Artificial Intel (AI) or Individual Authentication (IA) #5: Focus Point, differentiation as the key to deep-learning and the organic-machine.

SEE ARTICLES: #1, #2, #3, #4, UX#1, UX#2, Human Factors Articles 1 & 2

The following concludes a five part series comparing the role of Artificial intelligence (#AI) versus Individual Authentication (IA) as a human factor system.

Article one presented data in a basic presentation of the role of #AI (more accurately predictive analytics) versus IA with respect to job seekers and talent acquisition. The primary purpose was to highlight the pros and cons in the debate about the emerging role of #AI in #workforcedevelopment. Part 2 of that article touches on update candidate experience with respect to the relative inaccuracy of recruiters and job posters versus assessing skills and overall job-matching compared to performance of the #AI systems.

Subsequent articles examine an in-depth assessment focused on the role of learning as a human experience. This technical article is a direct follow-up to that Article "On learning" examining the future of AI versus #human factor learning systems and fifteen years of human factor research related to #learning.

Articles 3 and 4 examined the relationship of the evolving UX industry with respect to linking human interaction with machines and the role of understanding, researching, and a need to guide the ethical framework and development of AI systems. The key aspect of these articles - establishing that AI is not as advanced as many believe (or Hollywood has depicted). But, to identify for laymen that it is a rapidly evolving field thus what may seem to be five years out, could in turn be just one year removed from attainment. In these articles AI - defined by machine learning and deep learning - is the capacity for technology to respond to stimulus. Questions raised:

• Is this actually learning?
• What separates man from machine?
• And what measures are needed to ensure man governs machine while machine serves to make man a more functional organism?

The #FOCUS of this final article (5 in a series of 5) is to set a more technical basis for the future ethical framework and conversation about AI.

First and foremost, IT systems are fundamentally flawed. This is more than an argument that "man is flawed; therefore, machine is flawed as a product of man's creation." In some respects, IT systems are not actually business systems designed for functional automation based on input versus output with the ability for constant feedback loops (a job historically reserved for men or women to complete within a working system). Instead, at the fundamental level, IT systems were developed by individuals who thought and acted differently than about 2/3-3/4 of other people. While labels as anti-social or "geeks" were used in prior generations, the reality is these humans created a process-based programs that model systems, but were also entirely built around the common, inherent, and natural thinking styles, personality values, and behavior traits common within the "IT Industry." As many individuals were not the most social, many "IT systems" to this day require robust human "management" to keep them functioning and running - thus achieving an ultimate goal of original innovation for the IT profession: job security.

Before AI can advance it is absolutely imperative we seek to resolve the inherent flaws within IT before creating or adding to these problems. These flaws, however, are man's thinking and bias as much as IT processes design. The purpose of this final article remains the same - to provide a basis for conversations about the ethical guidelines for AI, to ensure respect for all human thinking systems and/or to limit AI design by ensuring human operating systems always remain different than AI process-learning systems. This articles provides a theoretical basis linking man and machine for the purpose of improving profitable experience through learning integrated with living.

FOCUS-POINT: on differentiation theory (Rand, 2019)

Over the course of ten years, Seattle Research Partners, Inc. worked in cooperation with Seattle Pacific University, the Society of HR Management (SHRM), and over 1200 Seattle based organizations to study and understand the role of human capital performance with respect to applied-learning. This included human factors research, UX research, program research (ideation to validation with both technical learning and qualitative human operating psychological research) and organizational performance measurements. Over this time, over 3000 qualitative studies were executed looking at thousands of individual human performance data-points. Measurements were defined by individual standards of well-being, team (or class) standards of well-being, organizational well-being and ultimately led to the development of the prosperity-motive in partnership with research by the Strategic Learning Alliance and RSolutions (Research Solutions).

Through this research one very distinct occurrence was revealed in multiple environments. The primary facet in human capital performance - whether conscious or unconsciously measured - that influenced ROI attainment (prosperity measures or profit-motives) was the concept of a FOCUS-POINT.

A FOCUS-POINT is an immediate point in space and time: it exists between stimulus and response that is well attended to in psychological literature. But the research revealed more complexity with regard to the importance of "FOCUS" as a larger construct, different from the focus-point.

