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Building Cognitive Applications
Introduction to cognitive computing Bellsouth database for sale

The explosion of data, mainly unstructured data, over the past few years led to the development of a new type of computer system known as a cognitive system. Unlike the

programmable computers that preceded it, the focus of cognitive systems is not about doing fast calculations on large amounts of data through traditional computer programs. Cognitive systems are about exploring the data, finding new correlations, and new context in that data to provide new solutions. Cognitive systems aim at expanding the boundaries of human cognition rather than replacing or replicating the way the human brain works.

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Cognitive computing is becoming a new industry. A new industrial revolution is coming, connected to job automation in transportation, customer care, and healthcare, to name a few. The livelihood of such a revolution will be a new generation of skilled developers who understand cognitive computing well enough to envision new business applications, and ultimately build the new cognitive web.

This chapter briefly summarizes the history of cognitive computing and the eras of computing because, to understand the future of cognitive computing, placing it in historical context is important.

This chapter also describes basic concepts that are relevant to any discussion about cognitive systems. It introduces the key characteristics and highlights some applications, widely used today, that are based on cognitive technologies.

Brief history of cognitive computing

The concept of intelligent machines has existed for a long time. Surprisingly, in the 19th century, mathematician George Boole’s 1854 book, The Laws of Thought, showed that logical

operators (and, or, not) provided the basis for the laws of thought. About that same time, Charles Babbage conceived of creating what he described as an analytical engine.

In 1950, Alan Turing, an English computer scientist and mathematician, addressed the problem of artificial intelligence and proposed an experiment that became known as the Turing test. It is a test of a machine’s ability to exhibit intelligent behavior similar to a human. The test was an adaptation of a Victorian-style competition called the “imitation game.” The Turing experiment was based on a human evaluator that judged natural language conversations between a human and a machine designed to generate human-like responses. The test studied whether the interrogator can determine which responses are given by a computer and which ones by a human. The idea was that if the questioner could not tell the difference between human and machine, the computer would be considered to be thinking.

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The term artificial intelligence was first coined by Prof. John McCarthy for a conference on the subject, held at Dartmouth College in 1956. McCarthy defines the subject as the “science and engineering of making intelligent machines, especially intelligent computer programs.”1

In 1960, computing pioneer J.C.R. Licklider wrote his seminal paper Man-Computer Symbiosis.2 The paper describes Licklider’s vision for a complementary or symbiotic relationship between humans and computers. The following quote is an example of Licklider’s research and insights:

“Man-computer symbiosis is an expected development in cooperative interaction between men and electronic computers. It will involve very close coupling between the human and the electronic members of the partnership. The main aims are: Bellsouth address lists

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To let computers facilitate formulative thinking as they now facilitate the solution of formulated problems.
To enable men and computers to cooperate in making decisions and controlling complex situations without inflexible dependence on predetermined programs… Bellsouth database for sale

Preliminary analyses indicate that the symbiotic partnership will perform intellectual operations much more effectively than man alone can perform them.”

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The eras of computing

To understand the future of cognitive computing, placing it in historical context is important. To date, two distinct eras of computing have occurred: the tabulating era and the programming era. We are entering the third and most transformational era in computing’s evolution, the cognitive computing era (cognitive era).

The eras can be described as follows:

Tabulating era (1890s – 1940s)

The first era of computing consisted of single-purpose electromechanical systems that counted, using punched cards to input and store data, and to eventually instruct the machine what to do. These tabulation machines were essentially calculators designed to count and summarize information and they did it really well but were ultimately limited to a single task.

These machines supported the scaling of business and society and were used in government applications such as process population census data and business applications such as accounting and inventory control. Tabulating machines evolved to a class of machines, known as unit record equipment, and the data processing industry.

Programming era (1950s – present)

This era started with the shift from mechanical tabulators to electronic systems and began during World War II, driven by military and scientific needs. Following the war, digital “computers” evolved rapidly and moved into businesses and governments. The programmable computing era begins.

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The big change is the introduction of general purpose computing systems that are programmable: they can be reprogrammed to perform different tasks and solve multiple problems in business and society. But ultimately, they must be programmed and are still somewhat constrained in the interaction with human beings. Everything we now know as a computing device, from the mainframe to the personal computer, to the smartphone and tablet, is a programmable computer. Some experts believe that this era of computing will continue to exist indefinitely.

Cognitive era (2011 – future)

As Licklider predicted, cognitive computing is a necessary and natural evolution of programmable computing. Cognitive computing systems are meant to extend the boundaries of human cognition. Cognitive computing technologies are not about replacing or necessarily even replicating the way that the human brain works; they are about extending the capabilities of the human brain. Humans excel at reasoning, deep thinking, and solving complex problems. But the human ability to read, analyze, and process huge volumes of data, both structured and unstructured, is quite poor. That, of course, is the strength of the computer system. The first role of a cognitive computing system is to combine strengths of human and machine into a collaborative situation. Bellsouth address lists

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Another key element of cognitive systems is a more natural interaction between human and machine, combined with the capability to learn and adapt over time.
The future of computing is cognitive Bellsouth database for sale

In his paper, Computing, cognition and the future of knowing: How humans and machines are forging a new age of understanding,3 Dr. John E. Kelly III states this:

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“Those of us engaged in serious information science and in its application in the real world of business and society understand the enormous potential of intelligent systems. The future of such technology — which we believe will be cognitive, not “artificial”— has very different characteristics from those generally attributed to AI, spawning different kinds of technological, scientific and societal challenges and opportunities, with different requirements for governance, policy and management.”

In the same paper, Dr. Kelly defines cognitive computing:

“Cognitive computing refers to systems that learn at scale, reason with purpose and interact with humans naturally. Rather than being explicitly programmed, they learn and reason from their interactions with us and from their experiences with their environment.”

Current demands driven by big data and the need for more complex evidence-based decisions, are going beyond the previous rigid rule and logic-based approach to computing. Cognitive computing enables people to create a new kind of value by finding answers and insights locked away in volumes of data. Cognitive computing serves to enhance human expertise with systems that reason about problems like a human does.

When we as humans seek to understand something and to make a decision, we go through four key steps:

Observe visible phenomena and bodies of evidence.

