Imaginational Intelligence: A New Frontier for ...

To cite this paper: Zhaohao Sun (2018) Imaginational Intelligence: A New Frontier for Innovation, Creativity and Intelligence Development in the Age of Big Data, BAIS No. 3(2):134, PNG University of Technology. DOI10.13140/RG.2.2.28759.98729. Revisited.

Imaginational Intelligence: A New Frontier for Innovation, Creativity and Intelligence Development in the Age of Big Data Zhaohao Sun, Department of Business Studies PNG University of Technology, Lae 411, PNG [email protected]; [email protected] & Federation University Australia Abstract. This paper explores imagination and imaginational intelligence as a core for artificial intelligence artificial life, and human intelligence. The key idea behind it is that imagination is the source of every creation and innovation. This paper examines the foundations of imaginational intelligence (philosophical, mathematical, logical and computational). It proposes a spectrum of imaginational intelligence and present imaginatics as a new discipline. It also looks at its impacts on data science, business intelligence and artificial intelligence, artificial life, robots and automation with applications. The approach proposed in the paper will facilitate the research and development of imaginational intelligence, artificial intelligence, artificial life, robots, automation and business intelligence as well as imaginatics as an umbrella of imagination science, technology and engineering.

Imagination is the source of every creation and innovation . Keywords: Imaginational intelligence, artificial imagination, imaginatics, imagination science, big data, data science. Alternative topics: 1.

Imaginational intelligence: A Fundamental Power of human intelligence and artificial intelligence ©

2.

Foundations of Imaginational intelligence ©

3.

Imaginational Intelligence: Foundations and Applications ©, This is delivered as a research seminar presentation, at DBD, UNITECH 28 Feb 2018

4.

Imaginatics: an emerging discipline©

Updated based on 5300 words to 8000 words. Dated on 24-310317. 020414, 27-280218, 1-15, 21-30 March 2018

1

Introduction

Albert Einstein, one of the greatest thinkers of the 20th century, told us that “The true sign of intelligence is not knowledge but imagination” (Einstein, 2017) . He also said that “Imagination is more important than knowledge.” (Wikiversity-Einstein, 2018). Then we can infer that imagination is more important than knowledge and data, taking into the close relationship between data and knowledge nowadays. Data and knowledge has been an important topic for many disciplines including big data analytics and data science (Sun, Strang , & Firmin, 2016), artificial intelligence (Russell & Norvig, 2010), and management information systems (Laudon & Laudon, 2016), to name a few. All these consider data and knowledge is a crucial and strategic asset for any organizations and individuals, especially in the age of big data (McKinsey, 2011), ( Lohr, 2012 February 11), analytics (Henke & Bughin, 2016) and intelligence (Sun & Wang, 2017). However, imagination has not been a topic for consideration in the abovementioned disciplines although we searched “Imagination Intelligence” using Google (on 010417) and found about 72,300 results (0.24 seconds). We are living in the era of trinity, consisting of big data, analytics and artificial intelligence (AI). They have been revolutionizing innovation, research, development as well as management and business (Chen & Zhang, 2014; Tableau, 2015; McAfee & Brynjolfsson, 2012). For example, big data analytics services have created big market opportunities. The researcher of IDC (International Data Corporation) forecasts that big data and analytics-related services marketing in Asia/Pacific (Excluding Japan) region will grow from US$3.8 billion in 2016 to US$7.0 billion in 2019 at a 16.3% CAGR (compound annual growth rate) (Roche, 2016). Big data and its emerging technologies including big data analytics have been not only revolutionizing the way the business operates but also making traditional data analytics and business analytics bring new big opportunities for academia and enterprises (Sun, Strang, & Yearwood, 2014; Sun, Zou, & Strang, 2015; McAfee & Brynjolfsson, 2012 ; Reddy, 2014). Big data analytics is an emerging big data technology, and has become a mainstream market adopted broadly across industries, organizations, and geographic regions and among individuals to facilitate big data-driven decision making for businesses and individuals to achieve desired business outcomes (Sun, Firmin, & Yearwood, 2012; Ali, 2016) (Vesset, McDonough, Schubmehl, & Wardley, 2013). The research of Sherrel Roche, IT Services Senior Market Analyst, IDC Asia Pacific reveals that more than half of organizations in the Asia/Pacific (Excluding Japan) region consider big data and analytics important and have adopted or plans to adopt it in the near future (Roche, 2016). Artificial intelligence (AI) has received increasing attention in academia, business and management since 2012 although it has been developing since 1956 (Russell & Norvig, 2010) AI has become not only an important technology for improving business performance of enterprises but also an marketing brand for driverless car, sharing economy, e-commerce, e-services (Sun & Wang, Big Data, Analytics and Intelligence: An Editorial Perspective, 2017). It is also the momentum for developing business, organizational intelligence, enterprise intelligence, management intelligence and marketing intelligence (Fan, Lau, & Zhao, 2015). However, the following issues have become even more interesting with the revolutionary development of big, analytics and AI.

1. 2. 3. 4. 5. 6. 7. 8.

Can a robot imagine what a 3 years-old child imagine? Can a driverless car imagine what a driver in another traditional car is laughing? What you imagine when you read an interesting book? Have you recorded what you imagine without any consciousness and thinking about any specific things? Can imaginational intelligence improve the micro-structure of the human brain? Have you experienced imagination as a process? Do you know how important your imagination is? Have you known artificial imagination? Have you studied imaginatics?

The above issues have not been drawn significant attention in the scholarly peerreviewed literature. This paper will address these issues through exploring imaginational intelligence, artificial imagination and imaginatics as a new frontier for innovation, creativity and intelligence development in the age of big data. This is a grand project or quest for our understanding of imaginational intelligence and imagination science, engineering, technology, management and system. Nevertheless, this paper first overview the age of AI, and then examines the foundations of artificial imagination, imaginational intelligence (philosophical, mathematical, logical and computational). It proposes a lifecycle and a spectrum of imaginational intelligence and imaginatics respectively. It also looks at its impacts on data science, business intelligence and artificial intelligence, artificial life, robots and automation with applications. The approach proposed in the paper will facilitate the research and development of imaginational intelligence, artificial intelligence, artificial life, robots, automation and business intelligence as well as imaginatics as an umbrella of imagination science, technology and engineering. The remainder of this paper is organized as follows. Section 2 overviews the age of artificial intelligence. Section 3 examines Intelligence 1.0 and Intelligence 2.0 Section 4 explores imagination and imaginational Intelligence. Section 5 examines the foundations of imaginational intelligence including philosophical, mathematical and computational foundations. Section 6 presents an evolutionary perspective on artificial imagination, artificial imagination and imaginational intelligence. Section 7 presents a spectrum of imaginational intelligence, which covers imaginational intelligence as a science, engineering, technology, systems, service and management in the age of big data. Section 8 explores imaginatics as an emerging discipline. Section 9 looks at imaginational intelligence’s impacts in the age of big data. The final sections discuss the implications and end this paper with some concluding remarks and future work. It should be noted that the approach on imaginational intelligence, artificial imagination and imaginatics will bring about impacts on many other fields including biology, psychology, philosophy, we still limit our focus on their foundations with applications in AI, data science and artificial life.

2

The Age of Artificial Intelligence

The four figures and discussion. In the near future, every most important decisions will be made by robots, bots and intelligent agents rather than by various leaders.

