What is cognitive computing?
Cognitive means “knowledge-related” and in this context refers to the ability of human knowledge processing: computers with cognitive capabilities are able to process natural language and knowledge and make well-founded decisions.
Cognitive computing uses advanced, AI-based technologies to analyze data. One of the underlying techniques is machine learning, for example. Here, the data is searched for patterns to gain insights and rules that the computer can later apply independently.
The process of cognitive computing can be divided into various steps:
- Data acquisition and preparation: once the data has been collected (from various sources), it must be organized and processed to create a coherent basis for analysis.
- Pattern recognition and data analysis: Using technologies such as machine learning (ML), patterns and correlations are discovered in the data to be processed. The use of natural language processing (NLP) enables human language to be captured and analyzed.
- Knowledge presentation: The acquired knowledge is integrated into internal models that make it possible to store and retrieve information.
- Context understanding: Recording and understanding the context is an essential component that allows the systems to make connections and draw well-founded conclusions from the data.
Application areas & examples of cognitive computing
The fields of application for cognitive computing are diverse and the technologies can be used in any industry in which data needs to be collected and processed. They are particularly important for companies that are in direct contact with the end customer. In today’s fast-moving world, customers expect an immediate response or processing of their request. The use of cognitive computing technologies makes it possible to respond to customer inquiries in real time, whether through the use of virtual assistants or intelligent data analysis. As these technologies work without human intervention, problems can be solved more quickly. Improved customer management can help to build customer loyalty, but also to attract new customers and retain them in the company.
Cognitive computing can support the analysis of market trends in the financial sector, for example. The technologies can help to understand complex financial markets and make well-founded decisions based on large amounts of data (big data).
In the manufacturing industry, cognitive computing is used for process optimization, quality assurance and even machine maintenance planning (predictive maintenance). By applying cognitive skills and AI technologies, complex data can be analyzed, patterns identified and measures derived. This enables more efficient and precise control of production processes and increases the overall efficiency of industrial companies.
Watson computer system
The term Watson is usually associated with cognitive computing. The Watson computer system developed by IBM is world-famous because it competed against humans on the English quiz show Jeopardy in 2011. This computer was based solely on learning and was not connected to the internet. Watson symbolizes the progress made in the field of cognitive computing and shows how computers can simulate human-like thinking abilities.
endless possibilities.
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Data processing with cognitive computing
Big data generates immense amounts of data that need to be strategically processed and analyzed. Cognitive computing goes beyond mere analysis: by integrating contextual understanding and decision-making, it is possible to react to changes, make data-based decisions and provide innovative solutions.
Cognitive computing therefore has enormous potential to revolutionize business processes. The use of such advanced technologies reduces the workload of employees and minimizes sources of error at the same time. As cognitive computing is a scalable system, it can be easily adapted to growing data volumes.
Cognitive computing faces these challenges in practice
One of the biggest challenges facing cognitive computing is unstructured data: As much of the information received by a company is usually in unstructured form, processing and analyzing such data requires advanced methods or data extraction and interpretation. However, data protection, ethics and the complexity of algorithms are also among the challenges in the field of cognitive computing.
Cognitive computing & ExB
Like cognitive computing systems, ExB can automate workflows that involve unstructured data. With more than 20 years of experience, we are one of the pioneers in the field of AI and document processing in particular. Get in touch with us without obligation and talk to our experts: We offer customized document processing solutions for every use case.
