Artificial Intelligence

Artificial intelligence from the onset 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.

Skills4Industry taxonomy uses benchmarks of performance to establish rules for determining competent individuals, with a set of logic to determine movement vertical from levels 1 to 10, with ten stages of progression within each level. Horizontally, Skills4Industry is determined by different industry, sectors, trade, and domain (ISTD), each domain represent functional groups whose competencies cut across the industry, trade, and domain, for example, administrative secretary, which unlike clerical work its not a competency level. This logical arrangement of Skills4Industry allows upward or downward movement depending on where the competency to close a skill gap is located, including associated work tools.

Several technological considerations, including data size, processing speed, security, bandwidth, and energy consumption resulted in the choice of a distributed machine learning that uses individual devices for processing courseware learning data.

This learning data create a summary for an update with Skills4Industry cloud data at intervals established by users.

In July 2018, Qualcomm announced that the company will be launching devices with embedded artificial intelligence this year. Moving data from cloud computers to machines and dramatically increasing the importance of structured data. On-device, machine learning requires rules-based machine learning frameworks.

The European Union’s Industry Standard 4.0 is requiring technology companies to provide users of artificial intelligence powered machines and robotics an explanation of how the decisions they made were reached. This means that software engineers must build machines and apps that are able to explain their reasoning, with ways for humans to tell them what to do.

A world where devices, machines, automobiles, and things are able to perceive, reason, and take intuitive actions based on awareness of a situation will simplify and enrich our lives. Skills4Industry design architecture and frameworks were born out of extensive research and implemented with on-device artificial intelligence, Industry Standard 4.0 and the Internet of Things (IoT) in mind.

Depending on the user environment, Skills4Industry may use wearable devices to measure individuals engagement, emotions, and behavior for scoring purposes that are meant to ensure a ‘skin in the game’ representing sacrifice in the course of performing a task for a reward. Accuracy in measuring all types of performances at home, school, work, play, and sports ground based on a set of rules and logic is important in solving real-world problems with artificial intelligence.

Our continued research in weighted values of the componential application of work skills, work tools, academic and relational competencies will exponentially increase the application of Skills4Industry as the clearinghouse in the measurement of effort and reward that Blockchain and Ethereum have earned their successes.

  • Skills4Industry framework meets the requirements of the following:
  • Industry Standard 4.0
  • Caffe / Caffe2
  • TensorFlow
  • Facebook Open Source Deep Learning Framework
  • NPE Framework