Day 3 | Friday, October 25

MORNING | Plenary Keynote Sessions

Coming soon.

AFTERNOON | Concurrent Tracks

Track 10: Monetizing Big Data

With data in-hand, machine learning aids in everything from cleaning datasets to managing multiple data sources to synthesizing data. With the help of machine learning, data can now be monetized. This track identifies key business strategies for data monetization and steps to be taken to maximize the impact of AI technology interaction with Big Data.

  • What process or framework do you use to create new data monetization business models?
  • How much data is enough? Do you have too much data or not enough? How do you to get the data that you need?
  • Identify how challenges in data storage, processing, and analysis can be overcome using AI technologies

Track Chair: Judith Hurwitz, President, Hurwitz & Associates

Using Intelligent Automation for Data Selection

Bigger Isn’t Always Better: Techniques for Analytics with Less Data

Understanding Data Risks with Artificial Intelligence

Data Management and Governance using AI Data Discovery

Track 11: Automating Strategic Enterprises Roles & Functions

Enterprise organizations have a range of core business operations able to utilize AI technologies, however, this one-size-fits-all functionality can be a non-starter for some industries. Whether due to regulation, government oversight compliance, or unique requirements, these vertical markets may appear to be laggard adopters. Hear how these companies can innovate using intelligent solutions for sales, marketing, finance, engineering, HR, customer service, change management, corporate governance, and more.

  • How, regardless of industry, AI is reducing human error and creating higher repeatability
  • The ways that workers are being trained to handle more complex, subjective, and creative tasks alongside AI deployment
  • The need for data sciences to enhance all enterprise roles and functions

Track 12: AI in Telecom & Mobile

According to a recent article in Forbes, “…a fully autonomous self-driving vehicle will be the epitome of AI and 5G technology.” The synergy between Artificial Intelligence (AI) and 5G is likely to lead to dramatic breakthroughs. Enterprises are especially interested in how the business case for their investments in Big Data, Cloud and IoT applications will be enhanced by AI and 5G networks.

  • What business growth strategies are mobile network operators considering for deployment of AI in 5G?
  • How are investments in AI startups differentiating 5G devices?
  • What AI applications will drive ROI in 5G wireless networks?

Track Chair: Berge Ayvazian, Senior Analyst and Consultant, Wireless 20/20

5G Networks, AI and Machine Learning Power Enterprise Digital Transformation
Ali Imran, PhD, Co-founder and Chief Technical Advisor, AISON

Panel: State of AI in Telecom and Wireless
Moderator: Berge Ayvazian, Senior Analyst and Consultant, Wireless 20/20
Panelists: Manish Vyas, President of Communications, Media & Entertainment and CEO of Network Services, Tech Mahindra
Tejas Rao, Network Practice Managing Director and Global 5G Offering Lead, Accenture

Panel: 5G and AI Partnership Collaboration Industry Roundtable
Moderator: Phil Marshall, Chief Research Officer, Tolaga Research

Panel: Venture Investments, Innovation and Start-Ups in AI, 5G and IoT
Moderator: Berge Ayvazian, Senior Analyst and Consultant, Wireless 20/20
Panelists: Ali Imran, PhD, Co-Founder and Chief Technical Advisor, AISON
Lu Zhang, Founding and Managing Partner, Fusion Fund

Track 13: AI in Healthcare

Artificial intelligence in the healthcare industry is predicted to save $150 billion annually for the US. As such, AI is being rapidly deployed in many areas of the healthcare landscape. This event will primarily focus on the Providers, attracting CIOs, CTOs, VPs of IT and Informatics along with senior Physicians and Clinicians from leading US hospitals who will share their experiences of using AI in clinical care and hospital operations.

  • Invaluable insight from the Payers, Patients and Investors
  • Integrating human and machine brains: The ethical issues
  • Using AI to generate trends and influence healthcare policy
  • Analyzing the economic models of AI: Who should pay and why?
  • Assessing the impact of recent M&As between payers, providers and PBMs and streamlining AI across all 3 sectors
  • How can chatbots help to evaluate symptoms, manage medications and monitor conditions?
  • Practical application in clinical/patient care: Image analysis, decision making, diagnostics, doctor consultation, personalized treatments, robotic surgery, virtual nursing assistants and electronic health records (EHRs)
  • Increasing efficiency in operations, workflows and administrative tasks (inc EHRs)

Deep Learning for Clinical Natural Language Processing
Sadid Hasan, PhD, Senior Scientist and Technical Lead, Artificial Intelligence Group, Philips Research

Additional Presentations from:John Mattison, MD, CMIO, Kaiser Permanente
Sandy Aronson, Executive Director of IT, Partners HealthCare Personalized Medicine
Uzair Rashid, Senior Manager, Healthcare Strategy & Innovation, CVS Health
Phil Hunter, Research Fellow, Rethink 


Track 14: AI in Pharma

Application and investment of AI in the pharmaceutical industry is rapidly gaining momentum. We bring together CEOs, CIOs, CTOs and Global AI, IT and Informatics Experts from leading pharmaceutical and technology companies to give strategic talks from a business perspective together with use cases from across the drug development pipeline.

