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Cognitive computing

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1
ABSTRACT
Cognitive computing is the new wave of Artificial Intelligence (AI), relying on
traditional techniques based on...
2
1. INTRODUCTION
'Cognitive computing represents self-learning systems that utilize machine learning models to
mimic the ...
3
What are the features of a cognitive computing solution?
With the present state of cognitive computing, basic solutions ...
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Cognitive computing

  1. 1. 1 ABSTRACT Cognitive computing is the new wave of Artificial Intelligence (AI), relying on traditional techniques based on expert systems and also exploiting statistics and mathematical models. In particular, cognitive computing systems can be regarded as a “more human” artificial intelligence. In fact, they mimic human reasoning methodologies, showing special capabilities in dealing with uncertainties and in solving problems that typically entail computation consuming processes. Moreover, they can evolve, exploiting the accumulated experience to learn from the past, both from errors and from successful findings. From a theoretical point of view, cognitive computing could replace existing calculators in many fields of application but hardware requirements are still high, even if the cloud infrastructure, which is expected to uphold its rapid growth in the very next future, can support their diffusion and ease the penetration of such a novel variety of systems, fostering new services as well as changes in many settled paradigms. Cognitive Computing has emerged from the comprehension that there will most definitely arise instances where machines will outperform humans by a significant margin especially regarding scenarios which have high levels of complexity. Cognitive computing thrives on massive amounts of data, the more data we supply the more efficient and accurate its outputs are. They are designed to understand, optimize and perform with the data that has been provided. Almost every field is investing seriously in the ventured advances with cognitive computing. Computers that have the potential to think and adapt like humans will surely be a game changer. It is predicted that machines will most likely exceed human performance in the not so distant future. The initial programming of the machine needs to be spot on and the machine must indeed be able to pick up on the alterations and diversifications of the task assigned and make the necessary corrections. A supervising human must indeed be present during the initial phase to ensure the smooth and error-free working for the future. They must also make sure that the trends the machine picks up are by no means biased and do not raise any ethical or social issues. The advancement in Cognitive Computing will indeed be a boon to the implemented fields. The newly developed strategies help to safeguard against unintended consequences as well.
  2. 2. 2 1. INTRODUCTION 'Cognitive computing represents self-learning systems that utilize machine learning models to mimic the way brain works.' Eventually, this technology will facilitate the creation of automated IT models which are capable of solving problems without human assistance. The result is cognitive computing – a combination of cognitive science and computer science. Cognitive computing models provide a realistic roadmap to achieve artificial intelligence. Cognitive computing represents the third era of computing. In the first era, (19th century) Charles Babbage, also known as ‘father of the computer’ introduced the concept of a programmable computer. Used in the navigational calculation, his computer was designed to tabulate polynomial functions. The second era (1950) experienced digital programming computers such as ENIAC and ushered an era of modern computing and programmable systems. And now to cognitive computing which works on deep learning algorithms and big data analytics to provide insights. Thus the brain of a cognitive system is the neural network, the fundamental concept behind deep learning. The neural network is a system of hardware and software mimicked after the central nervous system of humans, to estimate functions that depend on the huge amount of unknown inputs.