Interpret what we see by drawing on what we know in order to generate hypotheses about it means.

Evaluate which hypotheses are right or wrong.

Decide (choose) the option that seems best and act accordingly.

Just as humans become experts by going through the process of observation, evaluation and decision-making, cognitive systems use similar processes to reason about the information they absorb.

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Impact of cognitive computing to our lives

Whether you realize this or not, cognitive computing is already having an impact on our lives. Often when you talk to a call center, your interaction is likely with a computer. Articles that you read might have been written by a machine. In many cases, such as online shopping, cognitive computing understands your behavior and activities and makes recommendations based on that understanding. Chatbots that are powered by cognitive computing have been built to successfully support complaint-resolution services.

Many professions are being enhanced by cognitive computing. For example, a doctor diagnosing a patient with unusual symptoms would have to search through vast amounts of information to arrive at a proper diagnosis. Cognitive computing can assist that doctor by doing much of the research and preliminary analysis for the doctor and might also be able to recommend next steps.

Consider a wealth manager advising clients on their individual retirement portfolios. While basic facts and rules apply, individual needs, circumstances, and interests come into play. Sorting through all the related information and customizing the recommendations to a particular client can be an overwhelming task that is made easier by cognitive computing.

In essence, cognitive computing can put into context the information many professionals handle on a daily basis in order to drive real value from it.

Consider these basic concepts:

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Cognition, the “act of thinking,” is the mental process of acquiring understanding through thought and personal or shared experiences. Brain-based skills are part of every human action and are essential in carrying out any task, from the simplest to the most difficult.

Tasks include human senses (hearing, touch, smell, sight, taste, and even extra-sensory perception), learning, remembering, motor skills, language, empathy, social skills, and problem solving capabilities.

As stated, cognition is the process of acquiring knowledge through thoughts, experiences, and senses. Cognitive processing helps us understand and interact with the world around us from the basic to the complex.

Artificial Intelligence (AI)

The study and development of AI systems aim at building computer systems able to perform tasks that normally require human intelligence. AI-based machines are intended to perceive their environment and take actions that optimize their level of success. Today’s AI can be considered weak, in that it is designed to perform narrow and specific tasks. The goal of many researchers is to create strong AI that learns like a human and can solve human-type problems.

AI research uses techniques from many fields, such as computer science, philosophy, linguistics, economics, speech recognition, and psychology, which are manifested in applications, such as control systems, natural language processing, facial recognition, speech recognition, analytics, pattern matching, data mining, and logistics.

Cognitive computing

Humans are inherently capable of a set of skills that help us learn, discover, and make decisions:

– Humans can apply common sense, morals, and reason through dilemmas.

– Humans can think of new ideas and make generalizations when essential clues and pieces of information are missing.

– But humans are restricted by the amount of time spent to learn, process, and absorb new information, and limited by the unconscious biases we all possess that influence the decisions we make.

Cognitive computing is among the subdisciplines that shape AI. It is about putting together a system that combines the best of human and machine capabilities (Figure 1-2 on page 6). Consider capabilities that humans naturally have, such as imagination and emotions, combined with capabilities that computers excel at, such as number crunching, identifying patterns, and processing huge amounts of information. Cognitive computing uses machine strengths to “simulate” the human thought processes in a computerized model.

Cognitive systems use techniques, such as machine learning, data mining, natural language processing, and pattern matching to mimic how a human brain works. Such systems are ideal to interact with an increasingly complex world.

Big data

Often big data characteristics are defined by the five V’s: variety, volume, velocity, veracity, and visibility. Big data requires innovative forms of information processing to draw insights, automate processes, and assist in decision making. Big data can be structured data that corresponds to a formal pattern, such as traditional data sets and databases. Also big data includes semi-structured and unstructured formats, such as word processing documents, videos, images, audio, presentations, social media interactions, streams, web pages, and many other kinds of content. Unstructured data is not contained in a regular database and is growing exponentially, making up the majority of all the data in the world.

Question-answering (QA) technology

Cognitive systems can ingest millions of pages of text and apply question-answering technology to respond to questions posed by humans in natural language. This approach allows people to “ask” and get almost instantaneous answers to complex questions. Combined with other application programming interfaces (APIs) and advanced analytics, QA technology distinguishes itself from the conventional search (that is triggered by keywords) by providing a more conversational discussion.

Machine learning (ML)

Machine learning is a type of AI that gives computers the ability to learn and act without being explicitly programmed. This means that the computer model gets better over time by learning from its mistakes and new experiences (being exposed to new data), increasing its intelligence. If a computer program can improve how it performs certain tasks that are based on past experiences, then it has learned. This differs from performing the task always the same way because it has been programmed to do so.

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Natural language processing (NLP)

NLP is a field of AI and it refers to the processing by computers of natural language. Natural language is any human language, such as English, Spanish, Arabic, or Japanese, to be distinguished from computer languages, such as Java, Fortran, or C++.NLP is the ability of computer software to understand human speech. By using NLP capabilities, computers can analyze text that is written in human language and identify concepts, entities, keywords, relations, emotions, sentiment, and other characteristics, allowing users to draw insights from content.

Any system that takes natural language as input and is capable of processing it is a natural language processing system (for example, spam-detection software). A spam classifier is a system that looks at the content of the email subject line to assess whether the received email is or is not spam.

Cloud computing

Cloud computing is a general term that describes delivery of on-demand services, usually through the Internet, on a pay-per-use basis. Companies worldwide offer their services to customers. Services might be data analysis, social media, video storage, e-commerce, and cognitive computing in a way that is available through the Internet and supported by cloud computing. Bellsouth address lists

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Application program interfaces (APIs) buy  Bellsouth database for marketing

In general, APIs expose capabilities and services. APIs enable software components to communicate with each other easily. The use of APIs as a method for integration injects a level of flexibility into the application lifecycle by making the task easier to connect and interface with other applications or services. APIs abstract the underlying workings of a service, application, or tool, and expose only what a developer needs, so programming becomes easier and faster.

Cognitive APIs are usually delivered on an open cloud-based platform, on which developers can infuse cognitive into digital applications, products, and operations by using one or more of the available APIs.

