Day 2 - 29 November 2018
Big Data for Industry: Chairman’s Welcome
Director of Strategy & Business Development
09:30AM - Day 2
Keynote: AI and Its Promise to Business: Narrow AI vs General AI
AI is a red-hot topic, even overhyped, some say. We will show why the latter group may well be right–about general AI, anyway. One thing’s for sure: we’re not going to have personal AI assistants anytime soon. Narrow AI applications–that is, practical, focused ones–are a different matter altogether. We believe such applications in business will deliver on their promise and revolutionize the world as we know it.
VP of Sales
10:00AM - Day 2
03:45PM - Day 2
10:30AM - Day 1
Unlock every conversation and deliver voice data to the enterprise
Are you making decisions based on incomplete data? The first step in harnessing the power of analytics is having the right data set spanning the whole customer journey. Leaving voice data unexplored and unused means you many be missing a wealth of insights that can directly impact business performance, customer experience and risk management. Join this session with Call Journey to learn
- The 4 industry trends driving the need for voice data
- Use cases and impact of voice data across the organization ( Customer Experience, Marketing, Sales, Contact Center, Risk & Compliance)
- The blueprint on how you can easily add voice to the enterprise data mix.
Lead Data Scientist
Zappos Family of Companies
11:00AM - Day 2
12:15PM - Day 2
Machine Learning in Production: From Research to the Customer
If you had to describe the Machine Learning process in 5 steps, from research to customer, what would they be? This is a question I often ask candidates interviewing for the Zappos Data Science team. My hope is that one of them will be able to tell me, so I can stop trying to figure it out myself. There used to be a time when we didn’t have enough data but we had ideas. Today we not only have the data, but we also have the ideas implemented as models. The real challenge now is to put those models into production. In today’s talk we will go over the Dos and Donts of deploying Machine Learning and Artificial Intelligence solutions at scale and bridging the gap between research and production environments.
Vice President of Dark Fiber Business Development (West Region)
11:30AM - Day 2
Dr. Ren-Hao Pan
Chief Executive Officer
La Vida Tec
11:30AM - Day 2
Senior Data Scientist
Children's Hospital Los Angeles
11:30AM - Day 2
02:30PM - Day 2
Panel: The big data crunch: Transforming healthcare
- How much data does the healthcare industry have? What isn’t being utilised?
- Assessing the sheer amount of data the healthcare industry has and why it will take a long time to be digitised
- The rise of the Internet of Things – apps, wearable devices, sensors – in creating new data streams
- How other data – food shopping purchases, social media – can be utilised
- How this can all fit together to create a more streamlined, holistic view of the patient
S/Constable in Charge, Crime Analytics Advisory & Development Unit (CAADU)
Vancouver Police Department
01:00PM - Day 2
Big Data in Public Safety: Fueling AI in an Ethical and Transparent Way
The Vancouver Police Department is known worldwide for its pioneering work in the field of intelligence-led policing, and is the first police service in Canada to deploy a machine-learning, predictive system directly on police mobile computers. With a focus on high-end analytics, combined with competitive technology, the Department has achieved stunning results in reducing crime rates.
Key to this success is the use of ‘big data’ repositories that are required for deep learning and are the foundation of advanced crime forecasting technology. However, big data pose significant challenges to law enforcement, both in acquiring, storing and accessing decades of data, but also ensuring that the data is free of biases and prejudices, which could predispose forecasting outcomes.
Following the implementation of predictive technology and the consequent deployment of resources based on the forecasting, has helped to reverse skyrocketing residential burglaries. What sets this deployment apart from previous examples of predictive policing, is the way in which the technology was used, with the application of innovative policing practices in combination with advanced evaluative methodology to help guard against over-policing in ethnically and socio-economically diverse neighbourhoods.
02:50PM - Day 1
01:30PM - Day 1
Computing architecture challenges to extract value on big data
The introduction of the machine learning and AI into the industry will provide to the production chain enormous benefits at many levels, making real new kind of manufacturing processes and delivering to the market better products and services, AI will permit to optimize the costs and the resources to achieve the best level of quality and productivity.
Machine learning is a substantially a computational process. To that end, it is inextricably tied to computational power and computing architectures. The computational power and the computing architecture shape the speed of training and inference in machine learning and therefore influence the rate of progress in the technology. But, these relationships are more nuanced than that: hardware shapes the methods used by researchers and engineers in the design and development of machine learning models.
This paper aims to dig more deeply into the relationship between computational power and the development of machine learning chowing how the right computing architecture and the capability to fit the problem needs permits to achieve better results in shorter time and opens many new opportunities in the adoption of the AI into the industry.
Chief Data & Analytics Officer
02:15PM - Day 2
03:45PM - Day 2
Transforming into Big Data Analytics
Bin Mu, the Head of Data and Analytics of Brighthouse Financial, will share the journey and experience of the transformation to Big Data Analytics in a Brighthouse Financial, which just separated from Metlife in 2017.
Brighthouse Financial is in the process of separating from Metlife’s systems and environments. We are setting up our research environment and implementing Big Data analytics, which include transition into 100% open source analytics tools (Python/PySpark), and developing the integrated data environment with SAP HANA and BDS. This transformation includes not only setting up the Hadoop ecosystem, integrating with SAP HANA enterprise system, but also transition of the skillset of the Data and Analytics team.
Head of AI and Machine Learning Boeing AnalytX
02:45PM - Day 2
Future of aviation innovation through ML
As we embark on our 2nd century enterprise digital transformation, Boeing is driving growth with portfolio of analytics-driven products and services using Artificial Intelligence and Machine Learning.
At its core, we are making a difference in the aviation industry by delivering a new generation of manufacturing and logistic experiences. I will discuss how we are driving change internally and externally to drive growth with:
- Digital Transformation: Driving Data Growth
- Avionics transformation using Machine Learning
- Key Learnings: Driving Innovation while staying compliant and secure
- Advance manufacturing using IoT and Machine Learning
- Future of aviation innovation using AI and Machine learning