Compare Nara Logics AI

Businesses are blown away by the quality results Nara Logics Cognitive AI delivers.

When compared to perception AI and collaborative filtering, which drive deep learning systems, Nara Logics Cognitive AI approach has clear advantages for enterprise-scale AI.

Ours enables smart decisions based on explicit intelligence. Perception AI delivers assumptions based on patterns. Collaborative filtering makes recommendations based on seemingly similar people’s preferences.

Nara Logics Synaptic Intelligence compared with Perception AI and Collaborative Filtering

 Nara Logics Synaptic Intelligence / Cognitive AIPerception AICollaborative Filtering
How it WorksFocuses on cognitive processes: bringing together data once perceived, understanding it in context, and responding with options.

Uses brain logic to build and weigh connections between data points.
Perceives inputs based on patterns in images, data, video, movements, objects.

No correlation between elements.
Based on the principle that people who liked, purchased, watched and/or read the same “thing” will also be interested in the other “things” those people have bought or consumed.
OutputInformed rationale for deciding what to do, where and when.Recommendations based on historical patterns and assumptionsRecommendations based on other peoples’ actions.
ApplicationsEnterprise decision-making and personalizationNatural language processing and image recognitionThe Internet’s most popular recommendation filter for ecommerce
Transparency LevelFully transparent: detailed explanations behind all recommendations, always Black box: no way to know how it reaches conclusionsBlack box: no information about why someone bought certain products together or what the commonalities are
DataLittle data required to achieve accurate, game-changing results Massive data sets required to deliver recommendations. Cold start problem - recommendations require a sufficient amount of user ratings
Retraining/ModelsNo retraining or new models to evolve AI application; just update connections and rebalanceRetraining and new models needed for new inputsNew items have less data associated with them, may not surface as expected
Overfitting ChallengeNo overfitting: there isn't separate training data, there is the data in the ConnectomeCan model the training data too well, and new datasets not well enoughUsers try to reduce the number of features to lessen overfitting, which results in inaccurate connections between purchases