Enterprise AI Summit Canada schedule

•    Identifying the use cases and business problems that are best suited for AI solutions, and how to prioritize them
•    Developing an AI strategy and roadmap that aligns with the organization's overall business objectives
•    Building a team and infrastructure to support the implementation and scaling of AI
•    Addressing the technical and organizational challenges that arise when scaling AI from innovation centres to enterprise-wide solutions, such as data governance and security

 

•    How does your company identify which applications utilize artificial intelligence?
•    How does the company evaluate if these applications make sense from an economic perspective or if they are just hype?
•    Identifying the risks and benefits related to the large-scale use of artificial intelligence applications

•    How to overcome the challenges of aligning business and engineering on the AI journey
•    Unlocking the power of process optimization to drive sustainable profitability
•    Strategies for building a bridge between process and delivery in ai finance
•    Best practices for implementing ai solutions that deliver real-world results 

 

 

•    What is the next chapter in the usage of Data in AI?    
•    What is the next chapter in AI with respect to the framework of models, computational infrastructure, and social implications?
•    How do these new trends in Data and AI improve customer experience, risk management, and operational efficiency?

 

Panelists:
Maurice Leon, Director Service Governance, CIBC Mellon 
Huzaifa Noman, Global Head of Data & AI, Colliers 
Nitesh Soni, Global AI Solutions Leader, Sanofi 
Jesslyn Dymond, Responsible AI & Data Innovation Leader | Data Ethics, TELUS
Parisa Lak, Senior Data Scientist | Group Advanced Analytics, Manulife

•    How can possible areas for improvement in AI pipelines be identified? 
•    How can we optimize engineering, infrastructure, culture, teams and decision-making to see increased ROI (higher revenue, cost reduction, operational efficiency, value gains)?
•    Overcoming Barriers to ROI: Addressing data quality, integration, and change management 

 

•    Are you making the most of your NLP programs?
•    Different Use Cases of NLP in Financial Services
•    Learning how to harness the power of NLP to extract valuable insights from unstructured data
•    Discovering new ways to improve the accuracy of predictive models using NLP techniques such as sentiment analysis and topic modelling
 

•    An overview of the AI regulatory and legal landscape affecting the Banking sector
•    The importance of having an AI Governance program to manage AI-related risks
•    Key components of an AI Governance program and a practical approach to assess and mitigate AI-related risks

 

 

 

•    How can AI be leveraged to improve business strategy and production? 
•    What challenges may need to be considered and how can you reduce the impacts of such? 
•    What are the building blocks of a successful AI adoption to ensure that the strategy is holistic and achievable? 

Panelists:
Parastoo Dehkordi, AI and Algorithm Design Lead, Heart Force Medical Inc
Javier Mendel, Senior Machine Learning Engineer, WM
Harsh Singh, Manager, Data Science | Advance Analytics AI/ML Product Delivery, Sobeys 
Saad Rais, Senior Manager, Health Data Science, Ontario Ministry of Health

Moderator: Ramzi Abdelmoula, Head of AI Strategy, General Motors Canada
 

 

 

•    Building a financial sector-specific AI strategy
•    Integrating AI with existing business processes and systems
•    Establishing key performance indicators (KPIs) for AI adoption

Panelists:
Aneta Osmola, Data and AI Risk Director, Global Risk Management, Scotiabank
Amit Satpathy, Director of Artificial Intelligence, Munich Re Canada
Martin Bernier, Senior Director, Quantitative Strategies and Data Science, Caisse de Dépôt et Placement du Québec
Priya Alagarsamy, Senior Manager, Deposits and Investments, Pricing & Insights, Scotiabank 

Moderator: Awais Sher Bajwa, Head of Data & AI Banking, Bank of America

•    Misusing GenAI diminishes the value of AI in organizations
•    Determining whether GenAI makes sense for your use case
•    Considering alternative AI techniques
•    Combining GenAI models with AI other techniques

•    Defining what constitutes AI-ready data and why it is crucial for achieving successful AI outcomes
•    How should you do this, and what are others doing?
•    Exploring how clients and vendors are currently preparing their data for AI
•    Best practices and recommendations for getting data AI-ready

Panel Discussion: Preparing Your Data for AI Success: Strategies and Best Practices
•    Defining what constitutes AI-ready data and why it is crucial for achieving successful AI outcomes
•    How should you do this, and what are others doing?
•    Exploring how clients and vendors are currently preparing their data for AI
•    Best practices and recommendations for getting data AI-ready

Panelists:
Shenson Joseph, Vice President Data Engineering, JPMorgan Chase & Co
Steffen Klaere, Data Scientist, Definity
Maria Abrar, Data Scientist, Instagram-Meta

Moderator: Christie Mealo, Senior AI/ML Engineering Manager, CVS Health Corporation

•    Creating a Change Management with AI
•    How do you run the change?
•    Should I create new roles in my organization?
•    What are the economic benefits of AI?

