AI for Life

Artificial Intelligence (AI) has become a powerful and pervasive technology in recent years, influencing numerous aspects of our daily lives. We encounter AI through recommendation algorithms in online stores, voice-activated smartphone assistants, or the widespread use of technologies like ChatGPT. However, its rapid growth and integration into society raise complex questions and concerns among the public. Public opinions on AI vary widely; while some people are enthusiastic about its potential to revolutionize industries and enable breakthroughs in fields such as medicine, others fear that AI could lead to undesirable outcomes, such as a loss of human control or privacy. These views are often shaped by media narratives and the competing interests of different stakeholders, which play a significant role in influencing both public opinion and policy decisions.

Our team of scientists and communication experts aims to enhance the understanding of AI technologies among the Swiss people, with a particular focus on teenagers and female students, to create a positive societal impact. Building on the foundations of our previous project, NewsOnAI, we will expand beyond traditional media such as newspapers and employ diverse methods, including artistic performances and interactive exhibitions. We will design these activities to be highly interactive, encouraging active participation and dialogue. Activities will include themed theater plays that explore AI’s impact on everyday life, exhibitions where participants can interact with AI tools, and workshops specifically designed for teenagers and female students to discuss AI’s future role in society. Feedback collected will include real-time audience reactions, structured questionnaires, and focus group discussions, which will be analyzed to continuously refine and adapt our engagement strategies.

Our primary audience includes Swiss citizens interested in cultural activities, particularly teenagers who are keen to follow new trends. Additionally, we are committed to addressing gender aspects by designing content and activities that specifically appeal to female students. We aim to inspire and empower young women to take on more prominent roles in shaping the digital world, acknowledging that they have historically been underrepresented in these fields. As societal attention shifts toward greater inclusion, our project will contribute to fostering a more balanced and equitable digital future.

While many individuals in our target groups may lack in-depth technical knowledge of AI, they often encounter new AI products, companies, and social issues through various media channels, including newspapers and science fiction movies. As a result, they may be aware of recent developments but also susceptible to misunderstandings and controversies related to technologies such as ChatGPT, Elon Musk's brain-chip startup, and other emerging AI applications. It is crucial to recognize that media portrayal significantly influences public opinion on AI, both positively and negatively. Media creators, even if they are not experts in AI, often produce content that captures public attention, which high-profile figures, including entrepreneurs, CEOs, and politicians, may leverage to advance their agendas. This can sometimes lead to skewed public perceptions, whether intentionally or unintentionally. Given this landscape, it is essential for AI scientists to collaborate with media creators, providing evidence-based insights to ensure accurate and balanced information is shared with the public. Our project fosters such collaboration, ensuring that both the potential and limitations of AI are clearly communicated. By sharing our findings through diverse media outlets, we aim to reach a broad audience, extending beyond Switzerland. Furthermore, our proactive engagement efforts will foster dynamic, two-way communication between scientists and the public, using interactive methods in exhibitions and theater plays to engage teenagers and female students specifically. Analyzing the feedback from these initiatives will provide invaluable insights into public perspectives on emerging technologies. This understanding will guide scientists in pursuing research directions that effectively address societal concerns, demonstrating the tangible benefits of our project for both scientific advancement and societal well-being. We anticipate that our efforts will have a multiplying social impact over time, promoting informed public discourse and a deeper understanding of AI technologies.

Assessing adult-child social interactions objectively remains a complex but essential task, especially for child psychiatrists evaluating neurodevelopmental progress. This is particularly critical for children with Autism Spectrum Disorder (ASD), where early, accurate diagnosis can significantly improve long-term outcomes. The earlier autism is identified, the sooner targeted interventions can support cognitive development and social interaction skills. However, current diagnostic methods are time-consuming, subjective, and difficult to scale, requiring lengthy observation by trained professionals. As a result, many children face delays in diagnosis and limited access to early therapeutic support and follow-up.

This project addresses these limitations by developing novel Computer Vision and Deep Learning algorithms to automate behavioral coding, making ASD diagnosis faster, more objective, and scalable. These algorithms will estimate severity scores and detect key behavioral markers, supporting clinicians in early screening and continuous neurodevelopmental monitoring. While progress has been made in activity and non-verbal behavior recognition, current methods struggle with adult-child interactions captured in naturalistic conditions—such as those in the Autism Diagnostic Observation Schedule (ADOS), the clinical gold standard. These difficulties arise from the complexity and subtlety of target behaviors: attention-related cues (e.g., gaze, pointing), infant micro-expressions, and motor stereotypes.

To support this, the project will leverage the ACTIVIS dataset, a large-scale, clinician-annotated audiovisual collection recorded during ADOS sessions. It includes over 180 sessions with 160 children performing various tasks assessing developmental domains. These recordings are precisely labeled using standardized protocols. A goal is to use this resource to train deep learning models for automatic behavioral scoring and ASD profiling.