1. First, humans inherently think of the word "FOCUS" and they think "narrow clarity - like looking through a scope."
2. However, this more aptly defines a FOCUS-POINT and not "FOCUS".
3. A FOCUS-POINT, therefore, is a minute gap between present and future; it is the immediate here and now with respect to all complex human factors - seen and unseen - that can be measured, counted and defined within that single mathematical point in time.

A Focus-point is the immediate present moment.

This concept serves as a basis for computer science methodology, physics, psychology, and more; therefore, for it to be revealed in learning is not all that surprising. What is surprising, however, is the fundamental error-rate between FOCUS-POINT and situational clarity.

The point (or space between stimulus and response) conflicting with the appropriate response. In over 300 qualitative and 3000 quantitative studies, the accurate selection of response (based on two choice systematic responses) was often selected incorrectly in controlled studies. In fact, the error rate was a predictable 50%, with learning (depending on type, duration and frequency) this was reduced to an 8% error rate. This leads to the question, what is occurring to cause humans to inherently misapply basic fundamental responses to static situations?

Focus-Point: Problem Solving, upward or downward processing human systems

To investigate this phenomenon a series of studies focused on problem solving approaches, or heuristics, used by humans to interpret situations and apply an analysis driven decision-making framework. From this framework, applied-learning evolved along with applied-research, as methods to aptly measuring, monitoring, and differentiating between two types of problem solving: fear (downward thinking/remuneration), or ego (upward thinking/ideation)

Again, the concept of id, ego, and super-ego is not new in psychology (id being man's carnal instinct of fight versus flight; ego being the balanced measure of the self within the external existence, and super-ego being the inflated ideation of one's existence, the dreamer - to put years of Freudian research simply).

However, the concept that one simple FOCUS-Point that directed the relative response of do nothing, choose correctly, choose incorrectly based on controlled stimulus highlights an absolutely essential ingredient in data-driven, human capital performance initiatives: Differentiation Theory (C) (RSolutions, 2019).

Differentiation theory (C) is the assessment of a situation and application of the appropriate problem-solving approach more than chance (statistical) occurrence. In other words, within any given environment, condition, situation, the first fundamental learning that occurs is to differentiate. This is examined in more depth later.

While the process of learning is well attended to in article two (link above), the concept of a focus-point is the applicable human factor response to treat a situation as a problem to be solved based on immediate reaction to that problem versus long-range goal attainment.

The effective ability to differentiate highlights increased competency, confidence, attainment, satisfaction, well-being and/or what some might call - survival of the fittest. Thus, in regards to the role of machine learning the focus-point is the ability for a machine to be programmed to assess a situation and differentiate the appropriate sequence to follow.

This presents problem one of AI - AI requires a predetermined database for analysis with coded responses for correct and incorrect decision making. Therefore, how does AI work to self-learn when there is no concept of the human "Self" in its ability to determine (correctly) the ego or fear response?

In other words, it is programmed to respond, but humans programming reward-system responses on average are incorrect 20-50% of the time (best-case without specific learning and skill development to improve rates of awareness) - if those humans are involved in continuous learning about problem-solving differentiation methods, otherwise the error rate is considerably higher. Therefore, what measurement or basis determines the appropriateness or control over the proper coding that "teaches" artificial intelligence to "learn" in response to rewards?

Lets consider that humans are presented with three business cases following one day of educational content teaching the basis of fear versus ego problem solving (SRP & SLA, 2011-2015). They review the case in non-time bound environments, but produce a response within a relative time-frame for measurement that allows review of content from the educational exposure but ensures adequate movement toward solving the problem (time-laps approach). In this case, 50% of the individuals analyze the situation incorrectly as measured by the application of the wrong heuristic (problem solving system). They differentiate incorrectly thus limiting the ROI and performance of their recommended approach. On average humans are exposed to the content at varying degrees of depth twice more before improving differentiation rates to 92% collectively.

