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What is Symbolica?

Existing machine learning models are expensive to develop, complex to deploy, difficult to validate, and often produce misleading outputs. At Symbolica, we are fundamentally rethinking the machine learning paradigm. By utilizing the powerful framework of category theory, we design models capable of understanding and learning algebraic structures. This innovative strategy enables our models to possess a thorough and systematic worldview that is both explainable and subject to verification. We aim to empower both developers and end users to understand and communicate the rationale behind model outputs. Achieving this level of interpretability and control—such as the flexibility to exclude proprietary information from training datasets—is vital for applications that are crucial to achieving mission objectives. Furthermore, we are confident that improving transparency in the decision-making processes of models will enhance trust and collaboration between human users and artificial intelligence systems, ultimately leading to more effective partnerships. This commitment to clarity not only benefits users but also strengthens the overall integrity of machine learning applications.

What is Azure AI Language?

Azure AI Language provides a robust managed service specifically designed for the development of applications centered around natural language processing. Users are equipped with tools to identify key terms and phrases, evaluate sentiment, summarize text, and create engaging conversational interfaces. The platform allows individuals to annotate, train, assess, and launch customizable AI models with minimal machine learning knowledge required. It features predefined entity categories relevant to various sectors, including specialized text analytics for healthcare, enabling quick setup while also offering opportunities for customization and enhancement as needed. To optimize the training of machine learning models for unique applications, users can provide multiple labeled examples. Furthermore, it supports the creation of custom multilingual models that can be developed in one language and effectively applied across others. Utilizing Language Studio, users gain access to sophisticated GPT-powered language models that analyze and suggest labels for their content efficiently. The platform also facilitates the extraction, labeling, and redaction of vital information within text across diverse categories, ensuring that sensitive information is managed properly. This adaptability positions Azure AI Language as a crucial asset for organizations aiming to improve their capabilities in natural language processing, ultimately driving innovation and efficiency in their operations. With its extensive features and user-friendly interface, the platform stands out as a leading choice for businesses in today's digital landscape.

Media

Media

Integrations Supported

Crestwood Cloud
Microsoft Azure

Integrations Supported

Crestwood Cloud
Microsoft Azure

API Availability

Has API

API Availability

Has API

Pricing Information

Pricing not provided.
Free Trial Offered?
Free Version

Pricing Information

$2 per month
Free Trial Offered?
Free Version

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Company Facts

Organization Name

Symbolica

Company Website

www.symbolica.ai

Company Facts

Organization Name

Microsoft

Date Founded

1975

Company Location

United States

Company Website

azure.microsoft.com/en-us/products/ai-services/ai-language/

Categories and Features

Artificial Intelligence

Chatbot
For Healthcare
For Sales
For eCommerce
Image Recognition
Machine Learning
Multi-Language
Natural Language Processing
Predictive Analytics
Process/Workflow Automation
Rules-Based Automation
Virtual Personal Assistant (VPA)

Categories and Features

Natural Language Processing

Co-Reference Resolution
In-Database Text Analytics
Named Entity Recognition
Natural Language Generation (NLG)
Open Source Integrations
Parsing
Part-of-Speech Tagging
Sentence Segmentation
Stemming/Lemmatization
Tokenization

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