Playing Brains: The Ethical Challenges Posed by Silicon Sentience and Hybrid Intelligence in DishBrain

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Machine learning models (e.g., neural networks) achieve high accuracy in wind power forecasting, but they are usually regarded as black boxes that lack interpretability. To address this issue, the paper proposes a glass-box model that combines high accurac ...

Background aims: Age-related macular degeneration (AMD) is the most common cause of blindness in elderly patients within developed countries, affecting more than 190 million worldwide. In AMD, the retinal pigment epithelial (RPE) cell layer progressively d ...

Cell fate progression of pluripotent progenitors is strictly regulated, resulting in high human cell diversity. Epigenetic modifications also orchestrate cell fate restriction. Unveiling the epigenetic mechanisms underlying human cell diversity has been di ...

Machine learning and artificial intelligence methods have achieved remarkable success, matching and even surpassing human capabilities in various complex tasks. However, many demonstrations have generally neglected a critical part of the intelligence that ...

Proteins are the molecular machines of life, driving essential biological processes such as enzymatic catalysis, cellular signaling, and immune responses. The ability to design entirely new proteins with desired structures and functions has immense implica ...

Subcortical brain structures such as the basal ganglia or the thalamus are involved in regulating motor and cognitive behavior. However, their contribution to perceptual consciousness is still unclear, due to the inherent difficulties of recording subcorti ...

Animals exhibit remarkable locomotion skills despite significant sensorimotor delays and operating in uncertain environments. Moreover, mammals acquire these skills within minutes of birth. From the Cambrian explosion to the present day, vertebrate motor c ...

Cells are the smallest operational units of living systems. Through synthesis of various biomolecules and exchange of signals with the environment, cells tightly regulate their composition to realize a specific functional state. The transformation of a cel ...

This thesis focuses on understanding and improving the reasoning capabilities of neural networks. It develops theoretical results and empirical analyses to uncover reasoning potential and limitations, leveraging these insights to guide the design of improv ...

Traditional cell cultures have long been fundamental to biological research, offering an alternative to animal models burdened by ethical constraints and procedural intricacies, often lacking relevance to human physiology and disease. Moreover, their inabi ...

Artificial intelligence (AI) is increasingly used in an ever larger number of industries. Alongside this development, however, abundant works argue that AI-driven systems are lacking in terms of safety, ethics and transparency. As a direct consequence, the ...

Motivation Many machine learning (ML) models developed to classify phenotype from gene expression data provide interpretations for their decisions, with the aim of understanding biological processes. For many models, including neural networks, interpretati ...

Recent progress in computer vision has been driven by a simple yet transformative observation: the potential of 30-year-old neural networks can be unlocked by massively scaling their parameters and the amount of human-labeled training data. This breakthrou ...

The ability to reason, plan and solve highly abstract problems is a hallmark of human intelligence. Recent advancements in artificial intelligence, propelled by deep neural networks, have revolutionized disciplines like computer vision and natural language ...

Objective. A key challenge of virtual reality (VR) applications is to maintain a reliable human-avatar mapping. Users may lose the sense of controlling (sense of agency), owning (sense of body ownership), or being located (sense of self-location) inside th ...

Understanding the structure of real data is paramount in advancing modern deep-learning methodologies. Natural data such as images are believed to be composed of features organized in a hierarchical and combinatorial manner, which neural networks capture d ...

Semi-supervised semantic segmentation methods leverage unlabeled data by pseudo-labeling them. Thus the success of these methods hinges on the reliability of the pseudo-labels. Existing methods mostly choose high-confidence pixels in an effort to avoid err ...

The last two decades have seen the development of organoid models for many different tissues and organs. Organoids are three-dimensional organ-mimetics derived from stem or progenitor cells comprising various specialized cell types, resembling the architec ...

The current machine learning paradigm relies on continuous representations and fixed neural network architectures to approximate environmental structures, leading to challenges with continual learning, internal structure design, and goal-directed behavior ...

Throughout history, the pace of knowledge and information sharing has evolved into an unthinkable speed and media. At the end of the XVII century, in Europe, the ideas that would shape the "Age of Enlightenment" were slowly being developed in coffeehouses, ...

Playing Brains: The Ethical Challenges Posed by Silicon Sentience and Hybrid Intelligence in DishBrain

Chattez avec Graph Search

Explainable modeling for wind power forecasting: A Glass-Box model with high accuracy

Clinically compliant cryopreservation of differentiated retinal pigment epithelial cells

Single-cell epigenomic reconstruction of developmental trajectories from pluripotency in human neural organoid systems

Sequential Reasoning with Socially Caused Beliefs

Computational design of complex protein folds and soluble analogs of membrane proteins

Subcortical correlates of consciousness with human single neuron recordings

A Bio-Inspired Learning and Control Framework for Cross-Embodiment and Cross-Task Locomotion

Deciphering the nature of cell state transitions in single cells using quantitative modeling of temporal dynamics

Learning to Reason with Neural Networks: Principles and Methods

Accessible and highly observable gastrointestinal organoid model systems for studying host-pathogen interactions

Voices from the Frontline: Revealing the AI Practitioners' viewpoint on the European AI Act

A comparative analysis of gene expression profiling by statistical and machine learning approaches

Exploiting Representation Similarities in Self-Supervised Learning for Vision Tasks

Infusing structured knowledge priors in neural models for sample-efficient symbolic reasoning

Customizing the human-avatar mapping based on EEG error related potentials during avatar-based interaction

A phase transition in diffusion models reveals the hierarchical nature of data

Weighting Pseudo-labels via High-Activation Feature Index Similarity and Object Detection for Semi-supervised Segmentation

Bioengineering functional organoid models across scales

Modelleyen: Continual Learning and Planning via Structured Modelling of Environment Dynamics

Machine Learning for Modeling Stock Returns