Join our AI World speaking faculty covering a variety of critical business and technology topics in informal, small group format that allows all participants to meet potential collaborators, share examples from their own work, vet ideas with peers, and be part of a group problem-solving endeavor. This is a great way to expand your networking opportunities.
Wednesday, October 23 | 6:30 – 7:30 pm
TABLE 1: AI in Personalized Medicine and Digital Health
Moderator: Ana Maiques, CEO, Neuroelectrics
- Examining digital therapeutics
- Advances in brain technologies
- The challenges of bringing science to the market
- How to build a company without VC: A bootstrapped strategy
- New AI approaches to deal with brain diseases
TABLE 2: Intelligent Automation – No Data Scientist Required
Moderator: Kashyap Kompella, CPA and Chief Analyst, rpa2ai
- Improving operational efficiencies with reduced resources
- Creates value for back office and empowering knowledge workers
- Build trust to pursue machine learning
TABLE 3: Preparing for the Next Wave of AI/ML Technologies
Moderator: Mike Riordan, Vice President, Entanglement Institute, Inc. and former instructor at Ethics & Emerging Military Technology Graduate Program, U.S. Naval War College
- How enterprises can plan for the unknown
- Identifying signals and early indicators in technology experiments
- Using technology roadmaps for strategic enterprise planning
TABLE 4: Trends in The Evolving Intelligent IoT
Moderator: Ken Briodagh, Editorial Director, IoT Evolution World
- What does a mature IoT look like? What are its characteristics as an industry?
- How can a mature IoT leverage Intelligence at the edge (via AI, ML and Dynamic Analytics)?
- What are some of the new advantages that are enabled by this evolution from Sensors and efficiency-focused implementation to Intelligence-driven strategic thinking in the IoT?
- What are the pitfalls/dangers/risks of this new, mature IoT?
TABLE 5: Practical Application of AI and ML in Clinical Care
Moderator: Charles Jaffe, MD, PhD, CEO, Health Level 7 International
- The clinical demands dictate the varied implementation of Artificial intelligence and Machine Learning
- Clinical decision support that is augmented by AI and ML improves outcomes and mitigates risk
- AI applied to various clinical environments from imaging to population risk assessment reduce errors and improve workflow
TABLE 6: AI to Improve Patient Efficiency, Provider Efficacy and Treatment Effectiveness
Moderator: Senthil Kumaran, CIO, virtuwell by Healthpartners
- How is AI changing the telemedicine diagnostic industry?
- Reducing bottlenecks and improving patient flow and patient-specific needs
- Solving provider scheduling needs and provider efficacy by routing the patient encounter to the right provider and by funneling patient information
- Minimizing round trips to save time and money for patients and increase treatment effectiveness
TABLE 7: How Can the Use of Natural Language Processing Benefit the Pharma Industry?
Moderator: Sebastien Lefebvre, Senior Director, Data Sciences, Genomics and Bioinformatics, Alexion Pharmaceuticals
- Where do you get your data/information from to support your key decisions? (think BD, Strategy, new products, competitive intelligence)
- Information Sciences has disappeared from most of Biopharma companies so who picked up the work?
- How do you investigate/explore a new topic? How do you validate your thinking/hypothesis?
- AI driven NLP is here and new content curation companies are leveraging it. Where do they fit in your decision support capabilities/processes?
TABLE 8: Diversification of Pharma Portfolio Through Digital Services
Moderator: Angeli Möller, PhD, Head of IT Business Partnering Research, Pharmaceuticals Division, Bayer
- Opportunities presented by digital services
- Overcoming common challenges and pitfalls
- Reviewing public/cooperate partnerships
- Examining data access and artificial intelligence
TABLE 9: Breaking Down Silos: Creating Cross-Functional AI Teams and Making Data Available to All
Moderator: Brian Kesselman, Head, IT & Digital Transformation, Pharma, Bayer
TABLE 10: What is Better for Introducing AI to Larger Companies – Centralized COE vs Distributed Collective Development
Moderator: Alex Muller, MBA, Senior Vice President, Entrepreneur in Residence, Synchrony Financial
What is a better approach and operational model when introducing AI into larger companies, those with legacy systems and complex governance? Two options are the Centralized Center of Excellence or the Distributed Collective Development model.
- How do you prove to “Wall Street” you are investing in AI?
- In a centralized model – who is qualified to “own it” within the company? Is it the CTO or is there a separate AI group? How do you effectively establish a working COE, how can they prioritize all the potential AI projects? Does
this mean you can only hire PhDs in data science?
- In a decentralized model – does anyone get to use AI? Given the potential costs for quality issues – how is quality control managed? What is the risk of letting non-data science use these tools?
TABLE 11: Enabling Advanced Analytics Implementations at Enterprise Level – Tech and Business Perspectives
Moderators: Srinivaas Krovvidy, PhD, Head, Advanced Analytics Enablement, Enterprise Data, Fannie Mae
Prabhakar Bhogaraju, MBA, Vice President, Digital Products, Fannie Mae
As enterprises start looking at leveraging AI and ML technologies, it is important to make sure appropriate resources and support structure are made available. This includes:
- Managing Enterprise Data Lake as a shared service;
- Providing guidance in assessing appropriate use cases;
- Developing best practices and patterns for deploying AI and ML solutions and frameworks for integrating with enterprise applications; and
- Creating forums for close collaboration with business and analytics teams.
TABLE 12: Solving Anonymous ID Stitching – Handling Multiple Personas in Silo-ed Systems
Moderator: Sridhar Raman, Product Development Leader, Intuit Inc
Anonymous ID stitching is a pervasive problem where a person can browse websites and applications either as an anonymous user or as a logged in user. The problem is generally exacerbated by handling these multiple personas in different silo-ed systems,
and thereby leading to a dichotomy of data. How can companies solve Anonymous ID stitching by leveraging AI, and ML to deliver deeply personalized experiences within its products?
- Business impact of unstitched anonymous IDs
- Conway’s law and architectural issues
- Identifying the right customer problem to solve
- Problem formulation
- Solution options
TABLE 13: AI and the Financial Crime Arms War: Pairing Consortium Data with Advanced Analytics
Moderator: Rivka Gewirtz Little, Research Director, Global Payment Strategies, IDC
Let’s face it: Financial fraudsters are successful. That’s because they employ similar analytics and automation as Financial Institutions. So, how can banks outsmart – or out-arm – fraudsters? The answer is as much about the
data as it is the analytics. In this roundtable, we’ll discuss:
- Use of advanced analytics in fraud management
- The rise of consortium data-driven analytics
- Regulatory challenges with AI in financial crime management
- Creating AI strategies for a holistic, bank-wide fraud and AML management strategy
TABLE 14: Using AI to Improve Energy Efficiency & Transform the Energy Industry
Moderator: Kevin Prouty, Group Vice President, Energy and Manufacturing Insights, IDC
- How are large data sets being analyzed for identifying patterns, detecting anomalies, and making precise predictions?
- Identify smart applications that can autonomously make accurate recommendations based on learning.
- Where predictive analytics improves equipment O&M and predicts equipment downtime.
TABLE 15: Transforming Retail, eCommerce, & the Supply Chain with AI/ML
Moderator: Aili McConnon, Contributor, Wall Street Journal
- Orchestrating the transformation of physical work
- Building intelligent systems to solve commerce challenges
- Developing omnichannel programs that improve the customer experience
TABLE 16: Automating Conversations for an Improved Customer Experience
Moderator: William Meisel, PhD, President, TMA Associates
- Automating the understanding of human text and speech
- Best practices for successful use of NLP technologies
- Creating a flexible conversation
TABLE 17: Preparing Big Data for Automation & Monetization
Moderator: Henry Morris, PhD, Principal, Henry Morris Analytics
- Exploring data-driven revenue strategies
- Assessing the “data rich and insight poor” situation
- How do you address data sparsity (many rows of data, but with many missing values)?
- How do you manage the data cycle – data acquisition, data exploration, feature engineering?
- How do you form a team for preparing data for automation & monetization?
TABLE 18: Augmenting Human Intelligence & The Future of Work
Moderator: Steve Ardire, Force Multiplier 'Merchant of Light', Independent AI Startup Advisor
- Overcoming the anxiety of job automation
- Enabling knowledge workers to focus on complex, subjective, and creative work
- Greater emphasis on soft skills, such as critical thinking, cognitive flexibility, emotional intelligence, and lifelong learning
- Preparing the workforce for the future of work
TABLE 19: Making AI Trustworthy
Moderator: Pin-Yu Chen, PhD, Chief Scientist, RPI-IBM AI Research Center, Research Staff Member, Trusted AI Group, IBM Thomas J. Watson Research Center
- Why we need AI to be trusted?
- What domains will require AI to be trusted?
- What is lacking in the current AI technology in terms of trust?
- How do we make AI trusted?
- How do we verify AI to assure it is trusted?
- Is AI's trust measurable?
- Any progress you will like to see toward trusted AI
TABLE 20: Performance Breakthrough
Moderator: Roberta Stempfley, Director, CERT Division, Software Engineering Institute, Carnegie Mellon University
Automation and more advanced methods thought of as AI, will enable dramatic improvements in performance, however that is not a guarantee. This discussion will explore the dynamics of today’s environment and identify areas that require
attention to ensure that the AI solutions are integrated into business operations successfully
- Impact on human-machine teams? What role do the humans play? The AI? Under what conditions do these need to change?
- What are the engineering breakthroughs needed to bring AI to bear?
- What metrics should be used to gauge success? How do you evolve risk management processes with the introduction of these capabilities?