  3. 3. 3 What are the features of a cognitive computing solution? With the present state of cognitive computing, basic solutions can play an excellent role of an assistant or virtual advisor. Siri, Google assistant, Cortana, and Alexa are good examples of personal assistants. In order to implement cognitive computing in commercial and widespread applications, Cognitive Computing Consortium has recommended the following features for the computing systems – 1. Adaptive They must learn as information changes, and as goals and requirements evolve. They must resolve ambiguity and tolerate unpredictability. They must be engineered to feed on dynamic data in real time or near-real time. 2. Interactive Similar to a brain, the cognitive solution must interact with all elements in the system – processor, devices, cloud services and user. Cognitive systems should interact bidirectionally. It should understand human input and provide relevant results using natural language processing and deep learning. Some intelligent chatbots such as Mitsuku have already achieved this feature. 3. Iterative and stateful They must aid in defining a problem by asking questions or finding additional source input if a problem statement is ambiguous or incomplete. They must 'remember' previous interactions in a process and return information that is suitable for the specific application at that point in time. 4. Contextual They must understand, identify, and extract contextual elements such as meaning, syntax, time, location, appropriate domain, regulations, user’s profile, process, task, and goal. They may draw on multiple sources of information, including both structured and unstructured digital information, as well as sensory inputs (visual, gestural, auditory, or sensor-provided). Cognitive computing is definitely the next step in computing started by automation. It sets a benchmark for computing systems to reach the level of the human brain. But it has some limitations as AI is difficult to apply in situations with a high level of uncertainty, rapid change or creative demands. The complexity of problem grows with the number of data
  4. 4. 4 sources. It is challenging to aggregate, integrate and analyse such unstructured data. A complex cognitive solution should have many technologies that coexist to give deep domain insights.Human thinking is beyond imagination. Can a computer develop such ability to think and reason without human intervention? This is something programming experts at IBM Watson are trying to achieve. Their goal is to simulate human thought process in a computerized model. The result is cognitive computing – a combination of cognitive science and computer science. Cognitive computing models provide a realistic roadmap to achieve artificial intelligence. “Cognitive computing represents self-learning systems that utilize machine learning models to mimic the way brain works.“ Eventually, this technology will facilitate the creation of automated IT models which are capable of solving problems without human assistance Cognition comes from the human brain. So what’s the brain of cognitive systems? Cognitive computing represents the third era of computing. In the first era, (19th century) Charles Babbage, also known as ‘father of the computer’ introduced the concept of a programmable computer. Used in the navigational calculation, his computer was designed to tabulate polynomial functions. The second era (1950) experienced digital programming computers such as ENIAC and ushered an era of modern computing and programmable systems. And now to cognitive computing which works on deep learning algorithms and big data analytics to provide insights. Thus the brain of a cognitive system is the neural network, fundamental concept behind deep learning.
  5. 5. 5 3. WORKING Cognitive computing systems use computerized models to simulate the human cognition process to find solutions in complex situations where the answers may be ambiguous and uncertain. While the term cognitive computing is often used interchangeably with artificial intelligence (AI), the phrase is closely associated with IBM's cognitive computer system, Watson. Cognitive computing overlaps with AI and involves many of the same underlying technologies to power cognitive applications, including expert systems, neural networks, robotics and virtual reality (VR). How cognitive computing works ? Cognitive computing systems can synthesize data from various information sources, while weighing context and conflicting evidence to suggest the best possible answers. To achieve this, cognitive systems include self-learning technologies that use data mining, pattern recognition and natural language processing (NLP) to mimic the way the human brain works. Using computer systems to solve the types of problems that humans are typically tasked with requires vast amounts of structured and unstructured data, fed to machine learning algorithms. Over time, cognitive systems are able to refine the way they identify patterns and the way they process data to become capable of anticipating new problems and model possible solutions. How cognitive computing differs from AI ? Cognitive computing is often used interchangeably with AI -- the umbrella term for technologies that rely on data to make decisions. But there are nuances between the two terms, which can be found within their purposes and applications. AI technologies include -- but aren't limited to -- machine learning, neural networks, NLP and deep learning. With AI systems, data is fed into the algorithm over a long period of time so that the systems learn variables and can predict outcomes. Applications based on AI include
  6. 6. 6 intelligent assistants, such as Amazon's Alexa or Apple's Siri, and driverless cars are based on AI. The biggest difference between cognitive computing and AI is its purpose. The term cognitive computing is typically used to describe AI systems that aim to simulate human thought. Human cognition involves real-time analysis of environment, context and intent, among many other variables that inform a person's ability to solve problems. A number of AI technologies are required for a computer system to build cognitive models that mimic human thought processes, including machine learning, deep learning, neural networks, NLP and sentiment analysis.
  7. 7. 7 In general, cognitive computing is used to assist humans in their decision-making process. Some examples of cognitive computing applications include supporting medical doctors in their treatment of disease. IBM Watson for Oncology, for example, has been used at Memorial Sloan Kettering Cancer Centre to provide oncologists with evidence-based treatment options for cancer patients. When medical staff input questions, Watson generates a list of hypotheses and offers treatment options for doctors to consider. Where AI relies on algorithms to solve a problem or to identify patterns hidden in data, cognitive computing systems have the loftier goal of creating algorithms that mimic the human brain's reasoning process to solve an array of problems as the data and the problems change.
