Day 2 | Thursday, October 24
MORNING | Plenary Keynote Sessions
Khalid Al-Kofahi, Vice President, Research and Development. Head - Center for AI and Cognitive Computing, Thomson Reuters
Franziska Bell, PhD, Director, Data Science, Data Science Platforms, Uber
Jim Freeze, Chief Marketing Officer, Interactions & Ben Bauks, Sr. Business Systems Analyst, Constant Contact
Scott Lundstrom, Group Vice President and General Manager, IDC Government and Health Insights, IDC and AI World, Conference Co-Chair
Alex Sandy Pentland, PhD, Professor, Engineering, Business, Media Lab, MIT
Heath Terry, Global Investment Research, Goldman Sachs
AFTERNOON | Concurrent Tracks
Track 1: Operationalizing Big Data to AI
The mechanics of collecting the data, which algorithm is the best-fit, and even deriving insights are all important. But the greatest business value from big data, analytics, and AI comes from acting upon it. Decision-making requires modeling both the
data and the people making the decision. Achieving desired outcomes and the ability to act upon the data matters most when operationalizing enterprise big data with AI.
- Why data is not the new oil or currency; why insights alone do not make the business better.
- How to create organizational value from data?
- The benefits of operationalizing value through action
Track Chair: Dan Vesset, Group Vice President, Analytics and Information Management, IDC
Market Overview: Automating Big Data in Operational Environments
Dan Vesset, Group Vice President, Analytics and Information Management, IDC
Using an Intent Graph to Understand Future Customers
Sumeet Singh, Vice President of Big Data, AI & Location Platforms, Yahoo (Verizon Media)
PANEL: Intelligent Automation with RPA
Moderator: Kashyap, Kompella, CFA, CEO and Chief Analyst, rpa2ai
Panelists: Lawrence Lee, VP of Incubation and Innovation Strategy, Xerox
Dorothee Baas, Business Process Integration Manager, Stant Corporation
Predicting and Prosecuting Crime in Rio de Janeiro: A Machine Learning Story
Daniel Carvalho Belchior, Senior Tech Lead Big Data, Public Prosecutor’s Office, Rio de Janeiro, Brazil
Machine Learning in Practice: Anomaly Detection for Army ERP Data
Tanya Cashorali, CEO, TCB Analytics
Track 2: Emerging AI Technologies
There is no shortage of opinions on the potential for AI technologies in business. However, the current round of solutions is often viewed as expensive, proprietary, and complex to deploy and manage. When will AI solutions scale industry-wide? Is it possible
to measure ROI for automation? How does AI rank against other corporate initiatives? The state of AI technology and its future is spoken here. From the development of neuromorphic chipsets to democratizing deep learning toolsets and from the next
wave of machine vision, emotion, gestures, NLG, new algorithms, HPC and quantum computing will all be shared by the industry’s best and brightest.
- Are there AI standards in development to unify current fragmentation of tools and methods?
- How does current and impending regulation impact development and use of algorithms in the enterprise?
Track Chair: David Schubmehl, Research Director, Cognitive & Artificial Intelligent Systems and Content Analytics, IDC
David Schubmehl, Research Director, Cognitive & Artificial Intelligent Systems and Content Analytics, IDC
Synthetic Data: Using Simulations to Build Datasets
Panel: The Impact of Quantum Science on Artificial Intelligence
Emotional Intelligence and Affective Computing
Best of Both Worlds: Blockchain and AI
Track 3: AI and Real-Time IOT in Manufacturing
Reviewing data from thousands or millions of IoT sensors is beyond the capability of humans. Manufacturing is the largest and most advanced industry where AI is required in the deployment and operation of IoT applications. The addition of intelligence
and processing on small devices at the edge raises additional challenges. This track features use cases from the manufacturing industry that sit between the intersection of AI and IOT.
