Cobots in the 4th Industrial Revolution
Cobots are collaborative robots that are based on human-robot interaction to complement human actions. (Google is an example of a Cobot.)
In business, there are two possible uses:
- As part of a backward integration, to sustain decision processes.
- As part of a forward integration, to transform decisions into actions.
The business application of Cobots became possible due to the development of the fundamentals-based AI and the binary actions that ensure the generation of results. Cobots are not based on empirical rules; they use functional rules to build empirical solutions.
There are 4 basic application fields for Cobots in business:
- Industrial application
- Marketing application
- Managerial application
- Operational application
The first version of these Cobots, 5-click Strategy, was developed in 2012 and was followed by the development of interactive expert systems that were upgraded to Cobots to simplify and ensure the functionality of business processes.
The Structure of Cobots
The development of industrial cobots opened a new stage in human-robot collaboration in business management. It provided a technological solution for object driven organization processes.
Cobots became possible due to the IoT that provided the resources to introduce them in work and business processes. It became necessary in organizations who decided to enter the adaptability and customer orientation fostered by the 4th industrial revolution.
Cobots provide a safe framework to generate value in adaptive environments. They are now the next standard of the object driven organization that became necessary to manage this stage. It is also needed in all types of telework processes including telemedicine.
The collaborative approach, the functionalist approach, the use of binary actions, and the installation of business objects made cobots the solution for the next stage.
The purpose of Cobots is to be able to generate client centered processes that ensure results by being customer oriented through the assurance of added value. This purpose is achieved through the support of both the efficacy of work processes and their efficiency.
The wide context that establishes the framework of cobots is the new stage introduced by the 4th industrial revolution, which fosters the adaptability of business processes. While the 3rd industrial revolution introduced robots, the 4IR introduced adaptive automation sustained by the IoT.
The 4IR was transformed into actions by the customer orientation of businesses, which requires entering businesses from the point of view of the needs of customers.
This customer orientation implies knowing the structural, urgent and latent needs of customers and requires managing each segment as a universe to ensure customer value and generate bestsellers that fulfill the needs of both the customers and the company.
The fundamentals of the ontogenetic map of the context allow defining the binary actions that ensure the functionality of cobots, which includes on the one hand the adaptive customer orientation and on the other hand the adaptive value generation. The development of these two actions makes cobots necessary.
The final binary actions that make business cobots functional imply, on the one hand, the support of the efficacy of people and, on the other hand, the support of the efficiency of the processes to sustain the functionality of the client centered management.
The Ontogenetic Map of Cobots
The final purpose of cobots is to support the assurance of results. They provide the complements that make the achievement of results reliable. This requires two different functionalities. On the one hand, a backward integration by providing knowledge and resources and, on the other hand, a forward integration that allows introducing adaptive automated actions to ensure the operation of processes.
This is sustained by the customer orientation, which requires managing the true needs of customers. This customer orientation requires managing the segmented fundamentals of the business of customers. This requires managing each segment as an independent universe.
The results assurance is sustained by the assurance of the generation of added value, which implies introducing a quality assurance system. This quality assurance system is materialized in binary actions and the use of business objects.
The Maximal Strategy
The maximal strategy of cobots is given by their support to the efficacy of people. This is triggered by the need of generating adaptive automated actions that ensure both automation and customer orientation.
This maximal strategy becomes possible when the cobots ensure the functionality of the processes. Such functionality requires using functional design to manage the fundamentals of the specific processes that are being managed and the knowledge of the technical and operational aspects. This allows managing both the know-why and the know-how of business processes to ensure their functionality.
The internal catalyst that ensures the support of the efficacy is the design and use of binary actions that allow developing maximal and minimum strategy actions to manage each business function to ensure the achievement of its purpose.
When these actions have been designed and installed, they can be introduced into the adaptive automation processes. This requires using fundamentals-based AI to sustain the adaptive processes using indicators and predictors.
Cobots need to provide secure results. Therefore, their knowledge must be reliable, which means that the output of actions is predictable. In the cases in which there is no possibility of having fully reliable knowledge, the use of big-data management is a palliative that can be used to build knowledge.
The Minimum Strategy
The minimum strategy, which defines actions that do not depend on the environment, is based on having fully reliable functional knowledge. Probabilistic knowledge does not allow building cobots. They need to be fully reliable to support the efficiency and efficacy of processes. Probabilistic knowledge can be used, and under certain conditions must be used, as a complementary system but not as a collaborative robot.
