— By Daphne Lenders, Turning Magazine
“United in Diversity”. According to Holger Hoos, professor at the University of Leiden, this is not only the motto of the European union but can also serve as a guideline for the development of AI. That it’s actually possible to turn this statement to reality was demonstrated during this year’s AI synergies conference. From the 6th to 8th of November, AI- and ML-researchers from Benelux gathered in Brussel to present and share the latest developments in the ever-changing field.
A quick look in the program of the conference already shows that ‘diversity’ was more than just an empty promise. For the first time ever, the conference had dedicated tracks for business and academics, showing that AI will strive more if knowledge between both is shared. Like in the previous year, AI synergies was also a combined conference of
ML-focussed BeNeLearn and the broader AI conference BNAIC. As a result the conference covered topics to everyone’s heart’s desire: from Knowledge Representation to Deep Learning, from Robotics to Creative AI or Natural Language Processing. Read more about the conference highlights here!
Machine Learning/Deep Learning:
Talking nowadays about AI, there’s no way to avoid the topic of Machine Learning. After all, many of the recent advances are made in this field and challenges like Computer Vision are successfully being tackled through Machine Learning. There were multiple sessions covering research related to this field during AI synergies. With dedicated tracks for “ML for Bioinformatics & Life Science”, “AI for Health and Medicine” and “Applied ML & ML for Medicine”, many talks were about deploying Machine Learning for the highly societal relevant area of healthcare. The talks about using algorithms for HIV-, breast- or skin-cancer-, or sepsis detection highlighted AI’s promising potential for diagnosis .
Explainable AI:
Closely connected to the field of Machine Learning, but still worthy of its own track was the Explainability session during AI synergies. Following the Peter-Parker principle of “with great power comes great responsibility”, researchers have recognized that in order for AI to be more trustworthy it needs to be more understandable. Many talks were dedicated on how to make AI more transparent, demonstrated in the field of robotics or on the example of convolutional networks.
Moreover, a key-note talk was dedicated to this particular topic. In the presentation “Explainable AI: explain what to whom” Silja Renooij warned about just using white-box models like Bayesian Networks as a sufficient explanation to AI systems. Not everyone has the necessary background knowledge to actually understand the difference between correlation and implied causation, so we should be careful to just assume that Bayesian Networks are inherently understandable. She therefore argued that provided explanations should be adjusted to users’ understanding of AI/statistics in general.
Agents, Multi-Agent Systems and Robotics:
Next to a number of research-talks about agents- and multi-agent systems, AI synergies included a key-note about this topic, given by Jeremy Pitt. In “Democracy by Design” he describes how a simulated civilisation can generate new rules to reduce risks of tyranny or autocracy when being implemented on the principles of Ober’s basic democracy.
Focussing more generally on robotics, another key-note was given by Ana Paiva. Predicting that more and more robots will be integrated in our society, she argued that we should strive for a harmonic collaboration between humans and machines. Presenting a case-study of robots teaming up with humans to play a card game, she showed what factors need to be considered when designing and evaluating Human-Robot-Interactions.
Knowledge Representation:
One of the oldest and most traditional approaches to AI is the field of Knowledge Representation. While currently much more attention is put on more modern techniques, Marie-Christine Rousset demonstrated in her key-note talk “Reasoning on Data: Challenges and Applications” how modern challenges regarding data quality (like e.g. data inconsistency) can be tackled by deploying first- or second order logic rules.
In the track “Knowledge Representation & Hybrid”, multiple speakers elaborated on this idea, showing e.g. how the improvement of Knowledge Representation languages open up new possibilities for combination between old and new Data Science approaches.
Of course this overview by far is not enough to cover all the diverse and engaging talks given during the conference. Therefore, make sure to check out the conference (pre)proceedings for a detailed overview of all research topics. One last thing that might be noticed there, is how student-friendly the AI synergies conference is. Not only Master, but even Bachelor students admitted their abstracts and got accepted to present at the conference.
So can AI be truly “United in Diversity”? Looking at the varying expertise-levels of the speakers, the range of topics covered by the conference, and the combination of business and research, AI-synergies shows that this mission can indeed be fulfilled.