CW TEC 2024


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27 June 2024  |  The Computer Laboratory, University of Cambridge
09:00 - 09:30
Registration | Exhibition & Networking
09:30 - 09:40

Welcome from Cambridge Wireless

Michaela Eschbach, CEO, Cambridge Wireless


Welcome from our host

Ian Wassell, Senior Lecturer, Department of Computer Science & Technology, University of Cambridge

09:40 - 10:10

Introduction to AI

Chair: Michaela Eschbach, CEO, Cambridge Wireless

This session outlines AI basics, its evolution, and the current surge in technologies like convolutional neural network (CNN) and Generative AI, setting the stage for understanding AI's role in modern applications.


What is AI?

Mary-Ann Claridge, Founder, Mandrel Systems

Phil Claridge, Founder, Mandrel Systems

  • Introduction to the family of AI tech and where Machine Learning (ML) fits in.
  • Technical Jargon Buster
  • Why now? Why the upsurge in AI?
  • What is the latest?
10:10 - 10:35


Learning to Communicate, Cooperate, and Coordinate in Multi-Robot Systems

Prof Amanda Prorok, Principal Investigator, Prorok Lab and Professor of Collective Intelligence & Robotics, University of Cambridge

How are we to orchestrate large teams of agents? How do we distil global goals into local robot policies? Machine learning has revolutionized the way in which we address these questions by enabling us to automatically synthesize decentralized agent policies from global objectives. In this presentation, I first describe how we leverage data-driven approaches to learn interaction strategies that lead to coordinated and cooperative behaviours. I will introduce our work on Graph Neural Networks, and show how we use such architectures to learn multi-agent policies through differentiable communications channels. I will present some of our results on cooperative perception, coordinated path planning, and close-proximity quadrotor flight.

10:35 - 11:05
Break | Exhibition & Networking
11:05 - 12:45

Applications of AI in Critical Industries

Chair: Paul O'brien, Independent Consultant

This session explores AI's impact across telecommunications, cybersecurity, healthcare, and banking, showcasing its capacity to enhance infrastructure resilience, cybersecurity defences, healthcare diagnostics, and banking services. We will explore a selection of use cases where AI has and is being applied to critical industries.


Chris Murphy, Regional CTO, EMEA, Viavi Solutions
How is AI helping deliver resilient telecommunications networks, now and in the future?
This session will identify the challenges to delivering resilient telecommunications and how, despite being data-rich, the application of AI is not straightforward. It will explore:

    • How AI is improving the state of the art for many parts of the network lifecycle and allude to what the future will hold.
    • How ML means that AI models for propagation can be employed that are more accurate than analytical models but without the cost of a full ray-tracing approach.
    • How mistakes in installation of physical infrastructure means that the logical representation of the network inevitably ends up in disagreement with the physical reality and how this can be mitigated by using models to recognise the characteristics of these errors.
    • The operational phase of the network and how this has evolved from a manual process of reacting to alarms and a demanding pro-active process for seeking of poor RF performance that can be optimised.
    • How AI models can more accurately detect anomalies, dramatically reducing the false positives
    • The troubleshooting process where mistakes in how diagnostic measurements are performed mean that the process of triage, root-cause and problem resolution is error prone and based on unreliable data unless advanced AI models are employed.
    • The emergence of the digital twin concept – how it is already delivering value for more resilient networks and promising to become a foundation of many areas of the network lifecycle


    Stephen Clemmet, CEO, Silogic Technology

    The Future Of Wind Turbine Cybersecurity

    This talk will explore using AI to detect anomalies in SCADA data for national critical infrastructure. The focus will be on machine learning to detect live cyber-threats, and related operational performance on wind turbines. The story to be shared with pictures examines how AI would have detected a two-stage attack, the first stage was a denial-of-service, and the second stage was an on-turbine attack. The asset owner didn't know about the attack or want to pay the ransom demand, so the attack lasted for six months. Whereas, if they had AI, it would have been picked up within a day and AI would have told them what was being attacked during the on-turbine attack.


    Dr Camille Terfve, Partner, Mewburn Ellis

    AI in health, pharma and biotech

    Life sciences have been data driven for a while now: from omics and in vitro imaging to healthcare records via medical imaging and sensor data, there is an absolute wealth of noisy information to work with at every scale. This means that the opportunities for application of AI are equally abundant. This talk will provide a snapshot of the breadth of application of AI in the health, pharma and biotech sector, in terms of problems tackled (e.g. diagnosis, drug design, patient monitoring, etc.), data used (e.g. imaging, omics, etc.) and types of AI used (e.g. simple classifiers to large language models). We will also briefly touch on the specific challenges of working with AI in these fields.


    Cheryl Allebrand, Senior Consultant, Artificial intelligence (AI) and Automation, CGI

    Eyes on transport: Machine Vision support of critical services on the Edge

    According to Network Rail, scour is the leading cause of bridge failures in the last 100 years in the UK and 4500 structures are currently at risk of scour, of which 750 are rated as High or Medium/High risk. Despite best efforts, sending divers to monitor erosion doesn't match the speed with which erosion develops, and is ambiguous compared to the level of intel that real-time sensors and imagery can provide.

