Emerging Technologies and the Future of AI

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 development neuromorphic chipsets to democratizing deep learnin toolsets next wave machine vision, emotion, gesture, NL, new algorithms, HPC, quantum computing will all be shared by the industry's best and brightest.

Wednesday, December 5, 2018

7:45 am Registration Open

8:20 am - 12:45 pm AI World Plenary Session - View Details

11:00 am - 2:00 pm Concession Stand Open for Lunch in the Exhibit Hall

Track Program

Track Chair

Orr_JeffJeff Orr, Conference Content Director, AI World

2:00 Don’t Get Rid of Your Live Agents Quite Yet - Augmenting the Contact Center with AI

Jessica LangdorfJessica Langdorf, Director, Digital Engagement Lab, Nuance Communications, Inc.

Combining artificial intelligence with the human workforce will deliver a highly efficient contact center, but how do you design an experience that leads to reduced agent churn and high customer satisfaction? Jessica Langdorf will share her expertise on designing AI powered customer engagement solutions for customers and contact center agents.

2:30 PANEL: Emerging AI Trends: Removing Bias and Explainable AI (XAI)

The enterprise and end users need a way to explain why the AI made a prediction. Whether declining a loan or mortgage application, settling an insurance claim, or recommending a personalized medical plan to maintain optimal health, industry watchdogs and regulators are reluctant to embrace intelligent systems without some explanation of how the data input generated the machine output.  Some technology providers have claimed that they are already delivering explainable AI systems, but these are few and far between.

  • Discuss what is meant by explainable AI and what is it that businesses and industry regulators want to know about predictions
  • Understand the trade-off between AI transparency and performance along with the implications for intellectual property
  • What is the current state of the technology in delivering truly explainable AI systems
  • As narrow AI implementation scales to address complex business judgments and AGI, does the demand for explainable AI increase beyond finance, healthcare, and legal vertical markets?

Orr_JeffJeff Orr, Conference Content Director, AI World

Heather AmesVersaceHeather Ames Versace, PhD, COO and Co-Founder, Neurala

Raj MinhasRaj Minhas, PhD, VP and Director of the Interactions and Analytics Lab (IAL), PARC

Matthew CarrollMatthew Carroll, CEO, Immuta

Virani_ArifArif Virani, COO, DarwinAI

3:05 Refreshment Break in the Exhibit Hall - Last Chance for Viewing

3:40 PANEL: Implementing Advanced AI Technologies in the Enterprise

Machine learning is currently viewed as a single tool. However, ML is not a static environment. Researchers have already developed advanced technology to evolve ML to process larger amounts of data even faster. Some developers for example are examining how ML can incorporate blockchain for safety and security within the ML model.  ML in its various forms are being integrated into and with other highly advanced intelligent systems such as NLP, image processing, etc. for multitudes of applications. This panel of AI and data science researchers is pushing the bleeding edge of emerging technology and identifying the future of ML.

  • What are the opportunities for evolving ML in the enterprise?
  • How long can the current state of the technology evolve before we seen the next quantum leap?
  • What are the risks of market fragmentation and adoption as ML becomes more than one thing?
  • How can other emerging technologies, such as quantum computing and blockchain, be combined with machine learning to create the next frontier in data science?

Ransbotham_SamSam Ransbotham, PhD, Editor, MIT Sloan Management Review

Feiteira_ErnieErnie Feiteira, Manager of Strategy & Innovation, Liberty Mutual

Reese_CathyCathy Reese, GBS Integration Executive - The Weather Company, IBM

Gossain_VishalVishal Gossain, VP of Retail Modeling and Analytics, ScotiaBank

4:20 PANEL: AI Hardware: Storage, Networking, and Device Revolution

The race for making perfect hardware to accelerate artificial intelligence (AI) applications is heating up and many companies are jumping in with their products and solutions. Of the three key parts of hardware infrastructure – compute, networking, and storage – compute has made significant progress in the last couple of years. The other two areas, storage and networking, are lagging and have yet to see major innovations pertaining to AI applications. In the next few years, however, more research and development (R&D) will go into these areas and new products will emerge that are designed specifically for AI.

  • What are the market drivers and milestones that will enable AI-driven hardware to grow to $115.4 billion by 2025?
  • Which hardware segments will account for the majority of sales?
  • What impact will FPGAs, ASICs, SoC accelerators, and other emerging chipsets have on the current dominance of GPUs and CPUs?

Moderator: Kaul_AdityaAditya Kaul, Research Director, Tractica

Harrsen_JohnJohn Harrsen, VP Cloud Product Management, Graphcore

Shen_YichenYichen Shen, PhD, CEO, co-founder, Lightelligence

Watson_AndyAndy Watson, CTO, WekaIO 

5:00 Close of AI World 2018