Minds and Machines: The Fascinating Frontier of Artificial Intelligence

What is artificial intelligence 

Talking about artificial intelligence (AI) is something as simple as talking about intelligent machines . Specifically, machines that are trained to complete specific jobs automatically without the need for human oversight. In this way, artificial intelligence is presented as a branch of computer science, which is the discipline in charge of carrying out the programming of intelligent machines.

Robert bartram

A recent report from the McKinsey Global Institute(1) suggests that investment in artificial intelligence (AI) is growing at a rapid pace. McKinsey estimates that digital leaders like Google spent “between €17.5 and more than €26.2 billion on AI in 2016, of which 90% went to R&D and deployment and 10% to AI acquisitions”. According to the International Data Corporation(2) (IDC), by 2019, 40% of digital transformation initiatives will implement some variant of AI and by 2021, 75% of business applications will use AI, with spending to be estimated at about 46,000 million euros.

From perception to reality

But what exactly is AI? According to Wael William Diab, Chairman of ISO/IEC JTC 1 Information technology , Subcommittee SC 42 Artificial intelligence, the field of AI includes a whole collection of technologies. This newly formed committee has started with some fundamental standards containing AI concepts and terminology, such as ISO/IEC 22989. Diab also points out that there is a certain distance between what AI is today and what is often perceived as such. “People tend to think of AI as autonomous robots or a computer capable of beating a chess master. For me, AI is more of a set of technologies that enable, in effect, a form of intelligence in machines,” he states.

He also explains that AI is often seen as a group of fully autonomous systems – robots capable of movement – ​​but in reality, much of the AI ​​goes into semi-autonomous systems. In many AI systems, much of the data is processed before it is fed into a mechanism that exhibits some form of machine learning, which in turn will generate a series of insights. Among these technologies are machine learning, big data and analytics, although there are many more.

An umbrella of technologies

If there is someone who knows this field, it is him. Diab is currently a director of Huawei Technologies and Chairman of the ISO/IEC JTC 1/SC 42 subcommittee for good reason. Trained in Electrical Engineering, Economics, and Business Administration at Stanford University and the Wharton School of Business at the University of Pennsylvania, his career path has focused specifically on business strategy and technology. In addition, he has worked for multinational conglomerates Cisco and Broadcom, and has been a consultant specialising in Internet of Things (IoT) technologies, most recently as Secretary of the Industrial Internet Consortium Steering Committee. He has also registered more than 850 patents, of which he has obtained 400, with the rest still under study. The number is not negligible:

Diab’s true specialty lies in the breadth of his experience, from idea gestation to strategic industry driving. This is why she is so interested in standardisation, as she sees it as the perfect vehicle for fruitful expansion of the industry as a whole. She argues that it’s needed for AI for a number of reasons.

Part of the solution

This is where international standards come into play. SC 42, which reports to ISO/IEC/JTC 1, is the only body that examines the entire AI ecosystem. Diab is clear that the members of this committee start from the awareness that many aspects of the standardisation of AI technology need to be considered in order to achieve broad adoption. 

Foundational norms

With so many and varied stakeholders, a basic starting point has been the committee’s work on “founding standards”. This work examines aspects of AI that require a common vocabulary, as well as agreed taxonomies and definitions. At some point, these standards will mean that a professional will be able to speak the same language as a regulator and both the same language as a technical expert.

Computational Methods and Techniques

An essential part of AI is an evaluation of the approaches and computational characteristics of artificial intelligence systems. It involves the study of different technologies (for example, machine learning algorithms, reasoning, etc.) used by AI systems, including their properties and characteristics, as well as the study of existing specialised AI systems to understand and identify their approaches. , architectures, and underlying computational features. 


One of the challenges for the sector is “reliability”, the third area of ​​interest. It is something that is at the focus of many of the doubts that revolve around AI.  In the case of AI, there are systems that make decisions or inform people about decisions that have to be made, so it is vital to have a recognized and agreed form of transparency to prevent any unwanted bias. It is very likely that this study group will formulate a whole series of recommendations for standardisation projects. Such work will be a necessary tool and will proactively respond to concerns in this area.

Use cases and applications

The fourth relevant area is identifying “application domains”, the contexts in which AI is used, and collecting “representative use cases”. Autonomous driving and transportation, for example, constitute one of these categories.  The reports from this group will lead to the launch of a series of projects that could range from a complete repository of use cases to good practices for specific application domains.

Social issues

Another area of ​​emphasis is what Diab calls “social issues.” Broad technologies like IoT and AI hold the potential to influence our existence for generations, so their adoption has impacts far beyond the technology itself. One of them is the economic question, such as the impact of AI on the labour market – something that, obviously, is beyond the competence of the committee. Others, without a doubt, are in their field of study. These are issues such as algorithmic bias, eavesdropping, and industrial AI security directives, which are key issues for the committee to consider. 

Guest article written by: Kishore Senthil is a Digital Marketing Executive. He designs marketing strategies with the intention of using high-quality content to educate and engage audiences. His specialties include social media marketing specialist, SEO, and he works closely with B2B and B2C businesses, providing digital marketing strategies  that gain social media attention and increase your search engine visibility.