New cutting-edge technologies are essential for organisations to focus on faster deliveries, reduced spending, and enhanced user experiences
This is an exclusive interview conducted by the Editor Team of CIO News with Abhisek Chakrabarti, Chief Digital and Information officer at Vedanta Limited – Aluminium Business
New developments every technology leader should know about:
The world of technology is a swiftly evolving field, with several new advancements continuing to enter the marketplace at a rapid pace. These are designed to help organisations handle business complexity and increase overall performance. Their advantages, however, can only be realised when these new technologies are widely diffused.
At Vedanta Aluminium, we are accelerating our business by embracing technological advancements, leveraging digital enablers, actively investing in Industry 4.0, and building a strong culture of techno-innovation. This has helped us deploy several digital solutions mapped across our entire value chain to improve productivity, enhance the customer experience, promote connectedness and safety, ensure faster deliveries, and optimise costs. The secret sauce for every technology leader is to objectively look at how their existing technology stack is in tune with business requirements. Consider whether the new technology can truly benefit the business and the best time to invest in it.
Here, then, are some of the latest technologies set to make a huge difference in the near future:
A well-respected technology research and consulting company has organised emerging AI-related technologies under the umbrella term “AI TRiSM” to provide a better understanding of the emerging AI ecosystem. AI TRiSM is short for AI (T)rust, (Ri)sk, and (S)ecurity (M)anagement. It encompasses AI model governance, trustworthiness, fairness, reliability, efficacy, security, and data protection. If AI is the future of technology, then AI TRiSM is essential to helping us reach that future sooner and safer.
We can achieve AI TRiSM through a number of approaches. The first of these is guided documentation, which implies utilising specifications, document templates, and an automated report builder that pulls test results and presents them correctly within documentation. Objects from the codebase are analysed, and a flag is raised if any of these objects are missing from the documentation assets. Having a strong documentation structure not only improves reliability by placing focus on the data used to train an AI system, but it also enables reviewing the technology in the event that something goes wrong.
Secondly, automated risk and bias checks need to be incorporated. Bias occurs when patterns are found where they should not be, often due to an insufficient data set. When this occurs, the AI model may begin making conclusions based on certain undesirable parameters. When conclusions are made based on such illogical parameters, models become more prone to error. As a result, it is critical to closely monitor AI Bias and risk factors.
Lastly, enhanced transparency, a major challenge faced in general today is a lack of trust in AI models. Many end users are uncomfortable interacting with machines. This issue is exacerbated when the decision-making process deployed by it is difficult to explain, leaving consumers without answers. Establishments can help address customer concerns by embracing AI TRiSM principles. This approach to AI trust management, when done correctly, reduces the chance of inappropriate model performance, security, and privacy failures, while also minimising the chances of poor business decisions. The goal of AI TRiSM is to keep customers secure while still allowing for growth and innovation.
Applied observability stresses clarity rather than creativity, as it is founded on established stakeholder actions rather than intentions. By applying observability methodically, establishments can increase the speed of their response and augment business processes in real time. It is expected that by 2026, almost 70% of organisations effectively applying observability will achieve shorter delays in decision making, resulting in enhanced competitive benefits for the target business.
Observability is proactively gathering, visualizing, and applying intelligence to all systems of measurement, including proceedings, logs, and traces, so one can understand the behaviour and intricacies of otherwise complex systems. Observability gives technologists a practical tactic to optimise their systems. It provides a connected, real-time view of all the operational data in a software system, as well as the flexibility to ask queries and get the answers required for decision-making.
Observability also presents a novel tactic to manage business change within organizations. Instead of monitoring business procedures, which is a fundamentally reactive approach, observability trusts instrumenting procedures with the essential statistics and control mechanisms to empower proactive and preemptive data-driven actions.
Difference between observability and monitoring
Conventional monitoring won’t help you flourish in the complex environment of microservices. It can only track known and unknown threats. Observability gives you the insights to not only recognise that something is wrong, but also to understand why it is wrong. It gives you the elasticity to appreciate patterns you had not even considered earlier. Observability (a noun) is a method for appreciating your complex structure. Monitoring (a verb) is an act taken to support that method. Observability doesn’t abolish the requirement for monitoring but instead subsumes it into the techniques used to accomplish observability, making it easier to drive operating efficiencies and fuel innovation and growth.
Adaptive AI brings together a set of methods and practises that empower applications to self-correct their learning practises and behaviours, enabling them to rapidly adjust to evolving real-world scenarios while in production. It is armed with retraining competencies that abolish the requirement for human involvement, enabling it to even transform its own code so that its operations fine-tune according to existing requirements. These competences make adaptive AI accurate, well-organized, and agile.
For a traditional AI to change its operational conditions, collaboration with a human developer is mandatory. This development cycle of updating a prevailing AI could, however, take months. Meanwhile, an adaptive AI is intelligent and dynamic enough to accomplish these same updates in a fraction of that time. Because of its ability to learn from its own experiences, it is adaptable to any situation.
AI learns through machine learning (ML). At the simplest level, ML uses statistics to find patterns. It is through pattern recognition that AI learns. The various forms of ML comprise supervised learning, unsupervised learning, and reinforcement learning. Traditional approaches to AI train AI with massive amounts of data collected in the environment, which means that if the environment changes, the AI will need to be retrained to continue finding the correct patterns. Adaptive AI trumps traditional AI with its innate elasticity and enhanced learning capabilities. Its possible effectiveness across numerous fields permits it to fit into any professional application.
Digital Immune System
The Digital Immune System (DIS) comprises practises and technologies for software design, development, automation, operation, and analytics and uses these to generate a larger user experience and weaken system failures that influence business performance. Businesses face unprecedented challenges in ensuring robust operating environments, fast-tracked digital delivery, and a consistent end-user experience. The business expects to have the capability to respond to market fluctuations swiftly and transform at a fast rate.
In this scenario, end users assume more importance than sound functionality. They look for quality experiences and for their transactions and data to be adequately protected. The implementation of digital immune systems will make widespread use of data analytics, artificial intelligence, and machine learning to empower the platform to automatically respond to diverse situations and unexpected operational issues. The convergence of technologies and practises in DIS enables software development teams to gain visibility in addressing fears and vulnerabilities. Thus, digital immune systems represent one of the strategic trends in software system design.
Web 3.0, or Web3, is the third generation of the World Wide Web. It encompasses a vision of a distributed and open Web, signifying greater utility for its users. The evolution of the internet can be traced back to Web 1.0, a stationary information provider where people read websites but seldom interacted with them. It was shaped in 1989 by Tim Berners-Lee. Web 2.0, a term coined by Tim O’Reilly in 2004, represents an interactive and social web that permits collaboration between users.
The current iteration of the internet relies on systems and servers owned chiefly by big establishments, raising apprehensions over system susceptibility and regulation. When Meta’s connected platforms suffered a global outage in early October, worsened by the centralization of its servers, there were demands to adopt Web3 and its decentralised architecture. Web 3.0 will alter how websites are made and how people relate to them. In a Web3 world, activities and data would be accommodated on a network of computers using blockchain rather than corporate servers, helping diversify the storage and processing of information.
These new cutting-edge technologies are essential for organisations to focus on faster deliveries, reduced spending, and enhanced user experiences. With lots of new patents in artificial intelligence, it’s vibrant that these technologies will be influential in the future. As technology advances, new professions use cases, and businesses emerge. Technology, being a boon for mankind, succeeded in reducing human intervention. It is also important for the economic growth of an entity. It is for us to see how best we can make use of the technologies available to us.
Also read: Technology leaders must continuously attempt to learn with deep hands-on understanding
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