Integrating Artificial Intelligence into Rural Communities
Over a quarter of the world’s population is trapped in the informal sectors in rural communities, constituting the ‘forgotten half’ with elemental skills for subsistence food gathering. These populations are highly exposed to the rapidly changing environment, expansion of large-scale agriculture, poor sanitation, and disease. They are like their displaced K-nearest neighbor in western economies and America, with intermediate skills for repetitive work, whose employability has remained stubbornly difficult to solve.
At Skills4Industry level 1 lies rules-based rural skills designed to power the transition of elemental skills to primary skills, whose purpose is to formalize informal sectors at this level. The predictive models use a complete catalog of natural cash crops, trees, and minerals available with real-time geographic, distance and time domain context global data. With the intent of advancing learning, based on specifications valued by global markets for different applications.
Skills4Industry intelligent personal assistants integrate the global food supply chain into the development of skills for production, handling, and storage of low tech rural products and services. Using real-time location information to improve cultivation, soil management and implement environmental impact assessment for practice adjustments. Including, the application of data to water resource and irrigation management, operational safety, types of equipment and efficiency. Soil nutrition, storage and organic manures good application practice procedures. Understanding ground-water and nitrates/NVZs and the protection of water quality, as well as the relationship between agricultural sources of phosphate and eutrophication. Knowledge of sources of energy, farm waste, pollution management and recycling opportunities, analysis of practical, environmental and financial implications of a good agricultural practice.
Skills4Industry rural skills use complete map of herbal plants, handling, and administration for natural medicine, auto mechanics, weaving, coloring, hides and skins preparation, and sewing to mention a few, to advance learning and skills development. Skills4Industry rural skills level 1 domain learning data comes with endless templates for new trades and skills materials to support new programs and projects in rural communities.
Skills4Industry intelligent personal assistants formalize the informal sectors by giving power back to rural farmers, artisans, and entrepreneurs, by bypassing organized local cooperatives whose value to farmers is reduced information search costs. The information they skew to improve their bottom line.
The world is a complex place where events do not unfold in exactly the same way if repeated several times, but they serve as knowledge for the wisdom to do things differently to achieve better results. We know that impossible events have a probability zero and rare events do not have probability zero. As a result, any event that is possible must have a greater than zero probability of occurrence (Discounting).
In our judgments of what and which programs should be implemented to create a good future for human society. For example Skills4Industry level 1 programs: work-based learning to develop career competencies for high-quality jobs later in life; rural skills to formalize informal work activities; and intermediate skills for continued employment. The stakes are simply too high to get these programs wrong, data science and artificial intelligence provide prescriptive, unambiguous solutions.
If an image classifier fails to land a spacecraft successfully, because the predictive model training images were shut against a cloudy sky background for a system that has learned to link the sky with its destination, we have only lost the carrier and a few humans. However, when researchers tell us that young people are unable to get quality jobs later in life because they lack connections, an event with a probability of zero for poor parents. The impact on education, skills and development programs is generational.
As experts on human capabilities prediction, Adams Smith and other development economists provided how the system should work. Since data-driven models are based on observed correlations between input parameters and outcome variables our wisdom must impact the application of these theories to real-life problems through questions revealing answers to the future we desire. The application of these theories within statistical and probabilistic modeling is important because data science is about things that happen for understandable reasons.
At Skills4Industry we prefer models that are descriptive that is those providing us some insight into why they are making their decisions the way they do. Models that make decisions like the occupations in Industry, Sector, Trade, and Domain (ISTD). That is used to decide the amount of performance required to make progress from one level to another and predict the most suitable pathway to a career for an individual. A further example includes when a Skills4Industry model denies an employee a move from senior officer to supervisor sales position because the officer scored less than 30 points in overall performance by revealing that she/he made less than 5000 business contacts in the past two years in their relational score.
…. The future of learning