Blaise F Egan, BT Data Science and Statistics Specialist on " Statistics; the original data science"

Conference TFNetworkAutumn21

Data Science - The beating heart of AI
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Blaise Egan acts as a statistical advisor to senior management across the BT Group. 

He has been involved in high-profile legal cases and many projects where a deep knowledge of statistical science is required. He is a Chartered Statistician and a Council member of the Royal Statistical Society. He holds degrees in mathematics (BSc, Queen Mary College, 1978), Statistical Applicastion in Business and Government (MSc, University of Westminster, 1993) and an MPhil in Bayesian Statistics (Queen Mary University of London, 2007).

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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.

Michael Free, BT on "Models that cheat - Making sure it really works"

Conference TFNetworkAutumn21

Data Science - The beating heart of AI
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Michael Free, is an AI Research Manager in BT’s Applied research department. With his work focussing on business applications of Deep Learning, he has led machine learning projects across a variety of fields such as natural language understanding on customer interaction data, image recognition for infrastructure management and future chatbot technology. His main interest lies in how to best use this technology in an enterprise context, on real problems with limited training data, utilising unsupervised learning techniques to create useful functionality.

Current main projects involve utilising reinforcement learning for dialogue management, content recommendation systems and collaborative work with universities on speech analytics/NLP.

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.

I’ll discuss these issues in this talk, with examples of where things have gone wrong – and methods we can use to mitigate these issues and ensure our models “really work” when we test them.

Tim Whitley opening TFNetworkAutumn21 'Data Science - The beating heart of AI'

Conference TFNetworkAutumn21

Data Science - The beating heart of AI
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Prof Tim Whitley is a BT Distinguished Engineer and serves as MD Applied Research for BT and MD of BT’s Technology Campus ‘Adastral Park’ in Suffolk, England. He is accountable for all aspects of BT’s Global Research activities, which includes applied research, technology and partnerships with world leading universities.

From 2007 to 2011 he was BT Group Strategy Director. Tim holds a BSc in Physics and a PhD in Optical Fibre Systems. Tim is a Board member for the New Anglia Local Enterprise Partnership and a BT visiting Professor with the School of Computer Science and Electronic Engineering at the University of Essex.

In December 2016, Professor Whitley was appointed as a member of the Engineering and Physical Sciences Research Council (EPSRC) by the UK Government.

In February 2018 Tim was admitted as BT's William Pitt Fellow at Pembroke College Cambridge.

Tim joined BT in 1981 as a Telephone Engineering apprentice in North Wales and has held roles in Research, Technical Architecture, Strategic Analysis and Corporate Strategy.

Welcome to 'Data Science - The beating heart of AI'

Subhash Talluri, AWS on "Data Science on AWS"

 

Conference TFNetworkAutumn21

Data Science - The beating heart of AI
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Subhash Talluri is a specialist solutions architect in AI/ML working for AWS. He brings cross-functional expertise at the intersection of engineering; cloud computing, machine learning, computer science and business to provide quality and scalable solutions.

He'd like to position himself as a full stack data scientist but well knowing that such unicorns do not exist he is pursuing an alternative strategy. There are essentially five elements to a data scientist: data engineering, data visualization, machine learning, big data and cloud computing. He aims to be a specialist in two of these areas (machine learning, cloud computing) and a generalist in the other three. This allows him to handle the complete data lifecycle. From identifying the right problem, translating the problem in terms of data, getting the data, building the pipelines, analysing data, building models, presenting findings and putting models in production, he aspires to handle it all. 

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Data Science on AWS

AWS has been continually expanding its service portfolio to support virtually any cloud workload, including many services and features in the area of artificial intelligence. In the context of data science projects on AWS, the benefits of cloud computing include agility, cost savings, elasticity, faster innovation and smooth transition from prototype to production. Amazon SageMaker is a fully managed offering that addresses every aspect of machine learning by its modular design. All machine intelligence is powered by data. However, not all data are created equal. We need to critically evaluate machine-learning products from a standpoint that prioritizes the quality of the data streaming into them. This necessitates the need for a data lake or a data platform with considerations for horizontal scalability, a single source of truth, data governance and appropriate security frameworks.

Ben Taylor, CTO & Co-founder, Rainbird Technologies on "Bias, transparency and governance in automated decision making"

 

Conference TFNetworkAutumn21

Data Science - The beating heart of AI
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Ben Taylor, an authority on artificial intelligence – with an encyclopedic knowledge of its past and present – he is passionate about solving complex challenges through the use of innovative technologies. Where some see problems, he sees solutions.

Earlier in his career, while Director of Technology at a motor insurance start-up, he led the technical development of an award-winning AI system that revolutionised the motor insurance industry. As the co-founder and CTO of Rainbird Technologies, Ben is the driving force behind the fusion of human expertise and automated decision-making. He continues to push the boundaries of the platform’s capabilities, enhancing and developing it to serve a variety of data-driven processes. He holds a degree in artificial intelligence from the University of Sussex and is an active member of the All Party Parliamentary Group on Artificial Intelligence (APPG AI).

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Bias, transparency and governance in automated decision making

As they look at the great landscape of AI, organisations are getting to picture machine learning in finer detail. But the closer they get to the detail, the more they notice a chasm emerging between prediction and automated decision. And for no organisation does that chasm pose greater danger than those who operate in regulated industries.

In this talk, Ben Taylor, CTO of Rainbird, will discuss the challenges of turning data-first prediction into automated decision making, and how organisations can overcome them. He’ll discuss bias in data and decision making, the importance of transparency and how to embed governance into automation.

Professor Alessio Lomuscio, Imperial College London on "Towards verifying neural systems"

Conference TFNetworkAutumn21

Data Science - The beating heart of AI
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Alessio Lomuscio, Professor of Safe Artificial Intelligence at the Department of Computing, Imperial College London, Royal Academy of Engineering Chair in Emerging Technologies, ACM Distinguished Member

He leads the Verification of Autonomous Systems Lab, developing methods and tools for the verification of AI systems so that they can be deployed safely and securely in applications of societal importance.

At present the team are contributing to the following research areas:

• Verification of neural systems and autonomous systems realised via machine-learning.
• Explainability and Fairness in AI systems.
• Parameterised verification of robotic swarms.
• Logic-based verification of multi-agent systems.

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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.

Dr Detlef Nauck, BT on " Programming with Data"

Conference TFNetworkAutumn21

Data Science - The beating heart of AI
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Dr Detlef Nauck is the Head of AI & Data Science Research.

He runs BT’s AI and Data Science research programme where he focuses on making best use of data through AI and machine learning. A key part of his work is to establish best practices in Data Science and Machine Learning for conducting data analytics professionally and responsibly. 

He has a keen interest in AI Ethics and explainable AI as means to tackling bias and increasing transparency and accountability in AI. He has a PhD and a post-doctoral degree in Computer Science with a focus on Data Science and Machine Learning. He is also a visiting professor at Bournemouth University.

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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.