Research @ Alien Inn Space primary focus is on Artificial Intelligence in: the Space Industrial Complex, Small Business Capability enhancement, and What do we teach the children now that the Machine is proving to be smarter than the Human.

Research Project Plans already written, or underway include:

  1. Time, and why is it so important to the system administrator in space;
  2. A 1/5th Scale look at Mining on the Moon; and
  3. A self-healing, self-aware, and unmanned computing system. What should we teaching IT? (Allen G Miller, University of Canberra 2020 – Research Methods)

Artificial Intelligence:

A self-healing, self-aware, and unmanned computing system. What should we teach IT

Large knowledge gaps are presenting themselves in the Information Communications Technology (ICT) management fields. The industry is struggling to find potential employees with the necessary technical problem-solving skills.  The struggle increases dramatically with requirements relating to career exposures to large ICT systems in a management context. Adding to that pressure is a fast growing ICT trend in Artificial Intelligence (AI) (Sharkey, 2012). The purpose of this thesis to identify the current requirements of ICT management and decompose these into meaningful concepts for early childhood education for a Child Machine (Leiber, 2001, pp. 83-94)management.

This thesis looks at the education gaps that are presenting themselves in the Information Communications Technology (ICT) management field. The ICT industry is finding that more university educated employees do not have the necessary technical problem-solving skills as problem managers, while the technicians themselves do not have the underpinning theory or exposures to large systems in a management context.  The aim is to identify the requirements of current ICT management and decompose these into meaningful concepts for early childhood education.

ICT capability management skills.

The technical management skills required for high performance computers are becoming harder to find in the workforce.  As the world of robotics and super intelligence computers increase, education standards appear to be as rigorous or robust to provide the correct learning outcomes.

Through qualitative interviews and a quantitative survey, I aim to identify the education program appropriate for the management of a Child Machine (Leiber, 2001, pp. 83-94), as described by Alan Turing with his question “can a machine think?” (Wikipedia, 2019a). 

The generated skills matrix will inform training requirements applied to future studies or programs of work focused on large scale deployments of Artificial Intelligence (AI) (Sharkey, 2012), and ultimately augment business functions such as ICT support.

The skills shortage. 

Current education programs, or perhaps the delivery of them, do not appear to provide the long-term skills required in the community, in particular, ICT career paths. The education of new computer system administrators appears to fail with regards to general problem solving and end user communication skills.

Using a qualitative approach by interviewing a qualified Psychologist and an early childhood teacher, I aim to identify the appropriate criteria for current early childhood education programs in support of problem-based learning for the management of Child Machines.


The general hypothesis of this thesis is that there are no standards for long term career education programs in support of Australian ICT industries that focus on early childhood learning concepts.


This thesis makes the assumption that:

  1. There will be follow-on projects based on five-year blocks of child education programs;
  2. The follow-on projects will adjust focus based on current ICT standards at the time of study; and 
  3. The Australian education standards change over time requiring ongoing review for future projects.

Delimitations and Limitations:

This thesis aims to identify the recommended education program for a human child between the age of 0-5 as they apply to Child Machine management careers.  This thesis is delimited to:

  1. The education programs based on current 2019 Australian education standards, to highlight any appropriate gains appropriate to long term ICT career programs of work focused on Child Machine Management;
  2. The current 2019 Australian ICT employment standards as described by a sample Australian organization; and
  3. The Child Machine, as described by Alan Turing (Wikipedia, 2019a).

Architectural Guidance

Although “Implementing C4ISR Architecture Framework – An Australian Case Study” (Paul Prekop, 2001) is focused on the Defence Intelligence and Science & Technology capabilities, I will use the C4ISR framework to derive the business process models based on:

  1. The requirements brought about by the education programs of a child aged between 0 and 5; and
  2. The ICT management requirements of a child machine. 

Prekop & Kingston describe six architectural products that are required to adequately describe a system. 

