Human-like AI


"Towards more natural, interactive, personalized, and human-inspired AI systems. Seamless interaction between humans and AI in Multi-modal perception, Multi-modal instruction, Personalized interaction and responses, Complex control: navigation, reasoning, etc."

This research line focuses on the design of AI systems to communicate and interact with people as naturally as possible, including through natural language and image recognition. The AI ​​system must be able to build layered human-like reasoning by observing and understanding the complex environment. This allows computers to independently identify and solve problems. Humans can work with their intelligence and physical abilities in harmony with complementary machines through intuitive and social interaction. This goal is still a long way off. But in many practical applications, such as recognizing patterns and generalizing individual tasks, AI already makes a very interesting contribution.

Steven Latré UAntwerpen - IDLab Management team
Tom De Schepper UAntwerpen - IDLab Management team and WP5 Lead: Use Cases
Tinne Tuytelaars KULeuven WP1 Lead: Audio-visual Perception and Multimodal Representations
Walter Daelemans UAntwerpen - CLiPS WP2 Lead: Deep Learning-based Conversational Agents
Bart Goethals UAntwerpen - ADREM WP3 Lead: Interaction, Personalization and Recommendation
Geraint Wiggins VUB WP4 Lead: Cognitive Architectures & Human-like learning

Multiple research groups collaborate on this research domain. The above table only mentions the contact person and his/her affiliation.

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