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Smart Machines Ibms Watson And The Era Of Cognitive Computing Pdf

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Cognitive Computing

Cognitive computing promises to be the next big advance in computing systems — but what is it? Despite the buzz, there is no firm consensus on what precisely constitutes cognitive computing, an advanced field of artificial intelligence. In this in-depth guide, we demystify and explain cognitive computing, including how it works, how it is used, and how you can deploy it in your business. Cognitive computing spans both hardware and software.

As such, cognitive computing brings together two quite different fields: cognitive science and computer science. Cognitive science studies the workings of the brain, while computer science involves the theory and design of computer systems.

Together, they attempt to create computer systems that mimic the cognitive functions of the human brain. Why would we want computer systems that can work like human brains? We live in a world that generates profuse amounts of information. Most estimates find that the digital universe is doubling in size as least every two years, with data expanding more than 50 times from to Organizing, manipulating, and making sense of that vast amount of data exceeds human capacity. Cognitive computers have a mission to assist us.

Currently, cognitive computing is helping doctors to diagnose disease, weathermen to predict storms, and retailers to gain insight into how customers behave. Some of the greatest demand for cognitive computing solutions comes from big data analytics, where the quantities of data surpass human ability to parse but offer profit-generating insights that cannot be ignored.

Lower-cost, cloud-based cognitive computing technologies are becoming more accessible, and aside from the established tech giants — such as IBM, Google, and Microsoft — a number of smaller players have been making moves to grab a piece of the still-young cognitive computing market.

Little wonder, then, that the global market for cognitive computing is expected to grow at an astronomical To understand what cognitive computing is designed to do, we can benefit from first understanding the quality it seeks to emulate: cognitive intelligence. Cognitive intelligence is the human ability to think in abstract terms to reason, plan, create solutions to problems, and learn.

It is not the same as emotional intelligence, which is, according to psychologists Peter Salovey and John D. Cognitive intelligence is consistent with psychologist Phillip L. Cognitive computing seeks to design computer systems that can perform cognitive processes in the same way that the human brain performs them. They sought to define the field by its capabilities and functions: as an advancement that would enable computer systems to handle complex human problems, which are dynamic, information-rich, sometimes conflicting, and require understanding of context.

In practice, it will make a new class of problems computable. As a result, cognitive computing promises a paradigm shift to computing systems that can mimic the sensory, processing, and responsive capacities of the human brain. Cognitive technology , another term for cognitive computing or cognitive systems, enables us to create systems that will acquire information, process it, and act upon it, learning as they are exposed to more data.

Data and reasoning can be linked with adaptive output for particular users or purposes. These systems can prescribe, suggest, teach, and entertain. The cognitive system helps the oncologist harness big data from past treatments. In simpler applications, software based on cognitive-computing techniques is already widely used, such as by chatbots to field customer queries or provide news.

Cognitive computing systems can redefine relationships between human beings and their digitally pervasive, data-rich environments. Natural language processing capabilities enable them to communicate with people using human language. One hallmark of a true cognitive computing system is its ability to generate thoughtful responses to input, rather than being limited to prescribed responses. Among the other capabilities of cognitive computing systems are pattern recognition classifying data inputs using a defined set of key features and data mining inferring knowledge from large sets of data.

Structured data is information in the form that computers handle so well; think of it as pieces of data arranged neatly into a spreadsheet, where the computer knows exactly what each cell contains. IBM breaks unstructured data down into four forms: language, speech, vision, and data insights. Unstructured data typically requires context in order to understand. Computing systems typically struggle to handle unstructured data. With the increasing amount of analyzable data in unstructured form, the inability to extract insights from it represents a big drawback.

Cognitive computing systems are adaptive , which means they learn new information and reasoning to keep on track to meet changing objectives. They can be designed to assimilate new data in real time or something close to it — this enables them to deal with unpredictability. Cognitive systems are also interactive — not only with the human beings whom they work for or with , but with other technology architectures and with human beings in the environment.

Further, they are iterative, which means they use data to help solve ambiguous or incomplete problems in a repeating cycle of analysis. They are also stateful , which means they remember previous interactions and can develop knowledge incrementally instead of requiring all previous information to be explicitly stated in each new interaction request.

