Artificial intelligence is one of the most trending topics in the field of Computer Science which aims to make machines and computers ‘smart’. There are multiple diverse technical and specialized research associated with it. Due to the accelerating rate of technological changes, artificial intelligence has taken over a lot of human jobs and is giving excellent results that are more efficient and effective, than humans. However, a lot of time there has been a concern about the following: will artificial intelligence surpass human intelligence in the near future? Are computers’ ever accelerating abilities to outpace human jobs and skills a matter of concern? The different views and myths on the subject have made it even a more than just a topic of discussion. In this research paper, we will study the existing facts and literature to understand the true definitions of artificial intelligence (AI) and human intelligence (HI) by classifying each of its types separately and analyzing the extent of their full capabilities. Later, we will discuss the possibilities if AI eventually can replace human jobs in the market. Finally, we will synthesize and summarize results and findings of why artificial intelligence cannot surpass human intelligence completely in the future.

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Internet has become the most significant technology with tremendous impact on everyday human life, where IOT is a major technology which uses internet applications to make everything around us ‘Smart’ providing ease of accessibility of physical devices and reducing human effort. In this paper we analyze the role of Artificial Intelligence in field of Internet of Things or the IOT. .AI makes the network more ‘intelligent’ increasing the scope of its connectivity and vast data streams. In this paper, we will analyze the critical impact of Artificial Intelligence deployed in IOT. We will also provide a quick overview on the current approaches of IOT and the challenges faced. Later we discuss how IOT and Artificial Intelligence (AI) together play a vital role in the coming years with the emergence of Internet of Intelligent Things (IoIT) that make these devices even smarter.

With the emergence of social media and the Internet, bulks of data are generated every day in different fields. However, filtering out useful information, which can be turned into knowledge, requires significant effort. This can be achieved using various Big Data Technologies, statistics, data mining and a variety of machine learning algorithms that corporates and businesses are using to improve their performance every day for a long time now. In this paper, we analyze YouTube trending video data that analyses the target that grabs the user’s attention over a relatively short time. We present our analysis by measuring, mining, analyzing and comparing key aspects of time-series YouTube data with respect to its view and audience response statistics from 40 000 trending YouTube videos collected over 205 days. We have performed an exploratory data analysis (EDA) on all of it's aspect to get data insights and used statistics to find similarities between them to understand viewing pattern of different video categories. We also compare and observe the variation of activity over time with the nature of the event that affects the quality of our analysis. We make the viewership pattern clear belonging to each category separately by direct analysis. The results and findings can help us predict the category of the video that could probably be trending next in the upcoming days and help individuals get boosted views and performance when they upload video as per the predictions and analysis.

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