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.

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”.

Low tech demo outline, an example of a technical story:

1. What is it supposed to do – and ideally why
2. User’s perspective, top three user expectations
3. Key technical components with risk levels
4. An architecture, and
5. Short-term plan and assignments towards the simplest demonstration.

In contrast, the business learning path is intended to result in

1. An industry ecosystem of customers, partners, suppliers, etc. and
2. 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

1. 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.
2. 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.
3. 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.
4. 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.
5. 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.
6. 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.
7. 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.
8. Agile increments: After developing a minimal demonstrable solution, use agile increments to prioritize further development.
   1. Start with the simplest possible demonstration on the path to the best solution.
   2. Use a technology strategy that allows easiest adaptation.
   3. Be agile driven. We can’t predict final product in advance.
9. 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.
10. 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.
11. 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”.
12. 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.”

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.

1. The ability to solve problems.
2. The ability to act rationally.
3. The ability to act like humans.

1.4.1 History of Artificial Intelligence

The history of Artificial Intelligence in 20^th 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.

  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:
  1. Figure out the spatial information or map of areas, the spying aeroplane is used for taking the photographs.
  2. For diagnoses the patient, doctors are using a clinical expert system.
  3. 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):

1. Reasoning
2. Learning
3. Problem Solving
4. Perception
5. Linguistic Intelligence

1. 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:

   

   1. 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.
   2. 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.
2. 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.
3. 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.
4. 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.
5. 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.

1.4.7 Humanoid Robot and AI

• Sophia may be a social android golem developed by Hong Kong based mostly company Hanson artificial intelligence.
• Sophia was activated on April 19, 2015.
• She created her initial public look at South by Southwest festival in period of time 2016 in United States.
• In October 2017 Sophia became a Saudi subject, the primary golem to receive citizenship in any country.

1.4.8 The Explosive Growth of AI

• Since Al is applicable in most fields, they become the needs of our life. It’s the rationale behind the explosive growth of AI.
• The growth are often divided into two components based on the application area and what purpose they serve, they’re as follows:
• Growth in positive sense (useful to society)
• Growth in negative sense (harmful to society)

1.5 Types of Learning

Figure 1.5 shows the types of learning.

1. Artificial Narrow Intelligence (ANI)
2. Artificial General Intelligence (AGI)
3. Artificial Super Intelligence (ASI)

1. Artificial Narrow Intelligence (ANI)

   Artificial narrow Intelligence is also referred to as Weak AI, ANI is that the stage of artificial intelligence involving machines that can perform solely a narrowly outlined set of specific tasks. At this stage, the machine doesn’t possess any thinking ability; it simply performs a group of pre-defined functions.

   Examples of Weak AI embody Siri, Alexa, Self-driving cars, Alpha-Go, Sophia the humanoid then on. The majority the AI-based systems engineered until this date fall under the class of Weak AI.

2. Artificial General Intelligence (AGI)

   Artificial General Intelligence is also known as strong AI, AGI is that the stage within the evolution of AI whereby machines can possess the flexibility to suppose and build strong a bit like North American country humans.

   There are presently no existing samples of strong AI, however, it’s believed that presently be able to create machines that are as good as humans.

   Strong AI is taken into account a threat to human existence by several scientists, as well as Stephen William Hawking UN agency expressed that: “The development of full AI may spell the tip of the human race…. it’d commence on its own, associate degreed re-design itself at an ever-increasing rate. Humans, UN agency are restricted by slow biological evolution, couldn’t contend and would be outdated.”

   

3. Artificial Super Intelligence (ASI)

   Artificial Super Intelligence is that the stage of AI once the potential of computers can surpass persons. ASI is presently a theoretic situation as portrayed in movies and science fiction books, wherever machines have taken over the world.

   Machines are not very far from reaching this stage taking into considerations our current pace.

   “The pace of progress in AI is incredibly fast. Unless you’ve direct exposure to teams. Teams like Deep mind, have gotten no organize but quick. it’s growing at a pace getting ready to exponential. The danger of 1 issue seriously dangerous happening is at intervals the five-year timeframe. 10 years at the foremost.” —Elon Musk quoted.

   So, these were the various stages of intelligence that a machine will acquire. Currently let’s perceive the categories of AI, supported their functionality.

