The AI for Earth and Sustainability Science discovery series highlights seminal and recent progress in AI-enabled modelling and understanding of the Earth system from local to global scale and AI-science based diagnosis, prediction and remedy of environmental crises. Interdisciplinary researchers from academia, industry, UN and government agencies as well as NGOs will talk about tackling systemic real-world challenges with AI, including disaster risk reduction and preparedness, environmental degradation, climate change, societal impacts and dynamics, and the sustainable and responsible use of natural resources such as water and energy. The webinar series is curated by the ELLIS Unit Jena and the ELLIS program “Machine Learning for Earth and Climate Sciences”. ELLIS is the European Laboratory for Learning and Intelligent Systems.

For many years climate scientists have used comparatively simple statistical approaches to try and discern subtle changes in observational datasets, or to interpret abundant climate model data output. The opportunity now presents itself for climate science to exploit advances in Machine Learning to answer some of the most pressing challenges of our time – while they are still relevant for policy makers. This acceleration will be built upon: supervised and un-supervised learning of features and patterns in the vast amounts of Earth observation and climate model data that is now available, transforming our ways to constrain climate models and the detection of climate change; robust emulation of existing climate models and their components; and causal detection and attribution of regional climatic changes. Climate scientists have begun to enthusiastically explore these possibilities, but scaling these novel approaches to the exabytes of data that will be created over the next decade to answer urgent scientific and policy relevant questions in a timely manner will require a concerted collaboration between academia, industry and policy makers. Industrial partners in particular can play a leading role in bringing in their extensive expertise and know-how but also in ensuring that climate data is accessible and interoperable with the latest ML algorithms and the specialized computational hardware required to run them. This AI and Climate Science discovery series provides a forum for leading voices in these fields and across sectors to outline a vision for how we will achieve this – with the aim of accelerating Climate Science with AI.

Manufacturing is an integral and huge part of the economy and plays an essential and fundamental role in accelerating progress toward global development. With the rapid development of information technology and the continuous deepening of the digital transformation process, AI is gradually applied in the whole lifecycle of manufacturing and this trend is expanding. The AI for Manufacturing discovery series, organized in partnership with the United Nations Industrial Development Organization (UNIDO), provides a forum for leading research, policy insights, and best practices across various sectors. It focuses on how AI technologies can be applied to benefit the manufacturing domain, not only at the product lifecycle management level but also at the levels of smart factories and intelligent supply chains. The series draws on a wide range of techniques, including modeling and simulation, digital twins, blockchain, 5G, and edge computing.

Geospatial AI (GeoAI), the emerging scientific discipline at the intersection of geospatial data and artificial intelligence, is the new frontier of technological innovation that promises to transform entire business industries. Geographic information systems (GIS) have been used widely to present a view of our world based on geographic and geospatial data. Started as the basic capability to visualize information on maps to improve efficiency and decision-making, GIS has conceptually evolved to include the Digital Twin Earths for revisiting the past, understanding the present and predicting the future. Nowadays we are undergoing significant new developments expanding the use of geographic data in a way that promises to disrupt entire sectors as energy, transportation, healthcare, agriculture, insurance and institutions in the public/private sector (weather centres, national labs) Behind the rise of geospatial AI are three trends: increased availability of geospatial Earth Observation data both from flying (satellites, airplanes, and UAVs (unmanned aerial vehicles) and on the ground sensors, the advancement of AI (particularly machine and deep learning), and the availability of massive computational power. The GeoAI discovery series provides a forum for leading voices in the fields of geospatial and AI across private sector, academia, governments, and international organizations, to describe the latest research and real applications of GeoAI to solve global challenges.

Many stakeholders in the information and communication technology (ICT) domain are exploring how to make best use of AI/ML. But applying AI/ML in communication networks poses different challenges than applying machine learning in, say, image recognition or natural language processing. The reasons are:

1. Time scales vary a lot in communication networks, from annual (e.g. your subscription to a telecom provider) to millisecond timescales. If your network parameters changes on a millisecond timescale, you need to (re)train your ML model on a similar timescale.
2. The network environment is noisy.
3. Computing resources in a network are limited.

