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
James Luke, Distinguished Engineer, Public Sector, IBM
James has been delivering Artificial Intelligence solutions that solve real problems for over 25 years. In this presentation, the presenter will dig through the hype and use real examples to explain what it takes to deliver working AI solutions.
Peter Hafez, Head of Data Science, RavenPack
In order to maintain an edge in the marketplace, asset managers are to a large extent turning to unstructured content for alpha creation, using NLP and text analysis techniques. In addition, more and more managers are expanding their mandate, trading global portfolios, to ensure more scalable strategies. As part of his presentation, Peter will showcase how news sentiment can be a valuable input to such process.
Nishant Chandra, Data Science Leader, AIG Science
Deep learning has created a revolution in the natural language processing domain and corporations are leveraging it in various ways. The technology barrier is significantly reduced with open source technologies that are easy to configure and use. Several generic open source tools are available in machine learning, including deep learning, which can be customized for natural language processing. This presentation will help the audience to go beyond generic NLP problem solving by leveraging deep learning, customizing it for their industry. Specifically, they’ll learn that:
♦ Sentiment doesn’t have to be positive, negative or neutral but it can be extracted from the conversation
♦ Summarization doesn’t have to be entire document but only certain context
♦ Text classification doesn’t have to be exactly text/phrase/spelling based but can also include variation of acronym and synonym
♦ NLP can be applied broadly, and complex use cases can be built through intelligent iteration on simple examples.
Joao Gama, Professor, School of Economics, University of Porto
In recent years we witnessed an impressive advance in the social networks field, which became a ”hot” topic and a focus of considerable attention. The development of methods that focus on the analysis and understanding of the evolution of data are gaining momentum. The need for describing and understanding the behavior of a given phenomenon over time led to the emergence of new frameworks and methods focused in temporal evolution of data and models. In this talk we discuss the research opportunities opened in analysing evolving data and present examples from mining the evolution of clusters and communities in social networks.
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 Research Senior Associate Analyst, Macquarie Research
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.
Lyndsey Pereira-Brereton, Data Visualization Editor, Bank of England
With the explosion in the amount of data and the burden of information overload, how can we get the most out of our data and communicate this effectively? In my talk I will show how the Bank of England is using data visualisation to see through the data fog and better communicate our findings.
Moderator: Gautam Mitra; Panellists: Peter Hafez; Jakub Kolodziej; Anders Bally; Sanjiv Das; Pierce Crosby; Lyndsey Pereira-Brereton
Marleen Meier, ABN AMRO Clearing Bank N.V.
ABN AMRO Clearing Bank works with considerably large amounts of data every day and we design and implement Deep Learning models to approach some of our business cases. One example is, how to find real time anomalies (strange behaviors) in our data by using Unsupervised Anomaly Detection with TensorFlow and Spark. The output is being visualized with Tableau in order to express the anomalies and to make data-driven business decisions.’
Mohamed Latif, Managing Director, LynGro
AI can change the world, yeah ok ... but how? ...
AI in the content world can range from detecting patterns in user engagement, all the way to personalising the content experience on a site. This means you will no longer see anything that is irrelevant to you. You will never be served with something that does not interest you. The virtual world will revolve around you ... and that could all happen solely based on your behaviour online.
Elisabetta Fersini, University of Milano-Bicocca, Italy
Social media for opinion consumption is a double-edged sword. On one hand, they are low cost, easy to access, and suitable for a rapid dissemination of information that allow users to read and share news, reviews and rumors about product and services. On the other hand, social media can make viral “fakes”, i.e. low-quality information with intentionally false information. In this talk, we will give an overview of the current methodologies for identifying fake news, fake rumors and fake reviews, with a specific focus on machine learning and deep learning approaches.
Abhijit Akerkar, Head of Applied Sciences Business Integration, Lloyds Banking Group
Many non-tech CEOs have heard about AI and its disruptive power. But they are grappling to determine what it can really do and how to deploy it in their business. This session will introduce you to a set of curated levers that will help you embed AI into your business strategies to drive superior growth, increase return on capital, and manage both desirable and undesirable risks.
Elnaz Salehzadeh Nobari, Senior Quantitative Business Analyst
Recent political events and global economic volatility highlight the uncertainty of previously accepted deterministic models in analyzing downstream commodity transactions and need for case specific modelling. Data mining and machine learning have the advantage of removing econometric assumptions, however, the lack of integral data creates a data mining and classification challenge, explained within this presentation.
Alberto Chierici, Co-founder and Head of Product, Spixii
In this talk, I'd like to draw upon the experiences at Spixii from the past two and half years. We built hundreds of customer experiences in the insurance industry using conversational interfaces. We have seen what works and what doesn't. We want to share these stories to educate the audience about myths and misconceptions on AI and why good design and alignment to strategic and operational objectives are critical to moving from lab to business as usual.
Enza Messina, Professor in Operations Research, University of Milano-Bicocca
We show how deep learning and ensemble methods can successfully address challenging problems arising in sentiment analysis such as irony detection or domain adaptation. In particular, we propose an unsupervised framework for domain-independent irony detection built upon an existing probabilistic topic model initially introduced for sentiment analysis purposes. Moreover, in order to improve its generalization abilities, we apply Word Embeddings to obtain domain-aware ironic orientation of words. The acquisition of cross-domain high level feature representations through word embeddings combined with the generalization capability of ensemble methods can also be used for addressing the problem of domain adaptation also in the scenario where the testing target domain is completely unlabelled.
SK Reddy, Chief Product Officer AI, Hexagon
With the exponential increase in use and access of online shops, online music, video and image libraries, search engines and recommendation system are the best ways to find what you are looking for (and sometimes, what you are NOT looking for). Meanwhile Deep learning has made significant advances in speech, numeric, text and image processing. Typical recommendation systems can be organized into three groups: Collaborative system, Content based system and Hybrid system. I will discuss the design elements of a recommendation systems coupled with deep learning methods.
Moderator: Gautam Mitra; Panellists: Sanjiv Das; Ivailo Dimov; Jordan Mizrahi; Mark Grundland; Marleen Meier; Alberto Chierici; Francesco Cricchio/ Matteo Campellone
Mark Grundland, Functional Elegance
What enables an effective visualization to deliver insight at a glance? This talk presents practical techniques for how information visualization design can take better account of the fundamental limitations of visual perception, exploring the design choices that determine whether a picture can communicate the data it is meant to represent.
Disney Yapa, Territory Manager Nordics - ContentSquare UX Analytics, ContentSquare
Is the practice of specialised analytics teams working in silo's and creating vast report artefacts to the business team still the best practice? In the today's environment business users are subject to an amassment of data and restrictions on resources to act on them. Time to rethink analytics?
Disney will question the status quo of many digital teams today to provoke thought and highlight alternative analytics projects. Use cases will be shared from the digital and people transformation initiative that is being led by ContentSquare to empower businesses with UX Analytics as well as personal experiences working with leading brands.
Jeff Wellstead, Partner, The Pioneers
I'll be exploring how AI and Machine Learning are being incorporated into a multitude of applications across the entirety of the employee life cycle. My initial focus will help define the use of this exciting technology - highlighting the intersection between human and machine sits today, what the implications are regarding interacting with AI and Machine Learning, how HR and Productivity tech makers are incorporating these technical constructs into their applications today, and how you can benefit from their use vs. what you need to be mindful of in utilising these tools (such as algorithmic bias). I will cover off technology aimed at passive candidate attraction, recruitment, on-boarding, dynamic performance management aligned to learning and development, pay and reward management, promotion and succession planning, people analytics and insight analysis for targeted program development and decision-making.