1.6 Categories of AI

Based on the functionality of AI-based systems, AI can be categorized into the following types:

1. Reactive Machine AI
2. Limited Memory AI
3. Theory of Mind AI
4. Self-aware AI

1. Reactive Machine AI

   This type of AI includes machines that operate only supported the current knowledge, taking into consideration solely this scenario. Reactive AI machines cannot type inferences from the info to judge their future actions. they’ll perform a narrowed range of pre-defined tasks.

   An example of Reactive AI is the famous IBM Chess program that beat the world champion, Garry Kasparov.

2. Limited Memory AI

   Like the name suggests restricted Memory AI, can build conversant and improved selections by learning the past knowledge from its memory. Such an AI contains a short-lived or a temporary memory which will be accustomed store past experiences and therefore measure future actions.

   Self-driving cars are limited Memory AI, that uses the information collected within the recent past to form immediate selections. As an example, self-driving cars use sensors to spot civilians crossing the road, steep roads, traffic signals so on to form higher driving selections. This helps to stop any future accidents.

3. Theory of Mind AI

   The Theory of Mind AI may be an additional advanced form of computing. This class of machines is alleged to play a serious role in psychology. This kind of AI can focus in the main on emotional intelligence in order that human believes and thoughts are often higher appreciated.

4. Self-Aware AI

   Let’s simply pray that we tend to don’t reach the state of AI, wherever machines have their own consciousness and become conscious. This kind of AI may be a very little so much fetched given this circumstance. However, within the future, achieving a stage of super intelligence may be attainable.

   Geniuses like Elon Musk and Sir Leslie Stephen Hawking’s have systematically warned us regarding the evolution of AI. AI may be a terribly vast field that covers several domains like Machine Learning, Deep Learning so on.

1.7 Branches of Artificial Intelligence

Artificial Intelligence can be used to solve real-world problems by implementing the following processes/techniques (Figure 1.6 shows the elements of intelligence):

1. Machine Learning
2. Deep Learning
3. Natural Language Processing
4. Robotics
5. Expert Systems
6. Fuzzy Logic

1. Machine Learning

   Machine Learning is that the science of obtaining machines to interpret, method and analyze knowledge so as to solve real-world issues. Figure 1.7 shows the machine learning layout.

   

   Under Machine Learning there are three categories:

   1. Supervised Learning
   2. Unsupervised Learning
   3. Reinforcement Learning
2. Deep Learning

   Deep Learning is that the method of implementing Neural Networks [9] on high dimensional knowledge to achieve insights and type solutions. Deep Learning is a complicated field of Machine Learning which will be used to solve additional advanced issues. The layout of deep learning is shown in Figure 1.8.

   

   Deep Learning is that the logic behind the face verification algorithmic program on Facebook, self-driving cars, virtual assistants like; Siri, Alexa.

3. Natural Language Processing

   Natural Language processing (NLP) refers to the science of drawing insights from natural human language so as to speak with machines and grow businesses. We can see the layout of Natural Language Processing in Figure 1.9.

   

   Twitter uses natural language process to filter terroristic language in their tweets, Amazon uses natural language process to grasp consumer reviews and improve user experience.

4. Robotics

   Robotics may be a branch of computing that focuses on completely different branches and application of robots. AI Robots are artificial agents acting in a very real-world setting to provide results by taking responsible actions.

   Sophia the humanoid is a good example of AI in robotics.

5. Fuzzy Logic

   Fuzzy logic could be a computing approach supported the principles of “degrees of truth” rather than the same old trendy computer logic i.e. Boolean in nature.

   Fuzzy logic is used within the medical fields to resolve complicated issues that involve higher cognitive process. They’re conjointly utilized in automatic gearboxes, vehicle environment control so on.

6. Expert Systems

   A professional system is expert AI-based computing system that learns and reciprocates the decision-making ability of a person’s expert.

   Expert systems use if-then logical notations to unravel advanced issues. It doesn’t admit standard procedural programming. knowledgeable systems are principally employed in info management, medical facilities, loan analysis, virus detection so on.

1.8 Conclusion

With the help of numerous innovation engineering guiding principles, this chapter aims to focus on an overview of innovation engineering in artificial intelligence. The history, necessity, and applications of artificial intelligence are discussed in this chapter. It consists of a number of cognitive elements, including language intelligence, learning, thinking, and problem-solving. A wide range of tools, including search and optimization, logic, probabilistic methods for uncertain reasoning, classifiers and statistical learning techniques, neural networks, control theory, and languages, have been developed by AI to solve the most challenging engineering problems. Artificial intelligence in the year 2035. Artificial intelligence subfields are also explained in this chapter.

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