Those presented with the same business cases - based on a two-choice response option - without prior learning score on average less than 30% accuracy. Keep in mind article three that established clear validity for four domains of human operating systems. Further, the measurement of performance using well-being establishes four domains of distal attainment, and four elements of immediate wellness. In this measurement, accuracy of assessment is of critical importance between differentiating: element or domain; distal or immediate? On average - 33% accuracy (SRP, 2009-2015; SLA, 2015-2017; RSolutions, 2017-2019).

In real-life simulated situations, the differentiation-theory is simply resolved by directed response - think this way to CREATE, think that way to SOLVE.

In other words, situational complexity impacts differentiated accuracy rates, but in the absence of a stimulus the simple response is to create. Interestingly, in the data-era the trend is for humans to learn about and solve problems - the "Why" a business exists. Inherently, these business in post 5 year follow-ups struggled with corporate and culture identity, but were far more expansive in the creation and evolution of job (labor) markets than the alternative approach - to dream.

Those selecting to create a business built around goal-attainment or a dream, a Utopia, based on idealization to create something from nothing, struggled with creation of growth systems, yet yielded far more profitable humanistic measurements than the problem-solving environment. In a sense, they created their own Utopia, whereas the focus-point organization created jobs that were not the Utopia state of the dreamer but scored much more significantly in the tactical measurements of vocational clarity, fiscal security, and social interaction (short-term, focus-point measurements) than dreams score at any given time-frame (SRP & SLA, 2011-2014; RSolutions, 2017)

Thus, in applied situations the outcomes provided a measurement of success necessary to determine proper differentiation. Outcomes of success then requires assessing prosperity-motives (purpose-motives, passion-motives or attainment) versus profitable-performance (problem solving and immediate reward). One yields individual success, the other collective attainment. Collective attainment through focus-point attention became cluttered with complexity and ambiguity, often struggling with continued 50% error rates absent continuous learning within the operational environment. However, it impacted immediate outcomes of short-term well-being measurements at a far greater rate than dreamers created because the collective majority prospered versus the attainment of one idealized self.

In goal attainment, the profitable measures, growth, and creation was far less effective than answering the "WHY" a business exists, instead focused on the "Who" is business there to support (internally and externally). Keep in mind the concept of "Who first, then what strengths and how to execute" was the fundamental basis that resulted in 11 companies out performing the stock market, in the ground breaking study by Harvard scholar Jim Collins.

Amid all of this "clutter" one fundamental occurs: differentiation.

Differentiation: multidimensional processing heuristics past, present, future outcomes assessment

Differentiation theory is simply that within the stimulus and response times allows for the proper application of thinking that yields goal-attainment (distal) versus tactical attainment (short-term reward). The proper decision impacting the relative ROI of a controlled outcome, whereas in situational studies impacting reduced individual performance outcomes but greater collective good. Therefore, at minimum AI needs to be taught - differentiation and for what cause.

Differentiation starts with a point. The FOCUS POINT, defined previously. Because humans are complex and multidimensional, the point directs the following:

• Downward processing: remuneration of emotional (individual) needs, wants, desires; biologically fueled responses.
• Upward processing: cognitive mental creations, dreams, goals, ideals associated with unrealized or untapped potential.
• Reversed FOCUS (20/20 reflection): understanding of the past and comparison of ideal outcomes/desires versus responses to situations versus downward processing wants and needs as it relates to the specific focus-point in time, now.
• Forward FOCUS: the direction one will move that is either upward or downward focused in response to a situation - controlled or uncontrolled. Destiny.

To create a system that controlled for this variable - human error- a system was desired for testing that allowed autonomous free-will, but which also projected forward results at a better rate than 50%. To this extent, a three step goal-attainment model was hinged to a seven step problem solving method. The connection: differentiation, FOCUS (future) and FOCUS POINT (now).

FOCUS on Big Picture creation (strategic vivid image analysis)

Within these studies of learning a rather interesting phenomenon occurred, the creation of a vision statement or big-picture future focus. What was discovered is that when learning occurs in concert with coaching to clarify and crystallize goals, or future ideas, attainment occurred at a rate of 60% less time than expected and/or three-fold initial expectations of measured outcomes.