In the near future, almost every important business will be helped by robots, bots and intelligent agents as well as intelligent systems. In the near future, every human will be embedded with an intelligent chip as artificial brain which integrates with our own natural brain. Then one can easily to become a mathematician, a computer scientist, an AI algorithm engineer. … • • • • • • • •

3 3.1

George John, 2013, The Age of Artificial Intelligence, at TEDxLondonBusinessSchool. https://www.youtube.com/watch?v=0qOf7SX2CS4 The Age of Artificial Intelligence - The Rise of the Thinking Machines, 2016 https://www.youtube.com/watch?v=Wjk06IPJQOc John McCarthy, Kevin Minsky, Claude Shannon. 1956 I taught AI since 1990, also knowledge engineering, expert systems, logics for computer scientists. Annual worldwide revenue from AI is projected to hit $37 billion by 2025 (Ong 2017) Facebook, Amazon, Microsoft and Google formed a group Partnership on AI, a not-for-profit that plans to formulate best practices in AI technology, in 2016 [Ong 2017]

Intelligence 1.0 and 2.0. Intelligence 1.0

Basic: ability of learning + thinking + understanding + connecting AI focuses on the automation of human intelligence 1.0 Machine learning (ML) automates ability of learning, Deep learning improves automation of ability of learning, To make good better Natural language understanding automates ability of understanding of natural language But: AI has not automated ability of thinking nor ability of imagination Ref: Russell & Norvig (2010) Artificial Intelligence: A Modern approach, Person Sridhar Mahadevan (2017) Imagination Machines: A New Challenge for Artificial Intelligence, AAAI 2018.

3.2

Intelligence 2.0

Business Intelligence (BI) + emotional intelligence (EI)+ marketing intelligence (MI)+ service intelligence (SI)+ analytics intelligence + organizational intelligence (OI) +… This is Intelligence 2.0 , a kind of Advanced intelligence. 2D analysis of 1st D and 2nd D and then we have a spectrum of Intelligence 2.0 Why I is advanced here? What is I here? Or what intelligence means in the intelligence 2.0. (Sun, Sun, & Strang, 2017) • Temporality, Expectability (Expectable), relativity of intelligence We will below discuss business intelligence as an example of intelligence 2.0 3.3

Business Intelligence as a kind of Intelligence 2.0

There are many different definitions on BI from different perspectives. For example, •

BI is a framework that allows a business to transform data into information, information into knowledge, and knowledge into wisdom (Coronel & Morris, 2015, p. 560). BI has the potential to positively affect a company's culture by creating “business wisdom” and distributing it to all users in an organization. This business wisdom empowers users to make sound business decisions based on the accumulated knowledge of the business as reflected on recorded historic operational data (Coronel & Morris, 2015, p. 560).

•

BI refers to as a collection of information systems (IS) and technologies that support managerial decision makers of operational control by providing information on internal and external operations (Turban & Volonino, 2011).

•

BI is defined as providing decision makers with valuable information and knowledge by leveraging a variety of sources of data as well as structured and unstructured information (Sabherwal & Becerra-Fernandez, 2011). The first definition of BI emphasizes that BI is a framework and creates business wisdom for decision makers through business data, information and knowledge and their transformations. The second definition stresses “a collection of ISs and technologies” while specifies the decision makers to “managerial decision makers of operational control”, and information to “information on internal and external operations”. The last definition emphasizes BI “providing decision makers with valuable information and knowledge”. Based on the above analysis, BI can be defined as a framework that consists of a set of theories, methodologies, architectures, systems and technologies that support business decision making with valuable data, information, knowledge and wisdom. This definition reflects the evolution of BI and its technologies from decision support systems (DSS) and its relations with data warehouses, executive information systems (Holsapplea, Lee-Postb, & Pakath, 2014). The principal tools for BI include software for database query and reporting (e.g. SAP ERP, Oracle ERP, etc.), tools for multidimensional data analysis (e.g. OLAP), and DM e.g. predictive analysis, text mining, web mining (Laudon & Laudon, 2016). DM is also considered as a foundation of BI (Lim, Chen, & Chen, 2013).

3.4

Temporality, Expectability and Relativity of BI as Intelligence 2.0

The widespread development of BI in the past about three decades in the business world has so far ignored a major issue: What does the intelligence mean in BI? In what follows, we will address this issue through examining temporality, expectability and relativity of BI. As the name implies, BI is to create intelligence about a business. This intelligence is based on learning and understanding the facts provided by business data, information and knowledge about a business environment (Coronel & Morris, 2015). The ability of learning, understanding and reasoning belong to the category of intelligence (Wang, 2012 ). The term “intelligent” has been popular, not only in academia but also in the wider community, due to a long time, ongoing research and development of artificial intelligence (AI) and intelligent systems (IS) since 1955 (Russell & Norvig, 2010). There are about 243 million results related to “intelligent” in the Google world (searched on 27 may 2016). In the academia, the term “intelligent” frequently appears in titles of a great number of books, book chapters, papers, and international conferences as well as other media or products. In the wider community, the term “intelligent” often appears in home appliances and customer electronics including televisions, cameras, vacuum cleaners, washing machines (Sun, Zou, & Strang, 2015), and mobile phones, to name a few. Defining intelligent is not a simple question. According to the Macmillan Dictionary (2007, p. 787), the term intelligence means “the ability to understand and think about things, and to gain and use knowledge”. Similarly, the term intelligence has been defined in IS as “the ability to learn and understand, solve problems and make decision” (Negnevitsky, 2005, p. 18). The term intelligent means to be able to perceive, understand, think, learn, predict and manipulate a system (Russell & Norvig, 2010, p. 1). All these definitions on intelligence are mainly human intelligence, which has impacted the development of AI (Russell & Norvig, 2010). AI has been focusing on intelligence of machines or machine intelligence (Note that the web is also a machine.). In other words, AI is the science and engineering of making intelligent machines (Wang, 2012). However, a system may not be considered intelligent, even if it has these abilities associated with human intelligence, because the term intelligent implies some expectations from human beings or society. Practically, it appears that an intelligent system contains a set of functions that jointly make the system easy to use (Astrom & McAvoy, 1992), because ‘easy’ is a term related to human intelligence. More generally, a system or a product is intelligent if and only if it contains a set of functions that jointly make the system either easier or faster, or friendlier, or more efficient, or more satisfactory to use than an existing cognate system taking into account the time. Here easier, faster, or friendlier, or more efficient, or more satisfactory are all the expectations of humans or customers or society for the performance of a system or product. For example, a high speed train running in China is intelligent, because it is faster and friendlier, these are what the Chinese expect. The above consideration leads to three perspectives on “intelligence”. Firstly, term intelligence is temporal, or temporality of intelligence. There are two meanings for temporal intelligence. 1. Temporal intelligence is the ability to adapt to change. This has been motivated to develop temporal logic and evolutionary computing including genetic algorithms (Russell & Norvig, 2010). 2. Temporality of intelligence

