Using AI in Research
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I ran a recent poll on LinkedIn to ask what people would like a blog post about and the top of the list was, 'Using AI in research'. I now have to write a post about this for the lovely 71 % of you who responded requesting it. I can only offer my own perspective on this and what I have learned so far on this hot topic, I don't claim to be an expert...yet.
There are so many use cases for using AI in research, so I am going to focus on data visualisation which is one of the topics I teach. AI is playing an increasingly significant role in data vis. AI algorithms can automatically generate visualisations from raw data, choosing appropriate chart types, colours, and scales to create visual representations that communicate key insights. This is particularly useful when dealing with large and complex datasets. Tableau's 'Ask Data' feature is one piece of software that has integrated AI for this purpose.
Natural Language Processing (NLP) techniques are used to extract insights from textual data, such as research papers, and AI can analyse and summarise this information and create visualisations to represent the findings. This helps researchers quickly understand and communicate information contained in large volumes of text. Python is a versatile and widely used programming language in academia. It offers libraries like Matplotlib, Seaborn, Plotly, and Bokeh for creating a wide range of static and interactive visualisations.
Image and Video Analysis: In medical research, AI algorithms can analyse medical images like X-rays or MRIs and generate visual heatmaps to highlight areas of interest or concern. I am heading to Aberdeen soon to hear about how AI is being adopted by the NHS ONE Life Sciences Network | AI in Breast Cancer Screening (opportunitynortheast.com).
To summarise, AI is revolutionising the field of data visualisation by automating tasks, enhancing data analysis, and enabling more personalised and interactive experiences. Researchers and organisations are leveraging AI to make data-driven decisions, communicate insights effectively, and unlock the full potential of their data. Data visualisation is only one aspect being impacted by AI. If you have other use cases for AI in research that you want to share, please let me know.
Another couple of webinars coming up that will be of interest around the topic of AI are, The impact of Generative AI on the future of work - career-advice.jobs.ac.uk and Webinar: Generative AI for Learning and Corporate Training (Lindsey Coode) (virtualspeech.com).
Enjoy using AI for your research.