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Cognitive Computing Interview Questions
Cognitive computing is the use of computerized models to simulate the human thought process in complex situations where the answers may be ambiguous and uncertain. The phrase is closely associated with IBM's cognitive computer system, Watson.
Computers are faster than humans at processing and calculating, but they have yet to master some tasks, such as understanding natural language and recognizing objects in an image. Cognitive computing is an attempt to have computers mimic the way a human brain works.
To accomplish this, cognitive computing makes use of artificial intelligence (AI) and other underlying technologies, including the following:
* expert systems
* neural networks
* machine learning
* deep learning
* natural language processing (NLP)
* speech recognition
* object recognition
* robotics

Cognitive computing uses these processes in conjunction with self-learning algorithms, data analysis and pattern recognition to teach computing systems. The learning technology can be used for speech recognition, sentiment analysis, risk assessments, face detection and more.
Cognitive computing is the miniature of human notion and thought process in a computerized environment. It is a cumulativeness of self-learning systems which incorporates pattern recognition, data mining and natural language processing(NLP) to impersonate the way the human brain works. The main objective of cognitive computing is to create self-operating computerized systems that are proficient in solving even complex problems without any human help or intrusion. 

The cognitive process can be comprehended in a much simple way as “the mechanism which uses the existing knowledge for generating new knowledge”. The main theme of cognition is closely related to abstract concepts such as mind, perception, and intelligence. It is like understanding the obligation of a human brain and working on human kinds of issues. Such systems and set-up continually gain knowledge from the data. Cognitive computing system consolidates data from diverse and miscellaneous information sources while considering context and conflicting evidence to suggest the best feasible answers. 

It is one of the classifications of technologies that uses machine learning and Natural Languages Processing(NLP) to enable people and machines to interact and gain understanding more naturally for magnification of human expertise, perception, and cognition.
Cognitive computing combines several different technologies to develop its cognitive models.
Natural Language Processing : As its goal is to work side by side with and learn from human intelligence, cognitive systems must be able to understand human speech and written text. Natural language processing (NLP) is the area of computer science concerned with this. Cognitive systems can receive, understand, interpret, and offer feedback in written and spoken forms that emulate human syntax. Although this feat is incredibly difficult given the considerable variances in human communication, NLP technology is advancing at a rapid rate.

NLP is responsible for predictive text, Google’s ability to guess search parameters, communications with Siri and Alexa, and natural-sounding chatbots.
While NLP is the most innovative tool cognitive computing utilizes, other artificial intelligence processes work within its parameters as well.
Machine Learning : ML uses neural networks, a computer system modeled after how the human brain processes information. It is an algorithm designed to recognize patterns, calculate the probability of a certain outcome occurring, and “learn” through error and successes using a feedback loop.
Deep Learning : Deep learning is an approach to machine learning that falls under artificial intelligence (AI), which is most commonly used to label vast and complex data through ANN.