means that intelligence is related or limited to a time interval. For example, at the time of writing this paper, few people consider floppy disks as intelligent storage devices. However, a few decades ago floppy disks were considered intelligent in comparison to paper tape for data storage. Another story is that a pupil was only six years old. However, he could do calculus very well. The other classmates said that he was intelligent. However, that pupil dropped out when he was in Year 5. These two stories reflect the temporality of intelligence. In what follows, we limit ourselves to the meaning of item 2. Secondly, term intelligence can be considered as a substitution for easier, or faster, or friendlier, or more efficient, or more satisfactory. This is expectability of intelligence. We denote them using the degree of satisfaction. All these related concepts are a set of expectations of humans, as parts of human intelligence. We denote these expectations for a product as 𝐸𝑃 = {𝑒𝑖 |𝑒𝑖 𝑖𝑠 𝑎𝑛 𝑒𝑥𝑝𝑒𝑐𝑡𝑒𝑑 𝑝𝑒𝑟𝑓𝑜𝑟𝑚𝑎𝑛𝑐𝑒 𝑓𝑜𝑟 𝑓𝑢𝑛𝑐𝑡𝑖𝑜𝑛𝑖 𝑜𝑓 𝑎 𝑝𝑟𝑜𝑑𝑢𝑐𝑡 } = {𝑒𝑖 |𝑖 ∈ {1, 2, … , 𝑛 − 1, 𝑛} , where P is for a product, n is a fixed integer. For every 𝑖 ∈ {1, 2, … , 𝑛 − 1, 𝑛}, there is a perceived performance of customer for functioni 𝑝𝑖 , then a product P is intelligent if and only if there exists at least one 𝑖 ∈ {1, 2, … , 𝑛 − 1, 𝑛} such that (Larson & Gray, 2011, p. 436) 𝑝𝑖 𝑠𝑖 = ≥0 𝑒𝑖 where 𝑠𝑖 is the satisfaction degree of the customer to the i function of product P. For example, an iPhone 6S is intelligent, because its Touch ID, Apple’s fingerprint recognition feature, is noticeably quicker when unlocking the phone. “quicker” is what the user perceived, e.g. 𝑝1 , while “quick” is an expected performance for iPhone 6S from a customer (http://www.trustedreviews.com/iphone-6-review-performance-page3, retrieved on 28 Apr 16). Thirdly, intelligence is a consequence of comparison between two systems, which leads to the relativity of intelligence. Generally speaking, let X and Y be two systems. X is intelligent if X is better than Y with respect to E, where E is a set of human expectations. “Better” is a relativity concept based on comparison. For example, a new microwave is intelligent because it displays the temperature when microwaving food. A user believes that displaying the temperature is better than not displaying it. This example reflects the relativity of intelligence. This is the third dimension that intelligence is relative. Displaying temperature belongs to the set of expectations E. The set of human expectations can be considered as a set of demands. The expectation of human beings and society promotes intelligence and social development. Therefore, it is significant to define IS with respect to the set of human expectations or demands. In summary, intelligence, in general and intelligence in BI can be measured through three dimensions: Temporality, Expectability and Relativity. In other words, in any BI system there are three characteristics of its intelligence: Temporality, Expectability and Relativity. The degree of intelligence of a BI system or product can be measured using this triad, that is, Degree of intelligence = temporality+ expectability+ relativity 3 Equation 3 is more useful for BI and big data intelligence (for short, big intelligence), because BI is based on performance, business advantages, competiveness advantages of systems or products. This formula can be realized by using big data analytics and big

data, in other words, big data and big data analytics can generate big intelligence, for short, big intelligence = big data +big data analytics 4 Equation 4 indicates that either increase of either big data or big data analytics can increase the degree of intelligence of big intelligence. This is partially proved by what Professor Peter Norvig, the Google’s Director of Research, said that “we don’t have better algorithms; we just have big data” (McAfee & Brynjolfsson, 2012 ). In fact, the global competiveness among the giant companies lies in these three dimensions of intelligence in businesses, decision making, products and systems. Big data and analytics will intensify the competition of the giant companies in terms of temporality, expectability, relativity of intelligence. This degree of intelligence also differentiates BI from AI using three properties of its intelligence in BI. Throughout this paper, we use these three dimensions of intelligence as the basis to understand BI. Cloud services

Mobile services Big data analytics services

Social networking services

e-services

Figure 2. Interrelationship between big data analytics services and web services

It should be noted that for the state-of-art web services, Sun et al (Sun & Yearwood, 2014; Sun, Zou, & Strang, Big Data Analytics as a Service for Business Intelligence , 2015) explores that web services mainly consist of mobile services, analytics services, cloud services, social networking services, and service as a web service. Here we emphasize big data analytics services at the center to support cloud services, social networking services, mobile services, e-services to reflect the big data and big data analytics as an emerging new service (Sun, Zou, & Strang, 2015). Based on IDC’s prediction for the IT market in 2014 (IDC, 2013), spending on big data will explode and grow by 30%, to $14+ billion, in which, the spending on big data analytics services will exceed $4.5 billion, growing 21%. The number of providers of big data analytics services will triple in three years. This means that big data analytics services have become an important emerging market, together with the Internet of services including e-services, cloud services, mobile services and social networking services. All these five services and the technologies shape the most important markets for e-commerce and e-business (Sun & Yearwood, 2014). Furthermore, BI is a more general concept for improving business performance and business decision making. Big data analytics is a pivotal part for developing BI, at least from a technological viewpoint and data viewpoint. From a technological viewpoint, big data analytics is big data-driven and business-oriented technology and facilitates business decision making and then improves BI (Sun, Strang, & Yearwood, 2014; Sun, Zou, & Strang, 2015). From a data viewpoint, big data analytics relies on

data analytics and big data which have become a strategic natural resource for every organization, in particular for multinational organizations as well as for e-commerce and e-services. Discovering information, knowledge and wisdom from databases, data warehouses, data marts and the Web has become the central topics for business operations, marketing and BI (Sun, Zou, & Strang, 2015). This is just the task of big data analytics. Big data analytics service broker Publish Big data analytics service provider

Find

Bind

Big data analytics service requestor

Figure 3. BASOA: A big data analytics SOA

4 4.1

Imagination and Imaginational intelligence Imagination

Imagine = to form an image, an idea, in the mind of what something might be like (Oxford 2009). Imagination is the process of imagining. It is the creative ability to initiate and form images, ideas, and sensations in the mind without any immediate input of the senses (such as seeing or hearing) (wikipediaImagination, 2018). Imagination helps make knowledge applicable in solving problems and is fundamental to integrating experience and the learning process Albert Einstein said, "Imagination is more important than knowledge. For knowledge is limited, whereas imagination embraces the entire world, stimulating progress, giving birth to evolution. (Wikiversity-Einstein, 2018). Imagination is more important than intelligence (Bhardwaz, 2016). Imagination has played an important role in innovation, art, literature, tales and others

4.2

Imaginational intelligence

Updated on Sun 01 04 17, 27 Feb 2018, 1-7 March 18. Imagination is the unique source of any creation and innovation. It is also the unique source for human intelligence and artificial intelligence.

Georg Wilhelm Friedrich Hegel (1770-1831), a German philosopher, has studied the relationship between imagination and intelligence. He has referred imagination as a kind of intelligence which wields the stores of images and ideas belonging to it. [Sun 010417] Imaginational Intelligence (II) is not “imagination intelligence”, motivated by emotional intelligence (Goleman, 1995), which asks us a question why emotional intelligence is not Emotion intelligence. First of all, we have to know that “Imaginational intelligence” and Imagination intelligence“ are not in https://en.wikipedia.org in English (retrieved on 27 Feb 2018, also on ). Imaginational Intelligence can be defined as a kind of human intelligence and also a kind of artificial intelligence or machine intelligence. As a kind of human intelligence, imaginational intelligence can be defined as the capability of individuals or intelligent agents (including machines) to initiate and form images, ideas, and sensations in the mind without any immediate input of the senses (such as seeing or hearing), data, and knowledge, Introduced by Sun Z (28 Feb 2018). Imaginational intelligence was originally be defined as the “capability or skill of individuals to react and reflect what they listen, read, speak and hear to form, perceive and create new ideas, methods, and pictures instantly or effectively to solve a problem and improve a decision making” (Bhardwaz, 2016). [Sun 01 04 17]. As a kind of artificial intelligence or machine intelligence, imaginational intelligence can be defined as “a framework that consists of a set of theories, methodologies, architectures, systems and technologies that support to initiate and form images, ideas, and sensations without any immediate input of the big senses (such as seeing or hearing), and big data, and big knowledge” (Sun, Sun, & Strang, 2017) This definition does not emphasize that the “big” (volume of, and other four Vs of big data) senses, data, information and knowledge can facilitate imagination and imaginational intelligence. Imaginational intelligence is at least as important as emotional intelligence and IQ for success, including in academic, professional, social, and interpersonal aspects of one's life (Goleman, 1995). It is a basic skill that can be taught and cultivated either in school or in society. Imaginational intelligence might be more important than emotional intelligence and IQ for one’s intelligence development, at least it is the fundamental for developing emotional intelligence and IQ. It is the original force for one’ skill of innovation and creation. 4.3

The Evolution of Imaginational intelligence

Imaginational intelligence is a topic in my head since I published a few papers in JCIS in 2016 and 2017. On 24 March 17, I attended the presentation on emotional intelligence and I associated it with my Imaginational intelligence using a mathematical equation. On 30 March 17, I was invited to deliver a presentation to students and staff at PNG UoT, I have to draft a presentation slides on innovation and entrepreneauership. I found that Imaginational intelligence is the motive of any innovation, and then I mentioned it in the presentation to the students and staff at UNITECH on 31 March 17.

