Well done, you have got all this data but what are you going to do with it?

How do we make all this information accessible to users in a meaningful way? It’s about searching and matching content, context and similarity.

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Predict Human Behaviour ...Fraud, Risk, Spend

Symilarity applies Unsupervised Artificial Neural Networks to learning tasks that contain more than one hidden layer. The outcome creates a highly efficient system that learns representations from large-scale, unlabelled data sets.

 

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Unstructured Data, Chaos Becomes Harmony

Unstructured data: PDFs, documents, presentations, scanned documents, instant messages, emails, webpages, audio, video, and anything that doesn’t fit neatly into a tabular format. It all has relevant content that needs understanding

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Unleashing Data’s Potential in Every Industry

You have your historical data and are collecting new “stuff” every second. You can relax because you are searching, matching and monitoring this data in real time, waiting for any and all changes.

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THE WORLD’S EASIEST TO USE UNSUPERVISED DEEP LEARNING PLATFORM
Symilarity is an unsupervised artificial intelligence platform that automatically interprets natural language and extracts a structured representation of the content and context of data and/or images and their relationships.

SEARCH

Symilarity applies Unsupervised Artificial Neural Networks to learning tasks that contain more than one hidden layer. The outcome creates a highly efficient system that learns representations from large-scale, unlabeled data sets.

MATCH

Symilarity identifies and aggregates data records that correspond to the same real-world entity including data from multiple corpora and from different sources. The outcome enables and encompasses the matching of different tasks that have quite different data contexts.

DISCOVERY

Symilarity provides a range of tools such as clustering, word frequency analysis, automated classification, alongside machine learning, to enable users to find hidden content within large corpora

Deep learning (DL) is essentially a component of Machine Learning (ML) that extends ML capabilities across multi-layered neural networks to go beyond just categorizing data. DL can actually learn, unsupervised, basically from massive amounts of data. With DL, it’s possible to combine the unique ability of computers to process massive amounts of information quickly, with the human-like ability to take in, categorize, learn, and adapt. Together, these skills allow modern DL software to perform advanced tasks, such as identifying fraud from unstructured claims forms.

Machine learning (ML) relies on neural networks, computer systems modelled on the human brain, which classify information into categories based on elements that those categories contain (for example, song lyrics). ML uses probability to make decisions or predictions about data with a reasonable degree of certainty. In addition, it is capable of educating ML systems when given feedback from false positives.

The term AI (Artificial Intelligence) was first coined in the 1950s and refers to computer software that can reason and adapt based on sets of rules and data. The original goals for AI were to mimic human intelligence. As humans, our brains are collecting and processing information constantly. We take in data from all our senses and store it away as experiences that we can draw from to make inferences about new situations. AI is not very adaptable, but it can still be useful because modern processors are capable of working through massive sets of rules and data in a short time.

“the Symilarity team has been working with AI and Unstructured Data since the 1990’s. Today we bring you these decades of experience in a platform that extends Deep Learning accessibility to everyone”

EASIEST TO USE UNSUPERVISED DEEP LEARNING PLATFORM