As artificial intelligence (AI) technologies such as machine learning have advanced, they have broadened the applicability of AI. Moreover, the evolution of digital transformation has created a demand for services and more intelligent analytics, for instance in healthcare, manufacturing and finance.
During the hybrid IEC GM in Dubai, over 50 participants attended the AI session of the Young Professionals Workshop.
“Emerging applications of AI are numerous and diverse, including consumer, retail, digital assistants, expert systems such as smart grid, marketing intelligence tools, enterprise and more. The ecosystem is ripe for standardization and expert systems such as smart grid, marketing intelligence tools, enterprise”, said Wael William Diab, who chairs the IEC and ISO joint committee (SC 42) which develops standards for artificial intelligence.
A holistic approach to artificial intelligence
SC 42 develops foundational standards that allow communities to build upon such as terminology, use cases, application guidance and reference architectures. It links technology innovation communities such as proprietary implementations, research, standards development organizations and open-source communities.
“We take a unique holistic approach, which considers the entire AI ecosystem. We consider the technology by looking at technology capability and non-technical requirements, such as business requirements, regulatory and policy requirements, application domain needs, and ethical and societal concerns.”
The standards work programme
SC 42 “customers” that use the standards and participants, from over 50 nations, in the work are increasingly diverse, ranging from data scientists to regulators, application domain experts and social scientists.
With currently 24 active projects, the work is split into the following areas:
- Foundational standards - introduce an overview of AI, terminology, key concepts and frameworks and describes the AI machine learning ecosystem.
- Data - deals with extensive datasets by considering characteristics, such as volume, variety, velocity, variability, which allows scalable technology to efficiently store, manipulate, manage and analyzes these datasets.
- Trustworthiness - looks at a wide range of issues related to trustworthiness, security and privacy within the context of AI.
- Use cases and applications - identifies AI application domains, context of AI use in those domains and develops guidance, by collecting representative use cases and analyzing for derived requirements.
- Computational methods – considers computational characteristics of AI systems and the main algorithms and approaches used in AI systems, referencing use cases
- SC 42 – SC 40 joint group addresses governance implications for the use of AI in organizations, helping boards and executives ask and answer key questions about AI technologies.
Find out more about SC 42.
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