I defined Imaginational intelligence on 31 March and early April 2017, motivated by the definition of emotional intelligence. Late, I find that this definition is based on the assumption of “Imaginational Intelligence is a kind of human intelligence”. However, Imaginational Intelligence is also a kind of artificial intelligence or machine intelligence, therefore, I introduce the current definition based on what I define business intelligence in my work (Sun, Sun, & Strang, 2017). The latter one was defined based on a few well-known defintions on business intelligence. This section was imagined by my recent work on big data, AI and BI. 4.4

A Lifecycle of Imaginational Intelligence: A Process-Oriented Perspective

Association, search, generalize, and specialize are all the key for imaginational intelligence Association is an important attribute for Imaginational intelligence. One can associate A with B that he or she just read, listen, read and speak critically, and rationally. For example, when Peter reads a paper on fuzzy reasoning (A), he has an idea that fuzzy reasoning looks like to similarity-based reasoning. This is the first step of imaginational intelligence. A Lifecycle of Imaginational Intelligence: A Process-Oriented Perspective, consists the following stages. An initial idea, association, search, selection, form scientific hypothesis, validation, reuse of knowledge, creation of knowledge, dissemination of knowledge. An idea as start, this is not related to data, big data, therefore, imagination intelligence is not related to data, big data and data science at least at the earlier and most important stage of the lifecycle. 4.4.1 Search and Hyperlink Search and hyperlink is a kind of realization of imaginational intelligence. It is also a kind of engineering of imaginational intelligence. For search, when I read an article, I image an idea X is new, then I have to search on the web to know if X is a new idea. If X is available on the Web, how is different my idea from the existing idea. This research process starts from the search, which is motivated from the imagination when reading an article. In the age of big data, one does not know a lot before of intelligence limitation and memory limitation of the brain. The search is an extension of intelligence and memory limitation. 4.4.2 Generalization and specialization 020417 generalization, and specialization are all the key for imaginational intelligence. Some are good at generalization, while others are good at specialization, the former might become a leader, while the latter might become an team member. Therefore, generalization, and specialization are skill of imaginational intelligence

4.4.3 Association Association is a kind of imaginational intelligence. Association from one world to another, from a small world to a big one. Hyperlink is a kind of association realization.

5

Foundations of Imaginational Intelligence

This section explores the Philosophical Foundations, Mathematical Foundations, and Computational Foundations of Imaginational Intelligence. The Imaginational Intelligence can be defined differently from different perspectives. In what follows, we will propose cognitive, philosophical, business, computer science, mathematical definition of imaginational intelligence and then discuss their corresponding foundations. 5.1

Business Foundations of Imaginational Intelligence

5.2

Cognitive Foundations of Imaginational Intelligence

5.3

Philosophical Foundations of Imaginational Intelligence

Philosophical Foundations aims to answer the following questions 1. Can a robot imagine what a 3 years-old child imagine? 2. Can a driverless car imagine what a driver in another traditional car is laughing? 3. What you imagine when you read an interesting book? 4. Can Imaginational Intelligence improve the micro-structure of the human brain? Every human behaviors starts from an imagination Asma, S. 2017 on The Evolution of Imagination (Asma , 2017) https://www.researchgate.net/publication/323295571_Imaginational_Intelligence_A_ New_Frontier_for_Innovation_Creativity_and_Intelligence_Development_in_the_Ag e_of_Big_Data 5.4

Mathematical Foundations of Imaginational Intelligence

It is not related to probability at the core part of the mathematical foundations, and but related random process, because imagination maybe a random process. Fuzzy mathematics and fuzzy logic. Fuzzy logic can play an important role in the stages of from a rough idea to knowledge creation and knowledge industry Classic set theory might be in the core mathematical Foundations of Imaginational Intelligence, just as a mathematical foundation of big data A Mathematical Theory of Imaginational Intelligence (Sun & Wang, 2017) Mathematics is the door and key to the sciences - Roger Bacon, Philosopher (12141292)

For the things of this world cannot be made known without a knowledge of mathematics. If in other sciences we should arrive at certainty without doubt and truth without error, it behooves us to place the foundations of knowledge in mathematics. Reasoning draws a conclusion, but does not make the conclusion certain, unless the mind discovers it by the path of experience. Mathematical Foundations

• • • • • •

5.5

Computational Foundations of Imaginational Intelligence • • • • •

5.6

Linear algebra Mathematical logic (Propositional logic and predicate logic) Fuzzy logic and commonsense logic Random process and probability Similarity-based reasoning and non-similarity-based reasoning Optimization.

It is related quantum mechanics. Human and machine cannot record imagination timely. An excellent lifecycle of II is the start for the computational foundation of II. Hierarchical, process-oriented, structured model or integrated model or framework of II is also necessary. Optimization & AI, Computing and data engineering, data sciences. Alexander, I. 2001. How to build a mind: Towards machines with imagination. Columbia University Press. Imaginational reasoning

Updated on 13-15 03 18, Imaginational reasoning is a fundamental for imaginational intelligence. It can be defined as a reasoning motivated by an imagination. Imaginational reasoning as a process is significant for understanding imaginational intelligence and its important impact to artificial intelligence. where is the start (first step) of the Imaginational reasoning, where is the last step or the end of the Imaginational reasoning. Let's have a look at as follows (Sun, Strang , & Firmin, 2016) the first step or start is an idea, or a word, or a short text, or a short story or a rough picture appearing in the head without any preparation or consciousness. Therefore, imagination is related to thinking, maybe to understanding, but not to learning, nor connecting to others. the second step is curiosity. For average people, one does care the above-mentioned word, text, story, and the picture. then the imagination is dead before the continuing process, however, one, at that time, is curious about it. then the second step is curiosity. If there is no curiosity, the word, text, story and picture cannot be further processed and managed.

If one has strong curiosity at that time, he or she believe that why this word, or text, or story, or picture appearing to my head or mind. It is very strong, novel, interesting, then she or he will record this within a short time, I used this method to record imaginational reasoning, and wrote it on the paper as soon as I reach the desk, although I suddenly imagine "Imaginational reasoning" as a key word in my head. The other reasons behind is that I am working on Imaginational intelligence, and I have published articles on case-based reasoning (Finnie & Sun, 2003). This motivate my imagination on imaginational reasoning. the third step is association. as mentioned above, as soon as I record imaginational reasoning, I started to link it with Imaginational intelligence, because many intelligences are related to reasoning, although there are cases of intelligence without reasons (Brook, 1989) at the same time, I associate it as a process, because in mathematical logic, any reasoning or inference is a process. therefore, I wrote imaginational reasoning as a process, as mentioned at the beginning of this slip. The fourth step is search, because search aims to make our thinking, what we imagined, to a rational thinking, rational imagination or prepare scholarly writing. In this stage, I search Google web, google scholar. For example, today (13 March 2018) I google "imaginational reasoning" and found only 2 results (0.24 seconds). and then I search http://scholar.google.com and found zero results (13 March 2018). This implicates that imaginational reasoning has not drawn any attention in academia and industry. This is the reason why imaginational reasoning is important. The five and final stage is to form a hypothesis The six and final stage is to recommend a statement. Therefore, imaginational reasoning as a process consists of initiate an idea, curiosity, association, search, form and recommend a hypothesis or statement. This is the most important part of imaginational reasoning as a process. The following stages of imaginational reasoning consists of those for knowledge creation and knowledge dissemination and supporting decision making. These stages can be combined with other results of any research fields. I do not discuss it here anymore, at the moment. Finally, we can summarize imaginational reasoning as Figure 1.

