The “Discovery-Channel” digs deeper into thematic areas transformed by Artificial Intelligence/Machine Learning as well as into challenges of current AI/ML technology. Each “Discovery” episode dedicates one hour to a researcher to present their latest findings in one of the topic areas below.
Related sessions
7 July 2023
09:00 - 17:30 CEST Geneva | 03:00-11:30 EDT, New York | 15:00-23:30 CST, Beijing
Alessandra Sala (Shutterstock), Orly Lobel (University of San Diego), Samantha Maloney (The Surfrajettes)
17 July 2023
17:00 - 18:30 CEST Geneva | 10:00-11:30 COT, Bogotá | 11:00-12:30 EDT, New York
Juan M. Daza ( Universidad de Antioquia), Juan Sebastián Ulloa (Institute Alexander von Humboldt), María Cecilia Londoño (Humboldt Institute)...
25 September 2023
17:00 - 18:30 CEST Geneva | 16:00-17:30 BST, London | 11:00-12:30 EDT, New York
Mike Gill (NatureServe’s Biodiversity Indicators Program), Matilda Brown (Royal Botanic Gardens, Kew)
23 October 2023
17:00 - 18:30 CEST Geneva | 11:00-12:30 EDT, New York | 23:00-00:30 CST, Beijing
Eliot Miller (Cornell University), Grant van Horn (University of Massachusetts, Amherst), Mike Gill (NatureServe’s Biodiversity Indicators Program)
AI for Earth and Sustainability Science
AI and Climate Science
AI and Manufacturing
GeoAI
ML5G
Trustworthy AI
AI and Robotics
AI and Health
AI for Biodiversity
AI and Finance
Data-centric Machine Learning for Good
AI and Work
Open Source AI for Digital Public Goods
AI for Earth and Sustainability Science
The AI for Earth and Sustainability Science webinar 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.”
Enjoy and learn how to make our planet a better place and our future more sustainable through AI!
Curators
AI and Climate Science
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 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.
Curators
AI for Manufacturing
Manufacturing is an integral and huge part of the economy and plays an essential and fundamental role in accelerating progress toward the United Nations Sustainable Development Goals (SDGs). 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.
Together with the United Nations Industrial Development Organization (UNIDO), ITU launches the AI and Manufacturing series to provide a forum for leading research works, policies, and best practices in different sectors.
The AI and Manufacturing series focuses on how AI technologies can be used to benefit the manufacturing domain, not only product lifecycle management level, but also smart factory level and intelligent supply chain level, drawing from a variety of techniques such as modeling and simulation, digital twin, blockchain, 5G, and edge computing.
GeoAI
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 vehicle)) and on the ground sensors , the advancement of AI (particularly machine and deep learning), and the availability of massive computational power.
This series provides a forum for leading voices in the fields of geospatial and AI across various sectors (private sector, academia, governments, national and international organizations) to describe latest research and real applications of GeoAI to meet the Sustainable Development Goals.
ML5G
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:
- 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.
- The network environment is noisy.
- Computing resources in a network are limited.
5G, combined with AI, will speed up the advancement of the UN Sustainable Development Goals with significant contributions in healthcare, education, agriculture, energy, manufacturing and transportation, among others. The 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”
AI and Robotics
Advances in AI are driving the creation of more sophisticated, autonomous and specialized robots that can not only perform multiple tasks with great ease, but also analyze, learn and self-improve in dynamic environments. The AI and Robotics webinar series focuses on how intelligent autonomous systems, which are developed by integrating AI with robotics, can help advance the Sustainable Development Goals.
This expert talk series discusses the latest innovations and trends in robotics and AI, and how they can be harnessed to address some of the world’s most pressing societal challenges in fields such as ageing and health, smart transport, working in hazardous environments, sustainable food production and consumption, green energy, climate change, safety, and equality. Speakers address the technical aspects of robotics and AI, such as machine learning, computer vision, natural language processing, robotics hardware design, human-robot interaction.
This series provides a forum for cataclysmic collaboration around AI and robotics for the benefit of humankind. Learn about some of the most impactful areas of robotics from the leading experts themselves, and voice your thoughts, questions and ideas on how we can shape a better future, together.
Curator
Trustworthy AI
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. This expert talk series will discuss these challenges of current AI technology and will present new research aiming at overcoming these limitations and developing AI systems which can be certified to be trustworthy and robust.
Curator
AI and Health
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 and the World Health Organization established the ITU/WHO Focus Group on “AI for Health” to clarify responsibilities and building trust among AI developers, AI regulators and AI users.
Curators
AI for Biodiversity
While almost 25% of all species are at risk of going extinct mainly because of unsustainable human activities, biodiversity is essential for humanity and for achieving the Sustainable Development Goals. 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. Artificial Intelligence 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 towards SDG 14 (Live Below Water) and SDG 15 (Life On Land) and beyond.
Curators
Senior Researcher, Biodiversity Assessment and Monitoring Program
Humboldt Institute
AI and Finance
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. This series provides an important forum for leading experts in finance, economics, law, policy, and engineering to discuss the transformative impact of AI on finance and in support of the Sustainable Development goals.
Data-centric Machine Learning for Good
In model-centric machine learning (ML) – a common starting point in ML education – the focus is on improving the model itself, given a fixed dataset. In contrast, data-centric ML prioritizes methods and systems that incorporate the processing of the dataset itself; for example generation, annotation, or cleaning; into the learning pipeline.
Data-Centric Machine Learning Research (DMLR) for Good acts as a crucial link between the DMLR and United Nations (UN) communities. Its goal is to advance “AI for Good” by connecting top-tier ML research, including methods, code, and infrastructure, with public good assets like data, tasks, and problem owners.
Many ML researchers, from students to professors and conference organizers, strive to contribute to the public good. However, finding suitable and impactful public good tasks for ML applications can be as complex as pure machine learning research itself. This complexity often results in a focus on standard datasets like ImageNet or CIFAR. While beneficial for benchmarking, it also represents a concentration of collective compute, time and brainwaves on a limited problem space. Other components of reproducibility and utility remain underexplored as a result.
To address these challenges, the DMLR Discovery Track highlights ML works related to public good themes and increases visibility of UN assets for public good initiatives within the ML research community. We hope to facilitate a more diverse and impactful application of ML research in real-world scenarios.
Curators
Professor of Machine Learning, Artificial Intelligence and Medicine
University of Cambridge
Fellow at The Alan Turing Institute in London
Director, Research Scientist
Google
Professor of Artificial Intelligence, Université Paris-Saclay (Orsay)
AI & Work
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.
Together with the International Labour Organization (ILO), this series on AI & Work 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.
Curators
Open Source AI for Digital Public Goods (OSAI4DPG)
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.
With less than 10 years to achieve the Sustainable Development Goals (SDGs), AI holds great promise in supporting better country public services. 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 webinar 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 discuss the impact of (open source) AI on SDG 4, SDG 8, and SDG 9. A particular focus will be set on SDG 9 target 9.5, which involves the upgrading of technological capabilities of industrial sectors in all countries, and 9.b, which emphasizes domestic technology development, research, and innovation in developing countries.
Curators
Senior Project Coordinator of the EC-funded OSEE project
International Telecommunication Union (ITU)
AI for Good Discovery Past Curators
AI for Good Discovery partners
