Deductive Reasoning
Deductive reasoning starts with a usual statements and observation to the potential to reach specific or conceptual conclusions.
The statements are assumed to be true for all class members whenever any general thing in class is right.
Examples are: When Shibha is a grandmother because all women of age above 60 years are considered to be as grandmothers but Shibha is in 65 years.
Learning: It is the process of developing knowledge or skill using practicing, studying, taught or experiencing something. The Learning also increases the mindfulness of the themes of the study. The capabilities of learning are determined by humans, some animals and AI empowered systems.
The Learning are classified in the followings:
Auditory Learning: The Auditory Learning is defined by the skills of listening as well as hearing. The example are recorded audio lectures are listened to by students.
Episodic Learning: The Episodic learning is the linear and orderly learning method using memorizing the arrangements of events which one has viewed or experienced.
Motor Learning: The Accurate movements of muscles are called motor learning like picking objects, Writing, etc.
Observational Learning: The Learning comes through watching and imitating others such as a child tried to learn using mimicry of her parent.
Perceptual Learning: The learning skills for identifying stimulate that one has been observed before like, identifying and classifying objects and situations.
Relational Learning: In this learning to distinguish between the various stimuli based on relational facts instead of basic properties such as, Adding ‘little less’ salt at the time of cooking, potatoes that salty last time, when cooked with adding a table spoon of salt.
Spatial Learning: The Learning capability through the visual stimuli like images, colors, maps, etc. The example is a person can create a roadmap in mind before actually following the road.
Stimulus Response Learning: The Learning ability to execute a specific behavior when a particular stimulus is present. The examples are a dog raises its ear on the hearing doorbell.
Problem Solving: In this method one’s observes and attempts to reaches the preferred outcomes from current circumstances through taking some track congested by known or unknown obstacles. The problem solving was also involved in decision making, which is the approaches of choosing optimum alternative, out of different available alternatives for obtain the desired goal.
Perception: It is a mechanism of obtaining, understanding, picking and establishing sensory information. The Perception is assumes to sense [
13
]. In humans, perception is supported by sensory organs. However in the AI domain, the perception approach sets the data generated by the sensors and collectively in a significant style.
Linguistic Intelligence: It’s defined as one’s capacity to use, understand, speak, and write the vocal and written language. It is essential in the relational statement.
1.4.5 AI Tools
AI has developed an outsized variety of tools to unravel the foremost troublesome issues in engineering, like:
Search and optimization
Logic
Probabilistic methods for uncertain reasoning
Classifiers and statistical learning methods
Neural networks
Control theory
Languages
1.4.6 AI Future in 2035
AI is striking the subsequent of virtually every industry and every mortal. AI has acted as the main driver of make an appearance technologies like big data, robotics and IoT, and it will pursue to act as a technological innovator for the foreseeable (predictable) subsequent.
Looking at the features and its wide application we may definitely follow AI. Seeing at the event of Al is it that the future world is changing into artificial.
Biological intelligence is fixed, as a result of its associate previous, mature paradigm however the new paradigm of non-biological computation and intelligence is growing exponentially.
The memory capability of the human brain is perhaps of the order usually thousand million binary digits. However most of this is often most likely utilized in memory visual impressions, and alternative relatively wasteful ways that.
Hence we will say that as natural intelligence is restricted and volatile too world could currently rely on computers for smooth operating.