In the cognitive computing model, all these concepts are combined, eliminating the need for users to be experts in cognitive methods and to allow them to focus on creating better solutions
How do these concepts and technologies relate to each other?

The cognitive computing model intends to have high value in various domains. By applying this model, users do not need to spend time learning intricate details about tools in order to use the tools effectively or spend time interpreting vast amounts of information to draw conclusions. Instead, users spend their time identifying useful patterns, making decisions, and taking action to improve business and operational processes.

Because cognitive computing mimics thinking, the quality of the output is only as good as the algorithms and models used at the start. These models are improved with machine learning.

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While a human expert might spend weeks analyzing volumes of data, the computer model can do that in seconds. For example, a team of medical practitioners can carry out a study that monitors hundreds of children for many months to predict factors that cause diabetes in toddlers. In the near future, the same study can be accurately predicted by a computer model that takes seconds to analyze volumes of data, at a much lower cost. For even more added value, other sources of data can be included to improve prediction results. Examples of data to include are family history, life style, cultural norms, and family activities. These are the types of data that make the equivalent human-led research take several years to complete.

One big problem with most analytical tools is that they require a subject matter expert (for example, pilot, doctor, lawyer) to become a computer expert. One goal of cognitive computing is to demand only conversational skills from the subject matter expert to enable that person to draw valuable insights. Data mining and analysis now means “simply ask.” Over time, NLP and QA technologies have become better at identifying speech patterns and truly understanding what the user says in the context of the information available.

With much data to analyze, will you need a super computer so that you can gain insights? Here is where the power of the cloud computing can help. Various vendors have established cloud computing environments and offer access to the cloud over the Internet. Users request the services they need and provide access to their data. Vendors offer a pay-per-use model and provide customization of the environment to fit the particular needs of users. The cloud computing model greatly lowers barriers to access, and with global availability anyone in the world with Internet connectivity has access to these services.

Various APIs that provide access to various services enable quick, easy, and intuitive access to computing systems. Most of the APIs are independent of the programming language, which means your developers can work in any programming language. Using APIs for sharing data, services, and business functions between endpoints (such as applications, devices, and websites) creates the opportunity to lower the cost and time for integration.

Characteristics of cognitive systems

Many people believe that the only way to handle the onslaught of data today and the future is through the use of cognitive systems. Cognitive systems have several key characteristics:

An important concept to understand is that the first key element of cognitive systems is to expand the boundaries of human cognition rather than replace or replicate the way the human brain works. Humans excel at thinking deeply and solving complex problems, however our ability to read, analyze, and process huge volumes of data is poor. Reading, analyzing, and leveraging huge volumes of data is the strength of computer systems. A key element of a cognitive system is to combine those two strengths (human and computer) into a collaborative solution. More than searching through huge amounts of data, the cognitive system must combine different pieces of information together, and possibly do some reasoning to make connections and relationships.

The system needs to do enough analysis to pull out key elements, understand the problem the human is trying to solve, and based on that context bring information to bare on the problem. The goal is for a human to easily leverage the information provided by the cognitive system and enable the human to explore the evidence and use this insight to solve their problem or make decisions.

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The second key element is to have a more natural interaction between computers and humans. Until recently, to interact with computers, humans had to adapt the way they work to the computer interface, which was often rigid and inflexible. Cognitive systems provide a much more natural engagement between the computer and the human. Speech recognition, for example, enables the human to interact with the computer by using voice commands.

A third key element of cognitive systems is the use of learning, specifically machine learning. Machine learning has been pursued for a long time and cognitive systems must go beyond the core foundations of machine learning. Bellsouth address lists

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The intent is to broaden the potential for learning and the ability of a to adapt over time with use, which is a fourth key element of cognitive systems. So as you use these applications, a feedback mechanism captures the results of that interaction and the system must learn from the resulting interaction and evolve automatically over time, improving its performance. buy  Bellsouth database for marketing

With this base of understanding, you can think about cognitive systems as reaching to provide and, in many cases, already providing these capabilities:

Understand: Cognitive systems understand imagery, language, and other unstructured data like humans. Cognitive system operationalize virtually all data (structured and unstructured) like humans do.

Reason: Cognitive systems can reason, grasp underlying concepts, form hypotheses, and infer and extract ideas.

Learn: With each data point, interaction, and outcome, the cognitive systems develop and increase expertise, and continue to learn, adapt, and improve their expertise.

Interact: With abilities to see, talk, and hear, cognitive systems interact with humans in a natural way.

Solving real life problems with cognitive systems

Cognitive systems drive the use of big data to support business processes. Most big data has no formal organization or structure. Cognitive systems can penetrate the complexity of unstructured data and incorporate the power of natural language processing and machine learning. Cognitive systems create solutions for day-to-day problems.

Cognitive systems create new ways of generating value for consumers and enhance the experience across the purchase lifecycle. For example, a cognitive travel planner can consider language identification, tradeoff analytics, and personality insights to make travel recommendations that best meet customer needs. Another example is the review of massive numbers of insurance policies to obtain policy rules. With these rules, an insurance company can drive standardization, reduce risk, and more broadly learn from the expertise and experience of the underwriters.

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Vendors of cognitive systems provide a various offerings that are based on voice commands and the use of data from the Internet. Various vendors provide targeted cognitive systems for industries from healthcare to automotive to finance to insurance.

Cognitive business and IBM Watson

A cognitive business is an organization that creates knowledge from data to expand virtually everyone’s expertise, continually learning and adapting to outthink the needs of the market.

These three elements are the root of what becomes possible with cognitive businesses:

Grow knowledge from data: Translate expansive, ever-growing data sets into differentiation when you act on game-changing knowledge.

Enhance expertise: Redefine industries and professions when you make expertise a scalable resource.

Learn and adapt: Transform how you do everything together when you can learn and adapt perpetually.

Cognitive systems are crucial to a successful enterprise. Their capabilities are the key to enabling new kinds of customer engagement, building better products and services, improving processes and operations, making data-driven decisions, and harnessing expertise.

This chapter describes the industry landscape for cognitive computing and introduces Watson, the cognitive computing offering from IBM.

