‘For us, the focus is always on the human being’ What do you need if you want to solve the practical problems of governments and companies with the help of AI? Revolutionary algorithms, expensive hardware, large dataset? Not at all. AUAS’s brand new Expertise Centre Applied AI operates according to a much more important core principle.
Text: Sebastiaan van de Water
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ozens of shapes move through the image. People, pigeons, and vehicles criss-cross each other’s paths. An artificially intelligent algorithm analyses the video recordings of the scene, with one goal: to count how many people get in and out of taxis here at Amsterdam Central Station. The AI’s skills are beyond question. Using millions of examples, it has taught itself, under supervision, to recognise moving people and cars flawlessly. But confronted with the chaos of Amsterdam Central Station, including passing cyclists and screen-filling trams, the AI begins to stutter. Ten people just got out of one taxi at the same time, the algorithm reports. Nanda Piersma cites the anecdote with a smile. For the scientific director of the Expertise Centre Applied AI (ECAAI), it is no longer news that algorithms that work well enough in a ‘clean’ and controlled setting to extract academic promotions can sometimes stumble in unpredictable urban environments. ‘That in itself is understandable,’ she points out, ‘but if we want to share the fruits of the AI revolution with society as a whole, it is crucial to make that last translation as well. To concrete applications of AI that local businesses, 20 | New Scientist | AI technology for people
governments, and citizens can actually work with.’ The ECAAI of the Amsterdam University of Applied Sciences (AUAS) has an edge in this vital task, thanks to its deep roots in the practical world. Piersma: ‘Academic institutions conduct pioneering research; we build useful applications. That is why collaboration in Amsterdam is so important. If we create an AI to analyse the
‘Revolutionary AI and applicable AI are two totally different things’ s tructural strength of Amsterdam’s bridges and quay walls, that really doesn’t have to result in PhDs. Above all, it should prevent those structures from collapsing. Revolutionary AI and applicable AI are two totally different things, but they need each other.’
Co-creation What is essential for building practical AI is demonstrated by the way the seven different AI Labs (see text box) of the ECAAI work. Companies or other parties can develop in co-creation §solutions to the issues they are struggling with, with experts, programmers, and students in any
lab. For example, the legal tech lab works closely with ARAG, a legal aid provider, to train algorithms that can assess whether a battle for higher severance pay is a good idea. Together with ZiggoVodafone employees, the Responsible AI lab is trying to improve the AI that can see from a photo of the meter cupboard whether a mechanic is needed to install Ziggo’s decoder. ‘That can save a lot of frustrating customer service calls. Research has shown that customers regard this as the most important irritation factor,’ says Katrien de Witte, Operations Director of the ECAAI. Co-creation is the absolute core principle within every AI lab, Piersma emphasises. ‘We don’t write algorithms off the cuff just because they seem brilliant. We listen closely to the people in the field. They are the experts on the ground and understand best where the practical problems lie. So, when it comes to healthcare issues, we work with nurses. When it comes to garbage disposal, we stand side by side with the garbage collector. That’s what we mean by co-creation.’ The scientific director knows of plenty examples of ingenious solutions that do not work in practice due to poor co-creation. Like the smart respirators. These highend devices have sensors and algorithms that allow them to accurately measure how much breathing support an ICU patient needs. This is an excellent innovation, as