  • What are successful pharma companies doing today to prepare for a data-fueled, machine learning future?
  • Why is the pharma industry finding AI so difficult? Bridging the gap between life science and computer science
  • Breaking down silos: Creating cross-functional AI teams and making data available to all
  • Examining industry partnerships, collaborations and M&As
  • What are the best strategies for hiring AI talent with life science experience?
  • Disrupting drug discovery: Precision medicine, biomarkers, target identification and screening
  • Predicting clinical trial outcomes with the use of AI
  • Using AI to optimize regulatory processes, manufacturing strategies, supply chain, real-world evidence, HR, finance and the commercialization of products

Presentations from: Boehringer Ingelheim’s Digital Lab, Sanofi, Bayer, Pfizer, MIT and more – View Details

Track 15: AI and Machine Learning in Finance, Banking and Insurance

Artificial intelligence (AI) and Machine Learning (ML) are disrupting the financial services industry, and rightly so. The Finance, Banking and Insurance industries are sitting atop a mountain of customer data and are well positioned to benefit their business and their customers if they can utilize it effectively. AI can serve to improve decision-making, affect overall business strategy, generate new revenue, predict customer behavior, automate customer service, improve risk models, reduce costs, enhance business operations, improve customer experience, offer tailored products and advice, prevent fraud, and optimize internal processes. This track brings together business leaders and data science practitioners from the leading banks, insurance firms, asset management organizations, broker and investment firms, and fintech startups.

  • How organizations are adopting AI, ML, data analytics, image, voice recognition and NLP technologies across their enterprise to improve their businesses and better serve their customers
  • Integrate AI into business strategy development in banking, finance and insurance to make data-driven management decisions for the enterprise
  • How are innovators and Centers of Excellence bridging the gap between the tech and the business and developing a business case for AI
  • Applying AI to compliance, anti-money laundering (AML), fraud detection and digital identity
  • Using AI, ML and Deep Learning to improve personalization and predict customer behavior in banking, finance and insurance

Presentations from: Capital One, Wells Fargo, Citibank, Mastercard, MIT, Intuit, Nasdaq and more – View Details

Track 16: Robotics & Autonomous Machines

This conference track is specifically designed to impart to technical professionals the information they need to successfully develop the next generation of commercial robotics systems. Talks emphasize the design and development of commercially viable robotics and intelligent systems products including robots, drones, and autonomous machines.

Proposed Sessions:
  • Technologies, Tools and Platforms: covering the latest advances in the core technologies that are common to most classes of robots and intelligent systems, including Sensors and Sensing, Thinking and Cognition, and Actuation and Mobility.
  • Design and Development: covering the design and development of commercial robotic systems.
  • Manufacturability, Production, and Distribution: includes trends, designing for manufacturing, supply chain support, and robotics as a service.

Track Chairs: MassRobotics and John Santagate, Research Director Commercial Service Robotics, IDC

Armchair Interview: Safety and AI’s Role in Future Autonomous Vehicles

Panel: The Latest Advances in AI-Powered Technologies, Tools and Platforms

Panel: Design and Development of Commercial Robotic Systems

Panel: Manufacturability, Production, and Distribution of Intelligent Automation in Autonomous Systems

Track 17: Cutting Edge AI Research

This track will showcase cutting edge research and algorithms from both commercial and academic labs, that will be available for deployment in the next 1-3 years. The audience will learn what is currently being worked on and address several relevant issues, including:
  • How much data is needed to train a model?
  • Robustness of the models – trustworthy models
  • Ensuring privacy in the data

Track Chair: Ritu Jyoti, Program Vice President, Artificial Intelligence Strategies, IDC

How Much Data is Needed to Train a Model
Raj Minhas, Ph.D., Vice President, Director of Interaction and Analytics Laboratory, PARC
Karen Myers, Lab Director, SRI International's Artificial Intelligence Center

Robustness of the Models
Mark Stefik, Research Fellow, PARC
Pin-Yu Chen (and Team), Research, MIT-IBM Watson AI Lab, Thomas J. Watson Research Center
David Bishop, SVP, Research, Hitachi Solutions
Victor S.Y. Lo, Head of Data Science and Artificial Intelligence, Workplace Solutions, Fidelity Investments