The number of social media users is growing by leaps and bounds with each passing day. The count of the users increased by a whopping 202 million between April 2018 and April 2019, which accounts to a new social media user every 6.4 seconds! This bulk of users on social media platforms creates a playground for social spam. Wikipedia defines social spam as – "Unwanted spam content appearing on social networking services, social bookmarking sites, and any website with user-generated content (comments, chat, etc.). It can be manifested in many ways, including bulk messages, profanity, insults, hate speech, malicious links, fraudulent reviews, fake friends, and personally identifiable information." We can go on about the increase in spams on social networks.

The term Deep Reinforcement Learning is a new cool phrase in the world of Artificial Intelligence and Machine Learning. So, what does this phrase mean, and what is its impact? Deep Reinforcement Learning uses the combined principles of deep learning and reinforcement learning. Deep Learning, as we know, Deep learning is a part of machine learning methods and is based on artificial neural networks. Reinforcement Learning, on the other hand, is an area of machine learning which tells how software agents should take actions to maximize the probability of choosing the best possible path or behavior for a particular situation.

Retail Industry is looking at Artificial intelligence (AI) and machine learning (ML) as a solution to take their organization to the next level of productivity and customer experience. To explain this better, let us look at it this way – Retail companies have access to a massive amount of data about their customers and their shopping preferences. It is difficult for the companies to drill down into these huge mines of useful data and analyze it properly and derive actionable insights in real time. Therefore, massive amounts of this useful information could go waste which would otherwise have helped in increasing sales conversion rates or enhancing the customer satisfaction. With the help of AI and ML, the huge amount of big data could be used in creating web shops that take customer information and turn it into targeted shopping experiences or online chatbots that can easily answer questions and assist customers, or in-store intelligence to make the customer experience even more interactive.

Data Analytics is no more the future. Many people believed that it is just a flash in the pan and will wear off in some time. That fact is settled, and big data and analytics are here to stay. It is estimated that the market will grow at a CAGR of 30 percent between 2017 and 2023. This speedy growth will propel the market size for Big Data to reach a size of $80 billion.

Most of the sports competitions today are more of financial competitions along with being the athletic one. More financial resources mean better infrastructure for training, which results in better performance on the field. But in one case, a group of undervalued underperforming players came together to convert themselves into one of the most successful major league basketball franchise. This proverbial underdog story has been converted into a much-celebrated movie with Brad Pitt donning the hat of Billy Beane, the male protagonist. How did this underrated team with all the odds stacked against them win the battle against unfairness?

IoT or Internet of things involves embedding sensors in traditionally dumb devices and enabling internet connectivity in them to collect a range of ambient data. IoT devices can sense their surroundings and communicate this data over the Internet, and they can in turn also be remotely controlled. This expands the range of internet-enabled devices far beyond desktops, laptops, and mobile phones, and the applications of IoT are endless. Today, IoT is disrupting many industries: some examples of IoT systems are smart fridges, heart monitors, small weather stations. A Gartner study on IoT devices indicates that there will be more than 20 billion IoT devices by 2020, and as of today, there are about a billion PCs and two and a half billion smartphones on the internet.

CIOs across all industries have Digital Transformation as one of their key agenda items. There is a strong linkage between Robotic Process Automation (RPA) and Digital Transformation. RPA can greatly support organizations in achieving the end goal of digital transformation through automation. The major question is will RPA be able to do the same thing that iPhone did to the smartphone industry? Most of the key decision makers feel that it will.

As Data science continues to reign as a powerful differentiator across the industry today. Most of the organizations today are focused on automating tasks related to data integration and model building with the goal of simplifying data to present clear insights. There is a big demand for skilled data scientists in the market today. According to a report by NDTV, there has been a 200 per cent year-on-year increase in "Data Scientist" job searches since the last three years and a 50 percent year-on-year increase in job listings. In order to bridge the gap between this demand and supply, organizations are attempting to empower or train some of its employees who already know the business with some data skills – so that they can gain expertise on analyzing data themselves.

When it comes to Artificial Intelligence and Machine Learning, we seem to be entering a golden age. According to a Research Report by PwC, business leaders believe that AI is going to be fundamental in the future. In fact, 72% termed it a "business advantage." The big players in the tech game like Google and IBM are in the process of creating ground-breaking technologies that will transform life as we know it. The big players in the tech game like Google and IBM are in the process of creating ground-breaking technologies that will transform life as we know it.

Call it Industrie 4.0 as the Germans, Smart Manufacturing as the Americans or Smart Factory as the Koreans, the manufacturing industry is witnessing a technological overhaul that is propelled by the power of data and analytics. Given that the manufacturing involves complex production activities, it is leveraging greater digitization, the adoption of connected systems, and implementation of sensors in various manufacturing processes to improve the production accuracy and quality of products. With the help of the huge volumes of data that these systems generate, the manufacturing sector is using machine learning techniques and big data analytics to develop predictive statistical models that can help them make smarter decisions, improve operational processes and increase profitability. But what essentially is Machine Learning and is it any different from big data analytics? By definition, Machine Learning is a method of data analysis that provides computers the ability to learn without explicit programming.