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Automorphic

Automorphic enhances language models with knowledge infusion, data transformation, and security tools, integrating seamlessly with OpenAI API.
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Automorphic

What is Automorphic?

Automorphic is a platform that enhances language models with knowledge infusion tools, such as Conduit, and conversion tools like TREX. It provides security measures with Aegis, designed to protect against adversarial attacks. The Automorphic Hub allows users to access models trained and refined on the platform for their usage, supporting seamless integration with the OpenAI API.

Conduit, a tool by Automorphic, infuses knowledge into language models by allowing the fine-tuning of models, dynamically combining specific behavior or knowledge adapters, and rapidly iterating on models through human-in-the-loop feedback, thus streamlining deployment.

Furthermore, Automorphic integrates seamlessly with the OpenAI API, offering compatibility with existing codebases and enabling users to leverage its features without altering their current code. TREX, another tool provided by Automorphic, converts unstructured data into structured formats as defined by users, such as JSON, XML, YAML, or other custom formats specified by regular expressions or context-free grammars.

Automorphic prioritizes user and model protection against adversarial attacks, PII leakage, and toxic language through its firewall tool, Aegis, which continuously learns from usage to enhance detection and defense mechanisms over time.

Overall, Automorphic's features, including Conduit for knowledge infusion, TREX for data transformation, and Aegis for security, contribute to a secure, efficient, and flexible user experience with language models.

Who created Automorphic?

Automorphic was created by an organization that prioritizes model and user protection, particularly against adversarial attacks, prompt and PII leakage, and toxic language. The platform was launched on July 31, 2024, and the company founder and detailed company information are not explicitly mentioned in the provided documents. Automorphic offers tools like Conduit for knowledge infusion, TREX for data transformation, and Aegis for security measures. The platform allows easy access to models trained on Automorphic and supports integration with the OpenAI API, ensuring flexibility and adaptability for users.

Who is Automorphic for?

  • Data scientists
  • Machine learning engineers
  • AI researchers
  • Software developers
  • Data engineers
  • Artificial Intelligence Practitioners
  • Data Analysts

How to use Automorphic?

To use Automorphic effectively, follow these steps:

  1. Understand Automorphic: Familiarize yourself with Automorphic, a platform that enhances language models with tools like Conduit for knowledge infusion and TREX for data conversion, ensuring security with Aegis against adversarial attacks.

  2. Utilize Conduit: Learn how Conduit improves language models by infusing knowledge and enabling rapid model iteration through human feedback, fine-tuned adapters, and dynamic model combination.

  3. Explore Automorphic Hub: Access publicly shared models on the Automorphic Hub, facilitating model training and refinement for users.

  4. Leverage TREX: Utilize TREX to convert unstructured data into structured formats like JSON, XML, and more, offering high customization and predictable output.

  5. Secure with Aegis: Protect language models and users with Aegis, safeguarding against adversarial attacks, PII leakage, and toxic language.

  6. Integration: Integrate Automorphic seamlessly with the OpenAI API without modifying existing codebases for enhanced compatibility.

  7. Continuous Learning: Benefit from Automorphic's continuous learning features, allowing for model updates based on user feedback and manual labeling for improved model performance over time.

By following these steps, you can effectively harness the capabilities of Automorphic for enhancing language models, ensuring data security, and continuous model improvement.

Pros
  • Fine-tunes language models
  • Combines models dynamically
  • Allows rapid model iteration
  • Incorporates human feedback
  • Streamlined model deployment
  • Continuous model updates
  • Enables quick adapter loading
  • Stacks fine-tuned adapters
  • Publicly shared models hub
  • Converts unstructured data
  • Supports various data formats
  • Detects adversarial attacks
  • Prevents PII leakage
  • Filters toxic language
  • Learns from usage
Cons
  • Requires manual feedback
  • Adapts slow to initial feedback
  • Complex setup for TREX
  • Limited information on Aegis mechanism
  • No clarity on update frequency

Automorphic FAQs

What is Automorphic?
Automorphic is a platform that enhances language models with knowledge infusion tools such as Conduit and conversion tools like TREX. It also provides security measures with Aegis to protect against adversarial attacks, and the Automorphic Hub allows access to trained models for user utilization.
What is Conduit and how does it improve language models?
Conduit is a tool by Automorphic that infuses knowledge into language models by dynamically combining adapters for specific behavior or knowledge. It enables rapid model iteration, streamlines deployment, and incorporates human feedback for continuous model updates.
Can Conduit be integrated easily into an existing codebase?
Yes, Conduit can be easily integrated into existing codebases without requiring changes to the current codes as it is compatible with the OpenAI API.
What is the purpose of the Automorphic Hub?
The Automorphic Hub serves as a platform for sharing publicly trained models, providing users easy access to models refined on the Automorphic platform.
What is TREX and how does it differ from OpenAI's functions?
TREX is a tool that converts unstructured data into structured formats, offering high customization options for data processing, unlike OpenAI's default functions.
What does Aegis do and how does it protect language models and users?
Aegis is a firewall tool that protects language models and users from adversarial attacks by detecting prompt injections, PII leakage, and toxic language to ensure data security.

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