The popular digital challenge affecting all industries is the assimilation of fragmented data, the extraction of insights and the transformation of data into actionable information.
Fremont, CA: Businesses in all industries are keenly interested in the ability of artificial intelligence (AI) to solve the most pressing problems. AI is now known for its ability to speed up processes, streamline procedures and, of course, crunch large amounts of data faster than a person could ever have done. But when it comes to structures that can think about themselves? This truth is closer to one than they would expect.
Cognitive AI assimilates data from various sources in different formats and is able to weigh these data in order to provide insights. This form of AI differs from the others in its ability to imitate the way the brain functions.
Cognitive AI systems are collaborative, contextual and, most importantly, adaptive in that they learn and develop rapidly as new knowledge comes to light. Far from replacing humans, AI is trained to work alongside humans, helping to improve the work we do or meet needs in other ways. Early enthusiasts of cognitive technology see them as vital to the potential success of their company and the ability to develop digitally.
The Need for and Availability of Vast Data Sets
Without large data sets, there is no place for AI, never mind cognitive AI – but easily accessible databases or spreadsheets are enough to get started. Medical data is already vast in the healthcare world and is rising at 36% CAGR by 2025 due to developments in wearables and other IoT-enabled devices, medical imaging and real-time data generation.
One of the biggest enablers for cognitive AI in healthcare has simply been the sheer amount of data produced. Enabling connected systems to have access to this aggregated, anonymized data that already exists on patients enables cognitive AI to detect health trends and patterns, particularly when combined with real-time health monitoring information (such as wearables) and environmental information.
The popular digital challenge affecting all industries is the assimilation of fragmented data, the extraction of insights and the transformation of data into actionable information. In the insurance industry, for example, cognitive AI has already been used to collect large quantities of structured and unstructured data to increase the quality of underwriting, remotely manage claims, streamline processes, and minimize costs.