The following topics are covered in this chapter:

Landscape of cognitive computing in the industry Introducing IBM Watson

2.1 Landscape of cognitive computing in the industry

Organizations are just “scratching the surface” of cognitive computing capabilities, from improving customer engagement to enhancing research capabilities. The potential value of cognitive computing is boundless. Cognitive systems have three broad capability areas
The three broad capability areas are as follows:

Discovery: Finding insights and connections in the vast amounts of available information

Engagement: Changing the way humans and systems interact, extending the user’s capabilities

Decision: Evidence-based decision-making capabilities

Those capability areas relate to the ways people think and work and drive the use of big data in business processes. Most big data is unstructured data, having no formal organization or structure
Cognitive computing can penetrate the complexity of unstructured data and raise the power of natural language processing and machine learning.

From simple problem solving (for example, voice assistance and language translation) to complex challenges (such as a digital self-service system or healthcare solutions), cognitive computing creates solutions for the day-to-day problems that both organizations and users face.

Vendors that provide cognitive computing offerings take different approaches. For example, some vendors market cognitive appliances to target users (consumer market); others develop enterprise cognitive solutions to target businesses (enterprise market).

Consumer market: Cognitive computing offerings Bellsouth email database providers

The consumer cognitive market is targeted to users. Cognitive technologies generate new ways of creating value for consumers and enhance their daily experiences.

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Consumer cognitive products include personal assistants, wearable devices, home automation and more. These products often respond to voice commands and use data from the Internet, personal email, calendar, contacts, and devices that the owner uses to perform the tasks (Figure 2-3.)

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Cognitive technologies can give makers of household goods new ways to create value for consumers. These technologies, derived from the field of artificial intelligence, can enhance the consumer experience across the purchase life cycle, from pre-store planning through the in-store experience, product usage, and post-purchase interaction.

Upon closer examination at what is going on in the consumer space, those products provide customers with services that have access to specific knowledge domains or functions. Widely known examples are as follows:

Amazon Echo: A hands-free speaker you control with your voice. Echo connects to the Alexa Voice Service to instantly play music and provide information, news, sports scores, weather, and more.

Applie Siri: An intelligent personal assistant and knowledge navigator that lets you use your voice to do a variety of activities such as send messages, make appointments, control the apps in your phone, and update your calendar and to-do list.

Google Search: A web search engine that hunts (according to your typed in request) for text in publicly accessible documents located on web servers. The types of information available include books and book reviews, synonyms for words, weather forecasts, time zones and world clock, Google Maps, movie information, airport and airline information, real estate listings, sports scores, and more.

Enterprise market: Cognitive computing offerings

Large vendors that offer enterprise cognitive services include a broad range of solutions and tools in their portfolio. Most of them deliver their services and tools on the Internet, taking advantage of the benefits of cloud platforms, such as scalability, accessibility, and flexible billing options.

Also, hundreds of small startups apply artificial intelligence algorithms across industries, from healthcare to automobile to finance to insurance.

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Vendors that provide enterprise cognitive services focus on either core cognitive capabilities or they are applying artificial intelligence (AI) algorithms to specific industry solutions.

Natural language processing (NLP), machine learning (ML), and question-answering (QA) technologies provide the foundation for several core cognitive capabilities that are provided by enterprise cognitive services. These core capabilities are used by developers to build cognitive solutions. Core cognitive services available on the market include these examples:

Conversation services

Text mining, information extraction, and text analytics Machine translation

Computer vision and image recognition Speech recognition

Several vendors provide cognitive solutions for industry vertical sectors, as in these examples: Bellsouth email database providers

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In the agriculture industry, cognitive technologies are used in applications such as crop monitoring, automated irrigation systems, automated harvesting systems and AI guided drone systems.

Banking, Financial services and Insurance (BFSI)

In the BFSI sector, AI technologies are used for wealth management applications such as smart wallet, stock trading, fraud detection, and others.


In the manufacturing industry, cognitive technologies are using robot-integrated Computer Integrated Manufacturing (CIM) and sensor-assisted machining.


In healthcare applications, cognitive technologies are used in a plethora of applications to reduce drug discovery times, provide virtual assistance to patients, and diagnose ailments by processing medical images, among many others.

Oil and Gas

In the Oil and Gas industry, cognitive technologies are used in the exploration and production (E&P) lifecycle and drill floor automation.

Media and Advertising

Some of the core applications of cognitive computing in this industry are facial recognition, advertising, and customer self-service.

Transportation and Automotive

In this sector, some cognitive applications include these examples:

– Different modes of transport and their interactions

– Intelligent and real-time traffic management and control

– Transport policy, planning, design, and management

– Environmental issues, road pricing, security and safety

– Transport systems operation

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– Travel demand analysis, prediction, and transport marketing

– Traveller information systems and services

– Pedestrian and crowd simulation and analysis

– Autonomous driving

– Artificial transportation systems and simulation

– Surveillance and monitoring systems for transportation and pedestrians

Delivering cognitive services: Cloud and open source projects Bellsouth email database providers

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Different frameworks for artificial intelligence are offered through open source projects, which provide a platform for rapidly developing scalable machine learning software. Open source software available at free or for a much lower price than software that is proprietary gives more flexibility than commercial software and can be customized to meet the needs of specific users. However, many interfaces aren’t intuitive and difficult to use. In certain instances, open source has an intensive learning curve that can make it difficult to develop and deploy. The users are accountable to manage the technology and applications, and this is long-lasting, particularly when compared to the cloud-based delivery model. buy  Bellsouth targeted email list

The future of work and cognitive computing

In light of the technological changes High growth is anticipated in conventional IT and mathematical-based jobs focused on data analysts and software developers. This is true for many industries, such as media, financial services entertainment, professional services, since computing capacity and the power of big data analysis increase employment opportunities across all areas.

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The next generation of highly skilled developers who are adept at cognitive computing is in high demand to develop innovative solutions for companies that do not exist in the present. With the abundance of data and the requirement for more complex , evidence-based decision making the traditional approaches fail or are unable in keeping up the amount of available data. Cognitive computing allows people to make a new kind of value-based solutions that provide insights hidden within huge volumes of data.

IBM Watson is a cognitive system that allows a brand new relationship between humans and computers. This is the first cognitive computing service by IBM.

