The scope of artificial intelligence applications has been broadened thanks to advances in machine learning. This is opening up a new world of opportunities.
Together with IoT, it has created a demand for services and more intelligent analytics across industries. It is already enabling healthcare professionals to make better decisions for patients, the industrial manufacturing sector to streamline processes and in finance to create applications for asset management and fraud detection.
According to IDC research, by 2019 40% of digital transformation initiatives will use AI services, and that by 2021 75% of enterprise applications will use AI. Despite the speed at which AI technologies are being developed and adopted, much work still needs to be done to ensure safety, privacy and security.
AI needs standardization
Wael Diab, who leads IEC and ISO standardization activities for AI (through ISO/IEC JTC 1/SC 42), was invited to present this work to the ITU Workshop on Artificial Intelligence, Machine Learning and Security, during the panel session on the future of standardization activity.
JTC 1/SC 42 was setup as a systems integration committee to provide guidance to IEC, ISO and JTC 1 committees looking at AI applications. It benefits from access to broad, diverse and numerous committees that range from horizontal to vertical areas.
“The AI ecosystem is ripe for standardization. JTC 1/ SC 42 is the first international standards committee to look at the full AI ecosystem and we’re adopting a broad approach that includes and goes beyond traditional interoperability”, said Diab.
AI isn’t a single technology it’s a collection of technologies with numerous and different stakeholders who are approaching the deployment of AI systems from a business angle with a focus on customer needs, segments, services, products and regulatory requirements. AI will necessitate industry collaboration across domains, for example, IT and OT for applications in areas including transportation, medical, financial, robotics, manufacturing and more.
By considering AI technologies against the backdrop of market segments and needs, additional synergies are being identified, such as AI, analytics, Big Data, IoT and more.
Trustworthiness at the heart of AI
Within AI there are many issues related to trustworthiness, privacy and security, such as being able to comply with increasing regulations for privacy, addressing bias in algorithms and ensuring mitigation techniques and methods for risks and threats.
Additionally other societal concerns and ethics are hot topics which must be considered, such as the ability for AI and IoT technologies to influence and change how we live and work, the impact it is already having on the workforce and economy, as well as the need to ensure all the personal data we generate remains private and secure.
“In addition to the foundational standards we’re working on, we have three projects on Big Data, two on foundational AI, three on AI trustworthiness, one related to use cases and one on AI governance applications. We encourage broad participation and invite you to get involved in the work through your national standards committees.”
Find out more about the SC 42 activities