In this blog we will learn about what is cognitive computing, cognitive computing examples, cognitive computing tools, cognitive computing applications, and how does cognitive cloud computing work. So let’s begin!!
What is cognitive computing?
Cognitive computing depicts technology platforms that combine reasoning, machine learning, speech, natural language processing, vision, human-computer interaction, that imitates the functioning of the human brain and assists to enhance human decision making. Cognitive computing applications associate data analysis and adaptive page display to adjust content for a specific type of audience. Cognitive systems probably reflect a few features that are adaptive, interactive, iterative and stateful and contextual. Now we have learnt what is cognitive computing so now let’s look at its features.
Features of cognitive computing solution
With the existing state of cognitive function computing, fundamental solutions can play an outstanding role as an assistant or virtual advisor. Google Assistant, Siri, Cortana, and Alexa are good illustrations of personal assistants. Virtual advisors like Dr AI by HealthTap are cognitive solutions. It depends on individual patients’ medical profiles and information gleaned from 105,000 physicians. It assembles a prioritized list of the symptoms and pertains to a doctor if needed.
Now, professionals are working on executing cognitive solutions in enterprise systems. Several use cases are conspiracy or fraud detection using predictive analytics solutions, machine learning, foreseeing oil spills in Oil and Gas production cycles etc. The objective of cognitive computing is the innovation of computing frames that can solve sophisticated problems without continuous human intervention. To implement cognitive process computing in commercial applications, Cognitive Computing has recommended the following features for the computing systems:
This is the first step in creating a machine learning established cognitive system. The solutions should imitate the proficiency of the human brain to learn and modify from the surroundings. The networks can’t be programmed for an unusual task. It needs to be vibrant in data collecting, understanding missions, and regulations.
The cognitive solution should interact with all factors in the system like cloud services, processor, devices, etc. that are identical to the brain. The cognitive systems should interact bidirectionally. It should understand human input and deliver appropriate results using normal language processing and deep learning. Few skilled, intelligent chatbots such as Mitsuku have already accomplished this feature.
Iterative and stateful
The system should “remember” prior interactions in a process and retrieve data that is adequate for the particular application then. It should be competent to interpret the problem by asking questions or finding a further source. This feature needs a detailed application of the data quality and validation methodologies to assure that the system is constantly provided with enough information and that the data sources it regulates on to transmit sensible and up-to-date input.
They must comprehend, observe, and extract contextual elements such as syntax, time, meaning, location, applicable domain, restrictions, user’s profile, process, task, and purpose. They probably draw on numerous references of information, comprising both structured and unstructured digital data, in addition to sensory inputs like gestural, auditory, visual, etc.
How does Cognitive Cloud Computing work?
As we know what is cognitive computing so now that computers have been quicker at calculations and processing as compared to humans. But they failed to complete tasks that human beings take lightly, for example, analyzing the natural language or distinguishing unique objects in an image. Therefore, cognitive technology gives rise to such current classes of crises computable.
They can acknowledge problematic situations illustrated by vagueness and possess far-reaching impacts on our private lives, business, healthcare, etc. A report by IBM Institute for Business Value states “Your Cognitive Future“, the spectrum of cognitive computing consists of a conclusion, engagement, and discovery. These 3 abilities are associated with the ways people think and ascertain their cognitive abilities in daily life.
The cognitive systems have huge repositories of structured and unstructured information. These have the proficiency to improve deep domain insights and furnish professional assistance. The models erected by these systems comprise the contextual relationships between several entities in a system’s world that encourage it to develop hypotheses and arguments. These can harmonize ambiguous and even self-contradictory information.
Hence, these systems can immerse in deep dialogue with humans. The chatbot technology is a reasonable example of an engagement model. Numerous AI chatbots are already trained with domain knowledge for abrupt adoption in several business-specific applications.
A step forth of engaged systems, these maintain decision-making capacities. These networks are modelled in assigning support learning. The decisions created by cognitive systems frequently unfold based on new information, results, and actions. Independent decision making depends on the potential to trace why the specific decision was made and vary the confidence score of a system’s reaction. A prominent use case of this category is the use of IBM Watson in health care.
The system can collate and evaluate the information of patients incorporating their record and diagnosis. The solution bases suggestions on its proficiency to infer the meaning and examine queries in the context of complicated medical data and natural language, comprising doctors’ notes, patient records, medical annotations etc. As the solution learns, it becomes more factual. It furnishes decision support skills and curtailing paperwork authorizes clinicians to spend more time with patients.
Discovery is an extensively progressive scope of cognitive computing. It includes disclosing insights and understanding huge amounts of data and improving skills. These models are constructed on intense learning and unsupervised machine learning. With ever-increasing volumes of information, there is a clear necessity for networks that assist in exploiting information more effectively than humans could on their own. While still in the initial stages, some discovery capacities have already occurred, and the valuable recommendations for future applications are coercing.
One of the first cognitive solutions is the Cognitive Information Management (CIM) shell at Louisiana State University (LSU). The distributed intelligent agents in the model collect flowing information, like text and video, to create interactive sensing, inquiry, etc. that delivers real-time monitoring and analysis. The CIM Shell not only propels an alert but reconfigures on the fly to separate a significant event and rectify the failure.
Cognitive Computing Landscape
The cognitive computing landscape is monopolized by larger players like Microsoft, IBM, and Google. IBM, existing as the missionary of this technology, has financed 26 billion dollars in large data and analytics. Presently it spends nearly one-third of its R&D budget on expanding cognitive computing technology. Various other firms and corporations are acquiring products and services that are good, if not more reasonable than Watson. IBM and Google have developed some of the opponents and the market is shifting towards consolidation.
Let’s take a glance at the leading players in this market:
- IBM Watson
- Microsoft Cognitive Services
- Google Deepmind
- QCognitive Scale
- Spark Cognition
Healthcare is an extensively outstanding sector to accept cognitive solutions. Startups such as Enlitic and Lumiata have improved small and crucial analytic solutions that promote health care providers in the diagnosis. Other firms in this market are CustomerMatrix, Cisco cognitive threat analytics, etc.
We hope you are now clear about what is cognitive computing exactly!!