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.

AI-systems that communicate and collaborate seamlessly with humans

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.

Structure of the challenge

Overview of the structure of the research challenge

WP1, WP2, WP3 and WP4 are work packages that feature both perception and cognition: starting from multimodal sensors, clear semantics are derived, which are used in human-like learning algorithms, with a strong personalization and interaction aspect. The results of these methodological work packages are integrated in WP5: use cases and demonstrators.

Following the objectives set forth above, this challenge can be split in two main parts, which are both addressed in two methodological work packages. The overall work package structure is illustrated in Figure above.

How can we get a detailed understanding of the environment at hand? This is tackled in work packages WP 1 and WP 2
How can we respond and react to tasks in a way that is similar to a human? This is tackled in WP 3 and WP 4.

Work packages

WP 1 Audio-visual perception & multimodal representations

WP 1 'Audio-visual perception & multimodal representations' has a dual goal. On the one hand, it will develop new vision and audio analysis techniques to achieve visual and auditive understanding. On the other hand, it focuses on learning a clear and structured representation of the system’s surroundings.

WP Lead: Tinne Tuytelaars, ESAT, PSI, KULeuven

- KULeuven ESAT, PSI (https://www.esat.kuleuven.be/psi)

WP 2 Deep learning-based conversational agents

WP2 'Deep learning-based conversational agents' focuses on natural language processing and has the goal of creating a deep learning based conversational agent which can incorporate background knowledge, common sense and support interaction. This contextual knowledge can be extracted from other sensors such as vision and audio, studied in WP1.

WP Lead: Walter Daelemans, CLIPS, UAntwerpen

Research Groups:

WP 3 Interaction, personalization and recommendation

WP3 'Interaction, personalization and recommendation' focuses on achieving a natural and intuitive interaction with an AI system and giving the AI system a first “direction”. Out of a wide variety of choices (i.e., big decisions) an AI system can take, what is the best one that tailors to the human it interacts with? New recommendation techniques which are explainable are key to achieve this.

WP Lead: Bart Goethals, ADREM, UAntwerpen

Research Groups:

- UAntwerpen ADREM
- UGent MICT
- UGent IDLab
- UHasselt EDM
- VUB ETRO
- UGent WAVES

WP 4 Coginitive architectures & human-like learning

WP4 'Coginitive architectures & human-like learning' has the goal of developing new machine learning methods and solutions, which bring the reasoning closer to how a human learns and reasons about the world. This entails fast adaptation to unseen tasks, detecting and learning preferences, etc.

WP Lead: Geraint Wiggins, VUB AI Lab

Research Groups:

- VUB AI Lab
- UAntwerpen IDLab
- VUB DiMa
- VUB MOBI
- UGent WAVES

WP 5 Use Cases

Contacts

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

Erik Mannens

Management team, imec, UAntwerpen - IDLab, e-mail

Tom De Schepper

Management team & WP5 Lead: Use Cases, UAntwerpen - IDLab

Tinne Tuytelaars

WP1 Lead: Audio-visual Perception and Multimodal Representations, KU Leuven

Walter Daelemans

WP2 Lead: Deep Learning-based Conversational Agents, UAntwerpen - CLiPS

Bart Goethals

WP3 Lead: Interaction, Personalization and Recommendation, UAntwerpen - ADREM

Geraint Wiggins

WP4 Lead: Cognitive Architectures & Human-like learning, VUB