Active-Inference

AIM-TT

AiMTT aims to cultivate a highly skilled and diverse AI talent pool equipped to address the opportunities and challenges of AI in mobility, transport, and logistics. By combining real-world case studies with knowledge development, this initiative fosters deep expertise in the field.
FlexLab

FlexLab is an AI innovation lab that creates sustainable and societal impact by applying advanced AI technologies to enable flexible electricity consumption and ensure that this flexibility can be used to resolve congestion in medium- and low-voltage power grids. FlexLab achieves this by providing a secure environment in which startups and SMEs can test and validate their flexible energy solutions together with value-chain partners.
CONTACT-AI

Challenge. The International Labour Organisation reports over 300 million work-related accidents and diseases per year, with nearly 3 million being fatal (ILO report). Embodied Artificially Intelligent (EAI) agents can reduce this drastically, by for example inspecting construction sites or transporting cargo through hazardous areas. However, autonomously navigating unknown environments is difficult and requires adaptive decision-making. Suppose the agent detects a visually ambiguous obstacle: is it a crate that can be pushed away? Or a fence that needs to be navigated around? Rule-based algorithms and task-priority controllers could yield unsafe situations, while reinforcement learning (RL) requires enormous amounts of trial-and-error, potentially breaking the robot during training. The challenge is to design an EAI agent that cautiously and efficiently explores using multiple sensory modalities to find the best path through unknown terrain.

EmbodAI

State-of-the-art (deep) reinforcement learning systems, for all their fantastic achievements, struggle in real-world tasks that are trivial for humans, especially those involving physical interactions. At the same time these systems consume excessive power for training and operation. That is because they are inefficient with their model representations (many parameters) and their data (big data and many trials for training). We see the (robot) body as an enormous computational resource that is poorly understood and largely underappreciated. But how to harness embodiment remains an important open question.
FEP-Lab

https://icai.ai/lab/fep-lab/

The FEPlab (Free Energy Principle Laboratory) is a collaboration between Eindhoven University of Technology (TU/e) and GN Hearing. The mission of the lab is to ameliorate the participation of hearing-impaired people in formal and informal social settings. The lab will focus its research on transferring a leading physics/neuroscience-based theory about computation in the brain, the Free Energy Principle (FEP), to practical use in human-centered agents such as hearing devices and VR technology.
FEPQuad
Jan 1, 2022
Auto-AR

Automated Situated Design of Augmented Hearing Reality Algorithms
BayesBrain

BayesBrain-scheme

Computation in biological brain tissue consumes several orders of magnitude less power than silicon-based systems. Motivated by this fact, this project aims to develop the world’s first hybrid neuro-in-silico Artificial Intelligence (AI) computer, introducing a fundamentally new paradigm of AI computing. In this high-risk high-gain project, we will combine an in-silico Bayesian control agent (BCA) with neural tissue hosted by a microfluidic Brain-on-Chip (BoC) that together form a hybrid learning system capable of solving real-world AI problems.
CoHear

Hearing loss is a very serious health condition that has been associated with early dementia and cognitive decline. Still, hearing aid market penetration is quite low, in particular for the large population that is afflicted with ‘mild-to-moderate’ hearing impairment. This is mainly due to two reasons: Stigma (association with old age) and hearing aids (HA) sound quality. The recent commercial introduction of fashionable ‘hearables’ will likely alleviate the stigma issue. Recent advances in machine learning and cloud computing open new avenues for attacking the sound quality issue for hearing aids. In this project, we intend to develop a (crowd-based) collaborative design approach to improving the sound quality issues for the mild-to-moderately hearing-impaired population.