Watson is a combination of five capabilities:

Engages in conversations with people more naturally, depending on the individual’s preferences.

In a flash, you can ingest key industry components working with experts to increase and scale the level of expertise.

New products and services are able to understand, reason, and understand their users as well as the world that surrounds them.

Utilizes data to improve processes for business and forecasting, which improves the efficiency of operations.

Improves exploration and discovery, uncovering new patterns, possibilities and hypotheses that can be implemented.

IBM Watson is at the leading edge of a new age of computing known as cognitive computing. In a nutshell, Watson can understand all kinds of data, communicate naturally with humans and also think and reason at large scale.
ata knowledge, information, and skills make up the base to work together with Watson. Figure 4 shows instances of information and data that Watson can study and draw conclusions from, and gain new insights that have never been previously discovered.

Watson APIs: Create using Watson

You can include cognitive computing capabilities in your applications with the help of IBM Watson

The Language and Vision speech, Data, Language APIs. Watson APIs are provided by IBM Bluemix that is the cloud-based platform as service (PaaS) created by IBM.

These Watson APIs are available at present:

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– Conversation

Conversion of Documents

– Translator of Languages

Language Classifier : Natural Language Classifier

– Natural Language Understanding Bellsouth email database providers

Personality Insights

Retrieve and Rank

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– Tone Analyzer Speech:

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text to speech vision

— Visual Recognition Information Insights

Discovery – Discovery

Discovery News Discovery News

2.2.2 IBM Watson applied to businesses, industries as well as science

Nowadays, businesses are transforming their operations through the use of data. In this way, businesses and organisations can anticipate with greater accuracy what their clients will be looking for as well as where congestions in traffic will be, or the way in which diseases develop.

With the introduction of cognitive-based products, IBM is enabling organizations across all industries and any size to implement innovative cognitive features into their operations.

There are more than nine billion smart devices are operating around the globe today and generate 2.5 quintillion bytes of fresh information every day. Understanding the data contained in smart devices is creating a massive market that is anticipated to be worth $1.7 trillion in 20201.

Cognitive computing is used across a variety of sectors, helping scientists identify promising treatment avenues or helping lawyers discover connections between different cases.

Watson is a specialist in the field to help you solve the toughest problems. For more information, check out Watson Products and APIs.

The following list outlines the areas in which Watson can be used to address real problems across a variety of industries, companies, and sciences:

IBM Watson Commerce

This is the IBM research paper Commerce during the Cognitive Era: Unleash the potential of Watson Commerce

to improve customer engagement, increase customer loyalty and boost growth, says: “Watson Commerce combines business experience with world-class solutions that are infused with cognitive capabilities. This gives experts in the field of commerce the capability to provide constant, precise, and customized experiences that customers would like and appreciate. Watson Commerce understands, reasons and is able to draw on the collective wisdom of the company and business trends. Brands gain instant insight into customer behavior and performance and can take timely educated decisions and quantifiable actions to take advantage of opportunities in the market before their competitors take advantage of them.”

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For more information, go through the complete IBM Watson Commerce Point of View. IBM Watson Education

Through Watson Education, educators and students can work together to create an individual learning experience of their lifetime learning. Apps and solutions that are equipped with cognitive capabilities and analytics aid teachers in understanding their students in a holistic way. They are able to shape and guide individualized learning that is tailored to each individual student, supported by technology that recognizes the reasons behind learning, why and communicates. Students are empowered by “unique for me” tools that help make learning more enjoyable and more relevant. Teachers and students form bonds and hubs of collaboration, sharing and advancing their knowledge to create new opportunities in education which help to create an improved future for all. Explore the possibilities of transformative learning with Watson. buy  Bellsouth database online
IBM Watson Financial Services

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Make use of the cognitive capabilities of Watson to increase customer engagement, and to create experiences that are new, and improve the process of managing regulatory compliance. By incorporating Watson cognitive capabilities lets you transcend traditional rules-based policy and demographic views to gain a more thorough understanding of customer profitability as well as their preferences and needs throughout their lifecycle to provide innovative, personalized services and experiences. Watson will also assist you to change your approach to managing compliance and risk to ensure that you keep up with the ever-changing regulatory landscape. Visit Watson Financial Services. buy  Bellsouth targeted email list

IBM Watson Health

Watson Health cognitive systems understand how to reason, think, and learn, helping them translate information into knowledge that can help guide better decision-making.

Science is overflowing with information. When Watson makes connections that might not have been thought of previously, Watson can generate new ideas that accelerate the process of identifying patients to clinical studies, pinpoint promising research areas, and stimulate exploration.

IBM Watson Health solutions can aid to address the following problems:

– Improve performance: Drive the organization to change.

Engage customers: Increase the chances of your customers’ being successful.

– Ensure effective care Help your team with information and insight to improve their decision-making.

Manage health of the population Improve the patient experience while reducing costs.

IBM as well as IBM Business Partners IBM Business Partners and IBM are developing solutions for individuals and large health populations that will benefit from sharing and use their knowledge in real-time.

Watch some of the videos below:

The introduction of IBM Watson Health provides an overview of the service.

The Collaboration for Advancement of Genomic Medicine announces an collaboration between researchers from the New York Genome Center (NYGC) and IBM to bring about a brand new era in genomic medicine by making application of the IBM Watson the cognitive technology.

Search for ALS treatments demonstrates the ways in which Barrow Neurological Institute is using Watson to direct its research efforts on the most promising areas.

Also, see IBM Watson Health.

Watson Health provides these specific solutions:

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— IBM Watson for Genomics

On average 75% of patients with cancer do not be able to respond to one particular drug within a specific group of drugs. Oncologists are increasingly relying on genomics for insights into more precise and possibly beneficial treatments. Clinicians across the US are able to provide precise treatments to patients with cancer. Learn what Watson for Genomics helps doctors provide patients with hope with new perspectives by giving doctors feel confident in their personalized treatment strategies. 

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— IBM Watson for Drug Discovery

Watson for Drug Discovery helps researchers discover new drug targets and novel indications for the existing drugs. It can aid researchers discover new connections and create new therapies that could provide new discoveries and scientific breakthroughs ahead of the pack. Check out IBM Watson for Drug Discovery. buy Bellsouth targeted email list
— IBM Watson Health Patient Engagement

Watson Health Patient Engagement helps to identify patients who are suffering from care gaps and automates personalized interventions that keep patients engaged and assisting them in managing their own health care in between visits. Learn more about Engage consumers and patients.

