The Cognitive Engine: Unlocking True Performance Thinking
1. WRITTEN BY
Gary Morais, Bottom Line Results, LLC
Dennis Cagan, Caganco Incorporated
Chris Spivey, Spivey & Co. LLC
The Cognitive Engine:
a white paper
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TECHNOLOGY MEETS BRAIN SCIENCE
What is a cognitive engine and what is the right approach to developing one? The
chemistry or electronics of the mind are the source of thinking and the originating
cause that generates human behavior and actions. Manifestations of the brain’s
thought patterns, actions and behaviors comprise a human being’s performance in
any endeavor. We call this “performance thinking” In a workplace environment, this
equates to competency, contributions, performance, skills, and outcomes – which
may be either good or bad. To use a digital circuitry analogy, this performance
thinking – think of it as wiring or algorithms, are the original creation of the
impulses that generate the other performance behaviors and results. To continue, this
performance thinking, and its resulting behaviors, can be impacted and modified by
various factors over time.
Often, we see that demographics are used as a substitute for performance thinking.
What are demographics? Demographics are simply statistical data relating to the
population and particular groups within it. Demographics do not represent actions, or
behaviors, or performance, or thought processes. The statistical information includes
things like age, gender, geography, education levels, ethnicity, and perhaps even the
statistical proportion of people that have performed certain actions or exhibited
specific observable behaviors. But, demographics are not the actions or behaviors, or
their cause or root. Performance thinking is.
LOOKING WHERE THE LIGHT IS SHINING
One of today’s biggest buzzwords is Big Data – pun intended. Big Data and Data
Analytics go hand-in-hand. Collect it, and analyze it. We should not for a moment
confuse cognitive technology with the observation of actions and behavior, and
demographic statistics, massively compiled is a big database, and then analyzed and
dissected for measurable correlations and causality.
We propose that what most computer scientists and software vendors term a
cognitive engine is the massive aggregation, compiling, cross referencing, and
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correlation of observable actions and demographic data sets. In this approach, we
find “streetlight” bias.
According to Quote Investigator (www.quoteinvestigator.com) there is a brilliant
comical allegory that depicts the biases inherent in many types of scientific research.
The allegory is illustrated by a tale whose publication in slightly different forms
traces back to at least 1924, if not earlier.
A police officer sees a drunken man intently searching the ground
near a lamppost and asks him the goal of his quest. The inebriate
replies that he is looking for his car keys, and the officer helps for
a few minutes without success then he asks whether the man is
certain that he dropped the keys near the lamppost.
“No,” is the reply, “I lost the keys somewhere across the street.”
“Why look here?” asks the surprised and irritated officer. “The
light is much better here,” the intoxicated man responds with
aplomb.
Some sound scientific research is shaped by the need to perform and verify
replicable measurements. But these measurements do not always accurately reflect
the phenomenon that is being investigated. The term “streetlight effect” is sometimes
used to name this form of observational bias. Because the data is available, and
statistical correlations can be drawn, the industry is arguably looking for the keys
under the “Big Data streetlight”.
TRADITIONAL APPROACH IN REVERSE
In our extensive research, traditional investigative approaches are not truly
cognitive; rather it is specifically investigative research compiling multi-source data
sets, analytics, actions, outcomes and demographical data, in the hope of finding
ways to automate tasks that would mimic the perceptual and cognitive skills of
humans.
Many have approached talent engagement by assessing or researching styles,
personality, preferences, talent themes, or by using competencies and values-based
approaches, which fall short because these all rely on static identifiable data, and not
alterable coefficient of human behavior. They make it difficult to measure a
sustainable ROI. Their failure is based on a lack of baseline scientific performance
algorithms, and proven dynamic clinical tools, which can allow for the modification
of existing algorithmic brain functions, reforming a brain’s formulas to accelerate
measurable positive changes in productivity.
True cognitive science is much deeper than collecting external observable behavioral
data sets and correlation research.