  8. 8. 8 4. MAJOR PLAYERS IN COGNITIVE COMPUTING Present cognitive computing landscape is dominated by larger players – IBM, Microsoft, and Google. IBM being the pioneer of this technology has invested $26 billion dollars in big data and analytics and now spends close to one-third of its R&D budget in developing cognitive computing technology. Many other companies and organizations are developing products and services that are as good, if not better than Watson. IBM and Google have acquired some of the rivals and the market is moving towards consolidation. Let’s take a look at the prominent players in this market – 1. IBM Watson Originally Watson is an IBM supercomputer that combines artificial intelligence (AI) and sophisticated analytical software for optimal performance as a “question answering” machine famously featured in show ‘Jeopardy’. Now it uses a set of transformational technologies such as natural language processing, image recognition, text analytics and virtual agents. IBM Watson leverages deep content analysis and evidence-based reasoning. Combined with massive probabilistic processing techniques, Watson can improve decision making, reduce cost and optimize outcomes. 2. Microsoft Cognitive Services Microsoft cognitive services previously known as Project Oxford are a set of APIs, SDKs and cognitive services which the developers can use to make their applications more intelligent. With Cognitive Services, developers can easily add intelligent features – such as emotion and sentiment detection, vision and speech recognition, knowledge, search and language understanding – into their applications. We have made a chatbot ‘Specter’ using Microsoft Bot Framework to improve the efficiency of our marketing team.
  9. 9. 9 3. Google DeepMind DeepMind was acquired by Google in 2014 and considered to be a leading player in AI research. The team consists of many renowned experts in the field of deep neural networks, reinforcement learning, and systems neuroscience-inspired models. DeepMind became popular with AlphaGo, a narrow AI to play Go, a Chinese strategy board game for two players. AlphaGo became the first AI program to beat a professional human player in October 2015, on a full-sized board. 4. CognitiveScale CognitiveScale founded by former members of IBM Watson team provides cognitive cloud software for enterprises. Cognitive Scale’s augmented intelligence platform delivers insights- as-a-service and accelerates the creation of cognitive applications in healthcare, retail, travel, and financial services. They help businesses make sense from ‘dark data’ – messy, disparate, first and third party data and drive actionable insights and continuous learning. 5. SparkCognition SparkCognition is an Austin-based startup formed in 2014. SparkCognition develops AI- Powered cyber-physical software for the safety, security, and reliability of IT, OT, and the IIoT. The technology is more inclined towards manufacturing. It is capable of harnessing real-time sensor data and learning from it continuously, allowing for more accurate risk mitigation and prevention policies to intervene and avert disasters. Watson and DeepMind’s success has inspired other companies to develop cognitive platforms using open source tools. Other leading technology companies like Qualcomm and Intel are taking cautious steps to include cognitive solutions for specialized industries. Uber has established a research arm dedicated to AI and machine learning and acquired Geometric Intelligence and Otto. Otto is an autonomous truck and transportation startup and Geometric Intelligence is focused on generating insights from fewer data using machine learning.
  10. 10. 10 Gamalon has developed an AI technique using Bayesian Program Synthesis. It requires only a few pieces to train the system to achieve same levels of accuracy as neural networks. Healthcare is the most popular sector to adopt cognitive solutions. Startups such as Lumiata and Enlitic have developed small and powerful analytic solutions that assist healthcare providers in diagnosis and prediction of disease conditions. Other companies in this market are Cisco cognitive threat analytics, Customer Matrix, Digital Reasoning and Narrative Science.