Artificial Neural Networks : An artificial neural network (ANN) is a framework modeled after the human brain. This framework consists of inputs, outputs, and hidden layers. Artificial neural networks have artificial neurons that weigh input data and categorize aspects of that data, connect to other neurons, and feed it further down the classification funnel. ANN is an approach to machine learning that is most commonly used to label data. It is used in computer vision, speech recognition, medical diagnoses, and other ML categorization applications.
Cognitive Computing is used in almost every field, we will discuss a few of them here :
Retail Industry : Cognitive Computing in the Retail industry has very interesting applications. It helps the marketing team to collect more data and then analyse to make retailers more efficient and adaptive. These help companies to make more sales and provide personalized suggestions to the customers. E-commerce sites have integrated cognitive computing very well, they collect some basic information from the customers about the basic details of the product they are looking for and then analyse the large available data and recommend the products to the customer. Cognitive computing has brought numerous advancements to the industry. Through demand forecasting, price optimization, and website design, cognitive computing has provided retailers with the tools to build more agile businesses.
Apart from e-commerce sites, cognition can be very useful for on-floor shopping as well. It will help retailers to provide customers personalized products – what they want, when they want, and how they want to derive meaningful experiences, opportunities to reduce the wastage and losses by providing the fresh products by predicting the demand priorly, and by automating areas it will reduce the cycle time, effort and improve the efficiency.
Logistics : Cognition is the new frontier in the Transportation, Logistics, and Supply chain. It helps at every stage of logistics, like Designing Decisions in Warehouse, Warehouse Management, Warehouse Automation, IoT, and Networking. In the warehousing process, cognition helps in compiling storage code, automatic picking with the automated guided vehicle, and use of warehouse robots will help to improve work efficiency. Logistics distribution links use cognition to plan the best path improving the recognition rate which will save a lot of labour. IoT will help in warehouse infrastructure management, optimizing inventory, enhancing operations in the warehouse and the autonomous guided vehicle can be used for picking and putting operations. Apart from IoT, the other important technology is Wearable Devices, which helps to convert all the objects to sensors and augments human decision-making and warehouse operations. These devices have evolved from smartwatches to smart clothes, smart glasses, computing devices, exoskeletons, ring scanners, and voice recognition.
Banking and Finance : Cognition in the banking industry will help to improve operational efficiency, customer engagement, and experience and grow revenues. Cognitive banking will completely reshape the banking and financial institutions on three dimensions: Deeper contextual engagement, New analytics insights, and Enterprise transformation. We are already experiencing examples of such transformation for tasks like performing various banking transactions digitally, opening a new retail account, processing claims and loans in minutes. This technology has proved to be very helpful in the areas of product management and customer service support. 
Cognitive banking will provide customized support to the customers, it will help in deciding personalized investment plans based on the customer being risk-averse or risk-taker. Also, it will provide personalized engagement between the financial institution and the customer by dealing in the individual fashion with each customer and focusing on their requirements. Here, the computer will intelligently understand the personality of the customer based on the other content available online authored by the customer. 
Power and Energy : ‘Smart Power’ is the new intelligent future. The oil and gas industry faces huge cost pressure to find, produce and distribute crude oil and its byproducts. Also, they face a shortage of skilled engineers and technical professionals. Energy firms take various critical decisions where huge capital is involved, like which site to explore, allocation of resources, and quantity of production. For a long time, this decision was taken based on the data collected and stored and the expertise and intuition of the project team. 
With cognitive computing, technologies process volumetric data to support decisions and learn from those results. This technology will help us to make various important decisions of the future like commercially viable oil wells, ways to make existing power stations more efficient, and will also give a competitive advantage to existing power companies.
Cyber Security : Cognitive Algorithms provides end-to-end security platforms and detects, assesses, researches, and remediate the threats. It will help to prevent cyber Attacks  (or cognitive hacking), this will make customers less vulnerable to manipulation as well as provide a technical solution to detect any misleading data and disinformation.
With the increase in volumetric data, and rise in cyber attacks, and the shortage of skilled cybersecurity experts we need modern methods like cognitive computing to deal with these cyber threats. Major security players in the industry have already introduced cognitive-based services for cyber threats detection and security analytics. Such cognitive systems not only detect threats but also assess systems and scan for vulnerabilities in the system and propose actions. 
The other side of the coin is that for cognitive computing we need huge volumetric data, now securing the privacy of the data is also of utmost importance. To take full advantage of cognitive computing we need to build a large database of information, and at the same time also maintain its confidentiality and prevent data leakage. 
Healthcare : We have already briefly discussed the application of Cognitive Computing in healthcare. Recent advancement in cognitive computing has helped medical professionals to make better treatment decisions and improves the efficiency of the Medical professionals and also improves the outcomes of the patients. It is a self-learning algorithm that uses Machine learning algorithms,  data mining techniques, visual recognition, and natural language processing, dependent on real-time patient information, medical transcripts, and other data. The system processes an enormous amount of data instantly to answer specific questions and make intelligent recommendations. Cognitive computing in healthcare links the functioning of humans and machines where computers and the human brain truly overlap to improve human decision-making. This will empower doctors and other medical professionals to better diagnose and treat their patients and above all helps in planning customized treatment modules. For example, Genome Medicine is one such area that has evolved by cognitive computing. 
Education : Cognitive computing is going to change how the education industry has been working. It has already started bringing a few of the changes. It will change how the schools, colleges, and universities have been functioning, and it will help to provide personalized study material to students. Can you even imagine how fast a cognitive system can search the library or the journals and research papers from a digital library? 
A cognitive assistant can provide personal tutorials to students, guide them through the coursework, and can also help students to understand certain critical concepts at their own pace.  It can also guide students in selecting the courses depending upon their interest. It can act as a career counsellor.
Advantages of cognitive computing include positive outcomes in the following areas :
* Analytical accuracy. Cognitive computing is proficient at juxtaposing and cross-referencing structured and unstructured data.

