The emergence of Data Science is considered to be a great advance in the domain of modelling. The abundance and the explosive growth of recorded data in recent years has added a new dimension to the established paradigms of theoretical, empirical and computational modelling; these are now augmented by data driven modelling. Data Science encompasses the established domains of data warehousing, data mining, cluster analysis, pattern classification, machine learning and data visualisation. The application of Machine Learning in general and Deep Learning in particular, to very large data sets, has led to ground-breaking progress in recognising patterns of sounds, images, & data.
The human brain finds it difficult to make sense out of very large volumes (peta bytes) of unstructured data. Yet Deep Learning can discover hidden ‘connections and patterns’. Humans are not able to model these ‘connections and patterns’ because their cognition power limits them in making sense of these very large data sets and their inherent complexities.
This event brings together experts from industry and academe to explore and discuss the major advances and applications of these technologies.
Come along and open up to the new era of AI, Machine Learning and Deep Learning and find out how this can help you and your organisation in an increasingly ‘data-driven world’.
In addition to the conference, there are three pre-conference workshops:
Presentation 1: Leveraging Deep Learning for Image and Text
Presentation 2: Redefining Text Analytics: using deep learning for image and audio analysis
Who should attend
We are looking for Use Case Presentations on AI, Machine Learning and Deep Learning, particularly in the industry sectors of
and others. If you wish to submit a proposal to present at this event please fill in the speaker’s response form.
Bianca Furtuna, Data Scientist at Elastacloud
What is the future going to look like? When are we going to reach true Artificial Intelligence? Is the Singularity going to happen? There is a lot of talk today about AI and what it means for human society. Let's forget about the future for now and focus on what is possible today. We are going to look at the most promising area in AI research, Deep Learning and understand how it fits in the wider picture of Machine Learning. We are going to explore the fundamentals of Deep learning and deep dive into some common use cases to illustrate the applications of the technology in the real world.
Bogdan Ciubotaru, CTO, Everseen Ltd
♦ Process efficiency and integrity represent two main factors impacting many sectors including retail, manufacturing and transportation
♦ Vision has always been a critical information source, however it is mainly specific to human observers and hence difficult to use effectively
♦ Machine vision and artificial intelligence open the door for a wide range of applications including efficient process management
♦ The positive impact on various businesses and industries has already been proven with great growth envisioned for the future
Josh Sutton, Global Head, Data & AI, SapientRazorfish
AI is going to disrupt nearly every industry at a faster pace than we have ever seen. Tomorrow’s success stories will be those firms that became a cognitive business. This presentation will discuss pragmatic, real world approaches for identifying meaningful uses for AI within your organisation today. It will outline the seven steps for cognitive transformation within an enterprise business. The audience will gain a high level understanding of how to build a cognitive platform for their organisation inclusive of technology, experience, and change management that avoids creating silos and demonstrates meaningful business value in months instead of years.
Alistair Ferag, Senior Data Scientist, Satalia
Satalia creates production-grade data science and optimisation solutions for a range of clients and ever aspire to develop AI into them - but this can be tough. Leaving AI solutions in production with no oversight can lead to unintended consequences. This talk will discuss approaches to apply AI and provide a demo to highlight how difficult it can be to operationalize.
Gert De Geyter, Senior Consultant Data Analytics at Deloitte Belgium
As data scientists, we often spend a long time optimizing and endlessly trying to refine our models. All too often, this ends up in neglecting what may be the most important selection criterion: acceptance by the end user. In this talk, some tips and tricks are shown using real case examples how to improve to odds of convincing critical end users.
Karlijn Willems, Data Science Journalist at Datacamp
Data science requires more than traditional Integrated Development Environments (IDEs) can offer: the need to create and share data stories. That's why data scientists often resort to notebooks. In her talk, Karlijn Willems will guide you through the landscape of data science notebooks, from Jupyter to Beaker to R Markdown to Zeppelin and more, providing a comparison between the different notebooks that are out there for data science enthusiasts!
Jochen Leidner, Director, Research, Thomson Reuters
The Information economy combined with progress in computer performance and progress in machine learning pose great opportunities. In this talk, I will give some case studies of research projects conducted at Thomson Reuters Corporate Research & Development group, where we strive to improve information access for professional knowledge workers in different vertical domains, often applying machine learning to applications in information and information retrieval.
Aditya Satyadev, Co-founder & CEO, BizAcuity Solutions Pvt. Ltd.
