World within the palm of AI

Powered by various forms of machine learning that recognize patterns in data to enable predictions, AI can add value to your business by: Providing a more comprehensive understanding of the abundance of data available: Relying on predictions to automate excessively complex or mundane tasks.

BySanjenbam Jugeshwor Singh

Updated 17 Sept 2022, 7:28 pm

Representational Image (Photo: Pixabay)
Representational Image (Photo: Pixabay)

In the simplest terms, AI which stands for Artificial Intelligence refers to systems or machines that mimic human intelligence to perform tasks and can iteratively improve themselves based on the information they collect.

AI manifests in a number of forms. A few examples are: Chatbots use AI to understand customer problems faster and provide more efficient answers. Intelligent assistants use AI to parse critical information from large free-text datasets to improve scheduling. Recommendation engines can provide automated recommendations for TV shows based on users’ viewing habits.

AI is much more about the process and the capability for super powered thinking and data analysis than it is about any particular format or function. Although AI brings up images of high-functioning, human-like robots taking over the world, AI isn’t intended to replace humans. It’s intended to significantly enhance human capabilities and contributions. That makes it a very valuable business asset. Artificial intelligence is shaping the future of humanity across nearly every industry.

It is already the main driver of emerging technologies like big data, robotics and IoT, and it will continue to act as a technological innovator for the foreseeable future.AI is important because it forms the very foundation of computer learning. Through AI, computers have the ability to harness massive amounts of data and use their learned intelligence to make optimal decisions and discoveries in fractions of the time that it would take humans

AI has become a catchall term for applications that perform complex tasks that once required human input such as communicating with customers’ online or playing chess. The term is often used interchangeably with its subfields, which include machine learning and deep learning. There are differences, however. For example, machine learning is focused on building systems that learn or improve their performance based on the data they consume. It’s important to note that although all machine learning is AI, not all AI is machine learning. To get the full value from AI, many companies are making significant investments in data science teams.

Data science, an interdisciplinary field that uses scientific and other methods to extract value from data, combines skills from fields such as statistics and computer science with business knowledge to analyze data collected from multiple sources. Developers use artificial intelligence to more efficiently perform tasks that are otherwise done manually, connect with customers, identify patterns, and solve problems.

To get started with AI, developers should have a background in mathematics and feel comfortable with algorithms. When getting started with using artificial intelligence to build an application, it helps to start small. By building a relatively simple project, such as tic-tac-toe, for example, you’ll learn the basics of artificial intelligence. Learning by doing is a great way to level-up any skill, and artificial intelligence is no different. Once you’ve successfully completed one or more small-scale projects, there are no limits for where artificial intelligence can take you.


The central tenet of AI is to replicate—and then exceed—the way humans perceive and react to the world. It’s fast becoming the cornerstone of innovation. Powered by various forms of machine learning that recognize patterns in data to enable predictions, AI can add value to your business by: Providing a more comprehensive understanding of the abundance of data available: Relying on predictions to automate excessively complex or mundane tasks.

AI technology is improving enterprise performance and productivity by automating processes or tasks that once required human power. AI can also make sense of data on a scale that no human ever could. That capability can return substantial business benefits. For example, Netflix uses machine learning to provide a level of personalization that helped the company grow its customer base by more than 25 percent in 2017.

Most companies have made data science a priority and are investing in it heavily.  In Gartner’s recent survey of more than 3,000 CIOs, respondents ranked analytics and business intelligence as the top differentiating technology for their organizations. The CIOs surveyed see these technologies as the most strategic for their companies; therefore, they are attracting the most new investment.

AI has value for most every function, business, and industry. Some sectors are at the start of their AI journey, others are veteran travelers. Both have a long way to go. Regardless, the impact AI is having on our present day lives is hard to ignore, like:

(i) Transportation: Although it could take some time to perfect them, autonomous cars will one day ferry us from place to place

(ii) Manufacturing: AI powered robots works alongside humans to perform a limited range of tasks like assembly and stacking and predictive analysis sensors keep equipment running smoothly.

(iii) Healthcare: In the comparatively AI-nascent field of health care, diseases are more quickly and accurately diagnosed, drug discovery is speed up and streamlined, virtual nursing assistants monitor patients and big data analysis helps to create a more personalized patient experience.

(iv) Education: Textbooks are digitized with the help of AI, early-stage virtual tutors assist human instructors and facial analysis gauges the emotions of students to help determine who’s struggling or bored and better tailor the experience to their individual needs.


(v) Media: Journalism is harnessing AI too and will continue to benefit from it. Bloomberg Uses Cyborg-technology to help make quick sense of complex financial reports. The Associated Press employs the natural language abilities of Automated Insights to produce 3,700 earnings reports stories per year-nearly four times more than in the recent past.

(vi) Customer Service: Last but hardly least, Google is working on an AI assistant that can place human-like calls to make appointments at , say, your neighborhood hair salon. In addition to words, the system understands context and nuance. But those advances — and numerous others — are only the beginning. There’s much more to come. 

The emergence of AI-powered solutions and tools means that more companies can take advantage of AI at a lower cost and in less time. Ready-to-use AI refers to the solutions, tools, and software that either have built-in AI capabilities or automate the process of algorithmic decision-making.

Ready-to-use AI can be anything from autonomous databases, which self-heal using machine learning, to prebuilt models that can be applied to a variety of datasets to solve challenges such as image recognition and text analysis. It can help companies achieve a faster time to value, increase productivity, reduce costs, and improve relationships with customers.

Despite AI’s promise, many companies are not realizing the full potential of machine learning and other AI functions. Why? Ironically, it turns out that the issue is, in large part...people. Inefficient workflows can hold companies back from getting the full value of their AI implementations. For example, data scientists can face challenges getting the resources and data they need to build machine learning models. They may have trouble collaborating with their teammates.

And they have many different open source tools to manage, while application developers sometimes need to entirely recode models that data scientists develop before they can embed them into their applications. With a growing list of open source AI tools, IT ends up spending more time supporting the data science teams by continuously updating their work environments. This issue is compounded by limited standardization across how data science teams like to work.

Finally, senior executives might not be able to visualise the full potential of their company’s AI investments. Consequently, they don’t lend enough sponsorship and resources to creating the collaborative and integrated ecosystem required for AI to be successful.


First published:


AIArtificial IntelligenceChatbotsAI technology

Sanjenbam Jugeshwor Singh

Sanjenbam Jugeshwor Singh

Assistant Professor, JCRE Global College, Babupara, Imphal. The writer can be reached at sjugeshwor7@gmail.com


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