Nowadays, Machine Learning is one of the most dynamic fields, as it attracts strong research interest from both industry and academia alike. It is not surprising that a huge amount of funding from government agencies, universities, Tech giants and well-funded startups is currently being allocated exclusively to this field. Reinforcement Learning, one of the three major subfields of Machine Learning, has recently gained a tremendous traction due to the fact that algorithms can run more efficiently. This is mainly due to two reasons, firstly affordable and portable hardware, such as mobile phones, wearables and Internet of Things devices, now has the capacity to run these algorithms and secondly, new methods and models are being proposed that deal with the matter of efficiency from an algorithmic point of view. This proposal is concerned with dealing with open challenges in memory efficiency, while devising and applying such Reinforcement Learning algorithms for embedded systems in the domains of Games, Natural Language Processing and Robotics using Deep Learning models and Bayesian inference, a very powerful framework. Natural Language Processing is a domain of Machine Learning in which the input is given in the form of a text from a natural language that human agents use for everyday communication. It is also a domain that still faces a series of ongoing challenges, as opposed to more saturated domains, such as Computer Vision. One of them is the fact that ground truth is difficult to be decided due to the nature of text in general. Other challenges include the personalized type and tone of the conversation held by the human agents, such as formal, informal, aggressive, polite, etc. Therefore, this proposal deals with all of these matters, mainly in the subdomain of Question Answering systems, also known as chatbots, in a multi-agent setting.

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This whitepaper presents the vision, mission, and overarching role of the GUT-AI Foundation. The Foundation aims to promote the research, development and eventually the deployment of user-friendly, human-centred and developer-friendly Artificial Intelligence (AI) systems for the betterment of humanity through an Ecosystem of Concepts and Implementations (ECI). The Foundation recognizes the potential of AI to revolutionize numerous industries, ranging from Healthcare and Education to Financial Services and Self-Driving Cars. However, the development of such AI systems poses significant challenges and impediments, such as multiple single points of failure, lack of interoperability, and lack of user adoption. Therefore, this whitepaper proposes a multidimensional approach to promote a whole ecosystem that has the ability to overcome such challenges. Primarily, the Foundation will encourage research into AI systems that are accessible, intuitive, and ready-to-use. The research will focus on proposing AI system architectures that meet the needs of the users, while they address their pain points in order to enhance both the User Experience (UX) and Developer Experience (DX). Furthermore, the Foundation will focus on promoting the adoption of best practices and Optional Open Standards for AI development and deployment that is automated, cost-effective, and scalable. For instance, these best practices will include the use of modular architectures, microservices, and containerization. By adopting these practices, the foundation aims to enable interoperability and reuse of AI components. In addition, the Foundation envisions creating a marketplace, which will enable buyers to discover and adopt AI Solutions that meet their needs and preferences. The marketplace will also provide sellers with opportunities to showcase their AI Solutions, reach out to potential buyers and receive feedback from them. Finally, the foundation will leverage emerging technologies such as Blockchain and Decentralized Autonomous Organizations (DAOs) to enhance the transparency, security, and trustworthiness of AI systems, while incentivizing innovation and collaboration among humans.