One of the most important trends that has already started to impact businesses and consumers is the merging of artificial intelligence (AI) technology with IoT systems. Sensors measure various parameters and are connected to an AI-enabled cloud, or even distributed computers and servers (edge computing), where the vast quantity of data they supply is stored. In the very near future, new machine learning technology, based on increasingly powerful algorithms, will help to make sense of the data and automatically act in response to the findings. Experts anticipate that intelligent devices will become elements within a collaborative web of intelligent things, with minimal human intervention. Moving on from the Internet of Things, pundits call this new trend the Intelligence of Things and many expect it to bring huge benefits, starting with the business sector.
One of the immediate challenges for businesses and companies is the sheer quantity of data generated by individual sensors which is difficult to manage with conventional business intelligence and analytics tools. New machine learning systems will be able to automatically identify abnormal patterns in the data supplied and alert when things deviate from observed norms without requiring an advance set up by human operators. In other words, AI-enabled IoT systems can automatically surface relevant insights in the gigantic flood of data that would otherwise be totally overwhelming. This enables businesses to better monitor the supply and demand chain and deliver goods, with less human intervention, which in turn generates cost-savings. Such systems will ideally help to detect safety issues in smart manufacturing plants and automatically deal with them. This could save precious time and identify faults that may not have come to light - a bonus for employees and workers, as well as customers.
In the home, these systems will be expected to enable consumer connected devices to not only notify the owner or ring alarms, say when a fire starts in the oven for instance, as most smart systems already do, but also shut off the oven, the whole power system in the home and call the fire brigade.
Similar examples could be found to describe the near future in areas including health and medical, transport, and the power industry. The possibilities seem endless and the gains immeasurable. Unavoidably, however, there are downsides associated with these new systems and it is better to acknowledge them now rather than later, so the challenges can be addressed. And that is precisely where international standards can help.
One of the drawbacks of these new systems is that machine learning is only as good as the data provided. IEC and ISO together develop international standards for information technologies through a joint technical committee (ISO/IEC JTC 1). François Coallier, Chair of ISO/IEC Subcommittee (SC) 41, which prepares standards for the IoT explains: “It’s the rubbish in/rubbish out quandary. If you feed a learning system data that is corrupt you will not have a good result no matter how powerful the algorithms are. But that is where performance standards can help, by enabling users to monitor the quality of the data, for instance.”
SC 41 publishes several key documents that help to standardize the emerging industries impacted by the IoT and the intelligence of things. For instance, ISO/IEC 30141 provides a global reference architecture and common vocabulary for the IoT. (For more about these standards and ISO/IEC SC 41 strategy, read the interview of François Coallier)
Algorithms are only as good as their developers. Machine learning can reproduce sexist and racist bias from the real world. Examples include image recognition software that fails to identify non-white faces correctly. This occurs when the scientists who develop the algorithms unwittingly introduce their own prejudices into their work.
Biases can influence the way a medical sample is collected by not including some members of the intended statistical population, for instance. This could result in building an algorithm used for medical diagnosis, trained only on data from one subset of the population.
Another ISO/IEC JTC 1/SC 42 is addressing many of these concerns. It is looking into a wide range of issues related to trustworthiness as well as robustness, resiliency, reliability, accuracy, safety, security and privacy within the context of AI.
An essential project is the development of a big data reference architecture. “One of the unique things about what IEC and ISO are doing through SC 42 is that we are looking at the entire ecosystem and not just one technical aspect,” says Wael William Diab, who heads the subcommittee.
The more intelligent, autonomous and connected devices become, the more likely they are to be targeted by cyber attacks. International standards offer solutions that can be applied across a wide range of areas. The ISO/IEC 27000 series of standards, among many other things, helps to protect IT systems, ensuring the free flow of data in the virtual world. The IEC 62443 series offers a layered, defence in depth framework that applies to a wide range of industries and critical infrastructure environments. The IECEE (IEC System of Conformity Assessment Schemes for Electrotechnical Equipment and Components) includes a programme that provides certification to standards within the IEC 62443 series. In addition, ISO/IEC 27001 is now part of the approved process scheme that provides for the independent assessment and issuing of an international IECQ (IEC Quality Assessment System for Electronic Components) certificate of conformity for organizations that have demonstrated compliance with the relevant standards and/or specifications.
The IEC is the only organization in the world to provide an international and standardized form of certification which deals with cyber security. As “things” become more intelligent, with all the benefits and disruption entailed, the case for consensus-based widely adopted standards as well as independent international conformity assessments systems cannot be overstated.