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Cognika Corporation was founded in 2006 with a mission to bring leading edge analytics tools to market that had been developed at MIT. This breakthrough technology combines the best of Artificial Intelligence (AI) techniques with unique algorithms that emulate human cognition, and serves as the foundation for the Smart Analytics engine on which Cognika's technology is based.

Cognika Corporation has expanded its technology to fully accommodate structured and unstructured data sources and current work includes novel solutions for automated analysis, design and optimization solutions, business intelligence, knowledge management, and anticipatory search for leading organizations in information-intensive industries such as life sciences, healthcare, manufacturing and process industry, defense, homeland security, telecommunications, and the financial sector.

Cognika's "cognitive computing" engine surpasses current data-mining and Artificial Intelligence (AI) techniques, enabling organizations to extract maximum value from their data assets and generate meaningful insights in ways that have not been feasible until now.
Cognika provides smart analytics and practical decision support tools that empower analysts, statisticians and programmers by complementing traditional analysis methods and adding a new level of intelligent search and data analysis functionality.

Cognika's technology provides for (i) fusing information from multiple sources such as airborne, surface and sub-surface platforms and media such as video, imagery, text, sensory data etc., (ii) ability to query it in (near) real-time in a source-agnostic fashion, and (iii) explicitly represent uncertainties to the analyst for making critical mission execution decisions. The ability to query and correlate data across media is critical to resolving ambiguities and ensuring highest possible data integrity and visibility. Current approaches to querying textual or non-textual content such as audio, videos, images etc. typically rely on matching text metadata such as name or description tags, date-time and other information related to the non-textual data files. There have also been some approaches proposed using content-based analysis; however these too fall short since they do not address the issue of combining and analyzing data from all sources irrespective of the source media, without any restrictions and limitations.

Addressing the Challenges of Video & Imagery

Security agencies are inundated with an endless stream of video & imagery. In addition, some of it is structured (numerical) and some of it unstructured (text, sound and images). The challenge is finding the critical pieces of information amidst all the “noise” – and responding quickly and effectively, even as huge volumes of data and events continue to unfold. Data collection mechanisms and visualization technologies have traditionally been deployed to reason with the data, but they cannot handle the volume and variety of random data – or provide the real-time monitoring required to identify threats and take action immediately.

Cognika’s technology enables efficient analysis of multi-modal datasets –such as those from multiple video/imagery assets (e.g. UAVs, Vehicle-mounted cameras, and cellphone cameras) and SIGINT (such as combining COMINT and ELINT). This approach enables integration of information from multiple sources (including video) into a unified inverted index effectively combining cross-media information. Advanced query construction from cross-media elements combined to create powerful query formulations, including multi-modal queries such as those combining keywords and images. The “search-engine” like interface provides the users with a familiar yet unique and powerful mechanism for interaction with a single knowledge base combining complex mixed media data sources. Three basic characteristics define this approach:


Overview

The product’s underlying framework combines advanced machine learning (AI) technology with unique algorithms that emulate human learning, memory and predictive reasoning to generate hierarchical Bayesian models from structured and unstructured information. From these data sources, the system automatically:


What’s more, the smart analytics engine continues to “learn” over time, making higher confidence predictions (inferences) as new data arrives.
Potential applications in the ISR area include: