Among the most challenging and satisfying of human endeavors is to push the limits of knowledge beyond the known boundaries. I call those who pursue these objectives the "boundary hunters." Who are these people who go where no one has gone before? They are the scientists, the researchers, the engineers, the theoreticians, and the explorers who wonder what might lie beyond what we think we know and understand. They go past the familiar into terra incognita.

This article is sponsored by Oracle – redefining data management with the world's first autonomous database. In the coming years, the amount of data we create worldwide will grow to 175 zettabytes of data per year by 2025, up from 33 zettabytes in 2018. Over half of this data will be created by the Internet of Things devices and over 60% of it will be enterprise data. By 2025, 30% of all the data created will be in real-time, offering organisations great opportunities to constantly optimise their business. Clearly, the organisation of tomorrow is a data organisation.

Artificial intelligence (AI), is quickly becoming a hot topic in today's world. It can be used in process automation for businesses or in job market demand. In this age of digital transformation, real-time data is always the driving force behind any company. AI was created with one purpose: to make systems smarter and our lives easier. This is evident when you consider the rapid rate at which AI is being integrated across industries. IDC estimates that the global data generation by 2025 will reach 175 zettabytes.

Digitization has given rise to tremendous amount of data. The total amount of data created, captured, copied, and consumed globally is forecast to increase rapidly, reaching 64.2 zettabytes in 2020. Over the next five years up to 2025, global data creation is projected to grow to more than 180 zettabytes. That's a torrent of information created by the combined forces of social, mobile, and cloud technologies. This data contains a lot of hidden information.

Artificial intelligence (AI) is easily one of the most trending topics today: be it in the area of process automation for enterprises or its demand in the job market. Real-time data always is the driving force of any company in this era of digital transformation. Ushered into existence with the sole objective of making systems'SMART' and our lives'SIMPLE', the prominence of AI is pretty evident considering the massive rate at which it is being incorporated across industries. According to IDC, the amount of data generated globally by 2025 would reach 175 zettabytes, a staggering 430 per cent growth over the 33 zettabytes generated in 2018. In the coming years, the ability of AI to automate and augment jobs currently performed by humans will have the largest impact on business.

Artificial intelligence (AI) is easily one of the most trending topics today: be it in the area of process automation for enterprises or its demand in the job market. Real-time data always is the driving force of any company in this era of digital transformation. Ushered into existence with the sole objective of making systems'SMART' and our lives'SIMPLE', the prominence of AI is pretty evident considering the massive rate at which it is being incorporated across industries. According to IDC, the amount of data generated globally by 2025 would reach 175 zettabytes, a staggering 430 per cent growth over the 33 zettabytes generated in 2018. In the coming years, the ability of AI to automate and augment jobs currently performed by humans will have the largest impact on business.

Adoption of Artificial Intelligence (AI) has accelerated since the pandemic hit as the whole world moved towards digitization. A study by Oxford University and Yale University indicates that AI will outperform humans in many ways and will automate all human jobs in the next 120 years. By 2024, AI will be better than humans at translation, will write bestselling books by 2049, and will perform surgeries by 2053. Machine learning (ML), the proficiency of a machine to mimic human ability to accumulate knowledge and use it to drive insights, is generally considered the basis of AI. Although AI might depend on its machine learning abilities, we need to take a step back and realize ML doesn't happen in vacuum. ML is driven by big data, without which it can't take place.

Adoption of Artificial Intelligence (AI) has accelerated since the pandemic hit as the whole world moved towards digitization. A study by Oxford University and Yale University indicates that AI will outperform humans in many ways and will automate all human jobs in the next 120 years. By 2024, AI will be better than humans at translation, will write bestselling books by 2049, and will perform surgeries by 2053. Machine learning (ML), the proficiency of a machine to mimic human ability to accumulate knowledge and use it to drive insights, is generally considered the basis of AI. Although AI might depend on its machine learning abilities, we need to take a step back and realize ML doesn't happen in vacuum. ML is driven by big data, without which it can't take place.

Data science enables businesses to process huge amounts of structured and unstructured big data to detect patterns. This in turn allows companies to increase efficiencies, manage costs, identify new market opportunities, and boost their market advantage. Asking a personal assistant like Alexa or Siri for a recommendation demands data science. So does operating a self-driving car, using a search engine that provides useful results, or talking to a chatbot for customer service. These are all real-life applications for data science.

There are specific use cases like the spam filter, where doing traditional programming is hard. Also, the real use of machine learning, that is, cognitive problems, such as image recognition, speech processing, Natural Language Processing (NLP), and so on. These tasks are extremely data-driven and complex, and solving them using rules would be a nightmare. So, an increase in complexity and data-driven problems are the key areas where machine learning can thrive. For example, we have NLP models that can write entire movie scripts, image processing models that can colorize old black and white images, and so on.