Minerva is a continually learning AI that enables users to build their own AI with no code and no labeling. The purpose of Minerva is to enable users to boost cognitive throughput and surface game changing predictive insights from information complexity.
Minerva is comprised of hundreds of modular components for processing and contextualizing dynamically changing external and internal unstructured and semi-structured data. Minerva understands context with human level accuracy and explainability. As Minerva learns, it accumulates knowledge that it draws from overtime to understand increasingly general contexts.
Today, we use Minerva internally to build all of our products and solutions. In the near future, we’ll be releasing Minerva directly to end users with the aim of unleashing the extraordinary creativity and genius of our customers!
Accrete Data Hooks ingest data from a plethora of messy unstructured and semi-structured external and internal data sources including social media, news, filings, PDFs and foreign language documents.
Knowledge Functions are modular building blocks of cognition that process and contextualize data to extract predictive insight. Knowledge Functions perform cognitive tasks such as automated entity extraction, entity normalization, entity linking, influence modeling, topic classification, semantic search, anomaly detection and semantic translation.
Minerva enables natural user interaction with various dashboards and APIs for consuming insight, providing feedback and securely integrating Accrete’s AI products into existing enterprise workflows.
We benchmark our AI accuracy periodically against human experts for the task in question to ensure we meet the quality standards our clients expect. Only when our AI’s performance can both reach and maintain accuracy at expert human levels, together with diminishing amounts of human feedback, do we move forward with commercialization.
We believe explainability lies at the root of trust in AI. Accuracy without transparency leads to poor adoption which is why at the center of all our AI lies transparency and explainability. When users understand both the accuracy of the outputs and the reasoning behind them, the path to business impact is much faster.
Human bias is everywhere whether we’re aware of it or not. Our AI is as fair as it can be, requiring minimal human feedback and learning implicitly and continually from objective ground truths, hence vastly reducing human bias.
We’re building AI that is compliant but not limited to what is permissible by law. We adhere to precise ethical guidelines with regard to foundational human values. We build AI that has the utmost respect for privacy, individual rights, privacy, non-discrimination, and non-manipulation.
Real AI is AI that learns exponentially faster than the effort required to train it. That’s only possible when these three pillars are incorporated together. We’re delivering real AI.
Our architecture enables data to continuously enter the system while simultaneously incorporating it into our models dynamically. Our continual learning increases model accuracy over time and maintains accuracy even when data patterns change.
Accurately representing the meaning of data requires an accurate understanding of the context surrounding the data. Accrete’s continual dynamic learning selectively applies regularizer and progressive network approaches to grow and recalibrate its neural architecture while incorporating on-the-fly human feedback. The result is the ability to rapidly learn contextual meaning and adapt to contextual changes over time.
The AI industry’s dirty secret is that in order for models to function well, they need inordinate amounts of initial and ongoing human training and expert labeling. Accrete’s models require no explicit rules or labeling. Our AI automatically defines rules and extracts the features contained within expert provided ground truths.
It tackles the most challenging core industry problems with ease
The digital revolution has created unprecedented information complexity. Data is constantly growing in size and complexity while dynamically changing its context as well. The only solution is AI that is hands-off, continually learning, and applying new knowledge to new problems automatically.
The more people interact with AI in the form of explicit rules and manual labeling the more human bias is introduced to the model. Our AI requires minimal training and labeling, it learns implicitly and dynamically while reaching outstanding accuracy and performance levels.
AI that needs constant maintenance from both ends and that heavily relies on manual labeling simply cannot scale. Scalable AI is one that can automatically adjust to contextual changes, new data sources, and unfamiliar data points without human intervention.
Let’s discover what we can build together