Author: İsmail EL, SW Architect / Enterprise Applications
The concept of Artificial Intelligence (AI) first made an appearance in 1950, when British computer scientist Alan Turing put forward an ‘imitation game’ to assess if a computer could fool humans into such a thinking as they were communicating with some other human. Sooner rather than later, MADALINE which is the first artificial neural network applied to a real-world problem was built by researchers at Princeton University in New Jersey. This first AI system, which was modelled on the working principle of brain and nervous system, learnt to solve a maze through trial-and-error.
As is the case with any kind of uprising technology which is able to carry on the same tasks as humans do, also AI has potential to either replace or complement the human work. AI has already gotten off the ground and still continues to get ahead but will this dramatically advanced technology find room to impact the future with its real potential?
Each AI technology has potential to have its own effect
As defined by OECD, AI is “A machine-based system that can, for a given set of human-defined objectives, make predictions, recommendations or decisions influencing real or virtual environments”. Over the last few years, the availability of big data, cloud computing and the associated computational and storage capacity have conspicuously increased the power and impact of AI. And what is more, breakthroughs in machine learning technology together with Internet of Things (IoT) have made AI even more instrumental. Faced with the technological challenge, all those advancements need to be included collectively because there is no center of gravity and the value gained at the end of the day will be multiplied by each other’s additive contribution.
Natural language processing and generation, generating predictions that support decision-making, performing image and facial recognition, controlling the movements of a robot or a vehicle, etc. are just some few examples for the application areas of AI. These days, AI comes knocking on door in high tech, automotive and assembly, telecoms, transport and logistics, financial services and consumer packaged goods, retail and healthcare sectors. Besides, discounting the fact that large and established players have active role, the industrial adoption of AI still seems to be at an early stage where there still exist a great room for AI to create added value.
Acknowledged as a General Purpose Technology, which are likely to have significant impacts on society and also bring complementary innovations in their trains, AI plays a part in the league where steam engine, electrification and computing exist. As well, the importance of being regarded as a GPT results from the depth and scale when challenges and opportunities presented by AI and its extensive potential of applicability are considered.
In point of fact, by means of its problem solving, reasoning and perception capabilities and with all that and then some more, AI comes with solutions to perform some non-routine cognitive tasks. The evidence suggests that those high-skilled professionals which are exposed to the potential threats of AI are also likely to be more able or better positioned to take advantage of the benefits that AI brings. In other words, they can adapt more easily, can use AI in such a complementary way to do their work best and after all can avoid any negative impacts one way or another.
AI and IT Operations
As AI becomes more widespread as a software development approach, enterprise IT operations are supposed to get tied up with handling its complexity. The need for AI to help IT operations has picked up speed as organizations take steps in the direction of incorporating AI systems into their production environments. As far as the AI tools can make IT operation tasks automated, IT operations staff can be released for the other high volume works. That is why, the tools with smart analytics that can review the data collected from a wide range of applications and automatically give reaction to issues in real time are more preferable.
The use of AI for IT operations refers to the use of AI together with machine learning to collect and analyze huge volumes of data from the each corner of IT environment, via reducing data complexity by bringing data silos together to filter them, detecting patterns, and clustering meaningful information for taking actions more efficiently.
This enables performance challenges be managed proactively by IT teams, in real-time, before those issues become system-wide. AI integrated tools also provide capability to predict when issues are likely to arise, so that they can be proactively prevented. Here are some use cases where AI can be applied:
- Intelligence in Alerting: By gathering data from any part of the IT environment, the use of AI filters on and makes the meaningful data correlated into incidents. This makes the alert storms which are likely to result from domino effects prevented. Intelligent alerting also makes alert fatigue reduced and improves prioritization depending on user and business impact.
- Better Understanding in Cross-domain Situations: The use of AI provides an overview of what’s at stake in causality / relationships it created after aggregating all the data, and further provides a better understanding of the situation by slicing and dicing the information as the need arises.
- Cohort Analysis: AI burst into prominence in the analysis of huge amounts of complex data. With modern architectures which run tens of thousands of instances at the same time, determining outliers in application or system configurations is an unsurmountable task for humans.
- Automation of Remediation:AI makes contribution to the automation of closed-loop remediation for known issues. Once the problems are identified, depending on the historical data from past issues, AI puts forward the best approach to accelerate remediation.
AI and Predictive Maintenance in IT Operations
Predictive maintenance is one of the fast-growing, high return on investment (ROI) application areas of AI that seems to deliver true value to its users.
A simplified process for connecting IoT assets, major advances in cloud services, and improvements in the accessibility of machine learning / data science frameworks are the technical developments which contributed to the market expansion. Moreover, predictive modelling projects is expected to gain favor with the move to edge computing which is expected to enable algorithms to run at the point where data is collected and accordingly reduce the response latency.
The value gained from predictive maintenance stems from downtime prevention by means of monitoring operations and taking proactive actions to prevent the downtime or an unexpected or negative outcome. Enterprise software systems, such as Computerized Maintenance Management Systems (CMMS) or Enterprise Resource Planning (ERP) are just the two application areas of the predictive maintenance. Sensor technology which makes the volume of collected data exponentially increased, the ability to monitor industrial machines in real time and data analytics via cloud services are some few advances that makes predictive maintenance making more practical today.
Along with the technical developments in AI and IoT, a market considered still in its infancy today, predictive maintenance has overwhelmingly evolved and found room in software and service providers offering solutions.