Crowdsourcing AI and Machine Learning solutions for SDGs ITU AI/ML Challenges 2024 Report
Foreword
Acronyms
1 Executive Summary
2 Introduction
3 Domains and Areas of Competition
     3.1 AI/ML in 5G and 6G (Communication Networks)
     3.2 Geospatial Artificial Intelligence
     3.3 tinyML
     3.4 AI for Climate Action
     3.5 Fusion Energy
4 Participation
     4.1 Motivation to Participate
     4.2 Statistics
     4.3 Challenge Phases/Timeline
5 Problem statements
6 Winning solutions
     6.1 AI/ML for 5G-Energy Consumption Modelling
     6.2 Build-a-thon
     6.3 Graph Neural Networks (GNN)
     6.4 Smart Weather Station
7 Incentives
     7.1 Prizes
     7.2 Certificates
8 Webinars
9 Capacity building
     9.1 Technical Webinars
     9.2 Hands-On Workshops
     9.3 Mentoring Sessions
     9.4 Round-Table Discussions
     9.5 Online Learning Resources
     9.6 Certification and Recognition
10 Intellectual property rights
11 Challenge Solution Contributions
     11.1 Standards
     11.2 Open Source
     11.3 Journal and Conference Publications
     11.4 Ecosystem creation
12 Judging the submissions
     12.1 Common output format
     12.2 Additional output for open-source code
     12.3 Additional output for proprietary code
     12.4 Evaluation Criteria
13 Resources
14 Benefits
     14.1 Benefits for partners and collaborators
     14.2 Benefits for Participants
     14.3 Special Benefits for Certain Sponsor Categories
15 Impact
     15.1 Advancing Technological Innovation
     15.2 Promoting Global Collaboration
     15.3 Enhancing Practical Skills
     15.4 Contributing to Standards Development
     15.5 Addressing SDGs
     15.6 Recognizing and Rewarding Excellence
     15.7 Building a Thriving Ecosystem
     15.8 Showcasing and Disseminating Research
16 Testimonials
17 Conclusion
Annex 1: Data
Annex 2: Problem Statement Sample
Annex 3: Data Sharing Guidelines
Annex 4: Host Onboarding Guidelines