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AI Guidelines

Artificial Intelligence Guidelines for PGR students

The University is engaging with the emergence of Artificial Intelligence (AI) tools and adapting to the changing environment to ensure that you are equipped with the knowledge and skills to safely use these tools We have produced some initial guidance regarding the emergence of generative artificial intelligence (in the form apps like Chat GPT (open AI/Microsoft) and Bard (Google)) and how and when it can be used safely, and what to watch out for.

This guidance IS NOT intended to address existing tools such as Grammarly and Google Translate. 

Understanding

Prior to utilising large language models and other AI tools, it is crucial to develop a solid comprehension of fundamental AI concepts, including machine learning, deep learning, neural networks, and data pre-processing. 

Limitations

AI tools are not perfect and produce inaccurate or biased results. It is important to be aware of these limitations and to use AI tools in conjunction with other research methods. 

Transparency

If you use AI tools in your research, it is important to be transparent about this in your research papers and presentations. You should also cite the AI tools that you used and provide a link to the documentation for these tools. 

Objectives

Clearly define your research problem and objectives. This will help you determine which AI tools and techniques are most suitable for your study. Consult with your supervisor or mentors to refine your research question and ensure it aligns with the available AI resources.

Ethical Considerations

AI tools can be used for a variety of purposes. It is important to use AI tools ethically and to avoid using them in ways that could harm others. Ensure that your research adheres to ethical guidelines and be mindful of GDPR laws and Intellectual Property. Avoid inputting personal information or data when using AI tools. Respect privacy, obtain necessary permissions, and handle sensitive data responsibly. Avoid inputting personal information or data when using AI tools. Be aware of biases and potential ethical implications of AI algorithms. Consider the impact of your research and strive for fairness, transparency, and accountability.  

Developments

AI is a rapidly evolving field, with new algorithms, techniques, and tools emerging frequently. Stay updated with the latest research papers, conferences, and workshops. Engage with the AI community through forums, online groups, and social media platforms to stay informed about advancements and best practices. This will help you to ensure that you are using the most up-to-date AI tools and that you are aware of the latest ethical considerations. 

Choosing the right tool 

There are already a variety of AI tools and LLMs to assist you with your research and learning. Here are some specific AI tools that postgraduate students may find useful for their research and learning: 

Scikit-learn is a free and open-source machine learning library for Python. It is a popular tool for a variety of machine learning tasks, such as classification, regression, and clustering. 

TensorFlow is an open-source software library for numerical computation using data flow graphs. It is used for machine learning, data science, and artificial intelligence applications. 

PyTorch is an open-source machine learning library based on the Torch library. It is used for a variety of machine learning tasks, such as computer vision, natural language processing, and robotics. 

CNTK is an open-source software library for deep learning. It is used for a variety of deep learning tasks, such as image classification, natural language processing, and speech recognition. 

Caffe is an open-source software framework for deep learning. It is used for a variety of deep learning tasks, such as image classification, natural language processing, and object detection. 

Apache MXNet is an open-source deep learning framework. It is used for a variety of deep learning tasks, such as image classification, natural language processing, and machine translation. 

Keras is an open-source neural network library. It is used for a variety of machine learning tasks, such as image classification, natural language processing, and time series analysis. 

OpenNN is an open-source neural network library. It is used for a variety of machine learning tasks, such as classification, regression, and clustering.  

These are just a few of the many AI tools that are available. The best tool for a particular task will depend on the specific needs of the researcher. 

 Seek Collaboration and Feedback: AI is a rapidly evolving field, collaboration with peers, mentors, and experts in the field can greatly enhance your understanding of AI tools and techniques and enable us to share best practice.