* Business process efficiency. Cognitive technology can recognize patterns when analyzing large data sets.

* Customer interaction and experience. The contextual and relevant information that cognitive computing provides to customers through tools like chatbots improves customer interactions. A combination of cognitive assistants, personalized recommendations and behavioral predictions enhances customer experience.

* Employee productivity and service quality. Cognitive systems help employees analyze structured or unstructured data and identify data patterns and trends.
Cognitive technology also has downsides, including the following :
* Security challenges. Cognitive systems need large amounts of data to learn from. Organizations using the systems must properly protect that data -- especially if it is health, customer or any type of personal data.

* Long development cycle length. These systems require skilled development teams and a considerable amount of time to develop software for them. The systems themselves need extensive and detailed training with large data sets to understand given tasks and processes.

* Slow adoption. The slow development lifecycle is one reason for slow adoption rates. Smaller organizations may have more difficulty implementing cognitive systems and therefore avoid them.

* Negative environmental impact. The process of training cognitive systems and neural networks consumes a lot of power and has a sizable carbon footprint.
The term cognitive computing is often used interchangeably with AI. But there are differences in the purposes and applications of the two technologies.
Artificial intelligence : AI is the umbrella term for technologies that rely on data to make decisions. These technologies include -- but aren't limited to -- machine learning, neural networks, NLP and deep learning systems. With AI, data is fed into an algorithm over a long period of time so that the system learns variables and can predict outcomes.
Applications based on AI include intelligent assistants, such as Amazon's Alexa, Apple's Siri and driverless cars.
Cognitive computing : The term cognitive computing is typically used to describe AI systems that simulate human thought. Human cognition involves real-time analysis of the real-world environment, context, intent and many other variables that inform a person's ability to solve problems.
A number of AI technologies are required for a computer system to build cognitive models. These include machine learning, deep learning, neural networks, NLP and sentiment analysis.
Here is an example of a cognitive app that’s just over the horizon. Imagine you’re a farmer in Iowa or in sub-Saharan Africa. You’re out in the field working and your 10-year-old daughter comes running to you and says, “Dad, something bit me but I don’t know what.” You take a picture of the bite on your smartphone, and send it to the cognitive cloud. There it gets processed and tells you  there’s a 92 percent chance that it’s a spider bite, and a 47 percent chance it’s a snake bite. It displays the triage information on how to manage a spider bite along with a map to the nearest doctor. It also gives you the doctor’s telephone number, and asks if you’d like the phone to place the call.
This is an example of democratizing medical knowledge at the point of care. To make it possible, you need content in the cloud, you need the cloud itself to process the content and understand it, and you need a mobile device manufacturer.
There’s a role for entities that bring these parties together. Deloitte is doing that with its greenhouses. IBM is also doing this with its BlueMix Garages that were announced in April. Cognitive Scale and other startups are also spearheading a lot of these partnerships.
The first era of computing was the Tabulating Era, from 1900 to 1940, and consisted of machines which used punch cards to input data and perform extremely simple calculations. The Programming Era took over in the 1950s where complex data trees and feedback loops with a specific outcome became programmable. The limitation of this era is that these programs are only as good as their preprogrammed algorithms and data sets. The Cognitive Era is said to have begun in 2011. It is marked by its ability to adapt, make judgments, and self-learn.
The future of cognitive computing will touch every sector and industry vertical, from education, banking, and fraud detection to autonomous vehicles, conservational efforts, and even makeup recommendations. Wherever there is data and a problem to be solved, cognitive computing can play a role.