Discovery

Thematic series of technical talks on AI/Machine learning.

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.

ML5G

Trustworthy AI

AI and Health

AI and Climate Science

GeoAI

AI and Manufacturing

AI and 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.

Curator

Professor and Chair of Sustainable Manufacturing
KTH Royal Institute of Technology

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.

Curators

Professor of Geographic Information Systems and Digital Mapping
Politecnico di Milano
CEO
Open Geospatial Consortium (OGC)
Executive Director
World Geospatial Industry Council (WGIC)

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:

  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 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”

Curator

Independent Research Consultant
International Telecommunication Union (ITU)

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

Head of AI Department
Fraunhofer Heinrich Hertz Institute

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.

Curator

Chair of the Department of Biomedical Informatics
Harvard Medical School

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.

Curator

Head of Atmospheric, Oceanic and Planetary Physics
University of Oxford

AI for Good Discovery partners

Your Gateway to #AIforGood in your inbox!

Join our 60K+ global community to receive insights & invites to AI for Good! Just one email each week, and zero spam.