The last few decades witnessed Information and Communication Technology being pervasive with daily life. We have a persistent urge to remain online and wish to remain acquainted with the latest in the world at any point of time. In this growing trend, there developed, a wider acceptance of the digital world by a larger mass of people. Humans are being considered to be the biggest data source for digital contents. We are continuously switching between the roles of content generators and content users. Recently, there is a big debate on whether the #10 year challenge is a user initiated meme or another strategy of large social media giants to collect data for application related to age progression.
Whether we like it or not, our life revolves around digital content generations. Industries all over the world are banking on Artificial Intelligence (AI) and Data analytics based systems for better user experience and utility. The AI subfield machine learning (ML) and soft computing were predominantly used for inculcating human like learning abilities in machines. However, it couldn't surpass human performance. Hence, Deep Learning (DL), an advanced form of ML algorithms was developed to build intelligent systems that have outperformed human intelligence at several forums/applications. The increased importance of AI based approaches over the past 25 years has been boosted in large part by the adoption of statistical and probabilistic methods, the development of complex computing paradigms, the availability of large amounts of data, and increased computing processing power of Graphics Processing Units (GPUs).
"The AI subfield machine learning (ML) and soft computing were predominantly used for inculcating human like learning abilities in machine".
Deep Learning has enabled path-breaking capabilities and innovations in Computer Vision (CV) and Natural Language Processing (NLP) based applications. With the advent of computers, with several teraflops of computation, Deep Learning based models are harnessing the power of GPUs to train models whose accuracy are close to humans, and in many cases have even surpassed that. It's an emerging trend setter and has given way to various intelligent applications in domains like - consumer and business insight, precision agriculture, smarter healthcare, retail solutions, sports performance, homeland security, risk management, personal utility, industrial automation, risk management, traffic control and may other data based intelligent processing key terms in today's era. You think of a domain, AI and machine learning is almost pervasive with it. These requirements are generating opportunities for processionals in every domain and is not just something that is required to be done by computer engineers or scientists. The Era of AI wave has already set in and we need to keep preparing ourselves to accommodate the fast pace of changing technology worldwide.
Education is an intrinsic part of the growth of the human development, and hence, cannot alienate from the impact of the tremendous growth of AI in technology and services. Technology is known to empower the educators and the learners to foster the development of the 21st century. In the recent times, looking into the advent of the AI wave in every sector, it's imperative for the academicians to incorporate AI and ML based courses for the curriculum design for higher education.
As per the Artificial Intelligence Index - Annual Report 2018 published with the involvement of various global giants like Stanford University, OpenAI, Harvard University, MIT, McKinsey Global Institute; in the last few years, there is a surge in the inclination of students and researchers in the domain of AI and ML. The enrolment for AI and ML based introductory courses were 5x times higher than in 2012. Likewise, there is a significant growth in the number of ML based papers in conferences, ML based workshops and seminars. The ROS.org, a website for downloading robotic software, showed 18x greater number of page views in 2017 than in 2012.
The AI Index Annual Report 2018 also mentioned that from January 2015 to January 2018, active AI startups increased 2.1x, while all active startups increased 1.3x. For the most part, growth in all active start-ups has remained relatively steady, while the number of AI start-ups has seen exponential growth. Likewise, in comparison to Big Data and Cloud, the mention of AI and ML in IT Companies has increased in 2015 and further in 2017. This includes industries like IT, Consumer Discretionary, Financial and HealthCare.
In manufacturing, robots are being used for automation. AI can also be used for demand forecasting, supply chain, better prediction of the impact of change and for tuning of various other manufacturing tasks. Logistics sector can use AI to improve supply-chain management through adaptive scheduling and routing. In finance, AI can be used to provide early detection of unusual financial risk at multiple scales. Varied application of AI in transportation can be in the domain of asset management, passenger and freight control, emission control. Likewise in agriculture, AI can be used for overall yield estimation, crop health monitoring, automation for plant growing processes and harvesting. AI in communication can facilitate bandwidth allocation, automation of information storage and retrieval. Likewise in basic sciences, AI can play varied roles of automation, estimation, visualization and prediction.
In summary, the trend is already realized and accepted by majority of industries and research organizations. Students these days are already aware of the market trends and look for the latest technology in their curriculum for a more industry ready approach. So, it becomes all the more essential for academicians to adapt to the growing trend in ML based research and development and incorporate the related courses in both Engineering and non-Engineering Programmes.
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