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The secret is ‘between the ears’, the human brain science, real cognitive thinking. It
is the discovery of the ‘root source algorithms’ that provide what we will term
performance thinking. These are the driving force that impacts all aspects of human
behavior and workforce productivity, from leadership, sales, customer service,
meeting goals, following processes, procedures, working effectively in teams, and
building a high performance culture.
An algorithm is defined as a problem-solving procedure, or in the case of a computer
or brain, a program. The brain’s programs/algorithms that comprise thinking -
performance thinking, are the root source of all behavior and decisions in a human’s
workplace functions – therefore, the lifeblood of any business. By identifying and
emulating these, you have the true cognitive brain ‘files’, which we call performance
drivers. With these in hand, and coded into the proper software platform, you can
then predict outcomes, execution performance, and the results of people’s
engagement.
ARTIFICIAL INTELLIGENCE VS. COGNITIVE BRAIN
PERFORMANCE DRIVERS
Without debating the similarities or differences between artificial intelligence and a
cognitive engine, hopefully we can agree that the integration of the cognitive
formulas behind what drives behavior - performance drivers, has the potential to
contribute vital value added data and predictive analytics into current and new
technologies and applications. After over twenty years of research in this field – not
where the light is shining, but where impulses originate – we believe that as they are
identified and emulated, these cognitive brain algorithms will be the breakthrough
force for today's new disruptive technology companies. This leap forward in
integrating true cognitive performance thinking could be the ‘holy grail’ for future
applications and solutions.
THE REAL OPPORTUNITIES FOR BUSINESS
In a recent article in the Deloitte Review, Issue 16, titled Cognitive technologies:
The real opportunities for business, David Schatsky, Craig Muraskin, & Ragu
Gurumurthy noted: "We found that applications of cognitive technologies fall into
three main categories: product, process, or insight." They go on to summarize
“…Product applications can provide end-customer benefits, while Process
applications can automate or improve operations, while Insight applications uncover
insights that can inform operational and strategic decisions across an organization."
Given that performance thinking is the root source of all human productivity, the
brain’s performance drivers (performance thinking algorithms) are certainly the
source for new Product applications, or services, which deliver greater efficiencies
and added value productivity. Imagine being able to predict the productivity of an
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entire organization or being able to sustainably replicate the top performers in
organizations such as project or sales teams.
With regard to Process applications, applying these performance drivers to automate
or improve organizational development, will also allow transformational business
operations by getting to the ‘root source’ for solving diverse business engagement
challenges, and it will build sustainable high performance cultures faster and with
greater efficiencies.
With regard to Insight applications, applying performance drivers to a company’s
talent audit management technology solution can capture the critical talent
engagement data to uncover insights that can inform and shape operational and
strategic decisions, structures, and initiatives across an entire organization.
THE NEW FUTURE OF COGNITIVE TECHNOLOGIES
Cognitive technologies with embedded performance thinking algorithms will be the
new products in the field of artificial intelligence. One can imagine being able to
positively change human behavior and engagement that traditionally only humans
used to be able to do. Or being able to identify top performing talent before you open
a resume, or mining an entire organizations’ or nations performance capabilities.
Wise and appropriate investments in embedding true cognitive brain algorithms (as
opposed to data analytics) can dramatically improve current enterprise applications
and future innovative technologies.
Imagine actually automating the mind’s own thinking patterns to build future
innovative and disruptive technologies.
AUTHORS
Gary Morais is a psychotherapist and the inventor of Performance Drivers™
(Performance Thinking Algorithms) and the 10Rule® business performance
transformation suite of cloud-based software product (www.10Rule.com). Morais is
the managing partner of Bottom Line Results, LLC. and a business strategist
applying the 10Rule’s Hybrid Cognitive Technology strategy and solutions for
company CEO’s and President’s for over 25 years.
Dennis Cagan is a noted high-technology industry figure. He has founded or co-
founded over a dozen different companies, and has served as CEO of both public
and private companies, a consultant, venture investor, mentor and professional board
member – 53 fiduciary corporate boards. In 2011 he was elected to the IT Hall of
Fame. He has authored numerous articles in magazines including NACD
Directorship, Directors & Boards, Private Company Director, and Family Business.
