Biography:

Prof. Erwin Sniedzins has patented the Knowledge Generator™ (KG); a Machine Learning, “MicroSelf-Reinforcement Learning, Artificial Intelligence, Personalize ‘Gamification’ of ANY digitized textual content application in DYNAMIC real-time. The KG technology enables people to turn Data into Knowledge (DiK) 32% better and easier with more confidence and fun. No teacher or course designer is required. Erwin is the President of Mount Knowledge Inc. The company is a global leader in ML, AI, neural networks, automatic gamification of any textual data and reinforcement learning. Erwin has authored and published 12 books, Keynote speaker, Professor at Hebei University and Mt. Everest expedition leader.

Abstract:

Big Data is inundating educators, students, employers and employees causing a lot of stress, frustration and lack of confidence in data acquisition. More than 3.8 billion people are seeking relief from 3.4 exabytes of daily data bombardment. Genetic Algorithm Neural Networks (GANN) and machine learning provides a bridge and filtration solution between exabytes of data and megabytes of personalized data for knowledge acquisition by using Natural Language Processing (NLP) and automatic gamification in dynamic real-time. AI and ML is transforming humanity’s cerebral evolution as a replacement of repetitive habitual motions and thoughts. In its evolutionary process humans developed their primary biological interfaces to interpret the data that they were receiving through their five senses- seeing, hearing, smelling, touching and tasting. In recent years GANN and NLP have entered to provide, Data into Knowledge (DiK) solutions. Research with GANN and NLP has enabled tools to be developed that selectively filters big data and combine this data into microself-reinforcement learning and personalized gamification of any DiK in dynamic real-time. The combination of GA, NLP, MSRL and dynamic gamification has enabled people to experience relieve in their quest to turn DiK 32% better, faster and easier and with more confidence over traditional learning methods.

Biography:

Samir El Masri has completed his Electronic Engineering’s degree from the Lebanese University, his Master’s degree and Ph.D. from Grenoble National Polytechnic Institute, France. He has worked at Hokkaido University, Japan as a Researcher and Senior Project Manager and he was Assistant/Associate Professor at the University of Western Sydney and University of Sydney, Australia. He has also worked in the IT industry as a Senior Project/Program Manager in leading IT consulting companies in Sydney Australia. He has worked on large eHealth projects with several grants from KACST in Saudi Arabia where he has strong collaborations with local and international healthcare organizations. His main interest is in education, development and research activities. Moreover, he has more than 100 published research papers in international journals, books, and conferences.

Abstract:

Digital transformation is a journey which stems from strong beliefs in the digital economy by senior management supported by a digital transformation strategy. Strategy is much more difficult to deploy than develop and it may only be achieved when the transformation is led by CEOs reinforced by mature capabilities. Unfortunately, most digital transformation initiatives have failed in the past and many more will fail in the future. These failures have been mainly due to organizations undertaking digital change instead of digital transformation in addition to the lack of capabilities and non-readiness of the company to manage this transformation. New digital emerging technologies remain the backbone and the enabler of any digital transformation activities. The digitization of operations, workforce, marketing and new digital business models will be realized by the convergence of all new emerging digital technologies through new products/services, price, customer experience and platform values. In this talk, data science, machine learning, analytics, big data, IOT and their interrelationships will be demonstrated. Examples of how digital initiatives could help the industry by improving efficiency, avoiding trips, reducing unplanned downtime and transforming from time-based to condition-based maintenance will also be illustrated.

Biography:

Miguel Ángel Martínez completed his Computer Science Degree at the age of 23 years from Cadiz University. He is an Artificial Intelligence Mentor at Udacity, and the founder of Dirigendo Ltd., a recently launched startup which combines machine learning and location-based services.

Abstract:

Deep Convolutional Generative Adversarial Networks are a class of unsupervised machine learning algorithm, implemented by a system of two neural networks contesting with each other in a zero-sum game framework.

First introduced by Ian Goodfellow et al. in 2014, GANs are a new framework for estimating models via an adversarial process, in which two models are trained simultaneously: a generative model G that captures the data distribution, and a discriminative model D that estimates the probability that a sample came from the training data rather than G. The training procedure for G is to maximize the probability of D making a mistake. 

The most important benefit of GANs is their ability to learn deep representations without extensively annotated training data. Since their introduction, they have been proven to be useful in a variety of applications, including image synthesis, semantic image editing, style transfer, image super-resolution and classification.

Biography:

Sylvester Juwe is a highly experienced and qualified Artificial Intelligence Lead, currently a Senior Data Science Manager at British Gas, United Kingdom. Operating at strategic levels, he leads on the leveraging sophisticated machine learning and big data analytics and capabilities in enabling and driving business strategy thereby creating business value. Experienced in the exploitation of a range of data mining, advanced analytical and artificial intelligence techniques to understand customer behavior, derive critical insights, optimize operations and solve complex business problems.

Abstract:

Listening to the voice of customers plays a prominent role in a customer-centric business strategy. But with the business environment’s increased complexity and dynamism for a customer-centric business to thrive in its value delivery, there is a growing need for personalization of business offering and continuous evolution of business decisions in such a way that they align with changes in customer needs. These requirements could be challenging, particularly in organizations with a large customer base. In response, this talk presents how advanced analytics and machine learning techniques have enabled operational efficiency and business effectiveness in large organizations. Specifically, this address highlights how tree-based machine learning methods have been employed in understanding and prescribing solutions to complex and evolving operational business problems. Furthermore, it presents insights into, how uplift modeling has improved response rates and returns on marketing spends in large-scale targeted campaigns. Underpinning this talk is a discussion of the leadership approach that informed these innovations.

Biography:

Manoj Mishra has completed his Bachelor of Engineering in Computer Science and a Certification in Data Science from Johns Hopkins University. More than two decade of experience spreading across multiple geographies (US, Europe, India and Middle East) working with organizations like Adobe Systems, Dell, Perot Systems, CEB-Gartner, Rolta and Tata Group. He currently a Chief Manager-Business intelligence and data with union insurance and currently leading their data strategy and technology transformations through data analytics, research and various AI initiatives.

Abstract:

In order to have the competitive advantage, organizations worldwide are driving the need for better analytics (historical, real-time, predictive and cognitive) of data across various domains including customers, products, services and operations. Due to this, the data available for such analytics is exploding in size, technology and complexity. For many years companies have invested in technologies like data warehouses, data marts, OLAP tools, Big Data/Hadoop systems and streaming real-time analytics platforms to take advantage of these opportunities. Total value preposition to the business is maximized only when these are combined into an integrated analytics platform. However, traditional tools cannot integrate streaming data and data-at-rest especially when the data is spread on-premises, cloud, websites and documents everywhere. Data virtualization can be used to provide cross platform logical views of data and analytic insights across the enterprise to provide an integrated analytics platform. By utilizing native integration with in-memory data grids for data processing, data virtualization can deliver a unified and centralized data services fabric with security and real-time integration across multiple traditional and big data sources, including Hadoop, NoSQL, cloud and software-as-a-service (SaaS). Hence data virtualization is becoming a need to address the unique challenges of data explosion in today’s changing business climate.