Day 1 - 29 November 2017
AI for Developers: Chair’s Welcome
Keynote: The AI and Machine Learning Revolution.
- How to put ML models into production
- Current options and challenges of ML hardware
- Challenges faced by Enterprises using new deep learning and ML technologies
- What are the next wave of ML innovations?
Organizer of Big Data Science Meetup/CEO & Founder of AyushNet
Big Data Science MeetUp
Panel: Data and ML architecture
- Frameworks for data acquisition
- Creating big data and ML pipelines to power algorithms
- Model training and evaluation
- Designing a scalable and robust infrastructure
- How to utilize cloud-based systems for complex computing
11:30AM - Day 1
Case Study: Modeling Sequential Decision Making in Team Sports using Deep Imitation Learning
Disney Research has developed an automatic “ghosting” system which illustrates where defensive players should have been (instead of where they actually were) based on the locations of the opposition players and ball. Employing a machine learning technique called deep imitation learning, and modifying standard recurrent neural network training to consider both instantaneous and future losses, their algorithm is able to generate ghost player behaviors which anticipate the movements of their teammates and the opposition. The approach avoids the man-years of manual annotation that is needed to train existing ghosting systems, and can be fine tuned to mimic the behavior of specific teams or playing styles.
Group Lead at NASA
NASA - National Aeronautics and Space Administration
12:00PM - Day 2
AI powering missions to Mars
For over 50 years, NASA’s crewed missions have been confined to the Earth-Moon system, where speed-of-light communications delays between crew and ground are practically nonexistent.
This ground-centered mode of operations, with a large, ground-based support team, is not sustainable for NASA’s future human exploration missions to Mars. Future astronauts will need smarter tools employing Artificial Intelligence (AI) techniques make decisions without inefficient communication back and forth with ground-based mission control. In this talk we will describe several demonstrations of astronaut decision support tools using AI techniques as a foundation. These demonstrations show that astronauts tasks ranging from living and working to piloting can benefit from AI technology development.
Howard C. Consulting
12:30PM - Day 1
The importance of talent development aligned with UN Sustainable Development Goals to shape your AI business
- Why talent development is a key issue for the AI era?
- What is the importance of implementing organization health index(OHI) in every industry & nationality?
- What is the connection between OHI, talent evolution, AI, IoT & innovation?
- How could we combine the concept of UN Sustainable Development Goals for shaping AI business?
Panel: Driving Digital Transformation through AI & DL development
- What are the fundamental buildings blocks needed for build a digital organisation using AI, ML & DL in terms of people, skills and tech?
- Discussing the role of other companies within the process, from start ups to big ticket providers.
- Examining digital transformation from both industrial (digital twins etc) and business information (supply chain analysis) perspectives.
- Real life examples of successful transformations from across Enterprise.
Pratik Prabhanjan Brahma
Volkswagen Group Electronics Research Labarotary (ERL)
02:45PM - Day 1
Challenges and opportunities in applying machine learning for assisted and autonomous driving
- Deep Learning and its promising applications in perception for vehicles
- Real world and simulated data requirement for training and testing of machine learning models
03:45PM - Day 1
Woodside Capital Partners
03:45PM - Day 1
03:45PM - Day 1
Co-founder and CEO
03:45PM - Day 1
Investor Panel: Financing your AI Innovation
Our panel of AI investors and venture capitalists discuss what they look for before investing in a fledgling AI company. A must attend for all AI start-ups and entrepreneurs.
Using Deep Learning with Intel BigDL for optimized personalized card linked offer
- We implemented new Users-Items Propensity Models with deep learning algorithms base on Intel BigDL framework to help our PCLO (Personal Card Lined Offers) team in Loyalty to improve the quality , performance and accuracy of offer and campaigns design, targeting offer matching and linking .
- Intel BigDL is a distributed scalable deep learning library for Apache Spark; with BigDL, users can write their deep learning applications as standard Spark programs, which can directly run on top of existing Spark or Hadoop clusters without considering changing existing infrastructure and learning lots of new “terminologies”.
- I will also share the technical evaluation journey to choose an enterprise deep learning framework and the experiences for using Intel BigDL.
Start up Showcase- Using AI & Machine Learning in the Real World
A selection of rapid fire presentations from the hottest new AI start ups, showcasing the latest advances in this fast moving field.