Emergence of AI and ML:
Artificial Intelligence is deemed to be the main driver of the 4th Industrial Revolution. Until recently, practitioners have faithfully relied upon neo-classical models to make decisions and to measure performance, whether it’s in financial organisations or marketing corporations. AI is the new technology that offers an automated solution to these processes. It has the capability to replicate cognitive decisions made by humans and remove behavioural biases inherent in humans. Investment in AI has grown at a phenomenal rate with companies investing $26-39bn in 2016. Adoption in 2017, however, remains low. As a result, this has spurred companies from every industry to seize the trend and innovate – from virtual assistants to cyber security to fraud detection and much more. The majority of C-level executives have identified and agree that AI will have an impact on their industry. Only 20% of C-level executives admit they have already adopted AI technology in their businesses. For many industries there is an “imperative to catch-up”. The Finance industry is anticipated to lead the way in adoption of AI with a significant projected increase in spending over the near future.
Emergence of Big Data and Data Science:
The emergence of Data Science is considered to be a great advance in the domain of industrial problem solving. 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. Machine learning and sentiment analysis are specific techniques that are applied in AI. These techniques are maturing and rapidly proving their value within business and commerce.
Benefits of attending:
This conference will demystify the buzz around AI and differentiate the reality from the hype. Learn about how you can benefit from the unprecedented progress in AI technologies at this conference. Participants will be presented with real insights on how they can exploit these technological advances for themselves and their companies.
We are inviting speakers – thought leaders, subject experts and start up entrepreneurs – to share their knowledge and enthusiasm about their work and their vision in the field of AI, Machine Learning, Data Science and Deep Learning.
We understand that successful projects are written up as “White Papers”. Please share these with us. But projects that did not achieve their targets – “Black Papers” – are of interest to us too. They can be a very important topics of discussion / panels that you can present. Talk to us about both, we welcome your input.
Please complete the speaker’s response form and submit a proposal to present at this event.
UNICOM’s Code of Conduct & Views on Diversity
We at UNICOM strive to be a leading provider of knowledge to the business community and to engage the global business community as a specialised provider of knowledge. We strive to do this maintaining a culture of co-operation, commitment and trust. We want every UNICOM conference and training day to be a safe and productive environment for everyone – a place to share research and innovation and to build professional networks. To that end, we will enforce a code of conduct throughout all our events. We expect cooperation from all participants to help ensure a safe environment for everybody.
Our approach is that our events are dedicated to providing a harassment-free experience for everyone, regardless of gender, gender identity and expression, age, sexual orientation, disability, physical appearance, body size, race, ethnicity or religion. We do not tolerate intimidation, stalking, harassing photography or recording, sustained disruption of sessions or events, and unwelcome physical contact or sexual attention. We do not tolerate harassment of conference participants in any form. Sexual language and imagery is not appropriate for any conference venue, including talks, workshops, Twitter and other online media. Event participants violating these rules may be sanctioned or expelled from the event without a refund at the discretion of the conference organisers. Please bring your concerns to the immediate attention of the event staff.
Diversity: In our endeavour to be the provider of knowledge to the business community, we understand that this depends on hearing from and listening to a variety of perspectives that come from people of all races, ethnicities, genders, ages, abilities, religions, sexual orientation, and military service. We welcome diverse speakers for all our events, we do not always fully achieve this goal, but it is an ongoing process.
Programme under Development
SK Reddy, Chief Product Officer AI, Hexagon
Artificial Intelligence (AI) is transforming industries. Many CXOs have realized the benefits of AI and many of them want to take the organization towards AI. I would like to discuss practical approaches to cross the chasm of AI. I will also share some examples and checklists that could be used by orgs. I will try to layout a practical approach on how organizations can transform.
Adam West, Marketing, Satalia
♦ What is Data Driven Decision Making? And what technologies sit within it?
♦ What’s wrong with human decision making?
♦ What is AI, what it is not and why it is hard?
♦ Ethical AI – the challenges we face and the philosophical questions that must be answered
♦ Applications of AI within marketing, operations and organisational design
♦ Innovation and talent in the context of AI.
Jakub Kolodziej, Quantitative Associate at Macquarie Group
Macquarie analyse a large dataset of email receipts that covers the purchases of more than two million US customers. The data, sourced from QUANDL, contains weekly information on all the items purchased by each individual consumer from a large set of companies including Amazon, Walmart and Apple. In particular, for each product Macquarie gives a description, its likely classification in terms of broad goods categories, price paid, number of units, shipping costs, any discounts received and many more fields. Consumers opt in to share information available from their email accounts with a data vendor. The data is anonymised but each consumer is assigned a unique identifier which allows them to follow individual purchase histories over time and infer a profile.Using Amazon.com as a case study, they show that the data can generate real-time forecasts of quarterly sales that are at least as accurate as consensus. It is, however, in combining analyst insights and big data that they find the most significant improvement in predictive power. They also highlight the possibilities opened by this kind of large-scale database for a truly quantamental approach to equity valuation. Finally, they describe the technological solutions adopted to overcome the challenges posed by a dataset that can reach hundreds of millions of rows for a single firm.
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.