” IBM Watson for Oncology

The quantity of data and research available to inform the treatment of cancer is increasing exponentially. But the time healthcare teams need to delve into this information and find insights that are specific to every patient’s specific needs that could improve the outcome of treatment is less than ever. Watson for Oncology helps physicians quickly locate important information in the medical records of a patient and present relevant articles. They also look into treatment options to minimize unnecessary variation in treatment.

With Watson for oncology , your company can save time on for information and spend more time taking care of patients. Watson can offer clinicians treatments that are based on evidence and based on the expertise of Memorial Sloan Kettering physicans.
Check out Watson to learn more about Oncology.

” IBM Watson Care Manager

Watson Care Manager provides personalized care plans, automated management workflows, as well as integrated patient engagement features to aid in the creation of more informed actions plans. Learn more about Watson Care Manager.

Watson in the insurance industry.

The regulations for insurance and the policies they apply to are always changing. In handling claims, you need the expertise of highly experienced assessors who must read thousands of documents written notes, handwritten notes, blogs as well as other sources in order to keep up with changes in regulations and make sound decisions. To make matters more difficult the amount of variance in coverage of members makes obtaining assistance, such as training the new staff, even more challenging.

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Insurance companies are training Watson to recognize the interactions with rules, processes, and logic that could be applied to policies. Watson is able to analyse the structure and unstructured information as well as refer to appropriate policies and documents and make in-depth suggestions. This will assist employees to determine if claims are eligible and the percentage of the claim must be compensated. With Watson employees, they can make better choices and obtain better results in a shorter time. Belgium Phone Number Database

As the insurance industry becomes digital, smart customers expect insurance companies to recognize them ahead of time in the event of a call. actively interact with them and offer them prompt advice and direction. Cognitive computing provides an opportunity to connect with the customers and develop a greater understanding of their needs, from risk analysis , to fraud, to security. Employers make better decisions quicker. 

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IBM Watson Internet of Things (IoT) purchase  Bellsouth email lists

Internet of Things is changing the way companies operate and how people interact with physical objects. Cognitive IoT can comprehend every kind of data. It is able to choose its own sources of data and determine what patterns and relationships to be aware of. It makes use of the power of machine learning, as well as sophisticated processing techniques to organize the data and produce insight.

Cognitive IoTs can develop and grow through self-correction, learning and adaptation.

Watch some of the videos below:

How it works: Internet of Things shows how IoT allows us to access the data of hundreds of thousands of sensors.

– Industry 4.0 Meets Watson IoT illustrates the way IBM Watson IoT(tm) helps companies connect to all kinds of devices, from appliances for personal use to production equipment.
IBM Watson Cognitive Video

IBM Watson produces videos, using its visual recognition capabilities and tone analyzer. It analyses and learns from other videos to produce new compositions that are based on the requirements of the user.

Visual Recognition understands the content of images. Visual concepts label the image and locate human faces. find approximate age and gender and search for similar images within their collection.

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The IBM Watson Visual Recognition video provides information on visual recognition and the way it works.

Utilizing the an experimental Watson APIs and machine-learning techniques, scientists from IBM Research in collaboration with 20th Century Fox created the first movie trailer with a cognitive system for the film “Morgan.” This system looked over hundreds of thriller and horror trailers for movies. After analyzing what keeps viewers at the edge of their seat, the system recommended the top 10 most effective moments to use for the trailer of the film and the IBM filmmaker was able to edit and arrange.

Check out the following resources:

— IBM Research Takes Watson to Hollywood with the first “Cognitive Film Trailer” on the IBM THINK(r) Blog.

— Morgan film trailer to find out more about collaborations with IBM Research and 20th Century Fox in the creation of the trailer for the movie that uses cognitive technology. 

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Cognitive systems are able to simplify the work of security analysts by providing human-centric information like sophisticated visualization, as well as interactive analysis of vulnerabilities. Cognitive systems can detect flaws and inconsistencies and can provide evidence-based decision-making.

Cognitive systems shine illuminated view of the data that was previously invisible to security measures of an organization, to reveal new insights, patterns and previously unknown security contexts. Cognitive systems process data, build up their knowledge base from every interaction, and weigh the probabilities on the basis of a depth of knowledge, and then consider relevant variables that can help you make decisions. Therefore, cognitive security can help to lower the cost and difficulty of battling cybercrime. Watch these videos:

” Watson to Cyber Security in Action describes the way Watson aids an analyst in security analyze a specific incident to discover new patterns and context for security previously undiscovered.

The teaching of Watson how to use the Language of Security illustrates the method of training Watson to be a good candidate for Cyber Security by defining the security language, highlighting the relevant documents, and then manually connecting the terms. Watson will then by itself master the language and the connections necessary to respond to questions from security experts.

For more details, visit Watson to learn more about Cyber Security.
Watson uses cases to help Watson.

Some examples of ways Watson is utilized in various sectors, sciences, and applications that tackle real problems are provided in 2.2.2, “IBM Watson applied to business, industries as well as science” on page 18.

This section provides two use instances of companies that successfully implemented cognitive solutions built upon IBM Watson technology, to create new types of customer interaction, create more efficient solutions and offerings, enhance processes and operations, take decision-based on data, and tap into knowledge.

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OmniEarth The Cognitive Computing displays water consumption patterns based on Earth images

OmniEarth Inc. builds scalable solutions to process, clarify, and fusing huge amounts of aerial and satellite images along with different data sources.
The problem

Conservation of water is a major issue in California and is prone to drought-related conditions. In April of 2015 the state ordered a 25% decrease in water usage in cities over the course that lasted 10 months. It was essential to know patterns of usage of water and how they are related to weather patterns and the ability to establish realistic budgets for water usage.

The state’s water utilities in order to meet this goal, it requires more than asking consumers to reduce their use of water. The utility needed to have a solid understanding of patterns in water use and the areas where water was used up, how much the water they could save by what means and what actions to take, as well as where to target the outreach to consumers and educational efforts.