When the functional knowledge is available, the necessary minimum strategy is focused on the operational aspects, managing the know-how of processes. When this has been defined, cobots need to include business objects, which are encapsulated processes that are based on the concept of the function they manage to generate value, and have a quality assurance system to ensure results.
Therefore, the efficiency support is based on the use of functional knowledge, which allows managing the concepts and fundamentals of processes to build operational processes -including business objects, and catalysts in the case of adaptive functions- to generate reliable added value to the customers.
An Introduction to Robots (*)
The function of robots is to work and generate value. That is why they are homologous to human work. Each generation of robots develops work in a different way.
In relation to their capacities, robots have been typified according to the “generation” they belong to.
In essential terms, robots develop works that have a limited flexibility to ensure the efficiency of each activity.
Robots need to be 100% reliable. A robot that manages the landing of an airplane needs to ensure that all its rigid devices do not depend on a learning process that might occur when the airplane is landing.
Classification of Robots
First generation robots
They are robots with predefined operations that can only be reprogrammed by changing their hardware.
A first-generation robot has rigid parameters and quality assurance systems based on its exclusion when the operational limits are exceeded.
Second generation robots
They are robots that produce predefined operations, but they can be reprogrammed changing their software.
They have the possibility of having quality assurance systems to generate operational alerts to adjust their functionality based on the feedback of their system and not only their operation. Second generation robots are “slaves” with inflexible programs to obtain fixed results based on the knowledge introduced by human programming.
Third generation robots
They are robots that have a learning capacity based on the measurement of the results produced. Based on this feedback, they regulate the energy they use according to the power needed for each activity.
Learning is based on the restricted context they work with. Their learning systems will become improved based on the evolution of information technology.
Fourth generation robots
They are those robots that can integrate in an interdependent way with the context they work in.
They have the capacity to learn from their own operational experience and from the feedback coming from other interrelated systems.
They develop a learning process based on the feedback to generate simulations until the knowledge is integrated in their system to generate a reliable operation.
(*) Excerpt of the book on RobotThinking, 2011, of Peter Belohlavek and Diego Belohlavek.
Types of Cobots
Based on their functionality, there are four types of cobots:
- Operational Cobots
- Knowledge Cobots
- Efficiency Cobots
- Efficacy Cobots
Operational cobots are designed to sustain specific operational action in business processes. They are homologous to 1st generation robots.
They have the necessary functional knowledge that deals with the know-how of processes and they use operational objects and entropy inhibitors to sustain their activity. Examples: CRM/CDP cobots, risk management cobots, monitoring cobots, etc.
Knowledge cobots are designed to sustain management processes of any kind to ensure the accuracy of decisions. They are homologous to 2nd generation robots.
They include quality assurance processes to confirm that the proper knowledge is used. They use cognitive objects and inhibiting objects to ensure the functionality of their support. Examples: knowledge management cobots, business intelligence cobots, talent management cobots, functional design cobots, etc.
Efficiency cobots are designed to complement and support the efficiency of processes. They are homologous to the 3rd generation of robots. They are based on introducing adaptability in the automated processes and on the use of binary actions to ensure results. They use systemic objects and catalyzing objects to ensure their functionality. Examples: project management cobots, performance management cobots, marketing cobots, selling cobots, etc.
Efficacy cobots are designed to sustain the efficacy by providing knowledge to sustain decisions, adaptive automation to make them work, and quality assurance to sustain the functionality. They are homologous to the 4th generation of robots. They learn from the feedback based on indicators and predictors that were predefined based on the ontogenetic map of the functionality of the process they sustain. They use all types of objects to sustain efficacy. Examples: contingency room cobots, avant garde groups cobots, strategy building cobots, innovation marketing cobots, etc.
About the Existence of Mental Cobots
The mental conscious concepts people have, are the cobots that simplify the emulation of reality in the human decision and action processes. These concepts provide the functional knowledge to adapt to the environment.
The discovery of behavioral objects explained how concepts drive human conscious actions, integrating the data available in the long-term memory, involving the semantic, episodic, and procedural memory. It explained that the deeper the level of conceptualization of individuals is, the higher the level of abstraction capacity that is needed and the better their capacity to emulate a reality.