  • OV-1 High Level Operational Concept Graphic;
  • OV-2 Operational Node Connectivity Description;
  • OV-3 Operational Information Exchange Matrix;
  • OV-4 Command Relationship Chart;
  • OV-5 Activity Model, and
  • OV-7 Logical Data Model.

They also derived a development appropriate covering five stages that are used in the generation of the architectural products.

  • Stage 1. Describe the context and goal of the architecture;
  • Stage 2. Represent the activities to be performed;
  • Stage 3. Represent the force elements involved;
  • Stage 4. Represent information needs and information flows, and
  • Stage 5. Create the integrated dictionary, and finalise the architecture overview.

Significance, Innovation, and Practical Value

Teaching a machine to play chess is one thing.  Teaching a machine to problem solve and self-educate is a new level of achievement. A practical application for these machines could be applied to helpdesk environments where staff turnover is high, motivation is low, and the problem-solving abilities are not often found, but desperately required by industry. 

I believe that if personality and Intelligence Quotient (IQ) tests (Wikipedia, 2019b), can identify an early aptitude for computer management, then an early aged education program would provide a path to ‘how to manage a Child Machine’. This would also benefit future career paths of the child and provide increased benefits to the ICT industry.

One of my primary concerns focuses on the human education programs and how they are being utilised in the ICT industry.  Well educated and intelligent employees cost money and mundane business processes, such as changing passwords all day are hardly rewarding to some.  If the education programs identify a personality type or a particular IQ level and apply them at very early age, the resultant match would shift the focus from highly educated machines and dumb operators, to a well-matched team of human and machine built on the strength of both.

Literature Review

Existing Knowledge

The Child Machine. 

The first of the documents reviewed was “Making the Human Computer Marriage Work” (Carr, 1998). Carr identified two types of system architectures, Expert and Hypertext systems. Where an Expert System contains a rule-based decision cycle to achieve outcomes, each outcome returns to the user to the index. The subsequent hypertext system is closer to a human’s non-linear thoughts and efforts.  The Hypertext system starts with a topic and follows mental process to provide outcomes but does not return to the index (Carr, 1998).  He also identifies that such systems have an impact on the users.  At a high level, the user either has no job, or is shifted to some other position as a result of the computer system replacing the business process the user carried out.  This observation is followed up with a new system architecture, The Master System (Carr, 1998) , which merges the expert and hypertext systems to give the end user a more meaningful role in the systems use. “It will prevent the development of a highly skilled elite designing systems for a massively unskilled workforce” (Carr, 1998, p. 74).Zhaohui Wu, Zhejiang University, recants Alan Turing’s ideas of a Child Machine back in the 1950’s “…where a well-designed child machine could handle non-monotonic reasoning and introspection. Newell and Simon thought that AI should play chess and prove complex theorems.” (Wu, 2013, p. 29).

Let us skip to the current date in computer system architectures (25-08-2019) where a team created a computer system that has an unbelievable title of “unbeatable at chess”.

“The repurposed AI, which has repeatedly beaten the world’s best Go players as AlphaGo, has been generalised so that it can now learn other games. It took just four hours to learn the rules to chess before beating the world champion chess program, Stockfish 8, in a 100-game match up.” (Gibbs, 2017, p. web).

The World of Machine Learning (Wikipedia, 2019c).  This Chess System described by Gibbs in 2017 was taught how to play chess and not given any previous chess games as reference data to begin with. Let us think that about that comment for a moment.  We designed and built a computer system to win chess, a game of strategy and hypothetical combat between two opponents, no human training, just the rules of the game.  This prompts my first sub-problem’s core question, “what education processes would we use to educate Turing’s Child Machine? (Sterrett, 2012, p. 1)” and indeed, who would manage this chess machine now that it knows how to play?