    This brief talk will touch on:

    • CGI's solution to monitoring scour & other threats
    • Vision on the Edge & infrastructure protection
    • Enabling (inter)action & digital twins with generative transformer models
    12:45 - 13:45

    Lunch | Exhibition & Networking

    13:45 - 15:15

    AI Engineering Challenges

    Chair: Peter Whale, Founder & CEO, Vision Formers

    This session discusses the practical aspects of building AI solutions, including data management, risk mitigation, interoperability standards, and deployment tools, emphasising the organisational readiness required for AI integration.

    Focusing on the practice of engineering AI solutions and the challenges for organisations to become AI Ready, including business outcomes and technical results from deployment.

    • Engineering resilient AI solutions
    • Data challenges (AI - programming with data)
    • Managing risk when building critical AI solutions
    • AI for programming and software engineering
    • Technical standards for AI interoperation
    • Infrastructure & tools - AI at the edge / in the cloud, toolsets GCP/AWS/GPT/Copilot etc.
    • How to get started?


    Simon Thompson, Head of AI, ML & Data Science, GFT Financial Ltd.

    A secure and useful Banking application built with LLM’s?

    Managing personal finances can be complicated and confusing for many consumers. Retail banks want to be differentiated and target customers with products that are tailored to them but struggle to get their message across. Using an LLM to create an intelligent banking application might allow a greater level of customization and control for customers and create a new channel for retail banks. The challenge is that LLM’s are insecure and unreliable. In this talk I will describe an intelligent banking app that leverages LLM’s and describe the tactics that were used to put it together, as well as the limitations that we encountered.


    Anton Lokhmotov, CEO, KRAI

    Objective quality, performance and cost evaluations of AI solutions

    Adopting cutting-edge AI solutions in critical industries such as banking leads to numerous engineering challenges: from strict regulatory requirements to high development and deployment costs. Amidst constant innovation in hardware, software and "neuralware", it's becoming ever more difficult for practitioners to evaluate and select among many possible options and a myriad of combinations thereof.

    We aim to share our learnings of evaluating AI solutions in terms of quality, performance and cost trade-offs in the industry-leading MLPerf competition ("The Olympics of ML/AI benchmarking"). We describe an innovative approach based on automated reproducible workflows, which provide rigorous testing in development and continuous monitoring in production. Our approach is accelerator-vendor and server-vendor neutral, which helps de-risk hardware investments through best-fit selection and easy migration if necessary. The approach is applicable to a broad range of industries, in particular, heavily regulated ones: integrating checks and balances into automated workflows improves compliance and security.


    Bob Oates, Associate Director, Cambridge Consultants

    Cross-domain approaches to GenAI assurance

    The deployment of an AI-enabled system into a critical system context involves multiple stakeholders from multiple domains, including security, safety, data science, software engineering, and AI development. How can MLOps processes be augmented to deliver systems that provide confidence to the user, the developers, and the accreditors of the system? In this talk the author presents a high-level assurance framework that attempts to reconcile risks from multiple domains and a taxonomy of mitigation techniques that work in an MLOps setting for a GenAI application. Important lessons learned about the opportunities and pitfalls of integrating assurance and risk management strategies into the MLOps process are shared.

    15:15 - 15:45

    Break | Exhibition & Networking

    15:45 - 17:25

    What does the future of AI look like?

    Chair: Julia Gwilt, Partner, Appleyard Lees IP LLP

    This session speculates on AI's trajectory, covering new models, hybrid systems, sustainability in AI training, multi-agent systems, and explainable AI, aiming to foster discussion on the future of AI technologies.

    Where will this technology be in 5 years’ time?


    Peter Whale, Founder & CEO, Vision Formers

    Future AI technologies

    In this bite-sized session, we will provide a sneak peek at some of the emerging areas in AI technologies, and provide pointers to services and models that you can experiment with yourself.


    Dr Jade Hind, Senior Consultant, Space, Defence and Intelligence, CGI

    Personalisation 2.0: AI's Role Ahead

    This talk delves into the transformative potential of Generative AI to create deeply personalised experiences. By moving beyond generic recommendations, AI can deliver services that truly reflect individual preferences, benefiting both consumers and businesses. It underscores the need for skilled professionals to design secure and adaptive systems, and the importance of balancing innovation with ethical and sustainable practices. Emphasising inclusivity and fairness, the talk addresses the environmental and economic challenges of AI. Ultimately, it envisions a future where AI provides highly customised and responsible solutions that significantly enrich our lives.


    David Pollington, Head of Research, Bloc Ventures

    Where next? AI's evolution in knowledge, understanding, reasoning and scaling

    AI innovation is continuing at breakneck speed with advances being published weekly by both academia and the private sector. This session will explore a number of these areas including: improving AI knowledge and reasoning through agentic workflows; boosting AI’s understanding through causal and neuro-symbolic AI; and the importance of integrated photonics and neuromorphic computing in combating the compute scaling barriers.


    Dr Detlef Nauck, Head of AI Research, BT

    The Future of GenAI

    There is more to Generative AI or GenAI than ChatGPT. While the public discussion and the media are dominated by large language models, and image or video creation, there is another development happening in the background that is bound to change how we do science and engineering in the future. The AI industry has learned from playing games and is applying it in drug discovery, optimisation and creating new materials. I'll give an outlook how the public and not-so-public developments are likely to impact us in the future.

    17:25 - 17:30

    Closing remarks

    17:30 Event close

    *Draft agenda, subject to change ahead of the event

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