From a learning perspective this big-picture (strategic/distal) outcome was "focused" to provide clarity when coaching was integrated with learning. In a sense, for machine learning this is a performance improvement would require a visual image for assessment, or a big-picture versus linear coding. Presently, AI can barely articulate single colors, images, or items - versus complex situations. However, as data and technology rapidly advance this will change. Therefore, the ethical discussion should revolve around coaching as a method for real-time situational analysis and response.

Let's consider an example: data is continuously uploaded through multiple devices tracking blood sugar, steps, heart-rate, food and water intake; data is assessed regularly based on optimizing human performance through routine analysis of the working systems. However, without prior established clarity on the FOCUS long-term (strategic/distal) outcomes, AI has no ability to implement new recommendations.

As we evolve AI to FOCUS on a defined understanding, whether through visual optimization or voice-command prompts, the system programmed to monitor focus-point decision-making influences such as, "don't eat more chocolate today" or more precisely, "take five breaths on based on the timing I provide you to balance your blood pressure... start..." that will lead us toward a predefined and desired (health in this example) outcome.

But imagine the possibilities as AI self-driving cars assess real-time situational factors and begin to predict situational risks via something as simple as integrated glasses, wrist watch, shirt-logos to create command prompts - stop! 30 seconds before "final destination" impact occurred. The computers machine to analyze predefined risk data and learn from situations presents a complicated picture of optimized human performance. But, when deployed correctly it opens the question of, can computers be trained to think in real-time, to learn consciousness. If so, how best is this governed to protect humanity as a unique entity?

In comparative examinations, the relative relationship between differentiation and FOCUS clarity of strategic goals is rather similar. On average, based on educated standard for goal-setting, humans tend to create goals that meet less than 50% of what is defined as a "SMART" goal - specific, measured, action-oriented, realistic, and time-bound. Coaching improves clarity as well as differentiation in thinking systems and goal clarity. Ironically, the clarity of big-picture and strategic focus improved goal attainment significantly in multiple studies.

More interestingly is the relative relation of situational affects that seem to impact goal attainment in a manner that the focus-point and focused big-picture outcome continued to manifest. In other words, in reviewing the data from 1200 companies and relative human capital performance depictions (images/outcomes) of success attainment, the more complex and the more vivid, the more likely the achieved outcome. This parallels the relationship of individual attainment of distal outcome measurements versus individual problem-solving (those remaining focused on a focus-point alone).

As such we discovered the unique relationship to link people, culture and technology actually vests with one constant (learning) defined by a the domain of performance (selected direction) and experience (coached clarity).

FOCUS Dynamics- HIGH/LOW success measurement

The ultimate factor in creating people and culture strategies that produce effective outcomes requires, then, a simple FOCUS on the big-picture and desired outcome. The more time spent clarifying the layers of that "onion" the better. Meanwhile, to produce success, the sooner people get to "Solving problems" the more revenue and job creation. When considering the macro ramification of linking people, culture, and technology it is important to remain mindful of the process-focus point decision-making and activity now (improve funding access to improve problem-solving) that is tied to long-range descriptive, vivid, thick, rich, and multidimensional creation.

The best example: AMAZON

An organization that evolved from "selling books; to selling what ever the F*** they wanted," turned digital and real-time delivery system. A true example of where pioneering vision meets constant and ever present learning matched by complex dream ideation by solving everyone's problems, now.

1. Framework to guide differentiation: amazon provides guiding values and they mean it. Every decision is coached/learned and based on what should be done based on those values.
2. Speed of innovation: solve it, now. By focusing on solving problems they have created jobs, in creating jobs they are constantly leveraging learning to create a more complicated big-picture focus - pioneer, they call it. And it extends beyond Amazon to Blue Horizon and development of rockets that will someday lead to space pioneering. Meanwhile, that toothbrush you needed, just arrived at your doorstep less than 12 hours post-purchase (for those commuting in areas other than Seattle, it takes 12 hours minimum to drive to work, work, and drive home - not including time needed to change and eat for the day).
3. Market-Driven Differentiation: products that sell, are built into focus points with supporting products being depicted. In time, when Amazon launches 3-D shopping you will be able to "see" your look before you purchase the clothes just to return them two days later. AI will allow you the ability to create yourself image and Amazon will help you purchase, feel, and sense the products you want - reducing the rate of returns, improving purchase satisfaction, and in theory improving market-competitiveness for tangible products. Everything AI lacks, but that AI can create!