•    Developing cost-effective strategies for hiring and retaining top AI and Data talent
•    Exploring how to maximize the potential of free and low-cost resources to build a robust data infrastructure and accelerate development
•    Promoting a culture of knowledge sharing and collaboration within the team to enhance skillsets and innovation

•    Pre ChatGPT: Natural Language Understanding (NLU) driven architectures and why they were successful
•    ChatGPT: Advent of large language models and their impact on conversational AI design, development, and deployment. Do we abandon the old in pursuit of the new?
•    Post ChatGPT: How do enterprise conversational AI systems evolve to meet existing and future challenges. What are some good design principles to keep in mind while building for the future?

•    Providing an overview of how Generative AI is currently being used in production within the banking sector?
•    What are the key benefits that Generative AI offers to banks in terms of production efficiency and customer experience?
•    What are some of the challenges that banks face when implementing Generative AI in production?
•    How can these challenges be overcome, and what are some best practices for successful implementation?

Panelists:
Yannick Lallement, Chief Artificial Intelligence Officer, Scotiabank 
Chris Patterson, Head of Enterprise AI Platforms and Solutions, CIBC
Jeff Kurys, Director, Artificial Intelligence, BELL
Lin Liu, Senior Manager, Data Science & Engineering, Wealthsimple

•    Does artificial intelligence increase productivity as expected?
•    What are the biggest challenges financial institutions face when implementing AI solutions?
•    How can human expertise be best combined with AI to create a more robust financial ecosystem?
•    Implementing a Data-Driven approach

•    Identifying the current challenges facing by leaders, such as rising competition, security threats, and customer experience demands. 
•    The emergence of Artificial Intelligence as a game-changer in this environment 
•    Guiding leaders on evaluating their current processes to identify areas ripe for AI integration 
•    Encouraging leaders to embrace AI as a strategic tool for driving innovation and growth 

Panelists:
Amit Satpathy, Director of Artificial Intelligence, Munich Re Canada
Amin Atashi, Senior Machine Learning Engineer, The Globe and Mail
Justin Javorek, Digital Experience Design Leader, BELL
Roma Kojima, Senior Director, Enterprise Audience Data, CBC/Radio-Canada

Moderator: Paul Beaton, Sr. Manager, Data Science & Analytics, Rogers Communications

•    Evolution of Generative AI in the Workplace - From Personalization to Impersonation: Exploring the shift from AI enhancing personalized experiences to AI performing tasks that mimic human behavior, with cross-industry examples showcasing AI's diverse applications.
•    Human-AI Collaboration - Enhancing Capabilities and Ethical Considerations: How AI complements human work to increase productivity and efficiency, while addressing ethical concerns about AI impersonation and maintaining human interaction.
•    Future of Work- Adapting to and Predicting Change: Strategies for adapting to the evolving AI landscape and potential future scenarios, from utopian collaboration to dystopian job loss.

•    Designing a robust AI research infrastructure
•    Best practices for selecting and integrating the right tools and technologies.
•    Ensuring scalability and flexibility to accommodate evolving research needs
•    Promoting interdisciplinary collaboration and knowledge sharing among researchers

•    IAMDWarrior is MunichRe's upgraded machine learning platform is based on open- source stack and applies Gitops principles to machine learning in production. 
•    At MunichRe we use Gitops for most of our work, and this has many benefits including traceability, consistency, enhanced productivity, and provides a single source of truth. 
•    We will take a deeper look at the process, architecture, and how IAMDWarrior is helping us speed up our ML work and how Gitops principles fit into this setup.

•    Introduction to Reinforcement Learning (RL): An overview of RL and how its interaction-based learning differs from traditional machine learning methods, enabling complex decision-making. RL is a key technology behind advancements like ChatGPT, where it helps models improve through feedback and interaction as opposed to labels and model retraining. 
•    RL in Ad Recommendations: Explore how RL excels in measuring long-term effects in advertising, crucial for optimizing user interactions in large-scale recommender systems.
•    Evolution and Future Directions: Trace the evolution of RL in recommendation systems from the 2000s to today, highlighting advancements by companies like Google, Microsoft, and TikTok, along with a discussion of current gaps and future trends in the field.