Data Science – The beating heart of AI
12th Oct 2021, 10am
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.
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.
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.
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.
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.
Organiser: Tommy Flowers Network