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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

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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Most consumer offerings are based on voice commands and the use of data from the Internet. A cognitive travel planner, for example, can include capabilities such as language identification, tradeoff analytics, and personality insights to make travel recommendations that best meet the customer’s needs. buy  Bellsouth database for marketing

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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Agriculture buy  Bellsouth database for marketing

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.

Manufacturing

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

Healthcare

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.