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Content Analytics, Discovery, and Cognitive Software

Content Analytics, Discovery, and Cognitive Software

Cognitive computing refers to the technology platform that is based on scientific disciplines of signal processing and artificial intelligence. These platforms encompass automated reasoning, machine learning, speech recognition, and natural language processing. Creating and assessing the evidence based on the hypothesis made on human communication and natural language, along with further learning and adaption from user responses and selections is the standard build of a cognitive system.

Content analytics is a wide set of computer-assisted techniques for contextualized interpretations of documents. It is the amalgamation of mining and text analytics, which has the capability to visually explore and identify patterns, trends, and other statically available facts available in various types of content across several sources. Content analytics software is mostly used to give more perceptibility to the amount of content that is being generated. Data discovery is a type of system that scans the available data through different sources and extracts important information from data pertaining to the business goals. It is a decision-making platform that integrates, structures, and refines data.

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Oracle Corporation is an american multinational computer technology corporation headquartered in Redwood Shores, California. The company specializes... Read more
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F.A.Q about Content Analytics, Discovery, and Cognitive Software

What are the features of a cognitive computing solution?

With the present state of cognitive function computing, basic solution can play an excellent role of an assistant or virtual advisor. Siri, Google assistant, Cortana, and Alexa are good examples of personal assistants. Virtual advisor such as Dr. AI by HealthTap is a cognitive solution. It relies on individual patients’ medical profiles and knowledge gleaned from 105,000 physicians. It compiles a prioritized list of the symptoms and connects to a doctor if required. Now, experts are working on implementing cognitive solutions in enterprise systems. Some use cases are fraud detection using machine learning, predictive analytics solution, predicting oil spills in Oil and Gas production cycle etc.

The purpose of cognitive computing is the creation of computing frameworks that can solve complicated problems without constant human intervention. In order to implement cognitive function computing in commercial and widespread applications, Cognitive Computing consortium has recommended the following features for the computing systems –

1. Adaptive

This is the first step in making a machine learning based cognitive system. The solutions should mimic the ability of the human brain to learn and adapt from the surroundings. The systems can’t be programmed for an isolated task. It needs to be dynamic in data gathering, understanding goals, and requirements.

2. Interactive

Similar to brain the cognitive solution must interact with all elements in the system – processor, devices, cloud services and user. Cognitive systems should interact bi-directionally. It should understand human input and provide relevant results using natural language processing and deep learning. Some skilled intelligent chatbots such as Mitsuku have already achieved this feature.

3. Iterative and stateful

The system should “remember” previous interactions in a process and return information that is suitable for the specific application at that point in time. It should be able to define the problem by asking questions or finding an additional source. This feature needs a careful application of the data quality and validation methodologies in order to ensure that the system is always provided with enough information and that the data sources it operates on to deliver reliable and up-to-date input.

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).