Toronto, ON

2018 Agenda

February 7, 2018
  • 9:00 AM
    Opening Remarks from the Chair
  • 9:15 AM
    Keynote: TD Bank Group
    Laying the Foundation for a Successful Enterprise AI/Machine Learning Strategy
    Lovell Hodge
    Vice President, North American Fraud Analytics, Financial Crimes & Fraud Management Group, TD Bank Group

    Enterprise-wide AI programs are no small investment. Ensure meaningful returns on this investment with a strategy to support AI and machine learning. Develop a plan to:

    • Assess your AI/ML capabilities
    • Determine the objectives of AI/ML for your organization
    • Track the success of your program

    Make your AI and machine learning program a success with  a strategic plan.

  • 9:45 AM
    Industry Expert:
    Getting Beyond the Hype of AI and Machine Learning to Make a Real Impact on Your Business

    There’s a lot of hype surrounding AI. Get beyond the hype and find out what practical effects AI can have on your organization today. Gain expert insights to:

    • Identify AI applications that you can implement now
    • Manage your expectations for AI
    • Create a plan to develop your AI capabilities

    Identify real applications that AI can have on your organization today and in the near future.

  • 10:15 AM
    Networking Break
  • 10:45 AM
    Case Study: Region of Peel
    Use Artificial Intelligence and Machine Learning to Develop Autonomous Business Solutions
    Faraz Zaidi
    Advisor, Health Analytics, Region of Peel

    Recent advances in AI and ML have led to the development of innovative solutions and capabilities for a variety of sectors such as Sales and Marketing, Financial Services, Automotive Industry and Healthcare Providers. Common goals and objectives pertaining to these domains are, respectively, ‘I want to make more money,’  ‘I want to have more clients,’ I want to develop a self-driving car,’ ‘I want to improve the health of my patients.’ Achieve these goals by:

    • Modeling these objectives as qualitative problems
    • Using advanced analytical and computational methods to develop solutions using state-of-the-art algorithms and techniques
    • Overcoming the challenges organizations face is developing and implementing these solutions

    Create autonomous business solutions with the support of AI and ML.

  • 11:15 AM
    Industry Expert
    Bridging the Gap Between Theory and Business to Operationalize and Monetize AI and Machine Learning

    AI and ML research is advancing at a steady clip, but organizations often struggle to put these theories into practice. Create a plan to transform theory into profitable projects. Source insights to:

    • Identify promising new areas of AI research
    • Recognize practical applications for AI in your organization
    • Roll out AI projects to generate new revenues

    Make AI and machine learning a profitable part of your business strategy.

  • 11:45 AM
    Panel
    Gaining Organizational buy-in to Ensure the Success of AI and Machine Learning
    David Sadek
    Vice President Research and Technology, Thales
    Manu Sud
    Manager, Analytics and Advanced Technology Branch, Ministry of Economic Development and Growth

    AI and ML programs can only succeed if your organization buys into them. Launch your programs with proper organizational support to ensure long-term success. Create a plan to:

    • Develop support from senior team members
    • Select influencers to champion your AI/ML program
    • Gain the support of employees throughout the organization

    Create a successful AI/ML program by developing organizational buy-in.

  • 12:30 PM
    Networking Lunch
  • 1:45 PM
    Case Study: Collins Barrow
    Leverage Your Financial Data With AI/ML to Improve Business Decisions
    Jonathan Nichols
    Director, Data Solutions, Collins Barrow

    Financial data offers incredible insights into your business operations, but only if you can process it into meaningful information. Use AI to process this data more efficiently to uncover valuable strategic information. Leverage AI to:

    • Examine general ledger data for fraud and error
    • Identify tax savings opportunities
    • Combine financial and other data to predict sales

    Make better business decisions by extracting meaningful information from financial data with AI.

  • 2:15 PM
    Industry Expert
    Maintaining Security and Privacy in the Era of Artificial Intelligence

    As more data is put into autonomous applications, it raises concerns about data security and privacy. Ensure that your data is protected. Sources expert insights to:

    • Protect personal data
    • Clearly explain how data will be collected and used
    • Develop algorithmic transparency

    Ensure the protection of data and privacy of individuals when using AI.

