Machine learning, artificial intelligence, natural language processing, deep learning, robotics, and several other technologies have enabled businesses to leverage human intelligence and evaluate inputs for maximum accuracy and precision. For example, you now have image recognition software that acts as a scanner and finds the best search options on Google after interpreting what the image is. So, the application is based on ML, natural language processing, and artificial intelligence. It imitates a human who uses the item or object through the eyes and interprets the results in mind. Although all these disruptive technologies are individually the best in their field, combining them, it's a challenge.

In recent years, the scope and reach of artificial intelligence and associated domains have increased. As artificial intelligence's popularity develops, there has been significant criticism about the technical jargon that surrounds it. Deep learning, deep learning, voice recognition, text analytics, cognitive computing, and neural networks are just a few of the terms that come to mind. Although these phrases are frequently used interchangeably, there is a significant difference in their techniques and objectives. Cognitive computing is one such technology, which is sometimes confused with AI technology but is actually quite distinct.

Today, the world is all about industry 4.0 and the technologies brought in by it. From Artificial Intelligence (AI) to Big Data Analytics, all technologies are transforming one or the other industries in some ways. AI-powered Cognitive Computing is one such technology that provides high scale automation with ubiquitous connectivity. More so, it is redefining how IoT technology operates. The need for Cognitive computing in the IoT emerges from the significance of information in present-day business.

In the ever changing world of information technology, business organizations are left with humongous amount of data with them. This data includes very critical information for business use, but business organizations are only able to utilize 20% of whole data available with them with the use of traditional data analytics technology. To process and interpret the reaming 80% of the data that is in the form of videos, images, and human voice (also called as dark data), there is a need of cognitive computing systems. Cognitive computing systems are typical combination of hardware and software that constitute natural language processing (NLP) and machine language, and have capability to collect, process, and interpret the dark data available with business organizations. Cognitive computing systems works exactly the phenomena of how a human brain works.

Background: IBM Watson for Oncology is an artificial intelligence cognitive computing system that provides confidence-ranked, evidence-based treatment recommendations for cancer. In the present study, we examine the level of agreement for lung and colorectal cancer therapy between the multidisciplinary tumour board from Manipal Comprehensive Cancer Centre in Bangalore, India, and Watson for Oncology. Methods: Watson for Oncology is a Memorial Sloan Kettering Cancer Center (New York, USA) trained cognitive computing system that uses natural language processing and machine learning to provide treatment recommendations. It processes structured and unstructured data from medical literature, treatment guidelines, medical records, imaging, lab and pathology reports, and the expertise of Memorial Sloan Kettering experts to formulate therapeutic recommendations. Treatment recommendations are provided in three categories: recommended, for consideration and not recommended.

ILA, the Intelligent Learning Assistant, was designed with classrooms in mind and aims to enhance both learning and teaching! Specifically, it gives teachers more time to actually TEACH by doing roll call, collecting assignments, distributing materials, finding and managing resources, and, more importantly, keep track of pupil progress and participation. Harnessing IBM's cognitive computing system, Watson, ILA collects, analyzes, and converts information into natural language. Not only does this allow educators to access, evaluate, and integrate new teaching material, but it also provides them with analysis and diagnostics on the progress of each individual student's verbal skills and literary understanding. This makes it possible for educations to assess and tailor the learning behavior and path of each individual student.

For Reynolds, the advances made by self-driving cars in the last couple of years are a case in point. Autonomous vehicles have undeniably come a long way in a very short period of time. They are now capable of traversing streets much more competently than ever before. They are also still not ready to replace human drivers on the road. So, in this period where AI may not quite be ready for prime time, but is still, nonetheless, capable of delivering limited benefits, Reynolds recommended businesses be very selective about where they apply the technology, and think really hard about what they hope to get out of it.

You might have probably heard about the artificial intelligence being developed by some big researchers around the world. The current period of era is also about creating technology that not only process faster, but also works efficiently just like the human brain. The innovation in such technologies has given rise to cognitive computing, which nothing but another miracle innovative development by a human brain to let the machine learn just like human being. Recently, IBM with its new cognitive system called as IBM Watson have entered into the segment of artificial learning to make system that is capable of learning and understanding knowledge to interact with human in a more natural way. The cognitive computing is a self-learning technology platform that uses data mining and pattern recognition to simulate itself in a way that human brain works.

The face of International Business Machines' (NYSE:IBM) transformation is Watson. Bursting onto the scene in 2011 by winning a game of Jeopardy!, IBM's cognitive computing system is now being used in a wide variety of industries to boost efficiency and cut costs. The company predicts that 1 billion people will use Watson, either directly or indirectly, by the end of 2017. There have been many Watson-related deals and partnerships announced in recent months, and the pace seems to be accelerating. Here are three ways Watson is being used today that speak volumes about its future potential.

A silicon wafer designed to sort particles found in bodily fluids for the purpose of early disease detection. A chip that can diagnose a potentially fatal condition faster than the best lab in the country, a camera that can see so deeply into a pill it can tell if its molecular structure has more in common with a real or counterfeit tablet, and a system that can help identify if a patient has a mental illness just from the words they use: IBM is betting that a mix of AI and new hardware can make all three possible within the coming years. IBM's research labs are already working on turning these concepts into fully-fledged healthcare tools, combining the company's existing machine learning and artificial intelligence systems with newer kit including revamped silicon and millimetre wave phased array sensors. Playing House: How IBM's Watson is helping doctors diagnose the most rare and elusive illnesses Marburg Hospital's Centre for the Rare and Undiagnosed Diseases is using the cognitive computing system to solve some of the most complex medical cases. The latter will be used in'hyperimaging systems' -- tools that will be able to pick up not only images from the visible light that humans can see, but other parts of the electromagnetic spectrum that we can't.