Dr Sandip Bordoloi, Associate Professor, Department of Electrical Engineering, GIMT, Guwahati.

Electrical engineers work on a wide range of components, devices, machines, and systems, from tiny microchips to huge power station generators. Electrical systems, many a times, are so complex that they require expert systems to monitor and supervise them. Such supervisory systems may comprise high-end computers that use complex algorithms and software to regulate the flow of electrical energy obtained from different sources to domestic and industrial consumers.

A modern expert system such as Artificial Intelligence can pave the way to leverage data for ‘data interpretation’ and ‘decision-making’. The application of Artificial Intelligence technology on power systems is an active area of research and significant success has been achieved in this area to date. So, what is Artificial Intelligence and how can it be associated with Electrical Engineering? “Artificial Intelligence (AI) is a wide-ranging branch of computer science concerned with building smart machines capable of performing tasks that typically require human intelligence”. Recent progress in areas like machine learning, big data analysis, and natural language processing has affected almost every area in academics, industry and business and, therefore, engineering is no exception. AI is the simulation of the process of data interaction of human thinking in a machine, so as to produce a smart machine, which can think like a human being to provide a problem, respond and deal with the problem. Machine learning and big data analysis can help leverage AI to build and optimise electrical systems by providing AI technology with new data inputs for interpretation and decision-making. In addition, harnessing AI’s potential may reveal chances of boosting system performance while addressing problems more efficiently.

AI is used to program machines to learn and mimic the actions of humans. These machines are able to learn with experience and perform human-like tasks. For years, researchers, engineers and scientists have been fascinated with the concept of how machines can be taught to replicate tasks ordinarily done by humans. Machines can now be fed with a huge amount of data to make them more ‘Artificially Intelligent’. The concept of AI is not new. Different forms of intelligent systems have been incorporated in Electrical Engineering for a long time, some of which are discussed below:

Expert systems: An expert system (ES) is a software system that captures human expertise for supporting decision-making. These systems are useful for online operations in the control field because they incorporate symbolic and rule-based knowledge, in the form of if-then rules to solve problems. The expert system consists of knowledge base, database, reasoning machine, interpretation mechanism, knowledge acquisition and user interface.

Fuzzy logic control systems: “Fuzzy logic is a basic control system that relies on the degrees of state of the input and the output depends on the state of the input and rate of change of this state”. The fuzzy control system uses a rule-based system for how machines will respond to inputs that account for a continuum of possible conditions, rather than straightforward binary rules.

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We are accelerating fast into an Artificial Intelligence (AI) driven digital era. Not a moment goes when digital is not part of our daily lives. And that’s not just about smart devices at home or collaborating on MS Teams or Zoom meetings but extends to cars we drive, payments we make or shopping we do. While so much of our lives are surrounded and enhanced by digital experiences, when it comes to the most crucial resource that helps companies achieve goals and scale to new heights, that is human resources, AI is a tiny component. It will be a pity if we can’t extend and use the very tools that make our lives so much better when it comes to talent or human resources management.

In fact not just on Earth, even when we have people spending time in outer space (space tourism might take off this decade) an array of AI powered sensors all over the smart space crafts are informing people on board about what’s happening. So why when scouting for bright talent why do we just limit the digital experience to sending the CV online and do the rest manually? That’s like a whole lot of tasks from shifting through hundreds or thousands of CVs, matching profiles to candidates, identifying training needs or even checking out body language and speech quality in an informal way, when AI tools can give us scientific, unbiased answers.

Think of AI as a sensor capturing the daily activity of each individual of the workforce without interfering in their routines. At the end of the year it can give a better picture on how people delivered, what roles can they be moved into, who all need training requirements and areas in which they need and overall how do organisation goals match to the talent that the company has, which will help deliver those goals. Much like in some hospitals now X-Rays and other reports are being diagnosed by machines, freeing up radiologists to focus on more complicated cases.

What is done by the HR team alone based on information shared by employees, their department heads, colleagues could be prone to biases (so common it is to hear: he likes him more than me so I got a bad rating!) and this is where AI steps in to give a smart, intelligent, unbiased output on talent metrics of the company.

AI helps get the right candidates as well. Recruiters have often found that going through individual resumes is not only time consuming but may not be error free. As a detail could be overlooked. While chances of an AI making such errors is almost zilch.

