Solutions For Relevant Cognitive Computing

Modern Artificial Intelligence (AI) has come a long way. Within the scientific community and digital marketplace, developers are applying deep learning and cognitive computing - through machine algorithms - to relevant problems. Deep learning had its' inception in approximately YR2000, and digitized mechanisms for deep learning have evolved. Cognitive computing has one goal: build systems that learn from, and interact with, humans. Rules-based and cognitive computing systems differ quantitatively.

Rules-based systems

Consists of rule set
Consists of knowledge base
Consists of an inference engine (forward or backward rule-chaining)
Consists of a user interface

Cognitive computing systems

Deductive/Inductive Reasoning
Inferential-based
Notational computing
Questions + data sets drive computations

Briefly, a rules engine drives software reaction to different scenarios. In a commercial setting, the software system (rules engine) executes business rule-making in a real-time production environment. Consumer business sales solve a need within a specific business framework of regulations, policies, and best practices. There also exists the possibility of combining cognitive learning with a rule-based system: as in, a customized sales solution to a specific customer uses both mechanisms - and is an interesting hybrid solution to problems. The inference engine is truly AI and inferential-based to provide new knowledge solutions. This latter mechanism is not a rule made by a person, but the algorithms created by a developer/scientist team.

Inference Engine Success

Similar to hybrid knowledge solutions, a recent article touched on cognitive call centers. Call Center staff are absorbing customer preferences and data and have been enabled for cognitive conversations previously not seen within the classic business model. Again data married to inductive reasoning = solution. Recently, developer research for skin-cancer detection came up with a surprising outcome. A "deep learning" algorithm was found to be more effective in detecting skin cancer than the board-certified dermatologist. Too, Deep Patient was a similar algorithm application, and successful in predicting patient disease from medical record data input. The data outcome showed success over physician diagnosis, again.

Business owners have figured out the essential (4) metrics to ensure a positive customer experience. This also has ramifications for any digital marketplace activity. These (4) factors include: valuing the customer experience to the level of a market differentiator; assessing the metric for changeable customer demand speed; assumptions that cognitive factors (learning/understanding) evolve in real time; and, absolute management value for the combination of knowledge/technology solutions. As a digital enterprise transforming solutions for clients, Enterra Solutions has successfully matched customer problems to cognitive computing services. Consider Enterra Solutions for relevant cognitive computing solutions. Explore our digital website at: https://www.enterrasolutions.com/.

Michel Smith is the author of this article. For further detail about Inference Engine please visit the website.