The concept an individual has defines the purpose the individual wants to achieve. The absence of concepts generates meaningless actions or inactions. Concepts have different depth levels according to the conceptualization capacity of an individual. These levels are:
- The idea of the concept
- The operational concept
- The functional concept
- The essential concept
Concepts are the behavioral objects that drive human conscious actions; the level of depth of these objects defines the actions that are driven. The lack of concepts makes the information stored in mind work as independent meaningless entities.
The Basics for Unicist Cobot Building
The Functionalist Approach
The building of cobots is based on the use of the functionalist approach that integrates the know-how and the know-why of processes.
This approach made businesses reasonable, understandable, and predictable. It empowers their customer orientation, adaptability, and growth, increasing their shareholder value, customer value and stakeholder value.
The unicist functionalist approach is based on managing this triadic structure of the concepts of things.
Functional design is focused on the value of processes, while operational design is focused on the processes themselves. Functional design includes operational design but not vice versa.
The unicist functional design is based on the use of the ontogenetic maps that define the functionality of adaptive entities whatever their kind.
The output of any functional design is the definition of the operational design that includes the use of synchronized binary actions, the use of catalysts and the inclusion of business objects to increase productivity and quality.
The Use of the Rules of Unicist Logic
The Unicist Logic is a synthetic logic that emulates the ontogenetic intelligence of nature and its maximal strategies to grow and minimum strategies to survive.
It was developed to validate the triadic functionality of natural and artificial complex adaptive systems and to design and build binary actions to manage them.
The unicist logic was developed to manage consciously the unified field of complex adaptive systems. Conscious reasoning allows developing fallacy-free decisions and actions to ensure the results of what is intented to be achieved.
Unicist Artificial Intelligence
Cobots are based on the use of the rules of the unicist logic that are included in the Unicist AI. The Unicist AI is based on the integration of data-based AI and fundamentals-based AI.
On the one hand, the categories defined by fundamentals-based AI provide the autonomous universes that are needed to minimize the subjective biases of data-based AI.
On the other hand, data-based AI allows quantifying the specific structure of fundamentals-based AI to establish the aspects of the categories and segments of entities to build solutions.
Binary Actions are the Catalysts of Cobots
Binary actions are two synchronized actions that expand businesses while they ensure their results. They were developed to manage the evolution of adaptive environments by managing actions to install maximal strategies to grow and minimum strategies to ensure results. Any adaptive system and environment (living being or artificial construction) is driven by binary actions. Some examples will help to grasp the idea:
- The active function and the energy conservation function of the intelligence of a tree drive its growth and survival.
- Lift and propulsion make airplanes take-off and fly.
- The cover and the back cover define the functionality of the packaging of a book.
- The music and the lyrics of a song define its aesthetics.
Universally known examples of binary actions are:
- Efficacy + Efficiency = Effectiveness
- Empathy + Sympathy = Personal influence
- Participation + Power = Leadership
- Marketing + Sales = New customers
- Productivity + Quality = Value generation
The use of binary actions to manage adaptive environments is a must.
Binary Actions also include the use of Catalysts
Catalysts are process accelerators that diminish the efforts needed to produce results.
The discovery of the structure of the functionality of biological and behavioral catalysts allowed developing business catalysts, which are necessary to accelerate processes and drive the evolution of businesses.
Binary Actions include the use of Business Objects
Unicist business objects are encapsulated adaptive systems that produce predefined results that can be inserted in work processes to increase productivity and quality and to save energy.
To imagine an object please consider an automatic pilot in an airplane. It can be considered a “paradigmatic” object. From a functional point of view there are different types of objects:
- Driving Objects
To drive processes
- Catalyzing Objects
To accelerate processes
- Entropy Inhibiting Objects
To inhibit the entropy of business processes
- Inhibiting Objects
To inhibit dysfunctional events in a business
- Gravitational Objects
To influence the results of processes
The level of complexity of cobots varies according to their functionality. The simplest ones are the operational cobots, followed by the knowledge cobots and the efficiency cobots and the most complex ones are the efficacy cobots.
The development of Cobots is based on the functional knowledge of the specific business, its environment and the work processes involved. This knowledge is synthesized in the ontogenetic maps of each of the functions and the operational and technical knowledge of the business and work processes.