  11. 11. 11 5. APPLICATIONS There are many possible applications of cognitive computing. It can handle a very minute activity of routine nature to a complex set of tasks involving logical reasoning. Here are some possible applications of cognitive computing in business: 1. Chatbots: Chatbots are programs that can simulate a human conversation by understanding the communication in a contextual sense. To make this possible a machine learning technique called natural language processing is used. Natural language processing allows programs to take inputs from humans (voice or text), analyze it and then provide logical answers. Cognitive computing enables chatbots to have a certain level of intelligence in communication. Like understanding user’s needs based on past communication, giving suggestions, etc. 2. Sentiment analysis:
  12. 12. 12 Sentiment analysis is the science of understanding emotions conveyed in a communication. While it easy for humans to understand tone, intent etc. in a conversation, it is far more complicated for machines. To enable machines to understand human communication you need to feed training data of human conversations and then analyze the accuracy of the analysis. Sentiment analysis is popularly used to analyze social media communications like tweets, comments, reviews, complaints etc. 3. Face detection: Face detection is the advanced level of image analysis. A cognitive system uses data like structure, contours, eye color etc. of the face to differentiate it from others. Once a facial image is generated, it can be used to identify the face from an image or video. While traditionally it used to be done using 2D images now it can also be done using 3D sensors which account for greater accuracy. This can be used in security systems like for a locker or even mobile phone. 4. Risk assessment: Risk management in financial services involves the analyst going through market trends, historical data etc. to predict the uncertainty involved in an investment. But this is analysis is not only related to data but also on trends, gut feel, behavior analytics etc. Thus it is both an art and a science. Big data analysis (i.e. analysis of past trends alone) is not sufficient to do a risk assessment. Due to the intuition and experience involved in predicting market future, it is necessary to make algorithms intelligent. Cognitive computing helps combine behavioral data
  13. 13. 13 and market trends to generate insights. These can then be evaluated by experienced analysts for further analysis and predictions. 5. Fraud detection: Fraud detection is another application of cognitive computing in finance. It is basically a type of anomaly detection. The goal of fraud detection is to identify transactions which don’t seem to be normal (anomalies). This also requires programs to analyze past data to understand the parameters to be used for judging a transaction. A range of data analysis techniques like Logistic regression, Decision tree, Random Forest, Clustering etc. can be used to detect anomalies.
  14. 14. 14 6. ADVANTAGES OF COGNITIVE COMPUTING In the field of process automation, the modern computing system is set to revolutionize the current and legacy systems. According to Gartner, cognitive computing will disrupt the digital sphere unlike any other technology introduced in the last 20 years. By having the ability to analyze and process large amounts of volumetric data, cognitive computing helps in employing a computing system for relevant real-life system. Cognitive computing has a host of benefits including the following: ● Accurate Data Analysis : Cognitive systems are highly-efficient in collecting, juxtaposing and cross-referencing information to analyze a situation effectively. If we take the case of the healthcare industry, cognitive systems such as IBM Watson helps physicians to collect and analyze data from various sources such as previous medical reports, medical journals, diagnostic tools & past data from the medical fraternity thereby assisting physicians in providing a data-backed treatment recommendation that benefits both the patient as well as the doctor. Instead of replacing doctors, cognitive computing employs robotic process automation to speed up the data analysis. ● Leaner & More Efficient Business Processes : Cognitive computing can analyze emerging patterns, spot business opportunities and take care of critical process-centric issues in real time. By examining a vast amount of data, a cognitive computing system such as Watson can simplify processes, reduce risk and pivot according to changing circumstances. While this prepares businesses in building a proper response to uncontrollable factors, at the same time it helps to create lean business processes. ● Improved Customer Interaction : The technology can be used to enhance customer interactions with the help of robotic process automation. Robots can provide contextual information to customers without needing to interact with other staff members. As cognitive computing makes it possible to provide only
  15. 15. 15 relevant, contextual and valuable information to the customers, it improves customer experience, thus making customers satisfied and much more engaged with a business.