1

initiate an idea

2

curiosity

3

associatio n

4

• search

5

form a hypothesi s

6

recomme nd statemen t

Figure 1. A model of imaginational reasoning It should be noted that it is process model. We do not believe that it can be an iterative and incremental process model.

6

Artificial Imagination and Imaginational Intelligence: An Evolutionary Perspective

Updated on 19-23 March 2018 This paper provides an evolutionary perspective on imagination, artificial imagination, and imaginational intelligence through the research approach of research as a search. It searches SCOPUS, Google scholar, and SemanticScholar and analyzes the searched results. The main three research contributions include 1. Imagination has been drawn attention in academia since 1936 or earlier. 2. Artificial imagination has drawn some attention in artificial intelligence since 1980s (maybe in 1989, based on the research), 3. Imaginational intelligence was first mentioned in Hillman in 1982. This concept can date back to Ancient Egyptians’ philosophy, in which imaginational intelligence is intelligence of the heart. However, there are not yet research on imaginational intelligence in business, management, as well as computing in general and computer science in specific. The research suggests that artificial imagination and imaginational intelligence are an emerging field, they should draw more attention in business and computing with the dramatic development of big data analytics and artificial intelligence. 6.1

Introduction

Imagination, artificial imagination (Sun Z. , 2018) and imaginational intelligence (Hillman, 1982) (Sun Z. , 2018) have drawn some attention in academia and industry. However, the research of them are still at infancy in business, management and computing. This research will address the following questions: 9. When have imagination, artificial Imagination and imaginational intelligence been drawn attention in academia in terms publications? 10. What is the relationship among imagination, artificial imagination and imaginational intelligence? To this end, this research focuses on a literature review on imagination, artificial imagination, imaginational intelligence based on the research methodology: research as a research, which is an iterative and incremental process from the scratch to the completion of the research (Sun Z. , 2017). Research as a search can be considered as a research based on small data extracted from big data in the age of big data. Or big data derived small data analysis. The latter will form a popular research methodology for research and development not only in higher institutions but also in industry.

The research is organized as follows. Section 2 looks at imagination. Section 3 discusses artificial imagination. Section 4 explores imaginational intelligence, and Section 5 concludes this research. It should be noted that this research is a part of Research on Imaginational Intelligence: A New Frontier for Innovation, Creativity and Intelligence Development in the Age of Big Data (You can search), as a series of publications on the computing of imagination or imaginational computing. Therefore, we do not provide definitions and fundamentals on imagination, artificial Imagination and imaginational intelligence. 6.2

Imagination

Imagination has been drawn some attention in academia since 1936 (https://www.semanticscholar.org/search?q=imagination&sort=relevance,retrieved on 21 March 2018). There are 88 found research articles on imagination in specific, related to imagination in general, available in SCOPUS (www.scopus.com). It searched on 21 March 2018. The search strategy is as follows. search: “imagination”, limited to "article title" and "Computer Science" and "article". From the author's viewpoint, the following articles are important and useful for further development of the computing of imagination, which can be defined as “all the research and development of imagination in a computing and related disciplines”. We provide a preliminary comment or remark on each of them. • Quin, C.A.C. 2016, The cybernetic imagination of computational architecture. International Journal of Architectural Computing, 14(1), pp. 1629 Cybernetic is related to cybernetics, therefore, cybernetic imagination is related to cybernetics of imagination. The latter is a big topic for computing of imagination. •

Davies, J., Bicknell, J. 2016, Imagination and belief: The microtheories model of hypothetical thinking. Journal of Consciousness Studies, 23(3-4), pp. 31-49

Belief is a research area in computer science, cognitive science, philosophy and psychology. Can we explain belief based on imagination as the core of this article, further, hypothetical thinking is useful for my proposed lifecycle of imaginational intelligence (Sun Z. , 2018). That is, hypothesis is a stage of lifecycle of imaginational intelligence. However, we also have imagination without belief, because what we experienced imagination seems not related to belief. •

Duch, W. 2007, Intuition insight, imagination and creativity. IEEE Computational Intelligence Magazine. 2(3), pp. 40-52

Duch aims to explore the relationship among intuition, imagination and creativity. Intuitively, imagination first, tuition second, creativity is the consequence of imagination and intuition. Intuition motivates the curiosity originated from imagination. This is the logical relation -Sun 220318 •

Crowther, P. 2013, How images create us: Imagination and the unity of self-consciousness. Journal of Consciousness Studies, 20(11-12), pp. 101-123

The image might come from an imagination, the latter is related to selfconsciousness. •

Bawden, D. 2013, Imagination, exciting mixes and the improvement of information research. Journal of Documentation, 69(3) This article examines the relationship between imagination and search, which conforms with the proposed lifecycle of imaginational intelligence. Sun 22 03 18. • Weaver, W. 2012, World of pure imagination: On the rise of materials informatics. Scientific Computing, 29(3), pp. 8-9 We do not know materials informatics, but we have big interest in the World of pure imagination, because it can consist of imagined image database, imagined text base, etc. from a computing perspective. •

Stuart, S.A.J. imagination.

2007, Machine consciousness: Cognitive and kinaesthetic Journal of Consciousness Studies. 14(7), pp. 141-153

This is related to artificial imagination. In other words, cognitive and kinesthetic imagination are a part of artificial imagination. Machine consciousness is also a fundamental of machine intelligence. To our knowledge, the majority of research on machine intelligence is market-oriented intelligence realization, at least it is not related to imagination. This paper is toward the reverse direction of the current research of machine intelligence. Its value is here. • Grier, D.A. 2006, The captured imagination. Computer, 39(6), pp. 7 The imagination can be captured if one has tuitional curiosity and more. • Phillips, F. 2005, Technology and the management imagination. Pragmatics and Cognition, 13(3), pp. 533-563 It should be changed into technology and the management of imagination. In other words, imagination technology, and imagination management are a part of computing of imagination. •

Nirkhe, M. 1995, Formal real-time Informaticae, 23(2-4), pp. 371-394

imagination.

Fundamenta

This might belong to elements of imaginational computing. •

Ward, T.B. 1994, Structured Imagination: The Role of Category Structure in Exemplar Generation. Cognitive Psychology. 27(1), pp. 1-40

Structured imagination is a part of classification of imagination. From a computing viewpoint, at least imagination can be classified 10 categories, one is structured imagination, another is process-oriented imagination. •

Nahas, M., Huitric, H. 1983, Scenes of our imagination. Computers and Graphics, 7(2), pp. 205-207

This means that what we imaged are scenes. In fact, what we imagined also includes text, story, word, a formula, a model, an architecture, and more, we cannot represent each of them easily. •

Maranda, P. 1989. Imagination: A necessary input to artificial intelligence Semiotica, 77(1-3), pp. 225-238

From the above, we can see that imagination has drawn attention in artificial intelligence since 1989, thanks to the work of Maranda. This can easily lead to artificial Imagination from a discipline-level research viewpoint. 6.3

Artificial Imagination

Similarly, we use SCOPUS, and found 10 searched results for searching “Artificial imagination”. From the author's viewpoint. The following articles are important and useful for the further development of artificial imagination. •

Silvestre, J., Ikeda, Y., Guéna, F. 2016, Artificial imagination of architecture with deep convolutional neural network "Laissez-faire": Loss of control in the esquisse phase CAADRIA 2016, 21st International Conference on Computer-Aided Architectural Design Research in Asia - Living Systems and Micro-Utopias: Towards Continuous Designing, pp. 881-890

This is a problem-solving level research on artificial imagination towards deep understanding of architecture. Can we explore Artificial imagination of architecture with X, where X is a set of domains. • •

Thomee, B., Huiskes, M.J., Bakker, E., Lew, M.S. 2008, Using an artificial imagination for texture retrieval. Proceedings - International Conference on Pattern Recognition 4761476 Thomee, B., Huiskes, M.J., Bakker, E.M., Lew, M.S. 2007, An artificial imagination for interactive search. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 4796 LNCS, pp. 19-28

Search has a close relation with retrieval. Therefore, the above two international conference proceeding articles from a research group aims to applying artificial imagination to information search or retrieval in 2007 and 2008. Or the application of artificial imagination includes information search and retrieval. However, in my proposed lifecycle of imaginational intelligence, search is at the 3rd stage of imagination (Sun Z. , 2018). Search is used to propose hypothesis. •

Gangopadhyay, N. 2006, The embodied machine: Autonomy, imagination and artificial agents. Proceedings of AISB'06: Adaptation in Artificial and Biological Systems, 1, pp. 136-143.

