Day 2 | Thursday, October 24

MORNING | Plenary Keynote Sessions

Coming soon.

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

PANEL: Intelligent Automation with RPA

Data Visualization and Knowledge Graphs

 

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

 

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, RTInsightsView 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 

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

Using AI to Improve Industrial Energy Efficiency

Transforming Asset Inspection in Energy Leveraging Computer Vision & NLP

 

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: IDC

Business at the Speed of AI: An eCommerce Journey

 

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.