Day 1 - 28 November 2018
09:15AM - Day 1
Enterprise AI and Digital Transformation: Chair’s Welcome and opening comments
Opening Presentation: AI Landscape
Keynote: AI Powering Digital Transformation
• Is your organisation ready for AI and digital transformation?
• Why should you embrace AI?
• Challenges faced by Enterprises using new deep learning and ML technologies
• Humans and AI working together – creating an AI culture and developing trust within the organisation
Global Head of Data Science and Analytics
10:30AM - Day 1
Panel: Driving Digital Transformation through AI & Deep Learning
• What are the fundamental buildings blocks needed for build a digital organisation using AI, ML & DL in terms of people, skills and tech?
• Discussing the role of other companies within the process, from start-ups to big ticket providers.
• Examining digital transformation from both industrial and business information perspectives.
• Real life examples of successful transformations from across Enterprise.
Head of Sustainability Campaign
11:45AM - Day 1
The role of AI in the workplace
• Discussing the role of AI in the present and future workplace for tasks such as recruitment, talent development and HR operations
• What will the impact of a AI based workforce be on the future of the human workforce?
• Examples of using chatbots and other AI technologies within the modern workplace to improve processes from across Enterprise
The road to AI Transformation
• Practicalities on how to start or continue your AI journey
• Which business problems to solve e.g. customer service, growth, operational efficiency, cost reduction and how to do it in a way that is responsive to future change
• How to drive change strategically and methodically when the technology is developing at an exponential rate
• How to leverage your data successfully
• Real life case study examples
Panel: Driving value from Enterprise AI
• What AI is and isn’t and how it is of use to business
• Examples of AI systems beyond machine learning and common errors made trying to extract value from AI systems
• A look at modern development practices and tools with examples of real life projects
• Rules for success in determining which projects to address and avoiding failure
• Building a stellar team and retaining talent
AI for Everyone
- What is AI, ML, DL and what are the differences
- Why your business should care
- The state of Enterprise AI today
- A vision of where Enterprise AI should be
Panel: The Cognitive Enterprise
• Is your business ready to be transformed by cognitive automation (CA) and robotic process automation (RPA)?
• Which processes are most suitable for CA / RPA?
• What business benefits are being delivered? What are the challenges?
• Timescales for adoption, investment, scaling automation projects
• Examples and best practices from Insurance, banking and energy
Vice President of Data Analytics
Stanley Black and Decker
03:45PM - Day 1
Audio Analytics in the Enterprise
In this discussion, we explore the different applications for audio analytics in the enterprise.
We discuss the prominent use cases and value propositions in both a B2C and B2B environment.
In addition, we look at a case study within Stanley Black and Decker, where we have used deep learning to
develop an audio analytics application. We unpack the application and discuss our approach to development
Griffin Open Systems
04:15PM - Day 1
AI in Process Automation
- Challenges in adopting AI for automation/process control.
- How visual programming tools and open systems lower the barrier to implementing AI applications.
- State of the art techniques combine neural networks and evolutionary learning methods with capture of expert knowledge and best practices for real-time process control.
- AI applications optimizing complex processes in the steel and energy sectors, resulting in substantial environmental and cost savings.
Distinguished Data Scientist
05:00PM - Day 1
Deep Learning Applications to Online Payment Fraud Detection
The talk will cover some applications and use-cases of deep neural network architectures applied to the problem of payments fraud detection. With the multi-fold objectives such as maximizing fraud catch rate, approving the good user volume reliably and quickly, the underlying problem formulation and considerations applicable to large-scale online payment transaction data: such as dimensionality reduction, sparsity, high cardinality and temporality will be covered. Covering an assortment of deep learning methodologies applied to each problem formulation, some empirical comparisons and results will be presented. Lastly, some high level aspects of run-time performance benchmarking as applicable to training/inferencing processes and model deployment at PayPal will be presented.