Traditionally, state government agencies come up with these figures by calculating averages for an entire year or over a long period, ignoring variations in microclimates as well as other local conditions. 

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Utilizing its own algorithms and aerial photos, OmniEarth was confident it could aid the state in understanding the consumption of water at an unimaginable detail. But, the company required an approach that could scale its capacity to process an enormous amount of images, allowing the unstructured data inside and making it accessible to analysis. purchase  Bellsouth email lists

The solution

OmniEarth makes use of IBM Watson to identify topographical aspects in satellite imagery providing water authorities with insight into the changing patterns of water consumption as well as weather. OmniEarth utilizes the software to create methods to conserve water for areas suffering from drought. OmniEarth helps water companies in California’s State of California to analyze aerial photographs to track the amount of water consumed on each parcel of land throughout the state.

Since a cognitive system is able to process more images than manual processes The solution provides an image of water usage that looks more like a motion picture of the surrounding landscape than just isolated images. It is trained by the system to recognize crucial features that signify the usage of water like swimming pools, turf lawns, irrigation areas as well as agricultural areas. It converts images that are unstructured into consistent data OmniEarth’s exclusive algorithms can analyse.

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The system blends aerial photos with water consumption and weather data to determine parcels of land which exceed their consumption limits. They then target on their efforts to help the water companies in California educate consumers on how specific actions can result in cost savings as well as environmental benefits. The detailed, localized data aids
Individual water utilities set realistic demand and budgets for water instead of basing their decisions on blanket restrictions based upon broad state averages.

OmniEarth utilizes IBM Watson to develop water conservation strategies for drought-stricken regions. OmniEarth assists water utilities in California’s State of California to analyze aerial photos to measure the amount of water consumed on each parcel of land in the state.

OmniEarth utilizes an IBM Watson Visual Recognition service via IBM Watson Developer Cloud to categorize the physical features which are captured in satellite and aerial images (Figure 2-7)) and then integrates the resulting data into its own analysis tools.

The software allows OmniEarth to analyze aerial photos forty times quicker than possible using the manual processes, as well as the capability of processing 150,000 photos in just 12 minutes instead of hours. The solution also offers OmniEarth substantially more capacity in analysis of terrain on a huge scale, which creates new opportunities for business across the globe. Furthermore the cognitive technology offers the business as well as state of California State of California deeper insights into satellite images, as well as the capability of analyzing the information at a greater scale.

The insights derived from data have the ability to enhance the effectiveness of regulatory response and more efficient, but also to improve forecasting of demand and other crucial drought-related decisions.
What is the reason that makes the solution cognizant?

Here are some important aspects:

Game-changing result

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Before and after impact

Prior to this, OmniEarth was required to translate and label aerial photos using a manual and slow process, which took hours to create one batch. Through teaching a cognitive system to read the images OmniEarth dramatically speeded up the process, making it possible to analyse greater quantities of data and to understand the earth’s terrain at a higher level.

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Systems of engagement, discovery or decision-making

The solution offers a system of cognitive decision-making that allows California’s State of California to extract patterns from images with sufficient depth to concentrate on particular parcels of land, and fast enough that the information can guide daily actions.
Woodside Energy: Employees instantly have access to over 30 years of knowledge

Woodside Energy is Australia’s largest publicly traded exploration and production firm and one of the country’s most profitable explorers, developers and producers of gas and oil.

The problem

A few Woodside employees work at off-shore facilities for deployments of two weeks. Their work requires extreme accuracy, and the conditions must be flawless prior to any action being performed. Woodside employees look at every aspect including wind and weather, to tidal flows, the patterns of animal migration. They are reliant on the context of history and procedural knowledge to make adjustments. They needed the ability to significantly simplify the process by which engineers and technical personnel could search, analyze and draw lessons from the existing corpus of knowledge in the subject area throughout the organization.

If you can find information on specific technical issues for instance, how often it is necessary to maintain specific equipment in operation, Woodside could make better choices while avoiding “reinventing wheels” for time-consuming and costly tasks.

The solution

Together together with Watson, Woodside Energy built an application that was customized to allow its employees to get the most precise solutions to their queries, even for remote gas and oil facilities. Watson took in approximately 38,000 Woodside documents which would require the average person five years to digest.

The knowledge corpus grew into Woodside’s cognitive service that is powered through Watson which is a part of IBM Cloud. Woodside employees are able to ask questions using natural language, such as “What is the weight limit of a helicopter that will land at the base?” Watson will respond in a manner that is appropriate.
Woodside Energy worked with Watson on IBM Cloud by following five steps that are easy to follow:

More than 38,000 Woodside documents were loaded into Watson on IBM Cloud that is equivalent to 30 years of practical engineering knowledge.

Based on this data, Watson considers historical context and provides procedural details on the operation, equipment and weather conditions, as well as tidal flows and more.

Employees can ask Watson an inquiry via natural-language, like “What is the weight limit of a helicopter when it lands at the base?”

Watson can determine results from earlier tests and suggest the best course of action.

Employees are able to generate insight and make adjustments according to the information.


Today, Woodside employees from all over the world have access to 30 years of knowledge and find technical information to make faster, more intelligent, more informed decisions. Bellsouth email id list

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Watson reduced the amount of time spent looking for expert information by 75 percent. In an industry with high risk every step using an offshore platform is a waste of both time and money. By utilizing Watson using IBM Cloud, Woodside can gain a return on their investment as well as to ensure that employees are

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What makes the solution cognizant?

The Watson APIs listed below are utilized to implement this service:

Natural Language Classifier Conversation

Retrieve and the Rank

Watson took in Woodside’s complete 30 year collection of engineering documents and is constantly learning from new experiences. Watson’s employees can interact with Watson using natural languages. Based on the responses Watson provides, employees are able to gain new knowledge and make better choices.

Watson demonstrations

Do you want to test Watson? Here are some examples which give you the chance to interact with the applications developed using Watson services.

