On Wednesday 4th June, SODA hosted a conference on AI in Data & Analytics for public sector employees across Suffolk the wider area. Click below to see the slides from each of our sessions. Coming soon: Videos from these sessions will soon be available.
Sessions
- Session 1 – Michaela Breilmann: ‘Introduction’ (see the slides)
- Session 2 – David Robinson: ‘The Current AI Landscape’ (see the slides) – This talk will cover what I’m seeing develop in AI, the tensions this might cause for me as a digital leader, the difficulties in making a strategy and how I see the next 12 months pan out.
- Session 3 – Spyros Samothrakis: ‘AI: Past, Present and Future’ (see the slides) – This presentation explores the past, present, and future of AI, starting from the historical topics that fall under the AI umbrella, conceived from the logic-heavy initiatives of the 1956 Dartmouth Summer Research Project, through the different paths, twists, and turns that have brought us to the dominance of large language models. We will explore current opportunities and limitations and identify natural fits for out-of-the-box products, as well as opportunities for development. We will close with how I expect current limitations of AI systems to be resolved and what lies in the future, focusing on Moravec’s paradox.
- Session 4 – Natacha Bines: ‘A little spark goes a long way: How AI helps me get things done’ (see the slides) – Ever wondered how AI can supercharge productivity and creativity in your work? In this session, I’ll share how I use my AI sidekick, Spark (aka ChatGPT), to streamline tasks, boost efficiency, and make complex challenges more manageable. From automating repetitive processes to helping with data analysis, my journey with AI is all about turning time-consuming tasks into quick wins. Join me for an engaging workshop where I’ll show you how AI can become your go-to partner for tackling everything from research to creative brainstorming. Let’s spark some inspiration together and explore the power of AI in your daily work! Disclaimer: Content may have been written by Spark!
- Session 5 – Elinor Bally: ‘Research Made Simple: How Claude has Helped Transform Academic Literature into Actionable Insights’ (see the slides) – Join us for “Research Made Simple,” where we’ll explore the do’s and don’ts of using AI for rapid literature reviews. Through a practical demonstration, you’ll see how Claude can support the transformation of complex academic papers into clear, practical insights.
- Session 6 – David Robinson: ‘Data and AI Integration – a way through the next 12 months’ (see the slides) – This session will focus on how we glue things together from a data and AI perspective. We’ll look at how we’re planning to tackle the next 12 months in Barnsley by pushing out certain ‘development’ activities to services, training in data, and using AI in the data layer; fleshing out some use cases that we’re working towards.
- Session 7 – Amy Ryan & Michael Gray: ‘AI Skills Assessment for Suffolk – How SCC and the Business Board are supporting the Suffolk economy to capitalise on AI’ (see the slides) – Norfolk and Suffolk County Council are collaborating with Code Institute in conducting an AI Skills Assessment for the region. This research will provide us with invaluable insight to our regions AI capabilities. During our session we will introduce you to the AI Maturity Framework, share initial findings and invite you to consider our next steps to support the Suffolk economy to capitalise on AI.
- Session 8 – Rebecca Allen: ‘Boost your productivity with Microsoft Copilot in Office 365’ (see the slides) – Join us for a 30-minute interactive session exploring how Microsoft 365 Copilot can transform the way you work across Word, Excel, Outlook, PowerPoint, and Teams. We’ll cover some real-life examples of how Copilot can summarise content, generate insights, and save time spent on everyday tasks. Note: You don’t need access to Copilot to take part in this session. However, to use many of the features demonstrated, a Microsoft 365 Copilot licence will be required.
- Session 9 – Steve Parsons: ‘Making machines moral: Ethics for the AI era’ (see the slides) – As technology becomes increasingly sophisticated, ethical decision-making is becoming no longer an exclusively human endeavour. In this talk, we’ll look at how we can begin to think about ethics as analysts, and how we can make our machine learning models and analytical products ethical by design.
- Session 10 – Daniel Beaumont: ‘Using human intelligence to overcome artificial errors: Managing the emerging security risks to our data’ (see the slides) – This presentation will go beyond some of the media headlines and place AI into the wider context of information security, and outline what risk assessment and management structures and processes organisations need to have in place to look at the issue holistically. It will suggest what is distinctive with AI/machine learning and the emerging threats we face when we develop, deploy and use new systems. The focus throughout will be on the AI security challenges for those working in data insight, analysis and management.
- Session 11 – Melissa Featherstone: ‘Machine Learning Essentials: From Data to Models’ (see the slides) – In this session, we will explore the fundamental components of machine learning workflows. We’ll cover key concepts such as supervised vs. unsupervised learning, data preprocessing, and model training. Additionally, we’ll highlight a few common algorithms and real-world applications. By the end of the session, participants will gain a high-level understanding of how machine learning models are developed and utilised in practice.
- Session 12 – Ryan Giles: ‘The Researcher’s Ally: Google Gemini’s Deep Research Function’ (see the slides) – A quick guide to Google Gemini’s Deep Research feature – how to utilise this AI tool to streamline research, provide action plans and summarise key supported insights. The Deep Research function can help you to save time, uncover new opportunities and make faster, smarter decisions for your projects.
- Session 13 – Andrew Jarman: ‘From Descriptive to Predictive: Machine Learning & AI for Data-Driven Foresight’ (see the slides) – Discover how organisations move beyond the drudgery of “rear-view-mirror” reporting to forward-looking, actionable insight. Join us for a brief look at how data work can evolve from explaining the past to hinting at what’s ahead. We’ll touch on where machine learning and AI might fit, share a few take-aways from real practice, and leave you with ideas to explore back at base.
- Session 14 – Andrea Williamson: ‘Efficient Data Processing’ (see the slides) – In this session we will explore tips and tricks to improve run times and memory usage of large data processing in Python (and beyond).