♦ Unsecured Instalment Loan Business and Complexity
♦ Conventional rule based Underwriting Vs. Data Science Based Underwriting
♦ Paradigm shift in Risk Management due to accessibility of in-house and global data
♦ Solution Architecture – Data Integration/Wrangling, Data Quality, Technology Stack
♦ Building the Model with Data Science and Deep Learning Algorithms
♦ Model Assessment and Optimization
♦ Business Impact Analysis
♦ Operationalization of Solution with Human Touch
Tarry Singh, Data Analytics Executive, Entrepreneur
"Tarry will give a whirlwind tour of the world of deep learning. How it all started -- yes, your linear algebra and spherical trigonometry is back. Explore the inner workings of how Deep Learning actually works -- how ANNs work and how they still have a long way to go before really understanding how human brain works. Then he will take a practical dive into how companies actually try to put this is practice and create great products and services. And if time permitting he will give a quick tech walkthrough into one of his AI projects from his upcoming book titled 'Practical AI / Deep Learning Projects' "
Barbara Fusinska, Data Scientist
Deep learning is the area that wins over the field of Artificial Intelligence. By using libraries like TensorFlow, it is now available to the wider audience. In this tutorial, Barbara will walk the audience through the process of creating several types of neural networks. The session will start with explaining key concepts of deep learning and introducing datasets the computation will be performed on. Along the way, attendees will have the practical opportunity to use TensorFlow to build deep networks, train them and evaluate the results. After the session, participants will become familiar with how to use TensorFlow when shaping the architecture of neural networks. By the hands-on form of the tutorial, the audience will have the chance to gain some firsthand experience of how to apply deep learning to computer vision and natural language processing tasks.
Armando Vieira, Data Scientist, ContextVision AB
Despite being a relatively new research field, Artificial Intelligence (AI) history has been shaped by huge expectations and colossal failures. After several stagnation periods or "long winters", AI is flourishing as impacting business at an unforeseen pace. Behind this success is a technology widely known as Deep Neural Networks or Deep Learning (DL). In this talk Armando summarizes the key elements of DL and why it is such a transformative force for almost every business.
Mandie Quartly, Worldwide Lead, Machine Learning and High Performance Analytics software, IBM
There’s a lot of talk about using Artificial Intelligence (AI) to gain business insights, but there are a number of key ingredients required to actually make it happen in a timely fashion. A vital element is the technology which underpins AI and machine learning applications. Come and hear more about "making it happen" for your organisation; learn how to take advantage of rapidly evolving and innovating technologies. The emphasis is very much on real world use cases encompassing retail and financial markets.
Radek Maciaszek, CTO, A4G
Real time bidding is a dominant way or purchasing volumes of online advertising inventory. What was yesterday a domain of high-frequency trading hedge funds became a norm in todays advertising world. I will talk about the fascinating world of RTB and will discuss the business cases as well as technological challenges advertising companies face today.
Ben Houghton, Senior Data Scientist, Advanced Data Solutions (ADS), Barclays
Data Science is fundamental in driving data-led decisions in a multitude of business areas. In this talk, we will discuss the breadth of opportunities for data science available in the finance sector and the methodologies we can employ to make smart decisions. We will also discuss how data science can be made accessible to everyone, not just data scientists.
Timo P Kunz, Data Scientist, catawiki
In a time where AI is the only game in town, traditional methods seem to vanish from the analyst’s toolbox. This talk describes how catawiki uses stochastic modelling and simulation approaches to better understand customers’ behaviour on its auction platform.
Konstantinos (Kostas) Perifanos, Lead Machine Learning Engineer, Argos.co.uk
Demand forecasting for new products is not an easy task. The challenge is how to forecast sales for a product in case of lack of data. Given a database of several hundreds of thousand products and their demand history, we developed an Information Retrieval / Machine Learning approach to solve this problem.
Elisabetta Fersini, Assistant Professor - University of Milano-Bicocca Italy
Nowadays the number of people consulting product reviews on the web before making any purchase has increased. If most reviews are positive the probability of buying a given product increases, while if most reviews are negative almost certainly customers will choose another product. This gives strong incentives for opinion spamming, which refers to the writing fake reviews to intentionally mislead readers. In this talk, we will give an overview of the current methodologies for fake review detection, extending a markov random field model with some concepts about "burstiness of reviews".
John Murray, Data Scientist & Academic Researcher, University of Liverpool/Fusion Data Science
The growth in GPS fitted smartphones, smartcards and other location aware technology, adds an extra dimension to consumer analytics. John Murray will discuss the role of spatial data in gaining additional insights and show, with practical examples, how it can be integrated into your customer database.
Guy Kirby, UK Head of Advanced Analytics, North Highland Consulting
How can companies move away from widely-targeted marketing campaigns and use their existing data more intelligently to laser focus their target on those most likely to convert? This talk looks at how North Highland worked with Express Oil to build a predictive model using 1st party data collected across 350 variables and 3rd party data from over 2,000 variables. Guy will outline how North Highland identified target customers most likely to convert to mechanical services, allowing Express Oil get 7:1 ROI in the first 6 months, convert thousands of customers and generate significant new revenue not previously possible.