The application for your Celebrity Match application

This demonstration utilizes Watson Personality Insights and Insights for Twitter services:

Audio Analysis Application

This video uses the Concepts feature of the Watson Natural Language Understanding Service in conjunction to the Speech to Text service in order to provide an analysis of the terms that are used within YouTube videos:

Conversation service

Watson Conversation service Watson Conversation service, you’ll be able to comprehend what people are saying, and then respond in natural words:

In this example, imagine that you’re in the driver’s chair and Watson will be your driver. Watson will be able to recognize your commands and react in a manner that is appropriate. For instance, you can ask Watson “where is the closest restaurant” or saying “turn off the light” to see if Watson can comprehend your commands.

The demonstration is focused on a specific set automobile capabilities. Click the What can I ask ? button within the demonstration to show the list of subjects that Watson is aware of.
Visual Recognition service

Visual Recognition uses deep learning algorithms to analyse images that can provide insight into the content of your images. It is possible to organize your image libraries and analyze an image, and design custom classifiers which are customized to your specific needs:

The demonstration can be viewed in two ways:

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You can try classifying an image. You can choose to either select an image that will be shown or you provide a URL to the photo (image URL). Visual recognition analyzes your choice (with the classifiers that are available) to categorize the image and give you ratings of the results.


Train can be used to build an example classifier. To build a trial classifier, choose at least three classes from examples of image bundles or supply your own bundles of images (at at least two positive bundles of images and, if you find it useful, you can include a negative bundle) as per the instructions. Choose the option to train your classifier. Once the classification is complete it is possible to apply the classifier to images by using the Try feature.

The Jeopardy! challenge

The Jeopardy! television show in the US premiered for the first time in 1964. The show has three contestants that solve general knowledge questions. The format of questions and answers makes Jeopardy! against other trivia games.

When Jeopardy! starts the game board displays six categories. Each category is comprised of the same number of questions (clues) So, each round of Jeopardy! can be as long as 30 questions. Each answer comes with a dollar value (US dollars). The dollar value rises as the questions get more difficult.

In the course of the game, the host reads out the clue and the player who first presses the buzzer (in less than three seconds) is able to choose the correct answer. In the event that the response is right the contestant is credited with an amount that is associated with the answer and then moves to the next group to receive another query (clue). If the contestant has answered the incorrectly, that contestant is liable to lose the same amount and two contestants are attempting to find the correct answer. It continues to play until there are no categories left or the time is up. When the show is over, show, the participant with the largest dollar amount takes home the prize. 

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A pivotal event in Jeopardy! historical records was Jeopardy! Jeopardy! and Watson match, in which Watson’s IBM Watson computer took on the two most successful Jeopardy! winners (Ken Jennings, and Brad Rutter). Bellsouth quality email

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In the early 2010s, Watson (in the IBM Lab) was capable of beating humans on Jeopardy! contestants regularly. In the beginning of 2011 Watson played against two of the most prestigious Jeopardy! players (Ken as well as Brad). After struggling for a while and making mistakes on a few questions, Watson started answering all the questions correctly. He went in a winning run, and eventually won the game.

In the second day of the race, Ken was in the leading. There was a chance that he might beat Watson. Ken picked an entry in the Legalese category. Watson did the correct answer and then picked a second question that had the “Daily Double” value. The term “Daily Double” Daily Double means that if the contestant is able to answer correctly and correctly, the contestant is awarded twice the amount of money in addition to their current earnings. Watson gave the correct answer, which placed Watson ahead of the competition and allowed Watson to be the winner of the contest.

The Watson project wasn’t focused on playing Jeopardy! It was about conducting research on natural machine learning and understanding of language before taking the knowledge then applying it to solving issues that people are really concerned about. This is what the Jeopardy! game was only the beginning of IBM investing in the development of cognitive computing. In the present, Watson technology is already being used to solve real-world issues in healthcare, medicine financial support, technical support government, and various other industries, companies and scientific fields.

With the capacity of Watson to sort through and further analyze massive amounts of information, and then deliver it in a timely manner Learn from previous experiences, and converse with users using their own natural languages, Watson can be utilized in a myriad of applications across the globe.

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This section gives an outline of questions-answering (QA) system that was created to allow Watson in order to participate in the Jeopardy! game. The implementation is called DeepQA. DeepQA is a software structure that allows for deep content analysis and reasoning based on evidence. It is a powerful tool that makes use of advanced natural processing of language (NLP) as well as data retrieval and reasoning as well as machine-learning. The fundamental premise behind the research method which led to DeepQA is that real intelligence will be born through the creation and integration of a variety of algorithms that look at data from different angles. The effectiveness of the Watson answering system for questions is due on the integration a range of artificial intelligence techniques. 

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The DeepQA design approach views the problem of answering questions automatically as an enormously parallel hypothesis-generation and evaluation process. DeepQA is an application that can generate an array of possibilities and in each case, it creates an amount of confidence through

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analysing, gathering and evaluating evidence that is based on data available. 

The fundamental computational principle that is backed through DeepQA architecture is that DeepQA architecture is summarized as follows: following statements:

Consider and explore various interpretations of the issue. Find a myriad of possible solutions or hypotheses.

Examine and collect a variety of competing evidence-based pathways that may either support or debunk those hypotheses.

DeepQA’s DeepQA architecture was developed in a manner that is adaptable and allows for the integration of various of technologies, such as machine learning and natural language processing reasoning, knowledge representation as well as other AI technologies. Bellsouth quality email

Each part of the system is based on assumptions regarding what the question could mean and what the possible answer could be, and why it could be the correct answer. DeepQA is built in conjunction with an architecture called Unstructured Information Management Architecture (UIMA) which is designed to allow the interoperability and scaling-out of deep analytics. Page 32, Figure 3, shows how to build the DeepQA structure at its higher scale. The remainder of this chapter offer more details on the various architecture roles.
The fundamental assumption behind the DeepQA structure is that answers and the evidence to support each answer are collected from both well-structured (knowledge base) and

Unstructured (text) information like in Figure 3. DeepQA analyzes hundreds of candidates (also known as hypotheses) and for each is able to generate

evidence through an extensive set of natural machine learning, language processing or reasoning techniques. These algorithms collect and weigh the evidence against both structured and unstructured content to decide the right conclusion with the highest degree of confidence.

The unstructured data utilized for the Jeopardy! system comprised a variety of

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ontologies, for example DBpedia.