In parallel, the project will collect a longitudinal dataset (BabySmile) capturing parent–child interactions between 3 and 18 months to investigate early neurodevelopmental markers in infants. Studies suggest that behaviors like early smiling and joint attention onset may indicate neurodevelopmental disorders, but the progression of these milestones remains poorly understood. Using computer vision and expert-labeled data, this dataset will help study early behavioral patterns and their predictive value for later diagnosis.

The project is organized around three main objectives:

- Methods. Develop advanced AI techniques to address key scientific challenges: (i) leveraging multimodal signals (e.g., video, audio, depth) to enrich behavior interpretation; (ii) detecting subtle, fine-grained motion cues; (iii) addressing data scarcity via weak supervision and efficient fine-tuning; (iv) fusing diverse cues across multiple processing levels; and (v) ensuring robustness and generalization to real-world clinical scenarios.

- Datasets. Extend the ACTIVIS dataset with additional behavioral annotations and ethical anonymization; collect the BabySmile dataset to enable early-stage developmental research with clinical relevance.

- Evaluation. Create interactive visual tools for clinicians to review videos, explore behavioral predictions, and validate ASD profiles. These interfaces will enable clinician-in-the-loop feedback to refine annotations and continuously improve model performance.

By addressing these scientific and clinical challenges, the project will close the gap between automated behavioral analysis and ASD assessment. The consortium brings together complementary expertise: UniCA for clinical leadership and ASD evaluation; Inria for activity and gesture analysis; Idiap for nonverbal behavior modeling; and LIRIS for emotion and action recognition. This multidisciplinary collaboration ensures innovation and strong clinical impact across all objectives.

Oral verbal communication represents the main communication channel among humans. In most communication contexts, speakers must speak clearly and accurately in order to be intelligible. Intelligible speech can be disrupted in a variety of conditions of motor speech disorders (MSD). MSD in adults refers to a broad set of altered speech dimensions (articulation, speech rate, voice, prosody) in the course of several neurological diseases, which can dramatically impact patients’ communication. MSDs are due to disruption in the processes transforming a linguistic message intoarticulated speech, i.e., (a) the retrieval/encoding, contextualization and coordination of speech goals into a speech plan, (b) the preparation of motor programs with detailed neuromuscular specifications, and (c) the execution of these programs. Impairments atthese different stages have been associated with different MSDs, with apraxia of speech(AoS) associated with impairments at the first stage, i.e., the planning stage, and dysarthria associated with impairments at the programming or at the execution stage. Nonetheless, defining planning and programming stages, as well as distinguishing impairments at these two levels in terms of speech features and clinical differential diagnosis, is far from being clear-cut. This proposal builds on the successful outcomes of the Sinergia MoSpeeDi project (2017-2021, https://www.unige.ch/fapse/mospeedi/) led by the same multidisciplinary consortium. Thanks to the complementary expertise in speech and language pathology, psycholinguistics, neurology, phonetics, and speech engineering, we have collected an impressive database of MSD speech, have developed procedures sensitive enough to assess and classify mild and moderate MSD, and have obtained converging experimental evidences for the characterization of processes occurring at the planning and motor programming stages. This knowledge gained from carefully designed experiments and laboratory settings should now be expanded to speech production elicited in a more natural clinical setting. The distinction between speech planning and programming processes should also be further tackled to overcome the difficulty in defining and operationalizing processes at these two stages. With the overarching goal of understanding and modelling speech planning and programming and their related disorders, we will pursue our synergic approach based on the integration of methods and on the convergence of evidence obtained with experimentally induced speech behaviours, electrophysiological brain signals, and acoustic analyses of typical and impaired speech. Based on the results and expertise developed in the ongoing project to pinpoint speech planning and programming and to classify speakers and speech samples, in this project we propose to (a) develop assessment and classification methods applicable to realistic clinical constraints and needs, (b) build on the convergence of phonetic knowledge-based approaches and knowledge-free approaches, (c) enrich our set of acoustic descriptors in order to capture alterations at different scales of speech organization, and (d) complement acoustic-based characterization of speech planning, programming, and MSD classification with EEG signals. The outcomes of the project will rely on substantial data of disordered speech collected from over 180 French speaking participants with different types of MSDs including AoS and subtypes of dysarthria following stroke or neurodegenerative diseases. Results will be used to challenge current models of speech production which need to integrate data from MSD and will contribute to the development of speech assessment systems adapted to atypical speech and to the needs of clinical practice.

Project DREAM aims to develop a scalable foundation model for multi-channel EEG signal analysis, spanning from basic polysomnography to high-density and intracranial recordings. The goal is to create a unified AI model capable of adapting to various EEG configurations while maintaining simplicity, interpretability, and computational efficiency. By leveraging convolutional neural networks and attention mechanisms, the model will extract robust features for diverse tasks, including sleep stage classification, epilepsy detection, and cognitive function assessment. Collaborations with leading hospitals will provide real-world datasets for validation, ensuring the model’s generalization. Ultimately, this project seeks to advance AI-driven biomedical signal processing, enhancing diagnostic and monitoring capabilities in neurology and sleep medicine.