  • 2:45 PM
    Case Study: American Enterprise Institute
    Using Machine Learning to Predict Policy Changes to Gain a Competitive Advantage
    Weifeng Zhong
    Research Fellow, American Enterprise Institute

    Companies that are quick to respond to government policy changes, domestically or abroad, can gain a first mover advantage. Apply machine learning to predict policy changes before they happen, to create a competitive advantage. Develop a method to:

    • Develop quantitative measures to assess policy priorities over a long period
    • Detect significant policy changes over time
    • Make short-term predictions about future policy changes

    Develop a competitive advantage by anticipating government policy changes.

  • 3:45 PM
    Industry Expert
    Creating Value from AI to Improve the ROI of Your Investments

    To impact your bottom line, AI projects should have clear financial targets. Develop and monitor targets to make the most of your AI investments. Create a plan to:

    • Measure the financial returns of AI
    • Monitor the impact of AI on business processes
    • Assess the overall financial impact of AI investments

    Improve the ROI of your AI investments to ensure they’re generating value.

  • 4:15 PM
    Case Study: Workplace Safety and Prevention Services
    Making the Journey from Analytics to AI and Machine Learning
    Vidyasankar Sundar
    Market Research Analyst, Workplace Safety and Prevention Services

    Making the move from analytics to AI isn’t without its challenges. Hear how one organization has successfully made this transition. Create a roadmap to:

    • Plan your transition to AI/ML
    • Monitor the success of your implementation
    • Build on successes to expand your AI/ML projects

    Develop a plan to successfully make the journey from analytics to AI and machine learning.

  • 4:45 PM
    Day 1 Adjourns and Cocktail Reception
February 7, 2018
February 8, 2018
  • 9:00 AM
    Opening Remarks from the Chair
  • 9:15 AM
    Keynote: Twitter
    Chatbots: The Evolution of Service When Your Consumer Never Sleeps
    Alyson Gausby
    Head of Research, Twitter Canada

    Each and every day, millions of people on Twitter reach out to brands looking for help. The opportunity to answer questions, engage in conversations, and respond to concerns in real time, 24/7 demands a new approach to customer care. Consumers insist on speed, transparency, and accuracy but that’s not easy at scale. Today, only 40% of Canadians say they see good customer service on social media, yet nearly 2/3 say the service they receive impacts their future purchase behaviour. This session will share:

    • New research insights and tips and tricks for how to improve engagement with your customers
    • A consumer-focused deep dive into the world of chatbots
    • Insights on how chatbots can help and what impact they have on customer care

    Leverage chatbots to meet consumer’s needs and improve customer engagement.

  • 9:45 AM
    Industry Expert
    Taking the First Steps of your AI/Machine Learning Journey With Low-cost Investments that Impact Business Results

    AI and machine learning can be expensive investments, but they needn’t be. Identify low-cost initiatives that can make an impact on your business. Develop a strategy to:

    • Leverage existing platforms to support AI/ML
    • Use open source tools to automate processes
    • Create small projects as proof-of-concept to be scaled up

    Develop affordable AI/ML projects to drive business results.

  • 10:15 AM
    Networking Break
  • 10:45 AM
    Case Study: Law Society of British Columbia
    Proactive Regulation Powered by Data Analytics and Machine Learning
    Thomas Kampioni
    Manager, Information Services, The Law Society of British Columbia

    Many non-profit regulatory bodies face a pressure of being an effective and efficient regulator but having limited funding. In order to achieve its organizational objectives and meet KPMs, organizations must take advantage of collected data and use machine learning to identify trends, patterns and certain indicators that are crucial in making informed decisions about the future. Take back to your office strategies to:

    • Introduce affordable machine learning solution to your organization
    • Use predictive analytics to optimize the usage of your resources and effectiveness of your programs and processes
    • Tell the story through data visualization

    Take away practical ideas that you can apply to your organization on how to introduce proactive approach toward regulation and foster a data-driven culture.