This article embodies a machine with autonomy, imagination. More specifically, Gangopadhyay aims to develop an intelligent agent with autonomy, imagination. intelligent agents with autonomy or an autonomous agent has been developed in early 2000’s. At that time, autonomy is a basic characteristic for any intelligent agents.

However, an intelligent agent with imagination is still an interesting topic, because artificial imagination aims to develop intelligent agents with imagination. •

A taxi ride to late capitalism: Hypercapitalism, imagination and artificial intelligence Punt, M. 2002 AI and Society, 16(4), pp. 366-376

Yes, this is an application of artificial imagination to society. •

Artificial Life: A feast for the imagination and Philosophy, 5(4), pp. 489-492

Dennett, D.C.

1990,

Biology

I believe that this article should be a part of imagination, although SCOPUS puts it in the category of artificial imagination. Similarly, we search http://www.semanticscholar.org/ on “artificial imagination” and have, however, not found any new results, because two found results are already in what we searched using SCOPUS. This also demonstrates that this website is a not general search engine but an advanced one. 6.4

Can we generate a problem automatically?

This section looks at what is a problem, and can we generate a problem automatically? 1. What is a problem? What is a problem? This is a simplest problem, but it is most difficult to answer. According to Dictionary (Orford, 7th edition) problem is "a thing that is difficult to deal with or to understand". However, in reality, there are a lot of problems that are not difficult to deal with or not difficult to understand. Therefore, based on the above definition, these problems are not problems. Behind it is that problem has characteristics of relativity. Further, there are really a lot of problems. For example, in any research project or research paper, one must raise research problems, they are really difficult to deal with or to understand, at least from a perspective of the researchers or authors. Sometimes, the reviewers of the project or papers might not agree with this point. They believe that this is not research problem. What do you believe? My idea is that there are a lot of problems on problems. We have not understood about these. If we have a complete understanding of problem, Then we will have a better understanding of problem based teaching and learning, problem solving, and so on. It seems that we do not care about problems around us and annoying us often. Prof Dr Zhaohao Sun 2017-10-11 Can we generate a problem automatically? Here, we mean human beings and intelligent agents or robots. For we as human beings, can we generate a problem automatically? This is an important topic for problem based reasoning or problem based teaching and learning, because one course has not a lot of problems for students. For example, for computer programming course of undergraduate students, there are only a few dozens of

algorithms that are worthy of studying and questioning. Therefore, if we can generate 1000 problems automatically for one course. at least the lecturers are happy. For intelligent agents or robots, can we generate a problem automatically? This is an important problem for AI, because intelligent agents or robots can generate world class research problems, significant research problems for researchers and developers, so that researchers need not think about research problems, they can then focus on how to solve these world class research problems, significant research problems using their own intelligence or artificial intelligence. Problemless research is more important than driverless car, because researchers have liberated from the hardest stage of research, that is what are the research problems for the research or research articles. Prof Dr Zhaohao Sun 2017-10-11 This section is based on https://www.researchgate.net/project/Science-ofResearch/update/59dedc9e4cde264a84afe524 6.5

Imaginational Iintelligence

We search SCOPUS and have not found any research results on “imaginational intelligence” limited to “article title” and we receive “No documents were found.” On 22 03 2018. Similarly, we search http://www.semanticscholar.org/ on "imaginational intelligence" and have not found any research articles with including "imaginational intelligence" Finally, we search Google Scholar http://scholar.google.com.au on 22 March 2018 and found “about 21 results (0.17 sec)”. We found that. The first item is • Z Sun (2018) Imaginational Intelligence: A New Frontier for Innovation, Creativity and Intelligence Development in the Age of Big Data. The other items have only mentioned imaginational intelligence, which has not appeared in the article title. •

•

•

Hillman (Hillman, 1982) has referred imaginational intelligence to as “Only when we engage the world with the hearts mode of knowing, do we open to soul, the soul in the world, and connect, with the depths, mystery, and incredible variety of the human experience” (MS Green, Poetic awareness: Imagination and soul in education, 2007). Hillman's writings on soul-making, imaginational intelligence, and revisioning psychology aims to change psychology through imagination (JM McCart, Chasing Aphrodite: Removing hubris from the sacred, 2015) The ancient Egyptians believed that the heart is the seat of love and imaginational intelligence; this is communicated through what they had called the intelligence-of-the- heart (FY Seif, The pathless journey of beauty: Experiencing the sublime through the enchantment of Eros- The phenomenon of beauty in culture, 2012).

•

Ancient Egyptians believed in the intelligence of the heart. Indeed, this imaginational intelligence, which re- sides in the heart of soul (Hillman 1992) (F Seif, Life Paradoxes and perseverance: Designing through antinomies of - Semiotics, 2014)

This means that from the viewpoint of Seif, imaginational intelligence is intelligence of the heart. Maybe it is right in psychology. But it is not right in the field of business and computing. Therefore, it needs business definition of imaginational intelligence and a computing definition of imaginational intelligence. •

BS Poulson, Knowledge derived from the imaginal heart: The process of perceiving an ambiguous reality, 2011, search.proquest.com.

In his book, Poulson writes that knowledge derived from the imaginal heart: The process of perceiving an ambiguous reality. Based on his research question: What is the imaginal heart's role in the process of knowing the imaginal… From the search of Google scholar, we can conclude, that imaginational intelligence was mentioned in Hillman (1982), although he has not focused on development of imaginational intelligence as a discipline. This concept can date back to Ancient Egyptians’ philosophy, in which imaginational intelligence is intelligence of the heart. However, there are not research on imaginational intelligence in business, management, as well as computing in general and computer science in specific. Therefore, the research questions for business and computing are • What is imaginational intelligence from a perspective of business and computing? • What is the relationship between artificial imagination and imaginational intelligence from a perspective of business, management and computing This article will address these two issues. It should be noted that James Hillman (1926-2011) was an American psychologist, among the founding thinkers of archetypal psychology and a leading scholar in Jungian and post-Jungian thought. (http://www.oac.cdlib.org/findaid/ark:/13030/c8vh5tns/entire_text/). Hillman considers a return of the soul as a central place in psychology (Drob, 2001). He characterizes the soul as (1) makes all meaning possible, (2) turns events into experiences, (3) involves a deepening of experience, (4) is communicated in love, and (5) has a special relation with death (Hillman, 1977, p. xvi, Hillman, 1976, pp. 44-47). The ultimate psychological value of Hillman’s psychology is a realization and deepening of the soul in its widest possible sense (Drob, 2001). Therefore, imaginational intelligence is not the focal point for Hillman to develop its psychology. 6.6

