All models are wrong! – The foundations of modern Data Science
10:00 – 10:15    Prof Tim Whitley, MD Applied Research for BT and MD of BT’s Technology Campus ‘Adastral Park’
Welcome to ‘Data Science – The beating heart of AI’
10:15 – 10:30    Dr Detlef Nauck, Head of AI & Data Science Research at BT, Visiting Professor at Bournemouth University
Programming with Data

Experienced Data Scientists know that the biggest challenge to using insights from data in an operational setting is to get hold of good quality data. Pretty much every talk or blog mentions that 80% of the effort in any data science project are spent on data access and data wrangling. My take is that this effort will approach 99% soon simply because the analytics and machine learning parts are becoming largely automated. It is time that we shift focus from techniques and software to data and make it clear to ourselves what we are doing in Data Science and in particular in Machine Learning – we are programming with data.

10:30 – 10:45    Michael Free, BT Michael Free – AI Research Manager
Models that cheat – Making sure it really works

Machine Learning has made great strides in hard, unstructured problems with the advent of deep learning. However, such progress does not come free of issues. Often treated as black box solutions, interpretability and explainability are complex issues when building deep learning models, and poorly framed experiments and ‘dodgy data’ have led to a litany of models that don’t really work in practise – they merely ‘cheat’ the limited test you’ve given them.

10:45 – 11:00 Blaise F Egan, BT Data Science and Statistics Specialist
Statistics: the original data science
Statistical science is the oldest of the components of what is now called Data Science. I will give a quick run-through of some of the bigger landmarks on the road to where we are now and how we got here.
11:00 – 11:15 Rob Claxton, BT Senior Manager – Big Data, Insight & Analytics
Model Safety
11:15 – 11:30    Prof Alessio Lomuscio, Department of Computing, Imperial College London
Towards verifying neural systems
A key difficulty in the deployment of AI solutions, including machine learning, remains their inherent fragility and difficulty of certification and explainability. Formal verification has long been employed in the analysis and debugging of traditional computer systems, including hardware and networks, but its deployment in the context of AI-systems remains largely unexplored.
11:30 – 11:45    Dr Raoul-Gabriel Urma, CEO Cambridge Spark
Skills for a Data World

Data is all around us and transforming how organisations are having to operate. As a result, data is significantly impacting the future of work. In this talk, we will cover what are the key skills for individuals and organisation to invest in across all roles for a successful future.

11:45 – 12:30
Panel on ‘Foundations of Data Science’ and Q&A
When we analyse data we often search for a model to describe what we find, to make decisions, or to make predictions. A model is always a simplification of what we find in the data and so “all models are wrong but some are useful (George Box, 1978). What do we need to do to make sure our models are as good and useful as they can be?
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Organiser: Tommy Flowers Network

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