Global Competence Standard
Previously isolated elements of the production chain are linked today, via RFID (radio-frequency identification) chips or mini transponders. 5G is capable of handling 100 times more traffic and provide 10-100 times the speed of 4G. The importance of 5G is significantly higher throughput and lower latency. This will result in produce and products with digital information embedded and able to share data as they move along the distribution and production lines, with the ability to communicate with each other independent of human interference. This data will enable real-time detection and resolution of intractable issues, such as machine degradation, component wear and process snag requiring attention in the factory floor, supply chain and administrative processes, with serious implications for existing competencies.
In a world where devices, equipment, machines, automobiles, things, are able to perceive, reason and intuitively take actions based on domain knowledge inferred from us, provides opportunities for enrichment.
The evolution of standards is such that systems initially compete until one became dominant and was then widely adopted, these are the established critical requirements for development of the new industries we have today.
Skills4Industry is the artificial intelligence Gold Standard framework for capturing heterogeneous data from all connected Internet of Things (IoT), Internet of Services (IoS) and the Internet of Experience (IoE), into a rules-based standards for feeding machine learning with data to establish integrative curricula and pedagogy to aid human and machine learning.
Skills4Industry is the referenced architecture for integrative global competency standards supporting the strategic intent of Industry 4.0 with qualified candidates. Using real-time updated curricula, pedagogy and courseware from 5th grade to Ph.D., at ten levels for humans and four stages for machine learning. Skills4Industry career pathways dashboard and intelligent personal assistants target the most important segments of the population with immediate and long-term benefits to increased labor force participation and productivity.
Skills4Industry represents a universal communal learning culture powered by the Internet of Things (IoT), Internet of Services (IoS) and the Internet of Experience (IoE). This aligns learning at home with school, work, playgrounds, and experiences, thereby establishing a strong foundation for cross-cultural transitions.
Skills4Industry is the Gold Standard for the translation of, for example, teamwork skill learned in Jackson, Mississippi by a young lady (Dee), who transitioned to a corporate software development domain culture in Cambridge, Massachusetts (avoiding the culture of beat the animal first before we can trust him).
Competency is the ability to apply academic knowledge, work skills, relational skills, and domain cultural context to accomplish tasks, at home, school, work, play, and sports, using skillset that is transferable across experiences in global communities. To succeed in this trans-disciplinary connected world individuals need integrative competencies. Integrative competencies must be supported by trans-disciplinary curricula, and pedagogy capturing how individuals both in and out of formal education acquire, practice and reinforce the competencies they will use the rest of their lives. Skills4Industry uses artificial intelligence to capture machine and individual’s paths across the disparate IoT, IoS, and Internet of experience by ensuring the competency learned in one setting is valued in another.
From the onset artificial intelligence had two schools of thought. One school thought it made sense to build machines that reasoned according to rules and logic, to make their inner workings transparent to anyone who cared to examine and manipulate their code.
The other school felt that intelligence would more easily emerge if machines took inspiration from biology, and learned by observing, experiencing and imitating a process through learning-by-doing. This meant that instead of a programmer to write a set of commands to solve a problem, the written program would generate its own algorithm based on example data from behaviors it was designed to imitate.
While rules-based artificial intelligence systems are not commonly used, they are very useful whenever there is need to establish decision boundaries based on benchmarks of performance. It is particularly effective where continuous data from several sources must be processed and filtered for individuals to improve decision making. Today’s state-of-the-art deep-learning approaches train by being fed thousands of examples from the same category of data and are not good at transferring their learning to new problems, new environment, and new functional tasks.
While artificial intelligence machine learning methods require a lot of data that is similar to each category of tasks’ to be performed, integrative competency data is heterogeneous from disparate sources. That is characterized by transitions across different cultural domain contexts and progression from one level or stage to another, with specialized areas of practice which require specialized skills. Other important characteristics include guiding and protecting the machine learning from biased data.
It is noteworthy that when humans are shown an unfamiliar object once, they are able to recognize it, draw it, and understand its component parts, while machine learning requires being fed the same competency data at least five times, amounting to 5X training data.
Getting machines to learn the way humans do could prove crucial for artificial intelligence applications where training themselves on big data isn’t feasible. This is like teaching children how to pick up new concepts from different categories of subject matter every day. Also, humans change their observation through the blink of an eye, while artificial intelligence powered machines do not. Our global competency standards game is to combine these advantages into a single window for learning at home, school, work, play, sports grounds and across communities.
Skills4Industry represents the Gold Standard for a global universal learning standard that is designed to capture data real-time from IoT, IoS and IoE for a trans-disciplinary integrative competence curriculum and pedagogy. In this world, as humans interact with “Things” through work, school, services, and experiences they give up data willingly or unwillingly. It is also envisaged that individuals will transfer their learned skills from one cultural contextual domain to another where established practices recognize certain facets of culture. These facets, for example, communication – include power, trust, and know-how that assign an implicit value to social, professional and political domain specific cultural contexts that impact transitions and productivity.
The need for individuals to be proficient in facets of domain-specific cultural contexts as part of work and social transition skills, make a good argument for a universal global standard for transition skills that is embedded into a global integrative curricula and pedagogy.
Assessment is continuous and real-time, based on observation of learner’s behavior, attentiveness, and emotions, data parameters used to estimate the level of engagement and interest of learners.
We decoupled all learner’s devices from our cloud system, to help learners who may not have consistent 24/7 electricity to charge their devices and learners who may not have the money to purchase high data bandwidth from their local service providers.
Skills4Industry learner’s connected device download the current model (courseware) from the cloud server once, with all learning materials on your device to learn offline without an Internet connection and incurring data charges. Your learning is improved by the data on your connected device, and only a small summary of a learner’s work called focused update is collected by Skills4Industry cloud system whenever you choose to update your information. But remember this is your SkillScore™ encrypted data. Using your device you may establish a learning cohort (group) that include teachers, mentors and other learners across the globe. Your learning data is improved every time you use different relevant tools on your device to make decisions and take actions, including your workout, navigation apps etc. Your recorded data is average with your cohort and others using the same service apps to make decisions and take action. Your data is secured by you in your device, no personal information is collected or update without your permission and expressly for the purpose of learning.
The connected devices personalize the models locally, based on usage. The learning cohort updates are aggregated to form a consensus change to the shared model, after which the procedure is repeated. This method allows for smarter models, lower latency, and less power consumption, all while ensuring privacy. The approach has another immediate benefit; in addition to providing an update to the shared model, the improved model on the device can also be used immediately, powering experiences personalized by the way learners use their devices.