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Machine learning

Machine learning (ML) is a sort of artificial intelligence (AI) that allows software applications to improve their prediction accuracy without being expressly designed to do so. In order to forecast new output values, machine learning algorithms use historical data as input.

Despite its popularity, the phrase “machine learning” is frequently used interchangeably with “artificial intelligence.” Machine learning, in reality, is a branch of artificial intelligence that uses algorithms to learn from data and make judgments with little or no human intervention.

In this age of Artificial Intelligence, machine learning is a trendy topic. Computer vision and Natural Language Processing (NLP) are breaking new ground in ways that no one could have expected. Face recognition on smartphones, language translation software, and self-driving cars are all examples of how we are increasingly seeing both of them in our daily lives. What may appear to be science fiction is becoming a reality, and it is only a matter of time before we attain Artificial General Intelligence. Today, the discipline of machine learning is rapidly expanding, particularly in the field of computer vision. In computer vision, the error rate in humans is currently only 3%. This suggests that computers are already better than humans at detecting and analysing photos. What an incredible achievement! Computers used to be large pieces of technology the size of a room; now, they can comprehend the world around us in ways we never imagined. The current AI division head of Google Jeff Dean said “We’ve come a long way from a 26 percent error rate in 2011 to a 3 percent error rate in 2016. I prefer to think of computers as having evolved eyes that work.”

Machine learning is known for pretty much every recent trend and pattern seen in literary circles, according to Gartner, the world’s largest research, advisory, and consultatory institution, and rightly so. Machine learning has the potential to transform our lives in ways that were previously unthinkable. According to Gartner’s list of the top 10 important innovation patterns, computerised reasoning and new machine learning procedures have reached a fundamental tipping point, and will gradually develop and enlarge for all intents and purposes each innovation-enabled assistance, thing, or application.

Machine learning has become a critical competitive differentiator for many firms. As organisations’ critical processes continue to improve, machine learning solutions are becoming increasingly common in our daily lives. The global machine learning market is predicted to grow from $8.43 billion in 2017 to $117.19 billion in 2027. Because machine learning algorithms have the potential to produce more accurate forecasts and business decisions, several organisations have already started employing them. Machine learning startups will receive $3.1 billion in funding in 2020. Machine learning has the potential to revolutionise a variety of sectors.

It’s not just about worldwide and business advancements of machine learning but also the fact that ML has changed our lives considerably and continues to change our future much beyond what we foresee.

The World Economic Forum (WEF) claims that “The fourth industrial revolution will usher in a new era of human-AI collaboration, with potentially positive global implications. AI developments can assist society in addressing issues like as income inequality and food insecurity, resulting in a more inclusive and human-centered future.” For instance, the world is currently experiencing a devastating Covid-19 pandemic. It is critical for the government to have access to essential statistics in order to assess the severity of the pandemic. Models are being built using AI to foresee and predict disease spread trends. Machine learning can help Covid-19 vaccine supply chain management (SCM) become more efficient by automating quality checks, optimising production planning, warehouse management, and reducing forecast mistakes. When used in the many stages of vaccine SCM, machine learning can assist in achieving the objective of “vaccination for every individual.” Dr. Narayana Rao, a senior scientist in the field of atmospheric science research and radar technology and former Director of the National Atmospheric Research Laboratory, stated that current technologies would become obsolete by 2040, just as the world saw revolutionary changes between 2000 and 2020.


Dr. Narayana Rao stated, “AI and MI are some of the upcoming technologies that will impact our life in the next two decades.” It will be believed that the universe revolves around us thanks to ML. At a wink, AI will surround us with activities, recommendations, and actionable inputs while we converse, discuss, and act. Artificial Intelligence (AI) will resemble Natural Intelligence (NI). Intelligent cars that are self-driving and automated will be able to move around and park themselves. People’s work descriptions will shift. They will be need to labour less, and robots will perform the majority of mundane and hazardous tasks. The machine learning revolution, as well as machine learning’s future, will be with us for a very long time.

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