The Child Human.  Other studies focus on the human learning how to interact with AI (John N. Carbone, 2017) given in an AI System Design case study; “humans cannot become machines, but machines can become more human-like” (Joshi & Klein, 2018, p. 1); and using a problem-based learning (PBL) approach to teach AI (Cheong, 2008).  I believe Cheon’s PBL approach could be applied to the management of a child machine as Turing describes them. Raj Reddy (Carnegie Mellon University) outlines a roadmap to educate Turing’s Child Machines but limits them to the age of three (Wu, 2013).

Early Childhood Research Quarterly have quite a topical collection of studies focused on early childhood education programs, and the use of computers at an early age.  “One of the foundational assumptions in the education industry is that children learn through play” (Isikoglu, 2003, p. 27)while Janet Currie focused on the costs of education programs themselves and identifies that the Early Start programs had positive results (Currie, 2001). Both, and many other non-reviewed studies agree with an increased focus and use of ‘how’ to use a computer.  The Early Start programs described by Currie indicate that focused attention groups benefit and achieve more later in their life. 

Theoretical Framework

The theoretical framework for this thesis will follow a mixed methods approach using a small sample group of subject matter experts and the ICT industry specialists interviews specifically targeting:

  1. IQ testing and personality types through qualitative analysis;
  2. Australian Early Childhood Education standards through qualitative analysis;
  3. ICT industry employment standards through qualitative analysis;
  4. Gap analysis through mixed methods from both qualitative and quantitative analysis; and
  5. Recommendations on the appropriate long-term education program.

Literature Gap

I don’t believe Carr’s preventative goal was achieved that well.  With 19 years in the computer system administration fields, I see regular opportunities for the massively unskilled workforce to learn, but their method of learning (Johns, 2019) needs to be identified in the first instance.  I have same thoughts for the education processes required for an Artificial Intelligence (AI) as Alan Turing’s describes his Child Machine.

I don’t agree with Wu’s sample size of “up to three-year old”.  Age five is when children, in general, are observably more educated and ready to unleash on the preschool and primary school education capabilities, therefore Wu’s thesis on Human Level AI (HLAI) would need to be extended to include the full range from 0 to 5 years of age.  Subsequent research would benefit from following the education blocks, 0-5; 5-10; 10-15; and 15-20.


Carr, C. (1998). making the human computer relationship work. Training and Development Journal, 65-74.

Cheong, F. (2008). Using a Problem-Based Learning Approach to Teach an Intelligent Systems Course. Journal of Information Technology Education.

Currie, J. (2001). Early Childhood Education Programs. Journal ofEconomic Perspectives—Volume 15, Number 2—Spring 2001—Pages 213–238, 213–238.

Gibbs, S. (2017, 7 Dec 2017). AlphaZero AI beats champion chess program after teaching itself in four hours Retrieved from

Isikoglu, N. (2003). New Toys for Young Children: Integration of Computer Technology into Early Childhood Education. The Turkish Online Journal of Educational Technology – TOJET October 2003 ISSN: 1303-6521 volume 2 Issue 4 Article 5.


Johns, S. (2019). Four Learning Styles. Retrieved from

Joshi, M., & Klein, J. R. (2018). The Future of Human Workers.

Leiber, J. (2001). Turing and the fragility and insubstantiality of evolutionary explanations: A puzzle about the unity of Alan Turing’s work with some larger implications. Philosophical Psychology, 14(1), 83-94. doi:10.1080/09515080120033553

Sharkey, N. (2012, 21 June 2012). Alan Turing: The experiment that shaped artificial intelligence. Retrieved from

Sterrett, S. G. (2012). Bringing Up Turing’s ‘Child-Machine’ (revised). Retrieved from

Wikipedia. (2019a). Alen Turing. Retrieved from

Wikipedia. (2019b). Intelligence Quotient. Retrieved from

Wikipedia. (2019c, 7 September 2019). Machine Learning Retrieved from

Wu, Z. (2013). the convergence of machine and biological intelligence.