Automorphic

What is Automorphic?

Automorphic is a platform designed to boost language models by adding knowledge infusion tools, like Conduit, and conversion tools such as TREX. It also includes security features with Aegis, which is built to guard against tricky adversarial attacks. Through the Automorphic Hub, you can easily access models that have been trained and improved right on the platform, making it simple to integrate them with the OpenAI API.

Conduit, one of Automorphic’s tools, infuses knowledge into language models. It does this by letting you fine-tune models, combine specific behavior or knowledge adapters on the fly, and quickly iterate on models using human feedback. This really helps speed up the deployment process.

Plus, Automorphic works smoothly with the OpenAI API. This means you can use its features without changing your current code, making it compatible with existing projects. TREX, another handy tool from Automorphic, converts unstructured data into structured formats that you define. You can choose formats like JSON, XML, YAML, or other custom types using regular expressions or context-free grammars.

Automorphic really cares about keeping users and models safe. Its firewall tool, Aegis, protects against adversarial attacks, personal information leaks, and toxic language. Aegis keeps learning from how it’s used, so its detection and defense capabilities get better over time.

All in all, Automorphic’s features – Conduit for adding knowledge, TREX for transforming data, and Aegis for security – come together to offer a user experience with language models that’s secure, efficient, and flexible.

Who created Automorphic?

Automorphic was developed by an organization that puts a high priority on protecting both models and users. They focus specifically on guarding against adversarial attacks, preventing prompt and PII leakage, and filtering out toxic language. The platform officially launched on July 31, 2024. While the company founder and specific company details aren’t readily available in the provided information, Automorphic offers key tools like Conduit for knowledge infusion, TREX for data transformation, and Aegis for robust security measures. The platform also makes it easy to access models trained by Automorphic and supports integration with the OpenAI API, ensuring users have plenty of flexibility and can adapt it to their needs.

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 get the most out of Automorphic, here’s a simple guide:

1. Get to know Automorphic: First, take some time to understand what Automorphic is all about. It’s a platform that enhances language models using tools like Conduit for knowledge infusion and TREX for data conversion, all while keeping things secure with Aegis against adversarial attacks.
2. Put Conduit to work: Learn how Conduit makes language models better. It infuses knowledge, lets you fine-tune models, use dynamic adapters, and quickly improve models with human feedback.
3. Check out the Automorphic Hub: You can find and use models that others have shared publicly on the Automorphic Hub. This makes it easier to train and refine models for your own projects.
4. Use TREX for data: TREX is great for converting unstructured data into structured formats like JSON, XML, and more. It’s highly customizable, so you can get the exact output you need.
5. Secure with Aegis: Protect your language models and users from adversarial attacks, PII leaks, and toxic language using Aegis. It’s designed for strong defense.
6. Integrate easily: Automorphic integrates smoothly with the OpenAI API. You can add its features to your existing codebases without needing to change anything, which is great for compatibility.
7. Keep improving with learning: Automorphic benefits from continuous learning. You can update models based on user feedback and manual labeling, which helps improve model performance over time.

By following these steps, you’ll be able to effectively use Automorphic to enhance your language models, keep your data secure, and continuously improve your models.