Activities

  1. AI Research – research activities on AI for SDGs – enabled artificial intelligence applications with definition of case studies:

The two initial case studies are SDG3 and SDG4 where two projects are running; X5GON and Malaria Diagnosis. Artificial Intelligence technologies are being developed and “marketed” for educational and healthcare use since decades but a large implementation gap exists. There is a slow adoption rate of technologies in education, because of mismatch between real needs and supply. The lack of use of technologies is particularly affecting the primary and secondary education. There is a need for building the evidence base for more effective learning with technology. This will go hand in hand with tools and processes for collecting, storing, exploring and reasoning on large-scale educational data We will collect “big data” from students’ technology supported learning activities, transforming the data into information and producing, recommending actions aimed at improving learning outcomes.

  1. Prototype development, installation and deployment of state-of-the-art AI tools and technologies:

Prototypes to measure feedback and analysis of contexts, processes and environments. In order to have feasible research data, a real-life and functional ICT based network. AI is expected to contribute to advances in the data collected that will deliver the smart tools and analytical techniques required to generate actionable information from large and diverse datasets.

  1. Dissemination and demonstration

Development of AI platform, joint or single business plan, exploitation of results, investigate service models, clustering and liaison, active community building and standard means of dissemination (presentations, publications, events, meetings, data exchange). The main visibility objectives of this project are to make scientific, industrial and educational communities aware of the project and its results; to disseminate project activities and results in related fields or application sectors. Additionally, to build a community around both AI project results and actively maintain a communication channel to its members. The project will follow a dissemination and community building strategy and will ensure that the technology deployed and data collected and created within the project are available beyond the end of the project. It will actively communicate with communities outside of the project, collect their feedback and involve them in the software development and research activities.

  1. Exchange of research staff

Supervision, training of PhD and other research staff interested in computer science and technical aspects of AI. We recognize the need for researchers to work with large-scale data and we encourage them to develop collaborations with users to facilitate this exchange. We also encourage them to explore alternative routes to access sufficient computational resources (e.g. use of commercial clouds). However, the Chair will not try to imitate industry, and will focus on AI opportunities not yet identified by industry or not yet commercially viable.

  1. Networking

Sharing and promoting best practices, case studies, prototypes and research results. Here we define two types of research projects. The first type (Type A) reflects the research program of the AI Chair and therefore has its focus in AI and for SDG3 online learning for OER and SDG4 healthcare, underpinned by advanced knowledge and context technologies. These research projects will be supervised under the AI Chair. The second category (Type B) is a set of ongoing H2020, Erasmus+ research projects involving University College London in a variety of AI related subjects these include: learning and adaptation; sensory understanding and interaction; reasoning and planning; optimisation of procedures and parameters; autonomy; creativity; and extracting knowledge and predictions from large, diverse digital data. These applications of AI systems are very diverse, ranging from understanding healthcare data to autonomous and adaptive robotic systems, to smart supply chains, video game design and content creation, and will be connected to the AI Chair in order to enhance synergy with UNESCO Chair in Analytics and Data Science in the overall AI research agenda at University College London. The AI Chair is fully involved in these (Type B) research projects. In the first year and part of the second year the research activities (Type A) will build on ongoing H2020 research projects (Type B). At the same time University College London will bid and develop new research projects under the Horizon 2020 research program and the research program of the AI Chair and in collaboration with the other partners listed in c. partnerships/networking (Type A).