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 AI
|How it Works
|Focuses 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.
|Informed rationale for deciding what to do, where and when.
|Recommendations based on historical patterns and assumptions
|Recommendations based on other peoples’ actions.
|Enterprise decision-making and personalization
|Natural language processing and image recognition
|The Internet’s most popular recommendation filter for ecommerce
|Fully transparent: detailed explanations behind all recommendations, always
|Black box: no way to know how it reaches conclusions
|Black box: no information about why someone bought certain products together or what the commonalities are
|Little 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
|No retraining or new models to evolve AI application; just update connections and rebalance
|Retraining and new models needed for new inputs
|New items have less data associated with them, may not surface as expected
|No overfitting: there isn't separate training data, there is the data in the Connectome
|Can model the training data too well, and new datasets not well enough
|Users try to reduce the number of features to lessen overfitting, which results in inaccurate connections between purchases