Employee skills are of 2 types: – Domain and Soft. Domain skills are tangible in nature and easy to detect the presence or absence of. For example, a data analyst in the oil and gas industry will be aware of all types of products (petrol, diesel, kerosene, furnace oil etc), their subtle differences and what to look for. Soft Skills on the other hand are every hard to nail down. How does that oil industry analyst handle stress? Can he work long hours without a break and how does he collaborate with colleagues? No wonder research shows that on an average an organization loses up-to $100 million a year because of in-effective leadership and communication skills. Clearly since humans are not machines, they cannot be-embedded, with sensors which can signal the health of their soft skills.

Besides, how did you deal with a conflict? Whether it’s your written word or your spoken word, how effectively and astutely were you able to get it right? AI can identify individual behavior patterns and the environmental triggers which promote the right and or wrong behaviors.

And it helps both employees and companies. To an employee it can provide in time, on the job coaching and at the CEO /CXO level it can give deep insights into aspects of organization culture which are enabling versus hindering your growth. For companies, who invest considerable management time and resources in getting the right person for the job, a misfit in a role can impact not just that department but others who might be dependent on that department to deliver. AI platforms can empower companies and employees alike to fathom skills, capabilities and potential.

In a world where search for talent today starts on AI powered digital platforms, not using the same tools throughout the lifecycle of the employee would indeed be very surprising. Why leave that out for guesswork or chance? AI tools can not only improve individual performance but also company performance.

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When chief information officers think about their organizations and where machine learning might be deployed, the process often begins with an inventory of tasks. 

The CIOs and department leaders identify routine, repeatable processes that humans can pass off to computers. Then the operations and IT teams set up targeted programs to make those tasks more efficient. 

As legendary CIO Paul Strassmann has pointed out — not without controversy — it’s a piecemeal approach that has become standard practice in most businesses. It’s leading CIOs down a path of marginal returns and surprisingly limited innovation. 

Strassmann’s career includes serving as NASA’s CIO from 2001 to 2003 and serving in an equivalent role in the Pentagon before that. As far back as 1998 he has been on record suggesting software should be seen as a storehouse of knowledge and experience in an enterprise — what he calls “knowledge capital.” Software should not be the equivalent of a new forklift.

A new forklift does a job faster and better. But it does not learn or improve with every use. It doesn’t learn how it fits into the workflows of the business where it’s used, or how its work fits with the work of other machines. An even faster and better forklift is eventually bought, and the formerly new forklift is scrapped. All the use put into the scrapped forklift is lost, because obviously the machine never had the ability retain that knowledge capital. Strassman argues too many companies use enterprise technology this way, using it and then replacing it, rather than using it as a store for knowledge capital that becomes smarter and smarter.

That’s true for machine learning as well. It’s used as a tool to make tasks more efficient and faster, but it is not used enough as a store of knowledge capital not only for that task, but for how that task and others fit together, and can fit together better.

CIOs planning their organization’s evolution to machine learning, along with machine learning developers, need to dust off their Strassmann books.

More learning

CIOs should push to empower machines to do more learning, better, ahead of the task. This requires rethinking how machines take in data. Businesses should not think of themselves as a collection of tasks, but rather view their operations as brought to life by streams of data that run through workflows made up of those tasks. The tasks are just the muscles of the corporate body. Data is the blood flow and nervous system.

Focusing on how to turn that data into useful information and unique insights horizontally across the organization, no matter the task, is where CIOs can get a competitive edge and expand the return on machine learning investments. Deploy a smarter system for how data is ingested and interpreted by machines, and it will inevitably introduce greater efficiency and accuracy to the many tasks it touches. The goal is to move from a one to one benefit, to a one to many benefits.

Slow on the uptake

CIOs are having a tough time persuading skeptical business leaders to deploy machine-based intelligence in their organizations, and appropriately so. Enterprise tech marketers say the words “machine learning” very easily. But it’s harder to back those words with sustained, high quality results. Business leaders want more show, less tell.

A recent CFA Institute survey found that in the financial world, only 10% of investment professionals use machine learning. Instead they rely on traditional spreadsheets and desktop data tools. Across industries, only 50% of large businesses have artificial intelligence strategies. About 80% of enterprise businesses that have rolled out artificial intelligence or machine learning projects report stalled progress. And CIOs will continue to have a hard time modernizing their organizations and showing a return on the investment, if the effort remains task oriented.

As a team from Deloitte Australia writes, “if our social and economic systems persist in framing work in terms of tasks completed, and to value labor in terms of its ability to prosecute these tasks — then we can expect AI & ML solutions to continue to be used as they often are today: as cost-cutting enablers, substitutes for humans instead of partners with humans.”

The question should be: How will the entire organization benefit from smarter data systems that pervade across workflows? And if humans are not spending their time collecting and sorting data, what else can they be doing to add value to the organization?

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