Discussion and Implications Updated 150318, 2018-3-15

Artificial imagination is a part of imaginational Intelligence (https://www.researchgate.net/project/Imaginational-intelligence, retrieved on 15 March 2018). Imaginational Intelligence aims to implementation of artificial imagination. This is similar to the relationship between human Intelligence and artificial Intelligence. This is a challenging topic. This is the reason why I list Imaginational Intelligence as the 7th hard problem of artificial Intelligence One of the important task of artificial imagination is to produce original ideas, each of them can automatically be transformed into research problems (because one original idea might lead to many research problems). Then using the automation of research (i.e 6th hard problem of artificial Intelligence), we can use an imaginationer thousands of millions of research journal papers automatically, based on 3D printing technology. The imaginationer is an imaginational machine, or an imaginational bot. This imaginationer will declare the bankruptcy of all the existing press including Elsevier, Springer and Wiley in 2049. At the same time, all the scholars, researchers and developers stop imaginations, thinking, and undertaking any research. They (including the author) really enjoy the age of free from research. The research, research papers or outcomes will be automatically disappeared. The research offices or every universities will be closed in 2050. All these are not the result of artificial intelligence. It is the natural result of artificial imagination and imaginational intelligence. Prf. Dr. Zhaohao Sun 2018-3-15 6.7

Conclusions

Artificial imagination and imaginational intelligence are an emerging field. They should draw more attention in business and computing with the dramatic development of big data analytics and artificial intelligence. Artificial imagination and imaginational intelligence will facilitate the development of science of imagination in specific, and computing of imagination is general. 6.8

References

Sanford L. Drob, 2001, The Depth of the Soul: James Hillman’s Vision of Psychology, http://www.newkabbalah.com/hil2.html Hillman J. (1982). Anima Mundi*: The Return of the Soul to the World. Spring, 1982, 71-93. (Note that it means the world soul) Hillman, J. (1988) Cosmology for Soul. In Cosmos-Life-Religion: Beyond Humanism. Nara, Pakistan: Tenri University Press. Sun, Z. (2017). Science of Research, BAIS No. 17006, PNG UoT, DOI: 10.13140/RG.2.2.15233.15204. Sun, Z. (2018). Imaginational Intelligence: A New Frontier for Innovation, Creativity and Intelligence Development in the Age of Big Data. Retrieved from ResearchGate: DOI: 10.13140/RG.2.2.28759.98729

7

Spectrum of Imaginational Intelligence • • • • • • • • • • • •

8

Imaginational Intelligence as a Science Imagination science (Mahadevan 2018) Imaginational Intelligence as an Engineering Imagination Engineering Imaginational Intelligence as a technology Imagination technology Imaginational Intelligence as a system Imagination system Imaginational Intelligence as a Service Imagination service All the above belongs to imagination computing “All in one” produces imagination robots (Sun, 2018), imagination agents (Sun 2018), imagination machines (Mahadevan 2018)

Imaginatics: An Emerging Discipline

Imaginatics is coined by Zhaohao Sun in 2018 (24 March), officially released here today. This is a further imaginational evolution from imaginational intelligence in April 2017 (or earlier in 2014 in Australia, I imagined it and wrote a few sentences at that time, but I did not search and read anything and shift myself to other businesses), to artificial imagination on 15 March 2018 without the help of any data, any information as well as knowledge. Imaginatics can be defined as a science about imagination and artificial imagination, like informatics (corresponding Informatik in German, and computing in USA), mathematics and physics. Generally speaking, imaginatics includes imagination science, technology, engineering and management, imagination systems, tools and applications. Therefore, imaginatics is interchangeable with imagination computing. As an example of imagination, the author imagined all these three terms without any consciousness, without any association, without any discussion with others, without any reading nor thinking. For example, I imagined "imaginational intelligence" to my head when I walked on the campus by myself to my home after finishing my work. I imagined "artificial imagination" to my mind, when I went to my office on foots on the campus. As soon as imaginatics imagined to my head, I had big interest in it, and have to write it down without any search and reading of anything. I have big curiosity on imaginatics, because imaginatics is a new word, a new science, to my knowledge.

This is my research method for processing my imaginations towards any research I involved. After I published my paper on imaginational intelligence at RG (https://www.researchgate.net/publication/323295571_Imaginational_Intelligence_A_ New_Frontier_for_Innovation_Creativity_and_Intelligence_Development_in_the_Ag e_of_Big_Data), I still wrote based on my natural reasoning until last week after my teaching, I just professionally searched related publications using SCOPUS, Google Scholar and Semanticscholar (20-21 March 2018) and then reviewed the found research documents. The result has been published here entitled "Artificial Imagination and Imaginational Intelligence: An Evolutionary Perspective" (https://www.researchgate.net/publication/323946378_Artificial_Imagination_and_I maginational_Intelligence_An_Evolutionary_Perspective). This can be considered the summary of my research work in last week (20-23 March). The previous paragraph is an extension on "An Evolutionary Perspective". I have taught business intelligence (BI) for over 10 years. I really believe that BI is not a science (Sun & Strang 2017). BI researchers have not pursued to change BI into a science. However, in my publications on imaginational intelligence (https://www.researchgate.net/publication/323295571_Imaginational_Intelligence_A_ New_Frontier_for_Innovation_Creativity_and_Intelligence_Development_in_the_Ag e_of_Big_Data), I really like to change Imaginational intelligence into a science. What is this science? This is imaginatics. Therefore, the topic "Imaginational intelligence for Robotics and Automation" released here and published in the preprint can be changed into “Imaginatics for Robotics and Automation”. Finally, the relationship between imaginatics and imaginational intelligence is similar to that between informatics (computer science in specific) and artificial intelligence. In such a way, imaginatics and imaginational intelligence are a real challenge to informatics and artificial intelligence. In reverse, informatics and artificial intelligence will enhance the development of imaginatics and imaginational intelligence. Today is the age of artificial intelligence. Tomorrow is the age of imaginatics and imaginational intelligence. If you like this new development, then we can collaborate to develop it and find its applications. Prof. Dr Zhaohao Sun 2018-3-26

9

Imaginational Intelligence in the Age of Big Data Imaginational Intelligence in the Age of Big Data (020417) For This section will discuss

• • • •

Data Science and Imagination Science Imaginational Intelligence and Innovation Applying imaginational intelligence to Enhance AI Applying imaginational intelligence to Enhance Business intelligence (BI)

As the imaginational intelligence’s impacts in the age of big data. 9.1

Data Science and Imagination Science

Data science is an emerging discipline resulted from big data, and big data industry recently (since 2012, EMC 2015). Database, data management, and data warehouse, data mining all are the parents of data science. Now they are parts of data science Data science is a new abstraction in computer science towards marketing computer science and artificial intelligence. Imagination had drawn attention long ago before humans use data. Imagination has been a kind of human intelligence for more than 2000 years Data has been important only since 1946, the birth of modern digital computer Therefore, different from the work of Sridhar Mahadevan (2018) on Imagination Machines (Mahadevan, 2018), imagination intelligence is not related to data, big data and data science, at least to some extent. Only at some later, advanced stage we can integrate these two in the age of big data. 9.2

Imaginational Intelligence and artificial Life

Updated on 30 03 18 Artificial Life was coined by Chris G. Langton in 2004, and defined as “life made by Man rather than by Nature” (Langton, 2004). Artificial life is defined as a field that examines systems related to natural life, its processes, and its evolution, through the use of simulations with computer models, robotics, and biochemistry and develop lifelike systems (or "living" artifacts) (Langton C. G., 2007). It also studies its scientific, technological, artistic, philosophical, technological and social implications of such an accomplishment. If artificial life breakthroughs the obstacle of restriction of the carbon-based life in biology (Langton C. G., 2007) and studies and creates life-like systems, then we can study and create artificial human because human is a life. In fact, , that is, robots and intelligent agents are a kind of artificial human or a kind of automation of natural human. In other words, artificial life has a close relationship with robots and automation as well as artificial intelligence. If natural human has imagination, and imaginational intelligence, then imaginatics aims to automation of artificial imagination. 9.3

Applying imaginational intelligence to Enhance HI

Learning is a relatively simple intelligence of human being, because the core of learning is to imitate what others have done and what others said and what others written. Learning is a basic function of human intelligence to copy the past and store what one learned from the past to one’s head or brain. Sun 02 April 17 However, without any imagination, then learning, search, selection will become purposeless. This is what the author observed in the past 3 decades. For example, Y said that this book B is most famous, classic and had influenced billions of people

in the world in the past a century. X said, who care about it? And continues to play a computer game. This is an extreme example in the age of big data? Therefore, imagination training is more important than imparting of knowledge. Sun, 02 03 18 Neural networks are sometimes used to model the brain of an agent (Russell & Norvig, 2010). Can neural networks be used to model the initial stage of imaginational intelligence?

