Demystify AI hype and gain valuable insights with Mieke De Ketelaere, Director of AI at imec.
Belgium
Mieke De Ketelaere is the Director of AI at imec (IDLab) and an expert in defining AI business strategies with over 25 years of experience in data and analytics.
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Mieke is a unique blend of knowledgeable, thoughtful, and passionate. She explains complex and delicate matters in a simple manner, tailoring her message to the target audience. Mieke was a lot of fun to work with.
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About Mieke
Keynote
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Geertrui Mieke De Ketelaere is Director AI at imec (IDLab). She holds a master degree in civil and industrial engineering and specialised in robotics and artificial intelligence during her studies. Over the last 25 years, she has worked for several multinationals on all aspects of data and analytics (IBM, Microsoft, SAP, SAS, etc). At imec, Mieke De Ketelaere is responsible for the development of the AI strategy and roll-out.
From a consulting point of view, she is specialised in defining the AI business canvas, from potential value to predefined risks. With her understanding of the new digital data streams and her understanding of AI technologies, Mieke De Ketelaere is requested by different business schools as a guest speaker on digitalisation.
In her public presentations, Mieke puts the focus on the demystification of the hype around AI and covers non-technical subjects such as data privacy. In 2018, she was nominated “ICT Woman of the Year” in Belgium.
See keynotes with Mieke De KetelaereDrivers for Success:
Today, 80% of projects containing artificial intelligence (AI) ultimately fail to deliver on their intended promises to business. AI teams claims a major source of challenges is to be found in senior leadership who are lacking to see the value it can bring. However, is this the real problem? This keynote takes you behind the scenes of the AI world and looks in a non-technical way at the different components that are needed in order to succeed.
According to research, despite increased interest in and adoption of artificial intelligence (AI) in the enterprise, only companies able to position AI in the new economical model (people, profit & planet) return value when it gets to AI.
Multiple factors come into play when developing and implementing an AI project: Data, skills, domain understanding, company culture, AI strategy, data strategy, team setup, … are a just few topics that play a key role in the success of AI projects.
In this session, it will become clear that the major focus should not be on data and technology, but on people and processes. You will understand the current maturity of the technology and learn what’s needed to move beyond the typical PPP (Pilot, POC & Prototype) phase. The goal of the session is to guide you towards the successful drivers of AI projects in an accessible way.
For the successful adoption of AI, we need more female leaders. Why? As AI projects succeed on 4 factors (data, technology, people and processes), women normally possess the right qualities to lead projects to build real-world AI products more successfully as they fully master the enablement of an environment for collaboration and inclusion.
In this session I will share my personal views on what’s needed to work more holistically, outside of our own comfort zones. I will share my view on how to create the right environment where we look beyond gender, race, and cultural background.
Based on a set of personal examples, I will analyse what’s needed both at educational and company level to create a shift in mentality towards more collaboration. It will be a call for more role models to stand-up, male and female, in order to build a better future with AI.
AI has existed for some time, but we are coming to the tipping point: the popularity increases over time, yet there is tension caused by the negative effects of or fear for AI.
AI applications are regularly being developed, but the exciting world of opportunities and possibilities includes other issues: privacy, ethics, energy consumption and liability. The discrepancy between what the developers create, what the customer buys, and what the government wants has increased the complexity bias. In this session, we will split the issues in the good, the bad and the ugly.
The good: AI exceeds human intelligence when it gets to areas such as speed of processing, knowledge transfer, decision accuracy and operational modus. However, the human brain still wins on storage size, energy usage and parallel processing and many more points. We need to accept the fact that both forms of intelligence are good, but different.
The bad: A successful AI project isn’t only about data and technologies, but also processes and people. The bad refers to the typical mistakes made by a company. They can include incorrect training data to match the context, incorrect usage of learning algorithms and unconscious bias.
The ugly: Some issues aren’t mistakes, but purposeful approaches: fake news, autogenerated speech used to fake video speeches for instance, and more. It is a strong call for ‘security by design’ and protection for algorithms through legal frameworks, which are currently created.
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