Today when we look around, we see how technology has revolutionized our world. It has created amazing elements and resources, putting useful intelligence at our fingertips. With all of these revolutions, technology has also made our lives easier, faster, digital and fun. Perhaps at a point when we are talking about technology, Machine learning and artificial intelligence are increasingly popular buzzwords used in modern terms.
Machine Learning has proven to be one of the game changer technological advancements of the past decade. In the increasingly competitive corporate world, Machine learning is enabling companies to fast-track digital transformation and move into an age of automation. Some might even argue that AI/ML is required to stay relevant in some verticals, such as digital payments and fraud detection in banking or product recommendations.To understand what machine learning is, it is important to know the concepts of artificial intelligence (AI). It is defined as a program that exhibits cognitive ability similar to that of a human being. Making computers think like humans and solve problems the way we do is one of the main tenets of artificial intelligence.
AI exists as an umbrella term that is used to denote all computer programs that can think as humans do. Any computer program that shows characteristics, such as self-improvement, learning through inference, or even basic human tasks, such as image recognition and language processing, is considered to be a form of AI. As athumb rule, research in AI is moving towards a more generalized form of intelligence, similar to the way toddlers think and perceive the world around them. This could mark the evolution of AI from a program purpose-built for a single ‘narrow’ task to a solution deployed for ‘general’ solutions; the kind we can expect from humans.
Machine learning, on the other hand, is an exclusive subset of AI reserved only for algorithms that can dynamically improve on themselves. They are not statically programmed for one task like many AI programs are, and can be improved even after they are deployed. This not only makes them suitable for enterprise applications, but it is also a novel way to solve problems in an always-changing environment.
Machine learning also includes deep learning, a specialized discipline that holds the key to the future of AI. Deep learning features neural networks, a type of algorithm that is based on the physical structure of the human brain. Neural networks seem to be the most productive path forward for AI research, as it allows for a much closer emulation of the human brain than has ever been seen before.
The next generation of AI will be much more sophisticated and intelligent. The most obvious difference in these new intelligence systems will be that they will be able to learn rather than do what humans are taught. There will also be other technological differences, like the use of speech-to-text and the integration of computer vision, machine learning algorithms and deep learning. These technologies will create new opportunities in science, engineering, and technology.The future of technology is not only up for discussion. As technology is continually improving, there is nothing to stop it from going beyond the human limitations. The question is whether humans will be able to be part of the story.
The coming modern time itwill be defined as systems that are at least as intelligent as we are, but smarter and more resilient. They can perform feats of technology that today require human-level technology to perform. And they can do work that humans are not able to.
The first generation of AI is here. We know what the future might be like and know how to make it happen.It is our hope that in the coming years, people will see AI as an exciting opportunity and will help create the jobs of the future.
BY: Manoj Kumar Pansari, Chairman and Managing Director of Astrum