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AI Hub
Artificial Intelligence Hub (AIH) — RHFC
Artificial Intelligence Hub (AIH)
Our pursuit to provide pragmatic solutions that are sustainable and scalable leaves us with no other option but to tap into opportunities that Artificial Intelligence unveils. This realization has materialized into a cornerstone upon which RHFC has built the Artificial Intelligence Hub (AIH) to engineer solutions that are relevant to both businesses and development organizations.
AIH tackles problems that are at the forefront of our society by applying existing AI algorithms and developing novel techniques for most dire challenges faced. To achieve this, the hub has segmented its undertakings into clusters of specialized focus capable of delivering tailored solutions to end-users. The main clusters are AI for Social Good and AI for Business Growth.
Clusters
AI for Social Good
The AI for Social Good cluster has three main designated ares of specialized focus for the development of AI solutions. These areas of specialized focus have been carefully selected as domains where AI application are quite nascent and face peculiar challenges in developing solutions that can be life changing. Each of these domain is directly related to the Sustainable Development Goals with the benefits of successful application of AI to problems expected to be in exponential folds. We conceptualize, experiment and prototype solutions to problems that are really challenging and worth solving in unlocking sustainable development. A key element of the AI hub is its focus on applications that can be easily adopted even at the local level and communities with minimal training. This informs how our the end-products for the clusters are packaged.
AI for Disaster Risk and Extreme event management (AIDre)
AIDre is a specialized focus of the AI for Social Good cluster where machine learning and deep learning algorithm are used in forecasting hazards such as flooding, drought among others in order to enable the development of preemptive preparedness measures. Most of our work revolve around developing and experimenting with new deep learning architectures that improve prediction precision. We make use of satellite images as well as weather information to train the AI algorithms.
A key element of the algorithm developed is scalability and ability to use and integrate them into end-user applications.
AI for Environmental Resource Management (AIEnv)
AIEnv as a specialized focus of AI for Social Good is being developed as a integral part of RHFC’s smart agriculture / precision farming project at a decentralized local level for communities. The AI algorithm being developed mainly focus on accurate prediction of environmental conditions that needs to be optimized and control to increase environmental resource harnessing. Examples include prediction of soil moisture level, crop water usage, air temperature among others to guide various farm management practices. We are currently developing a working solution for crop diseases prediction and even classifying the type of disease. Computer vision is being employed for such purposes.
The success of AIEnv is expected to be groundbreaking of a large scale and the challenges are equally tough in getting quality data of crops and plant with their appropriate labels to train the AI algorithm. The end-use of AIEnv will lead to local farmers reducing crop pest and disease infestation, increasing farm output, crop insurance based of predicted yields and losses among others. Most of our work are at the conceptualization and experimental stage with strong focus on developing AI solutions for the various use cases.
AI for Climate Change Adaptation and Mitigation (AICam)
Climate change mitigation and adaptation has a specialized focus in the AI for Good cluster where we develop applications for prediction of carbon emissions and adaptation strategies at high resolution. Our machine learning API for prediction of carbon emission at household level is already available for public use. The API can be integrated into applications.
AI for Business Growth
AIH makes available its AI solutions for use in business settings and environment through transfer learning. Businesses and organizations can collaborate with us and use our services through two main pathways as follows.
1. Use of our predefined AI solutions that match their business needs. This is where AIH makes available as open source its models developed as machine learning and deep learning APIs that can be integrated into applications for use. This includes our deep learning API for predicting product review status, forecasting inventory levels, business performance, carbon emission among others.
2. Request for development of tailored AI solutions. AIH receives requests for developing various AI applications for businesses and renders Data-As-Service (DAS) operations that satisfy business needs. Requesting for our service is as simple as contacting us. A meeting is then scheduled to discuss your business problem, we formulate the solution and further discuss it before implementing it. The solution is not predefined but rather developed from scratch to meet your requirements. This implies that we tackle diverse problems as stipulated by our partners to develop applications that include time series forecasting, recommendation engines for large scale transactions, Natural Language Processing related tasks, Computer vision and image processing among others.
The pivotal role of AIH in achieving RHFC’s mission and vision
AIH plays a pivotal role in not only enabling RHFC optimize the impact of its projects but more importantly also raise funding for them. Funds raised from the services delivered to businesses are directly used to support the implementation of various projects. Donations made for the use of our APIs and Data-As-Service are used for such purposes. Anytime you donate or request for our services, you support poor kids with materials for school, menstrual hygiene campaigns and keep the livelihood empowerment lab running to skill women with vocational skills. Your support for AIH trickle down to local people to reduce poverty.