Artificial Intelligence (AI), Natural Language Processing (NLP) and Machine learning (ML) are the buzzwords of the current technology landscape and we can hear everyone talk about its application across industries. Artificial Intelligence has enabled several industries to apply and achieve business process automation through Robotic Process Automation, Cognitive machine interpretations of large volumes of dataset analytics and lastly replicating human thinking and activities through both predictive and cognitive patterns based simulations. Artificial Intelligence is not a machine or a system but rather programmed intelligence built into systems to simulate activities or work packets that humans were only capable of executing few years back. And these simulations are made possible by patterns, sensors, decision elements under cognitive and predictive technologies underpinned by large volumes of bytes of data volumes. Natural Language process (NLP) is a subset technology under Artificial Intelligence which entails building systems to understand and grasp language. NLP draws from varied disciplines, including computer science and computational linguistics, in its pursuit to fill the gap between human communication and computer understanding.

Machine learning is simply the way to achieve Artificial intelligence through technologies like Deep learning where in systems are programmed to learn from patterns and data to acquire knowledge and build continuously improving algorithms. Some other machine learning technologies are decision tree learning and Bayesian networks. An example would be to have humans tag images of a bird in thousands of images and have training induced into the machine algorithm to such an extent that the machine is able to identify a bird next time it comes across. This are self-reinforcing training algorithms to continuously feed data and improve itself over periods of time.

Artificial Intelligence can be of two types like General and Narrow intelligence. Artificial narrow intelligence comprises of role tasks such as those performed by chatbots, personal assistants like SIRI by Apple and Alexa by Amazon. Artificial general intelligence comprises of human-level tasks such as performed by self-driving cars by Uber, Waymo. It involves continual learning by the machines. Artificial narrow intelligence are driving tremendous economic values like healthcare (imaging diagnostics, mental healthcare), automobiles industries (inspection of auto components for defects). These are supervised learnings driven by neural networks and Deeplearning. These are all input and output driven logics. Like what’s in front of your car is the input, other car reference positions can be output. If input is an x-ray image, output can be diagnosis of the x-ray image. Practically economic benefits have started flowing in online advertising, social media and Ecommerce like ability to predict whether a customer will click on a specific advertisement or not.

Andrew Ng the AI Guru:Andrew Ng co-founded and led Google Brain and was a former VP & Chief Scientist at Baidu, building the company’s Artificial Intelligence Group into several thousand people. He is an adjunct professor at Stanford University and also the founder/co-founder of Landing AI and Course era.  latest lecture on Enterprise path for AI at Samsung CEO Summit in San Fransisco.

Andrew delivering lecture on AI at Samsung CEO Summit

Key takeway from this video: AI is not just a technology but its application is going to be pervasive in almost every industry including Agriculture (Precision farming, consolidation of farms) or Banking systems (Fintech, KYC, Fraud detections). Only companies with strong corporate vision towards AI and an adaptable culture can be successful in maintaining economic value in businesses.


Deeplearning.AI is Andrew`s new venture to provide comprehensive AI education beyond borders. We can take Deep Learning Specialization which can help us build AI based systems post the course. Deep Learning specialization course can be taken at Courseera where Andrew himself takes the classes online. Each specialization material includes hands on projects as well. There are mutliple courses one needs to complete in the specialization like neural networks, Deep learning, Structuring machine learning projects, and convoluted neural networks. There is also a non-technical based AI course coming up in Deeplearning.AI / Courseera partnership in 2019. Machine learning book by Andrew Ng:Machine Learning Yearning, a free book that Dr. Andrew Ng is currently writing, teaches how to structure machine learning projects. This book is focused not on teaching ML algorithms, but on how to make them work. We can download a draft copy of this book at