Applications Of Cognitive Computing From A Research And Industry Side
We are increasingly talking about cognitive computing systems and this opens a new machine reflecting on both scientific research, R&D departments of industries, and products and applications as well. In the research, there is a growing interest around cognitive computing and related topics. In the accompanying, we report a few references, which cover distinctive parts of subjective figuring, not just the simple mechanical side. In this regard, present the system of a more extensive order grasping cognitive computing applications. They describe Cognitive Informatics as a transdisciplinary enquiry of computer science, information science, cognitive science and intelligence science that investigates into the internal information processing mechanisms and processes of the brain and natural intelligence, as well as their engineering applications in cognitive computing. such applications have a strict relationship with machine learning and this should contribute to the revamping of AI.
In fact, on one hand, machine learning can be described as a technique for detecting patterns and surfacing information and on other hand, it is mainly based on statistics and mathematical models. We can see machine learning at work every day in the technology used for searching the web, which provides quick and accurate result, and classification. From a practical point of view, machine learning uses pattern matching techniques to pull back information that’s relevant to the user, individualizing the search results.
We can say that the other side of cognitive computing is about making computers interfaces more user friendly and enabling them to understand more of what the user wants, by taking signals about what the user is trying to do and providing appropriate responses. AI includes all of these tools and solves a wide variety of problems, such as, e. g. , writing articles, driving autonomous vehicles, detecting fraud. At present, a lot of decisions that have typically been made by human beings can be made by means of AI and future research aims to enhancing such capabilities through further developments in the AI field. Adding more things we can say, a rapid development of cognitive computing application may also result in the investigation for a new generation of computers. In this respect, we describe Street Engine as a new, highly parallel computer architecture is replaced by the micro-architecture of the human brain, distributing both memory and computation. It was designed specifically for cognitive computing, together with a specific language, also called Street Language, which is executed directly in hardware, with the aim of realizing advanced cognitive agents more power efficiently than conventional computers.
From this we can give conclusion that, to have a glance on open research challenges in cognitive computing, recently made a survey where interested readers can find: (i) a classification of current research on cognitive computing according to its objectives, (ii) a concise review of cognitive computing approaches, (iii) open research issues. Besides, depicts the era of cognitive computing, giving an overview of the applications, the underlying capabilities, and some of the key challenges, of cognitive systems.
From the industry side, with the objective of providing a non-proprietary definition of cognitive computing that could be used as a benchmark by IT professionals and researchers as well, by the media and technology users and buyers, a cross-disciplinary group of experts from industry, academia and the analyst community, joined the Cognitive Computing Consortium, whose members were gathered from a variety of company, research centers and institutions, such as, Synthesis and NextEra Research (founders) with Pivotal, Basis Technology, HP, IBM, BA Insight, CustomerMatrix, SAS, Interactions, Bebaio, Microsoft, and Universities such as, UCSF and the Babson College. It is worthwhile noticing that most of the sponsors are companies involved in big data analysis. Moreover, several tools and platforms were created in recent years, which are going in the direction of making cognitive computing affordable and widely available.
In this direction, some substantial contributions come from both private research investments, made by companies, and scientists in universities and research centers. Significant examples come from some of the greatest players in the IT market, such as, e. g. : (i) Cognitive Reasoning Platform (CRP) by Enterra, which claims to combine advanced computations and semantic reasoning to create a system that can sense, think, act and learn like humans; (ii) Deep Learning, by Microsoft, that open sourced its Computational Networks Toolkit (CNTK) and made it available for anyone to use in their own work on GitHub. This tool allows creating deep learning networks for different activities such as, e. g. , speech recognition; (iii) DeepMind, by Google, that, in 2014, acquired the UK based AI company aimed to solving artificial intelligence problems. Then, in 2015, Google announced the creation of an AI that learns by itself and is able to win video games; (iv) IDOL (Intelligent Data Operating Layer), delivered by HP, which acquired Autonomy in 2011, within their big data software platform, offering many services and solutions for, e. g. , data analysis and IoT; (v) Watson, by IBM, which is a technology platform using natural language processing and machine learning to reveal insights from large amounts of unstructured data.
