By Harkrishan Singh, Director, Application Development at In2IT Technologies
2020 was the start of a profoundly challenging time for citizens, companies, and governments around the world. As Covid-19 spread, necessitating extensive health and safety restrictions, applications driven by Artificial Intelligence (AI) and Machine Learning (ML) played a critical role in saving lives and fostering resilience. As humankind learns to adapt to living and working through a pandemic, companies around the world are putting AI and ML to work to yield better business outcomes and advance the innovative purpose of their organisations through data, finding novel ways to tap into the ‘new oil’ of the digital economy.
Problem-solving for global transformation
AI involves developing smart machines that are capable of performing tasks that usually require human input and intelligence. ML, on the other hand, is a category of AI that uses statistical techniques to grant computer systems the ability to learn from data, without being explicitly programmed, for example, by progressively improving performance on a specific task. 90% of the world’s data has been created in the last two years alone, and with a staggering 2.5 quintillion bytes of data being created every 24 hours, it is expected that this volume of data will double every two years. This explosive growth in data and research has heightened the focus on ML, which powers the cultivation of a massive ecosystem of technologies, frameworks, and libraries. This means that when technologists need to solve a problem, they have many ML technologies and frameworks at their disposal.
Advancing human functionality
The spheres of AI/ML development can be categorised according to three functionalities: sensing, comprehending, and responding. Sensing involves functionality like document analysis and computer vision; comprehending extends to natural language processing, probabilistic decision-making, while responding involves the creation of a complete contextual learning agent, as well as functionality such as disease incidence prediction. The algorithmic capability in ML is fuelling change at an unimaginable pace, particularly in the fields of:
AI & Vaccine Development: Previously it took years, if not decades, to develop a new vaccine. But just three months after the first reported cases, we saw Covid-19 vaccines undergoing human tests, and this record speed of vaccine development was due in part to AI models that helped researchers analyse vast amounts of data about the coronavirus. This application of AI in vaccine development will revolutionise the way drug discovery, vaccine decisions and precision medicines are handled in the future.
Automation: ML technology is helping businesses to introduce cost efficiency through precision by automating many manual types of tasks. Organisations can reduce the excess of human involvement in repetitive tasks, while assigning human resources to more essential business activities, such as customer service.
Industry-wide impact: AI/ML touches our daily life in many ways, helping to better our healthcare systems, improve retail management, and bring new functionality to fields such as cybersecurity and IoT, to name a few.
Elevating the potential of data usage
The value of analysed data lies in enabling and facilitating the smooth functioning of everything, from the government to modern business. With analysed data, it is possible to gain visibility of availability, demand, supply chains, trends and more, all of which are the key ingredients in any business case, and without analysed data, business growth would halt. ML can give companies the power to efficiently extract precise forward-looking intelligence from their data. This means that not only can ML assist with greater process automation, productivity, and compliance, but it can also help businesses to create predictive forecasts and sales figures to optimise costs, pricing, assess risks, segment customers, and so much more.
Growing opportunities
ML forms the basis of the mechanisms through which AI can be implemented, making use of predictive analysis to enhance the machine’s human understanding and ability to decipher data.
As a mix of methodologies, practices and tools for the continuous delivery of software, DevOps has become popular due to the agility, simplicity and faster resolution it enables. Proven to speed up operational processes and support cross-functional teams, work can happen more effectively with less friction and enhanced productivity. What happens when we bring ML and AI together? It’s called AIOps, and it makes streamlining and integrating data to identify problems much easier. Predictive analytics, service analytics and automated workflows combine for better end user experiences and even faster production cycles by disrupting operational silos, making conditions more favourable for innovation.
Intelligent operations
Use cases for AIOps include the ability to perform in-depth log analysis to predict problem areas and recommend changes to the development teams, along with alerting mechanisms that can avoid mistakes and system failures. Development efficiency is greatly enhanced because AIOps enables data collection related to sprints, user stories, the development velocity for each developer and bugs raised/fixed. ML algorithms can perform analyses per developer or team, taking care of reporting requirements on sprints or releases all of which adds up to higher efficiency, lower rates of failure and less stress. AIOps is also instrumental in AI-automated testing, contributing to enhanced software quality, helping to manage and monitor infrastructure from and move from proactive traditional monitoring tools to become predictive, using AI and ML algorithms.
To effectively leverage ML, organisations need to embed AI methodology in their end-to-end business model in a way that elevates the human capacities for learning, perception, and interaction into the very core of an organisation’s data strategy. This needs to happen at a level of complexity that will ultimately supersede our own abilities. To this end, organisations will need to engage and work with data from all business perspectives to understand and test suitable ML algorithms and frameworks. In the not-so-distant future, ML will become core to every business.
However, today, from a quick-win perspective, it is possible for retail, e-commerce, and consumer analytics to apply ML to forecast demand, optimise prices, provide customer recommendations, detect, and prevent fraud. In the banking and finance industry, ML is already being used for credit scoring, risk analysis, trading exchange forecasting, and fraud detection. The opportunities for leveraging these emerging technologies are only going to continue to grow and proliferate as AI and ML platforms are open for new algorithms and past learning to support real-world use cases and scenarios.