Build once, use many: a reusable RAG boilerplate for large organizations

• What is and why do RAG

• Building and Deploying RAG IaaS

• Reusable and customizable components

• Getting and maintaining data for the LLM

• Quick start use case onboarding formula

• Common boilerplate frontend (Streamlit)

• Reusable observability and monitoring

Eric Joachim Liese

Strategic Advisor and Lead Architect for AI & Data
BSH Home Appliances Group

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Saif Addin Ellafi

Product Lead, MLOps, Data Engineer, Solutions Architect
BSH Home Appliances Group

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• Exploring LLMs for product portfolio management

• Leveraging LLMs in operational situations to drive

 product and service improvements

• Improving manufacturing through the use of AI

 tools

Iliana Portugues

VP Innovation Centre Berlin
Siemens Energy

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• Comparison of the computational efficiency and

 performance accuracy between LLMs and SLMs

 across various tasks.

• Application Suitability: Use cases for LLMs and SLMs based on model scalability and complexity

 requirements

• Cost-Effectiveness and Accessibility: Analysis of the cost implications and accessibility of deploying LLMs versus SLMs in real-world applications.

Reserved for Partner

• Data analysis of patient needs and real language

 used in specific disease areas

• Develop a Gen AI based solution that leverages

 insights and can cross check patient education

 drafts and advise on better language

• Next step: let solution create customized material

 from scratch

Dimitri Metzger

Digital, Data & IT Business Partner for Development & Medical Units & AI
Merck

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GenAI for Capital Markets: Are Chat Bots and Smart

Summaries the only solutions?

• The future of Chat Bots for supporting capital

 market research and portfolio construction

• Do Smart Summaries really help the professional

 analyst?

• Is there any potential for more use cases with

 GenAI?

Dieter Konrad

Expert Capital Market Data Science
Union Investment

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• What is the current art-of-the-possible with regards to Gen AI BI (what it can do and what it’s still missing)

• What are the most often encountered problems when Gen AI BI is introduced in the organization

• How to mitigate possible risks, biases and other challanges that you will encounte

Kazik Surala

Head of Data Governance & Analytics- Poland
Phillip Morris International

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• Ethics and AI in aviation

• Harnessing the potential of AI while prioritizing

 responsibility

• Ethics by design approaches

Sergei Bobrovskyi

AI Expert
Airbus

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• Operationalizing Transparency: Practical methods

 for maintaining transparency in AI operations, such

 as explainable AI techniques and open algorithms

• Addressing Bias in AI Models: Strategies for

 detecting and correcting biases in generative AI

 models to ensure fairness across all user interactions

• Building Trust with Users: Techniques for building

 and maintaining trust through consistent and fair AI

 practices, including user education and transparent

 communication

• Regulatory Perspectives on Fairness: How new

 regulations shape the requirements for fairness in AI

 applications and the tools needed to comply

• Feedback Mechanisms: Integrating user feedback

 to continuously improve fairness and transparency

 in generative AI systems

• Understanding Deepfakes: Overview of the technology behind deepfakes, including how they are created and the role of Generative AI in their development.

• The Dual Impact on Society: Examination of how deepfakes are reshaping societal perceptions, from influencing public opinion to the risks of misinformation and identity theft.

• Business Implications: Analysis of both the risks and opportunities for businesses, including the threat and the potential for innovative marketing strategies.

• Ethical and Legal Considerations: Exploration of the ethical dilemmas posed by deepfakes, alongside current and emerging legal frameworks designed to combat malicious uses of deepfakes.

• The Future of Trust: Discussion on the long-term implications of deepfakes for the future of trust in digital content, and how society can navigate the balance between innovation and security

Anna Hovsepyan

Visiting Lecturer
King’s College London

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• Introduction to Generative AI for Cyber Security

• Using AI for Threat Detection

• Implementation of Security Measures

• Enhancement: Which Tools Support It?

Reserved for Partner

Robert recently joined Newcross to help the business maintain it’s competitive advantage in the age of AI. His first project has been to leverage Generative AI to increase the operational efficiency of the business and effectiveness of its people. In this session Robert will cover:

• Developing a ‘NewcrossGPT’ to enable staff to leverage Generative AI capabilities with internal data and IP

• Creating unique contexts for each team, such as sales, marketing and engineering, to enhance the effectiveness

• The method for deciding what data to train the model on and the governance structures around data access

• Ensuring that data used in the system is both secure and not ending up in OpenAI’s data set

• Taking an agile approach to technology implementation – move quickly and avoid investing too much in any single system given the pace of change

Robert Chilvers

Chief Data & AI Officer
Newcross Healthcare

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• Adapting to Advances in AI Technology: Preparing Gen AI systems for integration with upcoming innovations such as advanced neural network architectures and AI accelerators

• Leveraging New Technologies for Enhanced Scalability: The impact of emerging technologies like quantum computing on the scalability of Gen AI

• Organizational Scalability for Gen AI Initiatives: Exploring how to scale team structures, governance models, and operational processes alongside Gen AI technologies

Reserved for Partner

• Evaluating Copilot M365: Investigating how Microsoft’s generative AI tool, Copilot M365, influences efficiency, quality, and employee experience within Repsol.

• Experimental Study: A four-month research with 550 participants, using pre and post-test design, practical experiments, surveys, focus groups, and interviews to measure the impact.

• Key Findings: Results show a weekly time saving of 121 minutes, a 16% increase in quality of deliverables, and a significant positive impact on employee satisfaction and productivity

Dr. Guille Lorbada

Head of New Ways of Working
Repsol

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