9.4

Imaginational Intelligence and Innovation

Imagination is the source of any innovation. Imaginational Intelligence: A motive to any Innovation Imaginational Intelligence is a motive to any Innovation Imagination intelligence allows us to take data, information, knowledge and apply them in the frontier of innovation, creativity and intelligence development in the age of big data. Imagination intelligence allows us to take data, information, knowledge and apply them in the frontier of innovation, creativity and intelligence development in the age of big data. 9.5

Applying imaginational intelligence to Enhance AI

Based on the analysis of the previous section, machine learning aims to copy what it stored in the knowledge base, or existing knowledge base, although it is open. For example, if we consider the Web as a knowledge base, then it is open and scalable. The another function of machine learning is similarity-based reasoning or problem solving (Sun, Finnie, & Weber, 2004), even in deep learning. II for AI aims to find how to discover and manage an new idea, new big idea. Based on the analysis of the previous section, machine learning aims to copy what it stored in the knowledge base, or existing knowledge base, although it is open. For example, if we consider the Web as a knowledge base, then it is open and scalable. Another function of machine learning is similarity-based reasoning or problem solving (Sun, Finnie, & Weber, 2004), even in deep learning. Similarity is useful machine learning, non- Similarity similarity-based reasoning is more useful for II. 9.6

Applying imaginational intelligence to Enhance Business Intelligence (BI) • • • • • •

An idea can produce an industry, An idea can produce a billionaire. An idea can disrupt a set of businesses. An idea can revolutionize any business, business intelligence. Please have a look at Facebook, Uber, Airbnb, JD.com, and Alibaba, Huawei, Weixi and QQ

• • 9.7

Therefore, do is easy and simple, whereas imagine is difficult and important for business and BI. We do not care about thinker, we would lose more and more. Imaginational Intelligence for Robotics and Automation

Updated on 190318 As I mentioned early, imaginational intelligence aims to implementation of artificial imagination. I also designed an imaginational robot, imaginationer, who can automatically generate thousands of millions of top research papers and then terminate the research of researchers and let human researchers free from any research. Then humans enter the researcherfree age, like the driver enters the driverless age. If Google is right, then the above-mentioned vision is also right. However, Google only provided a driverless car, while we provide a researcherless age. What Google is doing is only an example or a tool towards this age. This also means that imaginational intelligence has a close relationship with robotics and automation. Further, what we mentioned earlier on imaginationer. This is only an example of applying artificial imagination to researcherless research outcome. In fact, if we can enter researcherfree age in 2050, we can use imaginational intelligence and artificial imagination to produce all the intelligent robots and realize various automation, because we have realized 1. artificial imagination, which initiates all the novel and creative and original ideas for all the human thinking and innovation and invention. 2. any robot and automation is initiated from one idea, on practice and one or more intelligent applications. For item 2, as soon as the imaginationer initiates the required idea, and understands the mentioned practice and the applications. The following engineering process is only an application of a 3D technology. Therefore, we have Robotics and automation = artificial imagination + 3D technology = imaginationer + 3D technology. Just as there are various kinds of robots, bots, intelligent agents in 2018, there will be various kinds of imaginationer to realize all the robotics and automation in 2050. As an animal, a human did not need to work 10000 years ago and only enjoyed the nature and love and friendship. After 2050, the majority of humans will not need to work anymore, only work for 4 hours every workday from Monday to Friday. In the researcherless age, and age of artificial imagination, a human will enjoy the happiness and nature encountered 10000 years ago.

10 Discussion and Implications Updated on 01 04 17, 25-28 02 18, 1-2 03 2018 Imagination intelligence are core foundation of AI (Sun), just as imagination is a core part of human intelligence.

Few have drawn attention to imagination intelligence. Everyone eats foods every day, but few really focus on research of food. Everyone has imaginations every day, but few really try to understand imagination and look into imaginational intelligence and science as well as spectrum of imaginational intelligence. We have mentioned a number of scholarly researches on imaginational intelligence, big data, AI, and BI. In what follows, we will focus on related work and discussion on imaginational intelligence, imagination machines. We searched “Imagination Intelligence” using Google (on 010417) and found About 72,300 results (0.24 seconds). However, we searched “Imaginational Intelligence” using Google (on 010417) and found zero results. We use imaginational intelligence motivated from “emotional intelligence” (Wikipedia, 2017), it has not used “emotion intelligence”. Imagination science and Imagination machines are a new challenge for artificial intelligence (Mahadevan, 2018). Bhardwaz considers imagination as more important than intelligence (Bhardwaz, 2016). He asks where would we have been now? if human have been gifted only with intelligence rather than imagination. Obviously, he differentiates imagination from human intelligence. In this article we consider imagination intelligence as a kind of human intelligence. Intelligence not only include acquiring knowledge (Bhardwaz, 2016), but also imagination. Further, imaginational intelligence usually associates with human behaviors including reading, seeing, listening and writing and doing more generally.

11 Conclusion There are many intelligence representations: artificial intelligence, business intelligence, marketing intelligence, organisational intelligence, emotional intelligence, machine intelligence and multiintelligence, to name a few. We can have a web of intelligence, like a web of science, or a web of knowledge. However, what is most important intelligence for all these intelligences, we argue that it is imaginational intelligence. This paper explored imagination and imaginational intelligence as the core for artificial intelligence and human intelligence. The key idea and motive behind it is that imagination is the source of every creation and innovation. This paper examined the foundations of imaginational intelligence (philosophical, mathematical and computational). It proposed a lifecycle and a spectrum of imaginational intelligence respectively. It also looks at its impacts in the age of big data on data science, business intelligence and artificial intelligence with applications. The approach proposed in the presentation will facilitate the research and development of imaginational intelligence, artificial intelligence and human intelligence and business intelligence as well as imagination science, technology and engineering. I’d like to tell the readers that I started to develop this research paper on imaginational intelligence in March 2017 intensively. However, I have not time to

complete it as a research journal paper, because I have other three research journal papers to be completed and published. Recently (21 Feb 2018), I read the just- in email from CCC and found that Sridhar Mahadevan, Imagination Machines: A New Challenge for Artificial Intelligence today from CCC news, Blue Sky Ideas Conference Track at AAAI-18 from [email protected], 21 Feb 2018. His work pushed me to publish my own preprint or working paper on imaginational Intelligence published at ResearchGate.net before I read his paper. From the published at researchgate.net the readers can know what I believe and what I like to develop roughly and many places are inconsistent. But the minimum idea is that imaginational intelligence is a new frontier for research and development. The interesting questions are as follows 1. Can a robot imagine what a 3 years-old child imagine? AI 2. Can a driverless car imagine what a driver in another traditional car is laughing? - machine intelligence 3. What you imagine when you read an interesting book? have you recorded it timely? -artificial intelligence 4. Have Imaginational Intelligence improve the micro-structure of the human brain?- Brain science. Every human behavior starts from an imagination! Therefore, if we unveil the secret behind imagination, then we can develop all the mentioned intelligences. To this end, in the future work, we will publish a series of peer-reviewed papers on imaginational intelligence based on the proposed spectrum of imaginational intelligence.

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