Lastly, cognitive systems are typically contextual in the way they handle problems, which means they draw data from changing conditions, such as location, time, user profile, regulations, and goals to inform their reasoning and interactions.

In summary, cognitive computing is the creation of computer systems that are capable of solving problems that previously called for human or near-human intelligence. In order to assist humans or work autonomously, cognitive systems have to be self-learning, a task which they accomplish through the use of machine learning algorithms. These machine learning capabilities, along with reasoning and natural language processing, constitute the basis of artificial intelligence.

AI allows these systems to simulate human thought processes — and, when dealing with big data, to find patterns and see reason.

Cognitive computing systems work like the human brain. They are able to process asynchronous information, to adapt and respond to events, and to carry out multiple cognitive processes simultaneously to solve a specific problem. How are the concepts of cognitive computing and AI related, in theory and in practice? It depends on who you ask. Simplified, that means that while artificial intelligence powers machines that can do human tasks, cognitive computing goes several steps further to create machines that can actually think like humans.

Another key difference lies in the way artificial intelligence and cognitive computing are designed to tackle real-world problems. While artificial intelligence is meant to replace human intelligence, cognitive computing is meant to supplement and facilitate it.

But other experts see the distinction differently, and believe cognitive computing is really just a subfield of artificial intelligence. Under this theory, multiple AI systems mimic cognitive behaviors we associate with thinking — and even that may be going too far. That last opinion certainly has some evidence behind it. Because AI has gained some negative associations think of all the books and movies about robot uprisings , some in the artificial intelligence community believe that the term cognitive computing is a convenient euphemism for the latest wave of AI technologies, without all the baggage of the term AI.

Cognitive computing is being harnessed to drive data analytics in an especially powerful way. Cognitive technologies are especially suited to processing and analyzing large sets of unstructured data. Since unstructured data is hard to organize, data analytics based on conventional computing requires data to be manually tagged with metadata data that provides information about other data before a computer can conduct any useful analysis or insight generation.

Cognitive analytics solves this problem because they do not require unstructured data to be tagged with metadata. Pattern recognition capabilities transform unstructured data into a structured form, thereby unlocking it for analysis. Machine learning enables these analytics to adapt to different contexts, and NLP can make data insights understandable for human users.

Users pull the information they need from a computing system, navigate relationships between different pieces of information, and may request that information be pushed back to them. Cognitive computing reduces the need for information access protocols and helps the user determine what information is needed. Interactions are stateful, which goes a long way toward simplifying and improving the information pull process.

Analysis is driven by the user, with the computing system serving as a facilitator. Analysis may be initiated and guided by the user, while the cognitive computing system is capable of testing hypotheses, collecting evidence, and learning. In this model, the computing system is capable of reasoning with greater independence.

A cognitive computing system is capable of recommending intelligent courses of action. While users remain responsible for decision making, the eventual decisions are fed back into the cognitive computing system as learning material. So, how does cognitive computing work in the real world? Cognitive computing systems may rely on deep learning and neural networks. Deep learning, which we touched on earlier in this article, is a specific type of machine learning that is based on an architecture called a deep neural network.

The neural network, inspired by the architecture of neurons in the human brain, comprises systems of nodes — sometimes termed neurons — with weighted interconnections. A deep neural network includes multiple layers of neurons. Learning occurs as a process of updating the weights between these interconnections. One way of thinking about a neural network is to imagine it as a complex decision tree that the computer follows to arrive at an answer.

In deep learning, the learning takes place through a process called training. Training data is passed through the neural network, and the output generated by the network is compared to the correct output prepared by a human being.

If the outputs match rare at the beginning , the machine has done its job. If not, the neural network readjusts the weighting of its neural interconnections and tries again. As the neural network processes more and more training data — in the region of thousands of cases, if not more — it learns to generate output that more closely matches the human-generated output.

