Talk to 10 people about AI and you’ll get 10 different definitions. Same with Business Intelligence, Machine Learning, Deep Learning, and the like.
We think it’s important to be on the same page, so here’s how Nara Logics defines key terms associated with our industry.
The ability to use compute power to acquire and apply knowledge and skills in new ways. AI augments human intelligence; it doesn’t mimic it.
Generally backwards-looking – focused on what has already happened – rather than what we can learn from data about what can/will happen in the future. This is in contrast to AI, which is forward-looking.
Brings together different streams of data, relates them for better decisions, and provides rationale for those decision options. It uses brain logic to build and weigh connections between data points, and does not require large data sets to achieve results.
Nara Logics Synaptic Intelligence Platform is based on Cognitive AI.
Compare Nara Logics Synaptic Intelligence with perception AI and collaborative filtering.
Perceives inputs based on patterns in images, data, video, movements, objects. It makes recommendations based on historical patterns and assumptions. This requires large data sets and does not provide the reasons behind recommendations/decisions.
Many internet recommendation engines and cloud platforms, such as Google Cloud Platform, Microsoft Azure and Amazon, are based on Perception AI.
Compare Nara Logics Synaptic Intelligence with perception AI.
An advanced statistical approach, it makes recommendations based on seemingly similar peoples’ actions, i.e., people who liked one “thing” will have other “things” they like in common.
It’s the internet’s most popular recommendation filter for ecommerce, although often inaccurate.
Compare Nara Logics Synaptic Intelligence with collaborative filtering.
Uses Collaborative Filtering and Perception AI to make recommendations.
Compute-heavy techniques that allow a machine to use data to continually improve at tasks over time, with experience. It is focused on acquiring knowledge rather than applying knowledge.
Nara Logics AI leverages machine learning. We use the machine’s ability to compute (i.e., learn) in order to augment and discover new avenues for applying knowledge (i.e., intelligence).