This depiction is provided to help establish the cultural, or global, impact of the "high-low" method. The "high-low" method was created in response to the relationship of FOCUS-Point (a finite, detailed, and limited measurement of the present moment) versus the FOCUS: the big picture outcome-objective.

The goal: to create a system that allows individuals to move forward by "doing something dynamic, no matter how small" but also encouraging problem-solving to create a collective outcome greater than the self.

High-low theory has been tested in individual and collective well-being measurement; prosperity-measurements, in controlled, and revenue restricted environments. Like differentiation, it serves as a constant connection between the immediate FOCUS-POINT versus the measured FOCUS outcomes of future attainment.

It allows for a positive measurement of outcomes that ensure goal-attainment levels are at baseline (constant focus-point) or greater). Recall, individuals seeking to solve problems (downward or upward focus) often experience immediate improvement of short-term well-being measurements specific to capitalization and growth (in organizational contexts) than those focused on the strategic-outcome. Those focused on the strategic outcome often create a much smaller system compared to modern "focus-point" companies seeking to solve problems for clients (today!), but rate significantly higher in multiple short-term and long-term well-being measurements than those regularly entangled in FOCUS-Point method.

AMAZON has mastered both.

The high-low method is how. In other words, the concept is that innovation occurs based not on the speed of technology, but the "speed of patience" (Rand, 2019; 2017); and the more REAL-REACH (Rand, 2019; Rand & Rankin, 2017; Rand & SLA, 2014) your innovation creates- the larger your creation IF you constantly are lifting individuals by helping them differentiate more effectively - shifting between problem-solving and goal-attainment with 92% accuracy, not 50% norms.

The basic premise of the theory is that to create something differentiated to by dynamic - to you and to others - is a success, no matter how small the creation. In this regard the only failure is success itself - the failure to individually achieve and attain while endeavoring to solve a dynamic situation or problem for another. Thus, in that process if you master the ability to expand the "high" baseline to imagined levels, it is because your dynamic solution created a culture that empowered innovation and attainment of dynamic outcomes by others. Such a system, think Amazon, allowing for real-time focus-points while shifting toward larger, more complex, more vivid, more dreamy ideals that are also less clarified, less structured, and less traditional jobs - allowing failures to produce success because the speed of technology analyzes prior data, while providing improved insights to differentiate and refocus toward future attainment.

For individuals, this can be as simple as choosing to read one book a month and conduct a book reading group; in the end, you may not recreate the original Amazon Book Store (but you could) but the dynamic creation may save the life, produce a friend, or create situations that - despite how small - have profound impact on your own life. As such, you do not have to create nor even work at Amazon, but by following this method you will improve your long-term well-being outcome measurements despite the 50% differentiation failure-rate that may occur. Gradually, as the focus-point continues, you will become more specific in your actions, more focused on your next steps, and gradually increase your rate of differentiated performance and problem-solving and naturally improve your ability to achieve big-picture outcomes.

FOCUS POINT, REFOCUSED

To recap, this article identified that time exists on a single-line continuum but that it extends all directions from a single FOCUS-Point (a dot); this includes forward, backward, up, down, left and right. In other words, time - like people - is complex and multidimensional. From a single FOCUS-point we execute daily decisions, decisions that do not improve random chance based on the "Differentiation theory" (Rand, 2019) which posits that people effectively differentiate the proper application of a heuristic (thinking process) to apply in any given situation, including post-learning and without time-bound restrictions - aka instant chance.

The relative depiction of the FOCUS-point, then projecting outward in all directions, expands from a single point to a much larger full "depiction." Think of a 3-D triangle being projected from the tiny Focus-Point outward in a specific direction to create an expanded "image" or "portrait" (FOCUS). In reference to "reflecting back" we might call this 20/20 FOCUSED HIND-SIGHT - where we see the "whole" picture. The same concept is true with fear, ego, and goal-attainment heuristics.

