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We have recently established a series of working groups to enable and promote the use of AI to support innovation that we are calling collectively the AI Ecosystem. One of these groups,��the AI Leadership Group has recently overseen the development of our AI guiding principles - the��.

We are seeking to provide a safe and secure environment for generative AI for �鶹��madou that can be used by staff and students. The principles are designed to balance both the regulation of AI (including generative AI) and innovation, supporting �鶹��madou’s positive attitude to AI, and being world leaders in AI research.

Please take the time to read and consider our guiding principles and use them to guide your engagement with AI.
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�鶹��madou’s AI Ecosystem is being fostered to support and guide the ethical, responsible and innovative use of AI within the university, with a focus on collaboration among our staff rather than strict regulation. The AI Leadership Group has established 3 working groups and a technology taskforce, agreed on guiding principles, and endorsed the full rollout of Microsoft Copilot with Commercial Data Protection for staff, emphasizing security and the opportunity to learn about generative AI.�� More technologies will be available broadly during the coming months as we continue to explore proof of concepts on what works best for �鶹��madou.

The AI Ecosystem provides a structure to convene��relevant experts broadly across �鶹��madou, as AI touches so many parts of our University.��
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To fully enable the AI Ecosystem, a small, cross-disciplinary fusion team will be established to pilot and support opportunities effectively.
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The AI working groups have recently endorsed the following key definitions for Artificial Intelligence.

‘AI System’ or ‘AI Tool’��–��A machine-based system that, for explicit or implicit objectives, infers, from the input it receives, how to generate outputs such as predictions, content, recommendations or decisions that can influence physical or virtual environments. Different AI systems vary in their levels of autonomy and adaptiveness after deployment

‘Non-operational AI’��– systems do not use a live environment for their source data. Most frequently, they produce analysis and insight from historical data.

‘Operational AI’��– are those that have a real-world effect. The purpose is to generate an action, either prompting a human to act, or the system acting by itself. Operational AI systems often work in real time (or near real time) using a live environment for their source data.

‘Responsible Officer’��– These include the Officer who is responsible for: use of the AI insights / decisions; the outcomes from the project; the technical performance of the AI system; data governance.

‘Artificial intelligence agents’ or ‘Intelligent agents’��– Systems that��can interact with their environment, collect data, and use the data to perform self-determined tasks to meet predetermined goals without the direct intervention of humans or others.

‘Robotic process automation’��–��Technology that automates��routine, rule-based and repetitive tasks found in business processes using automation software such as extracting data, filling in forms and/or moving files.

Algorithm��– A set of instructions that guide a computer in performing specific tasks or solving problems. Algorithms can range from simple tasks like sending reminders to complex problem-solving, which is crucial in AI and ML.

Generative AI��–An emerging field within AI that creates new content such as text, images, voice, video, and code by learning from data patterns.

Machine Learning (ML)��–is a subset of AI that allows computers to autonomously learn and improve without being explicitly programmed. ML algorithms are trained on data to make predictions or decisions.

Natural Language Processing (NLP)��– A field of artificial intelligence (AI) that deals with the ability of computer systems to understand and generate human language. NLP algorithms are used to analyse text, comprehend, converse with users and perform tasks like language translation, sentiment analysis, and question answering.

Computer Vision (CV)��– Empowers computers to 'see' and comprehend the visual world, analysing images and videos like humans. CV algorithms analyse images and videos for tasks like object detection, face recognition, and self-driving cars.

Deep learning��– A machine learning technique that uses interconnected layers of “neurons” to learn and understand patterns in data, especially in tasks like image recognition and speech synthesis.

Large Language Model (LLM)��– A subset of Gen AI model that specialises in generating human-like text. Unlike Generative AI, which encompasses a broad category of AI techniques and models designed to generate new content, such as text, images, audio, or video.

Neural Networks��– Computer models inspired by the human brain's structure. These interconnected artificial neurons, organised in layers, learn from data to make predictions in machine learning, underpinning deep learning.

AI in research definitions:

‘AI-enabled research’��- Researchers utilising existing artificial intelligence systems for the purpose of answering a (non-AI) research question.

‘AI Development research’��- Research into the development of artificial intelligence systems and tools.��
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