Preparing training data is an arduous process, but the big advantage of a trained neural network is that once it has learned to generate reliable outputs, it can tackle future cases at a greater speed than humans ever could, and its learning continues. The training is an investment that pays off over time, and machine learning researchers have come up with interesting ways to simplify the preparation of training data, such as by crowdsourcing it through services like Amazon Mechanical Turk.

Cognitive computing is the latest step in a long history of AI development. Early AI began as a kind of intelligent search function that resembled the ability to comb through decision trees in applications, such as simple games and learning through a reward-based system. The downside was that even simple games like tic-tac-toe have vastly intricate decision trees, and searching them quickly becomes unworkable. After AI-as-search came supervised and unsupervised learning algorithms called perceptrons and clustering algorithms , respectively.

These were followed by decision trees, which are predictive models that track a series of observations to their logical conclusions.

Decision trees were succeeded by rules-based systems that combined knowledge bases with rules to perform reasoning tasks and reach conclusions. The machine learning era of AI heralded increased complexity of neural networks enabled by algorithms, such as backpropagation, which allowed for error correction in multi-layered neural networks. The convolutional neural network CNN was one multi-layered neural network architecture used in image processing and video recognition; the long short-term memory LSTM permitted backward feeding of nodes for applications dealing with time-series data.

Whether you are a leader, technology professional, or manager, you may be wondering what cognitive computing can offer your organization. It provides better interdepartmental communication and feedback while working toward a common goal, customized products and services, and evidenced ways to manage risk. The benefits of cognitive computing generally fall within three areas where it portends paradigm shifts: discovery or insight , engagement, and decision making. Imagine being able to harness the natural language processing capabilities of cognitive computing to build highly detailed customer profiles including their posts on social media from unstructured data.

Watson (computer)

We are crossing a new frontier in the evolution of computing and entering the era of cognitive systems. In Smart Machines , John E. Kelly III, director of IBM Research, and Steve Hamm, a writer at IBM and a former business and technology journalist, introduce the fascinating world of "cognitive systems" to general audiences and provide a window into the future of computing. Cognitive systems promise to penetrate complexity and assist people and organizations in better decision making. They can help doctors evaluate and treat patients, augment the ways we see, anticipate major weather events, and contribute to smarter urban planning.

smart machines ibms watson and the era of cognitive computing pdf

short book, Smart Machines: IBM's Watson and the Era of Cognitive. Computing Computing. IBM's Watson computer created a sensation when it bested two.


Smart Machines: IBM's Watson and the Era of Cognitive Computing

Chatbots are the future of AI and much has been said about types of chatbots. But here's a list of types of chatbots for businesses that you must keep in mind! A brain-inspired chip to transform mobility and Internet of Things through sensory perception. Credit: IBM.

The concept of cognitive monitoring is defined. Some possible approaches to the construction of cognitive monitoring systems are considered and their generalized structure is described. The concept of a cognitive monitoring machine is introduced. A cognitive architecture approach to design monitoring systems that features the generation of on-demand architectures is proposed. The structure of a platform oriented to the use of this approach is described.

We are crossing a new frontier in the evolution of computing and entering the era of cognitive systems. Cognitive systems promise to penetrate complexity and assist peop Cognitive systems promise to penetrate complexity and assist people and organizations in better decision making. They can help doctors evaluate and treat patients, augment the ways we see, anticipate major weather events, and contribute to smarter urban planning.

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Search this site. Kelly III. Kelly III Synopsis: We are crossing a new frontier in the evolution of computing and entering the era of cognitive systems. In Smart Machines, John E. Kelly III, director of IBM Research, and Steve Hamm, a writer at IBM and a former business and technology journalist, introduce the fascinating world of cognitive systems to general audiences and provide a window into the future of computing.

Cognitive computing promises to be the next big advance in computing systems — but what is it? Despite the buzz, there is no firm consensus on what precisely constitutes cognitive computing, an advanced field of artificial intelligence. In this in-depth guide, we demystify and explain cognitive computing, including how it works, how it is used, and how you can deploy it in your business. Cognitive computing spans both hardware and software. As such, cognitive computing brings together two quite different fields: cognitive science and computer science.

Cognitive Technologies in Monitoring Management

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