Track Chair: Les Yeamans, Founder & Executive Editor, RTInsights – View Details
Track 4: 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 Technology Research
Track 5: 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 6: 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 7: Applied AI in Energy
Climate change and depletion of the Earth’s natural resources are frequent media headlines directly tied to demand for clean, affordable, and reliable energy. In parallel, gains in computing, memory, and storage have made artificial intelligence
technologies more accessible. The intersection of AI and energy may hold the key to unlocking some of the greatest challenges our world faces. “Exponential technology is rapidly pushing electricity to reach the point of eventually becoming nearly
free,” remarks Pascal Finette of Singularity University. Research into renewables and the use of simulations to creates digital twins are two examples of the accelerated value that machine learning brings to the energy sector. This track explores
the tremendous promise possible today and in the near future.
- 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.
Track Chair: Kevin Prouty, Group Vice President, IDC Energy Insights, IDC
Beyond Asset Automation: AI as a Transformational Foundation in the Energy Industry
Kevin Prouty, Group Vice President, Energy and Manufacturing Insights, IDC
Using AI to Improve Industrial Energy Efficiency
Daiane Piva, Energy Efficiency Improvement Consultant, Tata Steel Europe
Addressing Multi-agent Challenges with AI in Energy Systems
Michael Franklin, PhD, Assistant Professor, College of Computing, Kennesaw State University, Marietta Campus
Using AI Applied to Subsurface Physical Sensor Data: Addressing the Challenges
Smaine Zeroug, PhD, Research Director, Applied Math and Data Analytics, Schlumberger Research
Track 8: AI for Retail & eCommerce
In 2019, AI and machine learning technologies in retail have eclipsed the human analytical capability. Simple, rules-based pricing and competitive response have given way to agile, SaaS-delivered solutions optimized for immediate market conditions. By
combining historical sales data with edge sensors and demand-shaping signals, retailers and ecommerce marketers utilize the massive scalability of machine learning to anticipate market events. Customer-facing applications powered by AI, such as recommendation
functions and self-service checkout capabilities, enhance the customer experience.
- Humans will continue partnering with AI to improve customer experience and business processes in the retail industry.
- From supply chain planning and demand forecasting, to customer intelligence, AI will revolutionize ecommerce and the entire retail sector.
Track Chair: Jon Duke, Research Vice President, Retail Insights, IDC
Jon Duke, Research Vice President, Retail Insights, IDC
Applying AI in Retail & eCommerce: A Market Overview
Jon Duke, Research Vice President, Retail Insights, IDC
Business at the Speed of AI: An eCommerce Journey
Bahman Bahmani, PhD, VP of Data Science and Engineering, Rakuten
Panel: Retail & eCommerce Practitioners
Moderator: Aili McConnon, Contributor, Wall Street Journal
Panelists: Michael Feindt, PhD, Founder, BlueYonder, A JDA Company
Transforming Physical Work with Applied AI for Supply Chain Robotics
Chris, Geyer, PhD, Engineering Fellow, Berkshire Grey
Track 9: Building Conversational, Customer-driven Applications
Hosted by: William Meisel, PhD, TMA Associates
Automating the understanding of human text and speech revolutionizes connections with your customers and employees. Natural Language Processing (NLP) technology—interpreting speech or text— combined with Artificial intelligence algorithms
is one of the most dynamic and rapidly developing areas of technology today. One key trend, for example, is “digital assistants” that converse with customers or employees to ease use of digital systems and services. A conversational platform
using NLP allows a close intuitive connection with users, minimizing frustration and allowing efficient automation of many tasks. NLP technology also allows effective analysis of unstructured text or speech data.
- The state of the underlying NLP and speech recognition technology available for commercial use
- Case studies of deployments
- Best practices for successful use of NLP technology
- Creating a flexible conversation, rather than an overly structured and non-intuitive challenge.
Automating Conversations with Customers: Efficiency and Effectiveness
William Meisel, PhD, TMA Associates
How Conversations Will Help You Build a Better Customer Experience
Ian Beaver, PhD, Chief Scientist, Intelligent Self Service, Verint
Adding Creativity and Body Language to the Conversational Interface
Mark Walsh, Founder and CEO, Motional.ai