Three editions of the AI for Good Global Summit have showcased numerous artificial intelligence (AI) projects with the promise to advance the United Nations Sustainable Development Goals (SDGs).
From summit to summit, participants have recognized the significance of the leap from AI promise to global impact, a leap that they plan to take together with the development of an ‘AI Commons’.
The 3rd AI for Good Global Summit, held in Geneva from 28 to 31 May 2019, shone a spotlight on AI projects in fields including education, healthcare and wellbeing, social and economic equality, space research, and smart and safe mobility.
Impact on a global scale, agreed summit participants, will require a common enabling infrastructure – ‘AI Commons’ – comprising shared knowledge, data, resources and problem-solving approaches to stimulate the development and application of ‘AI for Good’ projects.
The community supporting the AI for Good Global Summit is advancing AI technologies and applications, working to bridge skills gaps between AI developers and adopters, and defining safe and ethical approaches to AI. Their overarching goal is to create the new partnerships required to ensure that high-potential ‘AI for Good’ projects achieve impact on a global scale.
“AI will have the greatest impact when everyone can access its benefits. On the other hand, every government, company, university, international institution, civil society organization and every single one of us should consider how best to work together to ensure AI serves as a positive force for humanity,” said ITU Secretary-General Houlin Zhao. “At the core of this is data. AI and data need to be a shared resource if we are serious about scaling AI for good. The community supporting the summit is creating infrastructure to scale-up their collaboration – to convert the principles underlying the summit into global impact.”
The ‘AI Commons’ will provide an open framework for collaboration, a decentralized system to democratize problem solving with AI. AI adopters will connect with AI specialists and data owners to align incentives for innovation and develop AI solutions to precisely defined problems. AI development and application will build on the state of the art, enabling AI solutions to scale with the help of shared datasets, testing and simulation environments, AI models and associated software, and storage and computing resources.
Original source: ITU
Published on 06 June 2019
