2022 Updates
Energy Sector
Over the past 12 months, these four projects have made significant progress in their development.
Project “ninakaw” has continued to work on its anti-theft technology for emerging markets. The team has further refined the system’s IoT-enabled technology to allow it to accurately gather and transmit consumption data. They have also continued to improve their AI and ML models to better identify unusual consumption patterns and detect informal connections to the power grid. This has helped utility companies in emerging markets to prevent energy theft and reduce waste. Additionally, the team has worked on optimizing the system’s remote management system to improve efficiency and ensure reliable power supply.
Project “auxilium” has made strides in preventing power outages in developing countries through predictive asset failure. The team has improved the system’s ability to analyze data from various IoT sensors and apply ML and DL algorithms to identify potential asset failures. This has allowed utility companies to better plan repairs and prevent unplanned outages. Additionally, the team has worked on optimizing the system for seasonal demand to reduce maintenance costs and improve energy reliability for mission-critical services.
Project “influunt” has focused on optimizing geothermal plant operations through deep learning. The team has further refined the system’s IoT-enabled devices to improve data collection and standardization for future use and predictions. They have also continued to work on using AI and ML techniques to detect cracks and other potential defects in plant equipment. This has allowed for predictive maintenance to prevent future emergency situations and improve plant operations. Additionally, the team has worked on developing more sophisticated ML algorithms to automate decisions and recommendations based on the data patterns from plant processes.
Project “naustrdomi” has continued to work on grid usage modeling using advanced mathematical models such as expectation-maximization algorithms. The team has improved the system’s ability to gather data through IoT technology and refine predictions over time. This has allowed for better identification and prediction of grid usage patterns. Additionally, the team has worked on incorporating other ML and DL techniques to further improve the accuracy of their predictive models.
Overall, these four projects have made significant progress over the past 12 months in developing advanced technologies for energy management and optimization. Their use of IoT technology, AI, ML, and DL algorithms has allowed for more accurate data collection, predictive analysis, and automation of decision-making processes. This has led to improved energy efficiency, reliability, and cost savings for utility companies and improved service for their customers.
Training and Development
Over the past 12 months, the 10 training programs have made progress in terms of their popularity and the number of courses offered. Among them, Cybernetics has been the most popular program with the highest demand for its courses.
Cybernetics is a transdisciplinary approach to explore regulatory and purposive systems, and its core concept is circular causality or feedback. Cybernetics has its origins in the intersection of several fields in the 1940s, and since then, it has become even broader in scope to include work in domains such as design, family therapy, management and organization, pedagogy, sociology, and the creative arts.
Cybernetics is concerned with exploring such processes embodied in environmental, technological, biological, cognitive, and social systems. The program offers four courses, including Introductory Cybernetics, Intermediate Cybernetics, Advanced Cybernetics, and Application of Cybernetics workshop. The Application of Cybernetics workshop, which is 5-days long, provides participants with the opportunity to apply cybernetic concepts to practical activities such as designing, learning, managing, and conversation.
Appreciative Inquiry is another popular program, and it focuses on the positive by seeking to engage stakeholders in self-determined change. This program offers three courses, including Introductory Appreciative Inquiry, Intermediate Appreciative Inquiry, and Advanced Appreciative Inquiry. It also has an Application of Appreciative Inquiry workshop that helps participants generate new ideas and models for how to organize.
Design Thinking, Systems Thinking, Business Value, Value Realization, Measuring ROI and ROE, Why Startups Fail, Red Teaming, and Human Performance Technology are other programs that offer introductory, intermediate, and advanced courses. Design Thinking offers a “bottom-ups” Application of Design Thinking workshop, while Systems Thinking offers a “top-down” Application of Systems Thinking workshop. Business Value and Value Realization offer courses that teach participants how to build a business case based on business value and realize the business value of a project, respectively.
Measuring ROI and ROE offers a course that teaches participants how to measure the ROI (investment) and ROE (effort) of a project. Why Startups Fail offers a 4-day course that teaches participants the 16 reasons why startups fail. Red Teaming offers an introductory, intermediate, and advanced course, as well as an Application of Red Teaming workshop that helps organizations to overcome cognitive errors such as group-think and confirmation bias.
Finally, Human Performance Technology offers an introductory, intermediate, and advanced course, as well as an Application of Human Performance Technology workshop that stresses a rigorous analysis of requirements at the societal, organizational process, and individual levels to identify the causes for performance gaps and provide appropriate interventions to improve and sustain performance.
In conclusion, the 10 training programs have made progress in terms of the popularity of their courses and the number of courses offered. Cybernetics has been the most popular program due to its broad scope, transdisciplinary approach, and the relevance of its concepts to practical activities such as designing, learning, managing, and conversation. Appreciative Inquiry, Red Teaming, and Human Performance Technology have also been popular programs due to their focus on positive change, critical thinking, and performance improvement, respectively.
