Copyright. International Conference on Knowledge, Innovation and Enterprise 2015
knowledge-
Guest Speakers & KIE Channels (Down Below)
KEYNOTER: Professor Frank Habermann, Berlin School of Economics and Law, Germany
Frank Habermann (PhD) is a Professor at the Berlin School of Economics and Law in
Germany. He is the co-
Presentation: How slow thinking can accelerate interdisciplinary projects
In his best-
KEYNOTERS: Chris Wilson and Michael Brown, the University of Derby, United Kingdom
Based at the University of Derby in the UK, Chris Wilson and Michael Brown have over 40 years combined experience of music in higher education, and have worked together for many years developing, presenting, and publishing research into creativity. Marked by interdisciplinarity and a focus on the exploration of boundaries between concepts, disciplines, and practices, they have published work in subjects as diverse as creative anthropology, business, project management, education, and the arts.
Presentation: The Future of Creativity
This presentation explores a number of questions: What is the future for creativity?
How will we need to be creative in the coming decades? How will personal, social,
and organizational creativity operate in a future of Artificial Intelligence, networked
data and information? How might our perspective of creativity evolve and develop?
Beginning with perhaps the most important question; how do we distinguish between
natural and artificial creativity-
Chris Wilson
Michael Brown
KEYNOTER: Dr Dom Heger, CEO DHTechnologies/Data Nubes, Texas, USA
Presentation: The Impact of Deep Learning & Quantum Computing on Big Data
One of the goals and objectives of machine learning is that the networks should be
teachable. It is rather trivial at a small scale to demonstrate how to feed a series
of input examples and expected outputs into a model and to execute a training process
to produce more accurate predictions over time. The main problem is how to do the
same thing at a large scale while operating on complex problems such as speech or
image recognition related projects. In 2012, Hinton et al. published a paper outlining
different ways of accelerating that learning process. With most machine learning
projects, the main challenge is in identifying the features in the raw input data
set. Deep learning aims at removing that manual step by relying on the training process
to discover the most useful patterns across the input examples. While the current
Big Data ecosystem is considered as being very powerful by today’s standards, to
actually increase the computing power of these systems to address the ever-
KEYNOTER: Dr Alain Biem, vice president of Analytics and chief scientist of advanced solutions delivery at Opera Solutions
Presentation: Management of The Analytic Lifecycle for Big Data
The Analytic Lifecycle involves building, deploying, and maintaining a variety of
analytic models, on a variety of computing platforms, for a variety of tasks. The
Management of the Analytic Lifecycle for Big Data, at rest or in motion, is a challenging
endeavor requiring the delicate utilization and leveraging of various Big Data platforms
and software assets, as data evolve. In this presentation, we describe the management
of Big Data Analytics lifecycle as an essential part of the data lifecycle and as
a pre-
Prof Nabil El Kadhi, Deputy Vice-
Presentation: Information Systems: A Shift from Structured Data to Smart Cities, with Increasing Artificial Intelligence Capabilities …Read more …
KEYNOTER: Prof Ling Shao, Chair in Computer Vision and Head of the Computer Vision and Artificial Intelligence Group, Department of Computer Science and Digital Technologies at Northumbria University, UK
Presentation: Discriminative Feature Learning and Image/Video Categorisation for Visual Big Data Categorisation for Visual Big Data … Read more …