Many of us are familiar with the speech, facial, and text recognition AI technologies of today. These, while significant, are the tip of the iceberg when it comes to developing AI. Some challenges that have hindered the adoption of AI are its inability to adapt, reason, and personalize. This challenge has been addressed in the next generation of AI technologies that are poised to make a big impact on business.
Some examples of next gen AI are:
1) Fuzzy Logic
Binary logic evaluations have been a hinderance in the development of AI. Eliminating outliers and grouping entities that are alike has been a challenge for AI developers. Reason demands assumptions, estimations, and correlations. Fuzzy logic is the science that enables AI to be more reasonable. This will lead AI to better decision making.
2) Case-based reasoning
Case-based reasoning (CBR) is a higher level way of problem solving that incorporates machine learning. Machine learning allows AI to learn and respond on its own without human programming. With CBR, solutions and learnings from past problems are incorporated in solving new problems. Over time AI will solve problems efficiently and effectively using CBR.
3) Genetic algorithms
Genetic algorithms is a form of evolving algorithms. Poorer performing algorithms are phased out while better performing ones are utilized. These better performing algorithms are combined, when necessary, to reproduce even better algorithms for problem solving.
These technologies will aid businesses in the areas like cyber security, data analysis, decision making, and process improvements. The result will be the development of faster, more targeted products and services for the consumer.
