Building a smarter
organisation with AI
Tuesday 9th April 2019
Artificial Intelligence has become mainstream and is fuelling more and more aspects of businesses and day-to-day life. Beyond the hype, the question is how data-driven innovation can be brought to life and put in action to resolve real business problems. What steps are needed to move AI out of the lab and into business operations to realise the desired outcomes?
During the morning of our half-day event we will take a practical approach around some different techniques, with true life examples. We will help you understand how to join those organisations breaking new ground with AI and analytics. And crucially, how you can deliver transformative value within your organisation.
Learn why AI is evolving the way data is used to leverage actionable insight in organisations today. We will talk through the value that machine learning, natural language processing and computer vision can drive and why data management is key to successful AI implementations.
|Tuesday, 9 April|
|8:30 – 9:00||Registration Coffee Pastries|
|9:00 – 9:10||Introduction|
|9:10 – 09:40||Five lessons learnt from AI deployment|
We will cover real-world applications of AI deployments detailing both the results and more importantly the actionable lessons learnt.
|Dr Iain Brown, Head of Data Science|
|09:40 – 10:10||Data management is key to successful AI|
AI is seen by many as the best way to secure the future of their organisations, but there is significant public concern about its possible detrimental impact. Some are concerned about the concentration of power in the hands of huge tech companies, while some see automation as a threat to their employment. In this session we will cover how transparency will go some way to alleviating these concerns;
|Joseph Kneen, Head of Data Management|
|10:10 – 10:40||Using Natural Language Processing to further improve customer satisfaction|
Understand how some companies use Natural Language Processing (NLP) techniques, to improve customer satisfaction. Companies set out to investigate why customers get in touch and find better ways to resolve their complaints and enquiries, to take pressure off their customer services.
Discoveries can include tangible ways that the digital customer service experience could be improved, thus alleviating the pressure on customer services.
Matthew Stainer, Analytics consultant
|10:40 – 11:00||Break and Networking|
|11:00 – 11:30||Real world example of Machine Learning in Insurance|
Cutting through the hype of Machine Learning and AI, we will take you through a practical application of how Machine Learning was effectively used in the insurance industry. Learn how organisations are deriving actionable insight from AI applications and the business value they deliver today. This will further clarify the role of human ML expertise in the development process, both now and in future
|Georgios Kapetanvasileiou, Lead Data Scientist|
|11:30 – 12:00||How are computers becoming more image-conscious?|
How do you apply maths to photos, you may wonder? Computer vision is one the key AI applications for SAS; whether that be classifying images, detecting objects in images or simply assessing changes in images. Increasingly so, organisations are seeking how to apply these techniques to previously unused data sources to either:
|Kayne Putman, Analytics Consultant|
Global Technology Practice
|12:00 – 12:45||Panel Discussion and Questions from the Audience & Wrap Up||ALL|
|12:45 – 14:00||Lunch and Networking|
We look forward to seeing you there and supporting your AI and analytical ambitions on Tuesday, 9 April at Dogpatch Labs, Dublin 1
Successful execution of an analytics, and thereby AI strategy, needs the right balance of choice and control.
Creativity and innovation flourish in open spaces. You need flexibility and freedom to attract the best analytical talent, use a wide variety of techniques and develop processes that work best. You need the flexibility to use multiple programming languages and analyze any data in any environment and to keep up with accelerating demands.
Analytics chaos can creep up. Once you lose control of your data, you lose trust in the system and its outputs. Transparency, governance and security become essential for maintaining trust in models and analytical results. Becoming even more critical as you scale development, monitoring and refinement of analytics applications and their associated processes.
At the intersection of data, software, and ingenuity, the future is being redefined. After all, when curiosity meets capability, progress is inevitable.