As we consider a point - the apex of a triangle projecting from the focus-point toward the future outcome of focus (despite what direction the projection occurs). This projects in any direction toward the "base" of a triangle, we see the essential framework of learning defined in Article 2: On Learning, of this five-part series. Essentially, in continual learning there is an iterative process moving through exploration, discovery, to development of a vivid and well defined and depicted portrait/image of the desired outcome. But, given we are multidimensional, what is really being presented is a globe - a sphere- because we have the capability of moving multiple directions at one time through thought, through action, and through learning.

Learning was previously defined as the capacity to shape the future; in essence to expand the globe and all that can be encompassed. When we think of Amazon, the book store it used to be was a very small sphere with dynamic results for its loyal customers; Amazon today is a massive, global-influencing learning machine with considerable dynamic influence in learning and shaping the future. Coaching, was discussed as a the "flip side of the coin" with respect to the relationship for continued learning by creating better attainment of outcomes in short-time frames through creating more vivid, thick, rich, and attainable desired outcomes. More importantly, this flip-side of the coin ensures the FOCUS on positive outcomes of future attainment versus getting caught remunerating, over emphasizing ego-functions or fear-driven slowdowns.

In context of this series, the question has been how effective is Artificial Intelligence? What is AI really? What does it actually do? To what extent should an ethical framework be created to govern its creation and instruction? How can we coach AI to function, versus how can AI be training to coach our performance toward positive attainment and positive states of focus on improved change?

In this more technical piece we have considered the following:

1. Differentiation Theory (RSolutions, 2019): Humans differentiate, without learning, at a rate of chance; with one-time learning at 50% correct application of proper thinking models for maximum optimization; with multiple learning exposure (or constant learning) at a rate of 92% application.
2. Continual Learning versus Selective Learning: AI is constantly instructed to learn, and while learning is presently gradual, this will expand exponentially; however, AI is limited to predefined database analysis and image analysis based on reward-patterns. The first limitation is the humans whom defined the parameters and the rewards will be inherently less correct the more complex the situation. The second limitation is that AI is the output of an inherently flawed IT system that was designed around the presence of a human operator. However, the strength in AI remains the programmed basis for constant analysis of data and learning, whereas humans selectively learn.
3. Human Operating Systems/Core Values: In prior articles we examined the relative thinking patterns and human operating systems that define the human race, estimating that the fundamental thinking process of machine learning and AI represents as little as 20% and as much as 50% of the human populations primary or secondary thinking systems.
4. AI versus IA the "organic machine" (Rand, 2016): In prior articles we examined that AI is flawed as is IA (individual authentication) in situational assessment, this is furthered explained more technically in this article as the differentiation theory.
5. High-low Theory (RSolutions, 2019): is a FOCUS output depiction that ensures forward projection of measured output based on well-being, prosperity, and ROI performance that ensures greater return for the investment in future attainment; a process learning defines as the "positive" capacity to shape the future.

Here lies the inherent ethical dilemma. For every 80% positive, there is always an unintended negative; and for every 20% positive there is an 80% effort to negatively impact the same situation. (Yes, YIN/YANG).

Learning is the capacity to shape the future; so much so it is rather frightening in it's ability to project future outcomes and attainment. Key principles of attainment involve the principle of REAL-REACH - a concept that relationships do not occur in a vacuum and as such individuals and groups can collectively work together to shape a positive, or negative, outcome. This principle is off-set by the SPEED OF PATIENCE - the capacity to shape the future either individually or collectively means there is always room for chance, differentiated errors reduce accuracy of prediction to a point. The more time spent near the Focus-point clearly defining, clarifying, and creating a complex depiction of the desired outcome, the more rapidly and expanded the outcome. Amazon, for example, leverages the five points above to capitalize on Human Performance Capability with a system that is constantly learning in all facets while analyzing data and gradually shifting future creations based on its complete commitment to its internal core values as a guiding framework. The ultimate organizational "organic-machine."