  16. 16. 16 7. LIMITATIONS OF COGNITIVE COMPUTING ● Limited analysis of risk The cognitive systems fail at analyzing the risk which is missing in the unstructured data. This includes socio-economic factors, culture, political environments, and people. For example, a predictive model discovers a location for oil exploration. But if the country is undergoing a change in government, the cognitive model should take this factor into consideration. Thus human intervention is necessary for complete risk analysis and final decision making. ● Meticulous training process Initially, the cognitive systems need training data to completely understand the process and improve. The laborious process of training cognitive systems is most likely the reason for its slow adoption. WellPoint’s financial management is facing a similar situation with IBM Watson. The process of training Watson for use by the insurer includes reviewing the text on every medical policy with IBM engineers. The nursing staff keeps feeding cases until the system completely understands a particular medical condition. Moreover, the complex and expensive process of using cognitive systems makes it even worse. ● More intelligence augmentation rather than artificial intelligence The scope of present cognitive technology is limited to engagement and decision. Cognitive computing systems are most effective as assistants which are more like intelligence augmentation instead of artificial intelligence. It supplements human thinking and analysis but depends on humans to take the critical decisions. Smart assistants and chatbots are good examples. Rather than enterprise-wide adoption, such specialized projects are an effective way for businesses to start using cognitive systems. Cognitive computing is definitely the next step in computing started by automation. It sets a benchmark for computing systems to reach the level of the human brain. But it has some limitations which make AI difficult to apply in situations with a high level of uncertainty, rapid change or creative demands. The complexity of problem grows with the number of data sources. It is challenging to aggregate, integrate and analyze such unstructured data. A
  17. 17. 17 complex cognitive solution should have many technologies that coexist to give deep domain insights. Thus, besides AI, ML and NLP, technologies such as NoSQL, Hadoop, Elasticsearch, Kafka, Spark etc should form a part of the cognitive system. This complete solution would be capable of handling dynamic real-time data and static historical data. The enterprises looking to adopt cognitive solutions should start with a specific business segment. These segments should have strong business rules to guide the algorithms, and large volumes of data to train the machines.
  18. 18. 18 8. SCOPE OF COGNITIVE COMPUTING While computers have been faster at calculations and processing than humans for decades. But they have failed miserably to accomplish tasks that humans take for granted, like understanding the natural language or recognizing unique objects in an image. Thus cognitive technology makes such new class of problems computable. They can respond to complex situations characterized by ambiguity and have far-reaching impacts on our private lives, healthcare, business, etc. According to a study by the IBM Institute for Business Value, “Your Cognitive Future,” scope of cognitive computing consists of engagement, decision, and discovery. These 3 capabilities are related to ways people think and demonstrate their cognitive abilities in everyday life. 1. Engagement The cognitive systems have vast repositories of structured and unstructured data. These have the ability to develop deep domain insights and provide expert assistance. The models build by these systems include the contextual relationships between various entities in a system’s world that enable it to form hypotheses and arguments. These can reconcile ambiguous and even self-contradictory data. Thus these systems are able to engage in deep dialogue with humans. The chatbot technology is a good example of engagement model. Many of the AI chatbots are pre-trained with domain knowledge for quick adoption in different business- specific applications. 2. Decision A step ahead of engagement systems, these have decision-making capabilities. These systems are modelled using reinforcement learning. Decisions made by cognitive systems continually evolve based on new information, outcomes, and actions. Autonomous decision making depends on the ability to trace why the particular decision was made and change the confidence score of a systems response. A popular use case of this model is the use of IBM Watson in healthcare. The system can collate and analyze data of patient including his history and diagnosis. The solution bases recommendations on its ability to interpret the meaning and analyze queries in the context of complex medical data and natural language, including
  19. 19. 19 doctors’ notes, patient records, medical annotations and clinical feedback. As the solution learns, it becomes increasingly more accurate. Providing decision support capabilities and reducing paperwork allows clinicians to spend more time with patients. 3. Discovery Discovery is the most advanced scope of cognitive computing. Discovery involves finding insights and understanding vast amount of information and developing skills. These models are built on deep learning and unsupervised machine learning. With ever-increasing volumes of data, there is a clear need for systems that help exploit information more effectively than humans could on their own. While still in the early stages, some discovery capabilities have already emerged, and the value propositions for future applications are compelling. Cognitive Information Management (CIM) shell at Louisiana State University (LSU) is one of the cognitive solutions. The distributed intelligent agents in the model collects streaming data, like text and video, to create an interactive sensing, inspection, and visualization system that provides real-time monitoring and analysis. The CIM Shell not only sends an alert but reconfigures on the fly in order to isolate a critical event and fix the failure.
  20. 20. 20 9. CONCLUSION To sum up, Cognitive Computing doesn’t bring a drastic novelty into the AI and Big Data industry. Rather it urges digital solutions to meet human-centric requirements: act, think, and behave like a human in order to achieve maximum synergy from human-machine interaction. In this article, we tried to translate this broad and high-level concept into specific technological challenges and provide some practical recommendations on the way they can be addressed. We believe that soon every digital system will be measured on its cognitive abilities. Like User Experience was the next big step for improving application usability, Cognitive Computing will be a significant step towards digital humanism.

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