Grants and Contributions

Development of robust AI/Machine Learning-based controller for the performance and energy efficiency optimization of Power Amplifier (PA) elements used in Massive MIMO and Active-Phased Array systems.

The Project will support pilot testing of AI strategies, determine target architecture and quantitative technical objectives for implementation of an AI solution to automate and enhance Schema Markup processes, increasing scalability and operational efficiency.

An AI software development project to implement a machine learning-based natural language processing (NLP) system using large language models to streamline the order-taking process for a wholesale eCommerce platform.

The Project will support the development of an innovation plan to explore integrating AI into the Firm’s core business.

This project is to improve upon the performance of an innovative energy price forecasting algorithm that is uses machine learning and deep learning technology to predict the electricity power prices.

This project focuses on building an AI-powered assistant chatbot designed to enhance the efficiency of sales agents by streamlining their workflow and providing quick access to relevant information. The assistant will leverage a large language model (LLM) to act as a virtual assistant, helping sales agents with product details, customer inquiries, and other administrative tasks, allowing them to focus on strategic interactions with customers.

While humans possess innate reasoning capabilities and access to vast reservoirs of knowledge to aid in clarification, intelligent machines face limitations in these areas. Knowledge-aware Compute Efficient Agents (KACE) addresses this challenge by leveraging complex reasoning abilities through the integration of information from diverse knowledge sources.

The project will experimenting with data architecture pipes to train AI systems. The project develop a prototype of a product that will ultimately assists dietitians in better managing the needs of their community's residents.

The Project will support pilot deployment of DeepMSI based AI models into clinical environment, optimize compute architecture that will help speed up the analysis of real time data, improve quantification of DryAMD disease biomarkers including drusen, pigment and GA.

The proposed platform will help organizations rapidly digitalize and mobilize certain business processes, particularly those related to field operations, by leveraging a cloud-based application and a mobile application platform that facilitates quickly designing and developing digital forms through a drag-and-drop mechanism and AI capability that can create forms based on the type of business process. These forms can then be published with one click to generate a new workflow on the Mobile App for users to digitalize business processes. The aim is to complete a minimum viable product (MVP) resulting from this project, which can be continuously enhanced based on market feedback.