From Deep Learning to Deep Reasoning with Kinetix Vi
While Deep Learning is remarkable at pattern recognition, classification, and prediction, it falls short when it comes to personalization, explainability, and understanding its own rationale.
The underlying architecture of Kinetix' Deep Reasoning XAI technology — a radically different AI algorithm design that overcomes Deep Learning's shortcomings — is fundamentally different under the hood and purpose built for enterprise-grade, human-in-the-loop decision support systems.
From Black Box to Glass Box
Why Does Explainability Matter?
Not only does eXplainable AI offer the ‘why’ behind machine-based recommendation, it serves as the connective tissue between man and machine allowing the two parties to better communicate and augment one another.
Natively Explainable
Not all eXplainable AI is created equally. With most companies trying to convert a black box into a glass box, ours is natively a glass box with explainability engineered into the very foundation.
Rule-based Architecture
Humans’ underlying reasoning framework is built upon complex rules. Kinetix’ Deep Reasoning AI is built upon a rule-based architecture to model how we ourselves are hardwired to think.
Self Learning
Deep Learning is designed to draw correlations between the most obscure of data. Kinetix’ Deep Reasoning leverages ML differently — to infer causal relationships in the data.
Evolutionary
Black box models are difficult to tune once trained. Kinetix XAI’s models are more fluid and evolutionary, meaning they can adapt to changes in human decision-making through feedback systems.
Technical Overview
Learning Modes:
- Supervised (labeled)
- Unsupervised (unlabeled)
- Both Learn on Structured Data
Data Inputs:
- Numerical Features
- Categorical Features
AI Output:
- Explainable Classification & Categorization
Functions
- Recommendation
- Personalization
- Anomaly Detection
- Automatic labeling / tagging
- Estimation
- Optimization
Sample Use Cases
- Decision Support, Insight & Analytics
- Idea Generation
- Fraud Detection
- Detecting Style Drift / Maintaining Consistency
- Preventing Under/Over Reporting
- Risk & Compliance
- Process Optimization
With Kinetix XAI at the core of your enterprise decision support systems...
Trust the Results
It’s risky to take machine-based results at face value. Through explanation, Kinetix’ XAI technology delivers more value to end users by coupling recommended courses of action with intelligible insights to machine-based rationale.
Retain Control
Enterprise decision-making is too complex to automate. Kinetix’ eXplainable AI employs a human-in-the-loop design built for augmenting decision-making with humans calling the final shots.
Remain Compliant
Policy-makers are wising up to risky, blackbox AI. In regulated industries, compliance mandates bring issues of auditability, transparency, bias, and data privacy to the forefront. Kinetix’ XAI is designed from the ground up to natively satisfy all compliance demands.
An Intelligent Decision Support System
The World of Data
Connect public data and your proprietary, in-house data
Artificial Intelligence (AI)
Interprets the data world, recommends, predicts, or finds anomalies, and generates personalized analysis reports
Analyst Apps
Intuitive design, data visualization, and notifications to provide actionable information and analysis
Client Success Stories
$85B Hedge Fund (Recommendation Engine)
$50B Top Tier Hedge Fund
Top British Broker Dealer
Leading Canadian Bank (Strategic Advisory)
Publications and Whitepapers
Explainable Artificial Intelligence Based on Neuro-Fuzzy Modeling with Applications in Finance
The book proposes techniques, with an emphasis on the financial sector, which will make recommendation systems both accurate and explainable. The vast majority of AI models work like black box models. However, in many applications, e.g., medical diagnosis or venture capital investment recommendations, it is essential to explain the rationale behind AI systems decisions or recommendations.
Get the Book
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