Conference Agenda

8:00-8:55          Registration. Pastries, Coffee and Tea

8:55-9:00          Opening Remarks by Nikolaos Vasiloglou

9:00-9:45         KEYNOTE ADDRESS: Executive perceptions of Machine Learning 

  • How executives from various industries define machine learning

  • Is machine learning / artificial intelligence critical and why / why not?

  • What are the best applications for machine learning?

  • What are the biggest limitations and barriers?

  • The outlook of business executives on the future of machine learning

Beverly Wright

Executive Director, Business Analytics Center

Georgia Tech Business School Analytics Center, USA

9:45-10:10      Case study: Audience targeting solutions powered by data fusion and Machine


  • The changing advertising landscape

  • New data sources and the impact of Data Fusion

  • New advertising product offerings

  • Creating scalable solutions using Machine Learning

  • Results and Innovations    

​                          Wes Chaar

                          Senior Vice President, Analytics, Data and Decision Sciences

                          Turner/TimeWarner, USA

10:10-10:35     Case study: Machine Learning in mobile-phone based credit scoring at IBM

  • Insights from the largest PhD store on the planet – IBM

  • Boosted decision tree – a standard tool in any data scientist's toolkit

  • Using boosted decision trees to create credit scores for un(der)-banked individuals based on their recent cell phone usage

  • Demonstrating a lower default rate

  • Recommending the credit product to an additional 1 million customers compared to the bank's original method

                          Skyler Speakman

                          Research Scientist, IBM Africa

                          IBM, Kenya

10:35-11:05      Coffee Break

11:05-11:30      Case study: Prescriptive Machine Learning to optimise manufacturing processes

  • Traditional control philosophies do not uncover complex relationships between steps in manufacturing processes

  • Unsupervised machine learning techniques can be used to discover these relationships in order to reduce scrap, improve yield and lower operating expense variation

  • Case Study of DataProphet's solution and its results

                          Frans Cronje

                          Founder DataProphet, SA

11:30-11:55       Case study: Data-Driven operational efficiency for the enterprise

  • Major inefficiency sources across the different industry verticals

  • Why ML/AI? and Why now, not tomorrow?

  • Major roadblocks facing ML/AI-driven automation

  • Successful attempts for disrupting end-to-end value chains such as FMCG & Healthcare using scalable machine learning platforms

                          Mohamed Aly

                          Founder and CEO

                          Seeloz, USA

11:55-12:20      Deep Learning in Computer Vision

  •  An introduction to convolutional neural networks

  •  Understanding what’s possible by reviewing state-of-the-art approaches to various computer vision problems

  •  Real-world case studies

                          Alex Conway

                          Founder and CTO, Data Scientist

                          NumberBoost, SA

12:20-13:50     Hot lunch catered by the Hilton Hotel

13:50-14:30     Google Cloud & Machine Learning

  • How Google uses machine learning to optomise internal processes

  • Practical example of machine learning in action at Google

  • How different industries are working with Google and Google Cloud (TensorFlow, ML Engine, and APIs)

                          David Elliott

                          Global Product Lead, Enterprise Cloud

                          Google, USA

14:30-14:50   Towards greater brand relevance, consistency and authenticity in the age of  ML & AI

  • Brand tools: Finding nuggets of "predictive" knowledge in the waves of structured and unstructured data

  • The marketing data eco-system: automated data visualisation, content analysis, ML techniques and impact on marketing execution and digital marketing toolsets

  • Brand Consistency and brand AI and ML Innovation: creating organisational brand ambassadors through developing AI and ML – the role of algorithms and tools  (Social Network Analysis, Conjoint Analysis, etc)

                           Paula Sartini 


                           BrandQuantum, SA

14:50-15:15      A conversation about chatbots

  • Discovering different forms and 'personalities' of chatbots

  • Not every chatbot is made the same! Diving into their underlying architectures and capabilities to understand what makes them 'tick'

  • How chatbots can be relevant to existing businesses and fields, including current production systems and their impact

  • Practical advice and resources for relevant and accessible technology

                          Chris Currin

                          PhD Candidate in Computational Neuroscience

                          University of Cape Town, SA

15:15-15:40   Artificial Intelligence (AI) and economics. Skynet in the market 

  • The impact artificial intelligence on economic theories.

  • Examples on how AI is applied to diverse areas such as modelling of the stock market, credit scoring, HIV and interstate conflict are given.

  • Artificial intelligence ideas used in this talk include neural networks, particle swarm optimization, simulated annealing, fuzzy logic and genetic algorithms.

                          Tshilidzi Marwala

                          Professor and Vice Chancellor

                          University of Johannesburg, SA

15:40-16:05    Coffee Break and Book Give-aways

16:05-16:30    Using analytics in insurance

  •  How predictive analytics is changing the way insurers do business

  • Navigating the change from a traditional actuarial office to a modern multi-disciplinary team

  •  Dealing with black box models and interpretability

  •  Open-source vs proprietary software

                          Marc van Heerden

                          Senior Specialist, Advanced Analytics

                          Bain and Company, SA

16:30-16:55     Combining Behavioural Science and the Internet of Things with ML to increase


Incorporating insights from behavioural science 

  • Recent advances in behavioural science provide greater understanding of psychological factors that determine human behaviour

  • Well-designed experiments can be used to segment customer base (by psychological motivation, rather than observed behaviour)

  • Supervised classification used to map available client information on customer segments beyond experiment participants (Example: health insurer identifies psychological factors that cause deviations from intended dietary behaviour, and provides personalised, cost-effective interventions to augment behaviour)


Incorporating data from IoT 

  • IoT used to measure location of workers and equipment

  • Supervised ML used to obtain flexible model of value added by combining specific equipment and workers

  • Instead of merely measuring value of purchasing equipment / hiring workers, this allows insights into whether current workers and equipment are employed efficiently (Example: IoT data from hospital theatre equipment and location tracking of nurses used  to predict most efficient combination of staffs and equipment to maximise beneficial health outcomes and patient satisfaction)

      Richard BarryPolymorph Systems

16:55-17:20      Machine Learning for combatting cybercrime

  • Challenges of cybersecurity in the era of artificial intelligence

  • Methods and algorithms for fighting cybercrime

  • Practical examples

                          Nick Vasiloglou

                          Machine Learning Expert

                          Symantec, USA

17:20-18:00    Panel Discussion: In-house or outsourcing AI/Analytics in a large corporation. The role

                          of start-ups and consulting firms in the AI transformation of large enterprises.

                          Acquiring talent versus training human capital

                          Moderated by: Theunis Jansen van Rensburg

                          Head of Commercial Analytics

                          Wonga, SA