5G, combined with AI, will make significant contributions in healthcare, education, agriculture, energy, manufacturing and transportation, among others. This technology will transform these sectors by providing significantly higher speed and lower latency for people, devices and applications. ITU has been at the forefront to explore how to best apply AI/ML in future networks including 5G networks. Please see the “AI (Pre-)Standardization Section”.

Advances in AI are driving the development of autonomous systems with a physical presence that can not only perform multiple tasks with ease but also analyze, learn, and self-improve in dynamic environments. The AI and Robotics discovery series explores the latest innovations and trends in embodied intelligence, including AI-powered robots, autonomous vehicles, unmanned aerial vehicles (drones), and other smart machines. It highlights how these technologies can be harnessed to address some of the world’s most pressing challenges and examines the technical foundations driving these advances, including machine learning, computer vision, natural language processing, robotics hardware design, and human-robot interaction. These embodied intelligent systems are already being deployed across a wide range of domains. In healthcare, for instance, surgical robots and assistive care robots are supporting telemedicine. In transportation, autonomous vehicles and delivery drones are reshaping mobility. In manufacturing, smart industrial automation is made possible through robots and collaborative robots (cobots). In education, robotic tutors and telepresence tools are enhancing remote learning experiences. In public safety, disaster response drones and search-and-rescue robots are saving lives in critical situations. This series provides a forum for transformative collaboration in AI and robotics for the benefit of humankind. Discover some of the most impactful areas of embodied intelligence and robotics from leading experts, and share your thoughts, questions, and ideas on how we can shape a better future together.

Artificial Intelligence (AI) systems have steadily grown in complexity, gaining predictivity often at the expense of interpretability, robustness and trustworthiness. Deep neural networks are a prime example of this development. While reaching “superhuman” performances in various complex tasks, these models are susceptible to errors when confronted with tiny (adversarial) variations of the input – variations which are either not noticeable or can be handled reliably by humans. The Trustworthy AI discovery series discusses the challenges of current AI technology and presents new research aiming at overcoming these limitations and developing AI systems which can be certified to be trustworthy and robust.

The health sector, one of the most important sectors for societies and economies worldwide, is particularly interesting for AI applications, given the ongoing digitalization of health data and the promise for an improved quality of health and healthcare. Many investigators from the machine learning community are driven to applying their methodological tool kits to improve patient care, inspired by the impressive successes in image analysis (e.g. in radiology, pathology and dermatology). However, due to the complexity of AI models, it is difficult to distinguish good from bad AI-based solutions and to understand their strengths and weaknesses. ITU, WHO, and WIPO established the Global Initiative on AI for Health (GI-AI4H) to enable, facilitate, and implement AI in healthcare, following the work of the ITU/WHO Focus Group on “AI for Health”.

While almost 25% of all species are at risk of going extinct mainly because of unsustainable human activities, biodiversity is essential for the processes that support all life on Earth, including humans. As more species face the risk of becoming endangered, restoring and preserving nature requires urgent and massive investment, effort and innovation. AI can play a vital role in protecting wild animals and plants in new and innovative ways, from AI-based cameras and drones to monitor populations and track poachers, AI models for animal recognition via images or sounds, or algorithms to estimate environmental degradation and ocean health over time – and more. AI is, for example, also used as a new approach for biodiversity conservation (see Silvestro, Goria, Sterner, & Antonelli, 2022) to identify conservation priorities in space and time, within budget limitations. This technique allows to integrate multidimensional biodiversity data and can more efficiently use the information at hand compared to state-of-the-art methods. Highlighting the importance of biodiversity and the urgent need to accelerate action to halt the unprecedented degradation of natural habitats, this series brings the AI and biodiversity community together to identify potential scope for collaboration to overcome challenges and strengthen efforts to mobilize, create and drive solutions for progress in protecting life below water, life on land, and beyond.  

