Ali Kookani wants to make artificial intelligence not only more powerful, but also more human – in the best sense of the word. ‘Today’s language models already seem creative to many people,’ he says. ‘But in reality, they only repeat what already exists on the internet with statistical probability. They mix information, but they don’t have any ideas of their own.’ What is touted today as “reasoning” – for example, when a system presents a ‘chain of thoughts’ for complex tasks – is in reality not a genuine conclusion, but rather an algorithmically simplified puzzle.
Ali Kookani’s research focuses on recommendation systems. Customers encounter this technology in many ways – on streaming platforms such as Netflix, in online shops such as Amazon, or on social media. But as sophisticated as these systems appear, they quickly reach their limits. ‘I sometimes get good suggestions, but sometimes I get completely inappropriate ones,’ says Ali Kookani. ‘For example, when an online retailer continues to recommend other laptops to me even after I’ve bought one – instead of accessories such as a matching bag or mouse.’ The reason for difficulty in recommendations is obvious: people are unpredictable. They change their minds, develop new interests, break with old patterns. This is a major hurdle for machines that rely on past behaviour.
Ali Kookani’s dissertation project is embedded in Dietmar Jannach’s working group at the Institute for Artificial Intelligence and Cybersecurity, where there is already extensive expertise in recommender systems. The institute is a project partner of the Cluster of Excellence ‘Bilateral AI’ funded by the Austrian Science Fund FWF. The aim of Bilateral AI is to bring together the two strands of research in AI development: symbolic AI is based on logic, while sub-symbolic AI involves training artificial neural networks with huge amounts of data. With Bilateral AI, the hope is now to significantly expand the performance of AI systems by combining the strengths of both methods.
Ali Kookani found his way into AI research via a roundabout route. Kookani comes from the Iranian capital Tehran, a vibrant metropolis with over ten million inhabitants. When asked whether he has always been enthusiastic about mathematics, Ali smiles: ‘I was good at it, yes – but not a genius like Stephen Hawking.’
He studied electrical engineering as his bachelor but during his studies, he discovered his enthusiasm for programming – and thus also for artificial intelligence. This was followed by a master’s degree at the University of Tehran, the foremost university in the country with a focus on deep learning. Through a contact with Dietmar Jannach, he became aware of a doctoral position at the University of Klagenfurt – and started his new job in March 2025.
The move from the hustle and bustle of Tehran to a small Austrian town was a big but welcome step for him. ‘I used to spend more than two hours a day on the subway,’ he says. ‘Now I cycle to university in fifteen minutes.’ He can now invest the time he has gained in his research. He emphasises that there is still a lot to be done in the further development of artificial intelligence: “We are all familiar with the scenarios of robots getting out of control from science fiction films. I see little danger in this because we are still a long way from “understanding” AI that can think like humans for many useful technologies.‘ Ethical guidelines are built into the models Ali Kookani works with. ’We want to create models that make decisions with genuine creativity and intelligence – within the framework of responsibility, fairness and transparency.”