  • 11:15 AM
    Industry Expert
    Improve Natural Language Processing to Develop Better Chatbots

    Chatbots have the ability to improve customer experience and free up employees, but only if they are highly functional. Improve the way your chatbots function with better NLP. Gain insights to:

    • Develop better AI-based long conversations
    • Improve open domain conversations
    • Create AI with a consistent personality

    Develop better chatbots with the aid of improved natural language processing.

  • 11:45 AM
    Panel
    Collaborating With Researchers to Improve Business Outcomes With AI
    Dean McKeown
    Associate Director, Administration, Scotiabank Centre for Customer Analytics, Smith School of Business at Queen’s University
    Helen Kontozopoulos
    Co-Founder/Director, Department of Computer Science Innovation Lab, University of Toronto

    Canada’s AI strategy places a strong emphasis on collaboration with research institutions. Take advantage of Canada’s strong network of AI researchers to drive real business results. Source best practices to:

    • Develop partnerships with leading AI researchers
    • Support commercial activities with the help of academic partners
    • Develop real-world applications for AI research

    Take advantage of Canada’s AI research leadership to drive business results.

  • 1:45 PM
    Case Study: WestJet
    How AI Is Changing How We Travel
    Tania Hoque
    Manager, Mobile Strategy and Emerging Technologies, WestJet

    Passenger air travel is expected to double across the globe over the next 20 years. How can airlines plan for this increased demand, while both controlling costs and providing innovative solutions that truly make air travel better. Learn how airlines can invest in Artificial Intelligence technologies to meet this demand. Look at Artificial Intelligence technologies to:

    • Enhance Guest Experience throughout the travel journey
    • Reduce costs and increase revenue
    • Increase employee satisfaction and productivity

    Apply AI to improve guest experience, increase revenue and improve productivity.

  • 2:15 PM
    Industry Expert
    Building Your AI/Machine Learning Team to Support Your Strategy

    By one estimate, there are only 10,000 people in the world with the expertise to create machine learning systems. Overcoming the shallow  AI/ML talent pool requires creativity. Gain expert insights to:

    • Compete with Silicon Valley and other foreign job destinations
    • Train employees to take on new roles
    • Identify and attract new talent

    Give your AI/ML strategy a proper foundation by building a strong team to support it.

  • 2:45 PM
    Case Study: Goldspot Discoveries
    Unlocking Value in Exploration through Artificial Intelligence
    Denis Laviolette
    CEO, Goldspot Discoveries

    The mineral exploration business is data-rich.  But the industry is currently frozen by the thought of what to do with it. Hear how a leading company is using big data in the mineral exploration process by interconnecting traditional geoscience and machine learning. Source expert insights on:

    • Using data to de-risk your project
    • Using data to lower your costs
    • Using data to increase your rates of discovery

    Explore less and find more through the power of machine learning.

  • 3:15 PM
    Networking Break
  • 3:45 PM
    Case Study
    Understand the Legal Landscape to Make the Most of AI While Staying on the Right Side of Regulations
    Anthony de Fazekas
    Partner, Lawyer, Patent Agent, Head of Technology & Innovation, Canada, Norton Rose Fulbright

    The legal landscape in a fast-moving field like AI can be unclear. Get the clarity you need to make business decisions. Gain insights to:

    • Understand the relevance of copyright laws to AI/ML; and the protectability of AI/ML
    • Comply with privacy regulations
    • Anticipate potential legislative changes

    Gain an understanding of the legal landscape for AI in Canada to inform business decisions.

  • 4:15 PM
    Case Study: Shoppers Drug Mart
    Teach Machines to Learn for Better AI
    Pramod Dogra
    Senior Manager Advanced Analytics, Shoppers Drug Mart

    Today, experiential learning doesn’t apply just to humans — machines are increasingly able to sense, reason, act and adapt based on learned experience. Create a framework to teach your AI to be smarter. Develop a plan to:

    • Apply cognitive learning theory to machines
    • Teach your AI to be smarter
    • Automate complex tasks with AI

    Develop better AI by teaching machines using psychological principles.

  • 4:45 PM
    Conference Adjourns
February 8, 2018

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