Artificial intelligence promises to transform every aspect of our lives, from the way we communicate to how we drive, learn, consume energy, and obtain healthcare. There is no area where this is more true than financial services. However, the disruption of the finance sector by AI has only just begun. While AI models are being used in trading and portfolio management, and large language models are being used for information consumption and image generation, artificial intelligence is just on the cusp of stimulating changes in lending, investing, insurance, cybersecurity, infrastructure, and much more. The AI and Finance discovery series provides an important forum for leading experts in finance, economics, law, policy, and engineering to discuss the transformative impact of AI on finance.

While past fears over automation primarily concerned blue-collar workers, recent advancements in AI have revealed the potential for automating cognitive tasks. It is thus not surprising that many of today’s workers report fearing for their job or for the jobs of their children. But the effects of AI at the workplace go beyond potential redundancy, as the more likely outcome is the transformation of jobs, with important implications for job quality. In partnership with the International Labour Organization (ILO), the AI and Work discovery series will explore the opportunities and challenges presented by AI in the workplace. With invited speakers from economics, law, industry and labour studies, the series will delve into how AI technologies are reshaping jobs and skills requirements, the use of AI-driven decision-making in hiring and management, and the implications for productivity, inequality and the nature of work itself.

With the recent advances in AI, and in particular generative AI, there is a growing interest from the public sector to invest in AI developments to facilitate and improve public services. AI usages in the public sector span from simple redundant tasks automation, to more advanced chatbots to serve citizens and to decision support tools to improve public policies, investment and services. AI holds great promise in supporting better country public services and ITU is actively contributing to raising awareness and providing education and training on the potential uses and risks of AI in public services to help countries build capacities and move forward. Under the patronage of its EU-funded Open Source Ecosystem Enabler (OSEE) project and the ITU OSPO, this discovery series will discuss the compelling requirements of trustable, auditable and equitable AI-based public services. A key focus of this discussion will be on the importance of Retrieval Augmented Generation (RAG) and the fine-tuning of open-source AI for Low and Middle-Income Countries (LMICs), particularly when resources are scarce. RAG techniques can significantly enhance the efficiency and effectiveness of AI implementations in these contexts, making them more viable and impactful. This track will explore the impact of open-source AI on enhancing the technological capabilities of industrial sectors worldwide, while also supporting domestic technology development, research, and innovation in developing countries.

The humanitarian sector has been actively exploring the integration of AI, with each organization independently experimenting and deriving valuable insights. These efforts span technical, data management, financial, cultural, and linguistic dimensions. However, the broader humanitarian community has yet to fully benefit from these dispersed findings. To maximize the potential of AI in humanitarian contexts, it is crucial to foster a more integrated approach that consolidates these lessons and promotes collaborative learning. The AI in Humanitarian Action discovery series, in collaboration with AI for Good, seeks to bring together a wide range of stakeholders to share their experiences and learned lessons. Coordinated by UNOCHA and the IASC secretariat, this series aims to foster a more cohesive and integrated approach to AI in humanitarian contexts, enhancing the collective impact and effectiveness of AI-driven initiatives.

Join us for the From Molecules to Models discovery series, where leading experts from the ELLIS Programs present their cutting-edge research and innovations in AI. This is a unique opportunity to engage with high-impact topics across various AI fields, from semantic and symbolic learning to breakthrough applications that are shaping the future of technology. The European Laboratory for Learning and Intelligent Systems (ELLIS) is a top-tier initiative dedicated to advancing AI research in Europe. ELLIS unites some of the brightest minds in AI across Europe’s leading institutions, fostering a collaborative, interdisciplinary network of researchers. Its mission is to address the most pressing challenges in AI, from deep learning to interpretable AI, and to ensure that European research remains at the forefront of AI development. The ELLIS Programs are a network of cutting-edge research programs focused on high-impact AI challenges. Led by distinguished researchers and supported by leading Program Fellows, these programs drive collaboration across Europe to push the boundaries of modern AI. Inspired by the CIFAR Program model, ELLIS Programs also collaborate with the CIFAR LMB (Learning in Machines and Brains) Program, creating an environment for intensive scientific exchange.