Artificial Intelligence Demystification
Geertrui Mieke De Ketelaere is Program Director AI at imec. 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.
TOPIC 1: AI MEETS NATURE
AI is here to stay in our landscape today, if we want it or not. While getting inspiration from our brain, humans have literally spent decades of research perfecting the mathematical calculations to make wonderful complex learning algorithms work, with great results. But are these results truly that great at all levels?
AI is surely getting smarter day-by-day, however, it isn’t getting cleaner. Analyst reports indicate that creating an AI system today can be five times worse for the planet than a car. This, of course, is not acceptable in a world where everything turns around sustainability.
In this session we will look at intelligence beyond our own human brain. I will take you on a trip in the search for other intelligent systems which are currently used as inspiration for our next generation AI systems. We will look into a series of examples on how these new "roots" taken from Mother Nature are actively used by research teams in order to succeed in the common goal towards "Green AI".
TOPIC 2: IN AI WE TRUST ... OR NOT
It is a fact. Machines with common sense that actually understand what’s going on, just like we humans do, are far more likely to be reliable, and produce sensible results, than those that rely on statistics alone. But let’s be clear: these systems don’t exist yet and there are a few other ingredients we will need to think through first together.
Trustworthy AI has to start with good engineering and business practices, mandated by laws and industry standards, both of which are currently largely absent. Too much of AI thus far has consisted of short-term solutions, code that gets a system to work immediately, without a critical layer of engineering guarantees that are often taken for granted in other fields. Do we currently have design procedures for making guarantees that given AI systems work within a certain tolerance, the way an auto part or airplane manufacturer would be required to do? No.
The assumption in AI has generally been that if it works accurate enough to be useful, then that’s good enough, but that casual attitude is not appropriate when the stakes are high. But this time of over now.
The European Commission has put guidelines in place for trustworthy AI. 7 amendments have been defined. Do you know them? Have you acted upon them as a business or as an engineer?
This session will take you to the current state of the art information on the requirements for trustworthy AI. It will highlight the current pain points and deliver insights on potential solutions in order to make sure that you can take up your accountability as business owner and developer now and in the future.
TOPIC 3: DRIVERS FOR SUCCES IN AI
According to research, despite increased interest in and adoption of artificial intelligence (AI) in the enterprise, 85% of AI projects ultimately fail to deliver on their intended promises to business. AI teams claims a major source of AI challenges is to be found in senior leadership who are lacking to see the value it can bring, however, is this the real problem?
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. A common mistake. We will find answers on the questions: How to define high impact use cases? How to identify projects that will create the highest impact on company KPI’s, rather than picking projects where one only sees the scope of a technical breakthrough?
You will understand what’s needed to move beyond the typical PPP (Pilot, POC & Prototype) phase while looking at some real examples and common reasons why AI projects fail to deliver.
The successful drivers for AI implementations will be your new guideline when leaving the session.
ME, MYSELF AND AI
For the successful adoption of AI, we need more female leaders. Why? Well, 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.
John Naisbitt once said: “The most exciting breakthroughs of the twenty-first century will not occur because of technology, but because of an expanding concept of what it means to be human”. This seems odd in the world of artificial intelligence full of robots and automated machines, but when looking more closely at what’s currently needed to increase AI adoption, it all makes sense.
In order to solve the above challenges of AI adoption and in order to build more successful AI products, we need to focus on a more collaborative and community-driven approach. How can we create an environment which takes into account opinions from different stakeholders, especially those who are under-represented? What is limiting us from creating a multidisciplinary debate around our AI solutions? What is needed to have access to AI translators at different levels?
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.