Marine Sector
Over the past 12 months, Project "liquet" has made significant progress towards its goal of developing AI and ML-based systems for analyzing and predicting the health and wellness of coral reef ecosystems. The project team has been working on developing a self-contained, solar-powered hardware module that can stream remote video data to scientists. This module will be coupled with the analytics platform to provide invaluable datasets and insight into the reef ecosystem. The project team has also been working on developing new tools for analyzing population, migration patterns, breeding and growth patterns, species identification, and desegregation analysis. These tools are designed to help scientists better understand the health and wellness of coral reef ecosystems and take action to preserve them.
Over the past year, Project "pistris" has made significant strides in developing AI-enabled auto-pilot drone technology for patrolling beaches where shark nets are deployed. The goal of the project is to identify approaching sharks or signs of other large marine animal activity and deploy repellent technologies to prevent entanglement. The project team has been working on improving the cybernetic modeling used to predict the behavior of different marine species and the most appropriate repellent technologies to use. They have also been developing new technologies for detecting and assessing marine animal activity, including thermal imaging and sonar.
Project "quisquiliaq" has been making significant progress over the past year towards developing predictive refuse and debris mapping. The project team has been using automated drones to collect visual data on beaches and river systems and analyzing it using ML and cybernetic models. They have been developing new tools for identifying the type, density, deposit rates, volume, time-at-site, decomposition rate, and redistribution patterns of refuse and debris. The goal of the project is to provide detailed predictive mapping of refuse and debris patterns to help reduce the impact of pollution on the world's coastlines and river systems.
Over the past year, Project "exesa" has been making significant progress towards developing a Deep Learning system for predicting coastline and estuary erosion patterns. The project team has been using remote sensing to gather data over time and building predictive models that take into account many elements of the natural and man-made systems. They have been using topographical, geological, oceanographic, meteorologic, and climatological data sources to build detailed predictive models utilizing the power of AI, ML, and DL. The goal of the project is to help identify and predict coastline and estuary erosion patterns and take action to mitigate their impact on coastal populations and the economy.
Overall, these four projects have made significant progress over the past year in developing AI and ML-based systems for analyzing and predicting the health and wellness of coral reef ecosystems, preventing entanglement of endangered large sea animals, predicting refuse and debris mapping, and predicting coastline and estuary erosion patterns. These projects have the potential to make a significant impact on the world's coastal ecosystems and the people who rely on them.
Mining Sector
Over the past 12 months, all four projects in the mining exploration sector have made significant progress in developing innovative solutions for improved mineral exploration.
Project "remotus" has seen significant advancements in aerial imaging and drone technology, enabling startups to develop innovative exploration solutions and minimize risks involved with accessing dangerous and unstable terrain. These developments have also improved surveying techniques and geological prospecting, making exploration more efficient and accurate.
Project "inveniet" has focused on developing AI-based algorithms and models that significantly improve mining exploration. With the support of machine learning methods, the team has been building technology to identify mineral deposits in undeveloped locations. They have also been using drones to collect geophysical data, resulting in a high ROI based on the low cost of implementation and high return on effort. The use of automated drilling rigs has eliminated the need for labor, improving productivity and worker safety in mineral exploration operations.
Project "doctina" has utilized remote sensing data streams and deep learning algorithms to assist in mining exploration and potential environmental impacts. They have been developing an image analysis platform to aid in categorizing and segmenting geological imagery, topographical data, and video streams. With the automation brought by Project "doctina," geologists, geographers, and ecologists have been able to spend more time interpreting the results rather than working with the data.
On the other hand, two projects have faced negative news that held back their progress. Project "remotus" and Project "inveniet" have both experienced setbacks due to the negative environmental impact of mining operations. The environmental effects of mining are a significant concern, and this has caused these two projects to slow down as they try to develop solutions that factor in the ecological cost of mining operations. Despite this, the teams have remained committed to developing exploration solutions that minimize environmental risks.
Lastly, Project "exemplum" has focused on creating AI and machine learning models that are the basis for advanced mining exploration simulations by combining geological, geographical, and geophysical data. The use of machine learning has reduced the labor force prerequisite for performing complex data analysis, enabling efficient exploitation of resources, both human and physical. The project has been generating mineral deposit leads and enabling hot spots for exploration, which is a significant achievement.
Overall, the past 12 months have seen significant progress in the mining exploration sector, with projects like "remotus," "inveniet," "doctina," and "exemplum" making significant advancements in technology and innovation.
While some projects have faced setbacks due to environmental concerns, the teams have remained committed to developing solutions that minimize the impact of mining operations on the environment. The use of AI and machine learning has been a significant driver in the sector's progress, enabling efficient resource exploitation and improving worker safety.