The 3rd edition of Transform.AI will take place under the theme
“From what? To so what? To so how?”
AI was everywhere in 2018 and it will continue to be a major topic in 2019 as we begin to witness AI breakthroughs across businesses and society. Companies are no longer evaluating the transformative power of AI. We have transitioned from questioning what AI is and how it will change business and society to looking at how enterprises can implement and leverage AI responsibly and at scale.
Some of the questions we will explore over the course of the program include:
– When deploying AI should one go broad or narrow with AI within organizations?
– Are we still in the early days of AI or have we reached the limits of the easy wins and the low hanging fruit?
Current and past speakers
Our program is designed to be as interactive and engaging as possible. No formal presentations but rather frank exchanges and dialogues between peers and AI experts.
Over the past year ambitious national AI plans have been announced around the world: Macron unveiled in March 2018 a €1.5 billion strategy to make France an AI leader; in November 2018 Angela Merkel laid out a €3 billion plan to turn Germany into an AI powerhouse; President Trump in February signed an executive order establishing the American AI Initiative, with the aim of “accelerating our national leadership” in artificial intelligence and in July 2017, The State Council of China released a plan to become the leading AI power by 2030.
Despite European’s investment, according to some such as Kai Fu Lee or Mckinsey, Europe continues to lag behind the US and China while others feel that Europe is on track to capitalize on its strengths. Is the European AI gap shrinking or growing?
Pilot projects are the most common route for companies to dip their toes into the world of AI and explore how AI might deliver business value, new ways of engaging customers or drive new business revenue streams. What are companies learning today from their AI pilots and how can they scale up?
Data is often described as the new oil and without it, the AI machine cannot run. The amount of data required to train AI systems remains impressive and the lack of access to huge volumes of data remains a bottleneck for many firms.
How can firms do more with less while guarantying the quality of their data?
AI is touted for its ability to improve efficiency, increase productivity, detect fraudulent behavior, make better predictions, provide medical diagnosis, just to name a few, but when is the machine doing too much? At what point in the process do people want to engage or work with a human? How do companies determine the optimal machine/human limit?
Existing educational systems- especially across Europe have barely evolved in the last 30 years yet the world is changing rapidly. How is AI transforming the way we will learn in the future and given how AI is disrupting the workplace and the future of work, what capabilities, values and behaviors do we need to be teaching?
Sponsors and Partners