But, what happens when a system reaches forward from the focus-point to attain the focused-outcome? From there, it refocuses the large image and depiction to attain very specific new focus-points. It refocuses on problem solving. This capacity is what creates essential conscious thought that is different than machine learning; this may well be the very capacity to shape the future, and redirect the desired outcomes that outpaces machine and keeps the human operating system predictably, unpredictable.

The unpredictable nature of human experience may well be the key that either defines our successful evolution of AI versus allowing AI to more successfully define our future change. Within the essential system of continued learning, the role of coaching with respect to AI evolution and human performance becomes essential due to the concept of REFOCUS.

REFOCUS is derived from Change Theory (Rand, 2006). This theory applied heuristic thinking system research methods (Rand, 2014; see also Rand, Rand, & Rand, 2011; Rand & Rand, 2007; Collins, 2005; Covey, 1991; Moustakas, 1970). In short, Change Theory defines that along any constant focus-point or time continuum there is one predictable essence of all organics: change. From biological, psychological, physiological, astrological and astronomical, and theological theories, one constant defines life - change. This includes organic and inorganic objects because of the capacity for items to learn from the presence and attract toward a FOCUS outcome larger than the present focus-point. For example, from the Big Bang Theory we postulate that from nothing, matter suddenly changed to create something. From that point forward time moved in all direction - forward, past, present, future. Much like the depictions in this section the space is expansive and global, in as much as specific to the presence of a finite object on a finite time continuum.

For example, in human factors love is considered a great debate defining the future evolution of AI. Can a machine be programmed to love? Stenberg (2009) defines biological socio-psychological (evolutionary psychology) construct definition of "love" as being passionate, romantic, and intimate relations. Yet, in 2009 Bandura conducted one of the largest socio-psychological studies of infidelity measuring constructs from global perspectives. In this very real experiment it was demonstrated that there is no statistical difference between rates of infidelity between men versus women. This was defined as accurate in emotional and physical infidelity with an average rate of infidelity of 75%. Ironically, within the same time-frames the average human desired monogamous marriage at a rate of 75%. This raised a fundamental problem: why do humans crave monogamous connection, yet endeavor to cheat and break monogamous relationships at such high rates?

To understand the psychological influence, sociological, biological, and physiological theories were examined based on data of the human experience. This data created the "Change Theory." Specifically, from the point time began - the Big Bang focus-point - all things organic (and inorganic) have evolved with space, wind, elements, and profound and unexplained uniting of atoms has occurred. One constant regardless of all details: change. In other words, just as humans endeavor to become one WHOLE unit, they equally endeavor to create situations that fundamentally change the unity of one. Thus, as humans move toward creating a complex future attained outcome, we will suddenly shift to a new and opposite focus-point.

With the evolution of Artificial Intelligence, there is one constant reality: change.

With the evolution of human factors and learning, with or without respect to the evolution of Artificial Intelligence, there remains one constant reality: change.

To Refocus on solving a problem is man's opportunity to remain ever present in cultivating and developing an outcome greater and more positive than the sum of anyone on contributor, and with AI by his side helping solve changing organic functions a reality of man and machine - the "organic-machine" can emerge- a reality of high-low success far greater than the change and evolution of uncontrolled artificial learning and intelligence that will foster change, but very well could foster a negative outcome perspective due to the inherently limited and flawed fundamental basis of its origination.

In conclusion, the simple solution: create a guiding framework like Amazon to instruct the proper evolution of AI to serve the human experience and condition by improving organic functionality and performance in a manner the reduces man's need to be governed by fear and ego but to instead be governed by synergy and positive cultivation of a brighter, better, more positive future for all.

Basis for Well-Being as performance measurement of aforementioned articles: here and here.

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RAND is an awarded scholar for his proven method of linking people, culture and technology to measure ROI based on systems of prosperity. These systems are defined by constructs of well-being which ensure a focus on the "whole person and community learning and leadership culture" that Rand describes as his personal mission:

Inspira disciplina ducatus

Rand is a Regional White House Fellow and has advised President Trump and bipartisan committees on the policy problem and solution defined in this article.

Dr J Paul Rand, MBA, CPCN is awarded for his work with combat veterans; manages an executive "off-grid" retreat and organizational strategy

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