Construction of a Natural Language Processing (NLP) infrastructure to support domain experts in biomedical discovery over large scientific textual bases (papers and patents). ABRoad aims to develop an NLP software infrastructure which will support biomedical discovery using large-scale textual interpretation over scientific text (papers and patents). The project will use state-of-the-art methods in Deep Learning based text representation, such as transformers and graph neural networks, to support specialised inferences over large-scale corpora. The project aims to provide a universal (embeddings-based) textual interpretation platform to support the identification of new hypotheses in the life science space. The platform will integrate two main data modalities: textual and molecular representations. More specific target scenarios include support for drug discovery (e.g. drug repurposing), the determination of bioequivalent substances and the identification of novel antibiotics.

Nowadays, 1 in 59 children is diagnosed with autism spectrum disorders (ASD), which makes this condition one of the most prevalent neurodevelopmental disorders. The hereby project is grounded on the recognition that, on the one hand, early diagnosis at scale of autism in young children requires the development of tools for digital phenotyping and automated screening, through computer vision and Internet of Things sensing. On the other hand, current gold-standard approaches in autism are not intended to provide a precise quantitative estimate of ASD symptoms in children. We therefore aim to examine the potential of digital sensing to provide automated measures of the extended autism phenotype, for the purpose of stratifying autism subtypes in ways that would allow for precision medicine. Recent developments in digital sensing, big data and machine-learning have offered unforeseen opportunities for seamless sensing of body movement, social scene capture, and measure of object manipulation. Together, these tools are key for modeling social interactions and offer avenues for both improving screening and fine-grained characterization of autistic symptoms in young children. Despite considerable efforts invested to explore such automatic behavioral analysis, most studies in ASD digital phenotyping have been conducted on modest samples sizes, used mono-modal approaches, were focused on eliciting very specific behaviors by largely controlled prompts, and have suffered from technical difficulties in behavior sensing (view points, children population, image resolution for gaze). To address these limitations, we propose an interdisciplinary project combining the skills of experts in clinical research, engineering and computational social sciences in order to address these clinical, scientific, and technical challenges. It is grounded on the Geneva Autism Cohort consisting of young children with ASD and their age-matched typically developing peers, extensively assessed with gold standard standardized clinical and cognitive assessments, as well as neuroscience tools. Further, our preliminary results demonstrated that relying on a substantial dataset it is feasible to successfully train a deep neural network directly from on a global scene representation (people poses) to predict ASD with above 80% accuracy. This Sinergia proposal is set to stretch a giant leap forward, by investigating three key research directions. First, from a clinical research perspective, we will design digital tools for screening and automated profiling of autism phenotype. We will test these tools in a structured setting with well-established clinical protocol, as well as in a less structured environment (free play in day-care centers). Second, with Internet of Things (IoT) sensors, we will investigate the motor skills of very young children, through the integration of inertial and low-cost UWB indoor localization data. Additionally, we will develop a solution for the longitudinal monitoring of fine-grained motor skills development. Last but not least, our project is rooted in modern computational perception and machine learning. We will investigate novel deep learning and computer vision techniques by leveraging the availability of large behavioral and clinical annotation data. At the core of this effort, we will develop multimodal machine-learning methods and models for the analysis of motor and gaze coordination patterns which are at the core of ASD, and for ASD diagnosis and profiling with a focus towards interpretable models.

Motivated by the challenges, risks and opportunities that the wide use of AI brings to media, society and politics, AI4Media aspires to become a centre of excellence and a wide network of researchers across Europe and beyond, with a focus on delivering the next generation of core AI advances to serve the key sector of Media, to make sure that the European values of ethical and trustworthy AI are embedded in future AI deployments, and to reimagine AI as a crucial beneficial enabling technology in the service of Society and Media. The AI4Media consortium, comprising 30 leading partners in the areas of AI and media (9 universities, 9 research centres, 12 industrial partners) and 35 associate members, will establish the networking infrastructure to bring together the currently fragmented European AI landscape in the field of media, and foster deeper and long-running interactions between academia and industry, including Digital Innovation Hubs. It will also shape a research agenda for media AI research, and implement research and innovation both with respect to cutting-edge technologies at the core of AI research, and within specific fields of media-related AI. AI4Media will provide a targeted funding framework through open calls, to speed up the uptake of innovations developed within the network. A PhD programme will further enhance links to the industry and the fostering and exchange of talent, while providing motivation to prevent brain drain, and a set of use cases will be developed by the network to demonstrate the impact of the achieved advances in the media sector. The Excellence Centre that is established during the AI4Media project, and the ecosystem that will grow around it, will provide a long-term basis for the support of AI excellence in Europe, long after the project end, with the aim of ensuring that Ethical AI guided by European values assumes a global leading role in the field of Media.

Self-driving cars are around the corner. Biped uses such technology to help the blind and visually impaired walk safely and understand their environment. This project aims to build a completely novel deep learning architecture to reach improved performance, better scalability, and lower maintenance.