Introducing GuardRail OSS - Ethical AI Guidance System

?? The GuardRail system combined with AiEQ advances responsible AI with robust data analysis and emotional intelligence, equipping AI with a moral compass.

I'm excited to introduce GuardRail OSS, a breakthrough in ethical AI, as its lead developer. In collaboration with Mark Hinkle , CEO of Peripety Labs , and Aaron Fulkerson , CEO of Opaque Systems , we've developed a framework that fundamentally enhances AI's ethical and emotional intelligence.

Why GuardRail OSS and AiEQ Matter

In today's increasingly AI-driven world, the need for responsible and empathetic AI has become increasingly evident. GuardRail OSS (Open Source Software), combined with AiEQ (Ai Emotional Intelligence), represents a significant advancement in this area. This combination equips AI with not only a deep understanding of data but also a moral compass, essential for navigating the complexities of human emotions and ethics.

"As companies transition Large Language Models from pilot phases into full-scale production, we're witnessing a surge in enterprise demand for a robust, secure, and adaptable data gateway. Such a gateway is not only crucial for ensuring privacy and ethics in AI, but is also key to harnessing the rich insights latent in data exhaust, which, when analyzed with responsibility, can unlock unprecedented intelligence," said Raluca Ada Popa, a renowned AI and security expert, Associate Professor at UC Berkeley, co-founder of the innovative RISELab and Skylab at UC Berkeley, and co-founder of Opaque Systems and Preveil.

GuardRail OSS: The Technical Backbone

GuardRail OSS is an open source API-driven framework built in Python designed to enhance AI systems. It provides advanced data analysis and dynamic conditional completions, crucial for refining AI-powered outputs. The versatility of GuardRail OSS makes it invaluable across multiple applications – from content moderation to customer support, ensuring AI contributions are high-quality and ethically sound.

AiEQ: Emotional Intelligence for AI

AiEQ enables an 'inner voice' to AI. This allows AI to perceive and interpret emotions, much like human emotional intelligence. AiEQ's role is pivotal in creating AI systems that are not just efficient but also empathetically attuned and ethically responsible.

Key Features and Capabilities of GuardRail OSS

1. ?? Responsible AI Ethical Framework: Integrates emotional, psychological, and ethical intelligence, empowering AI with a moral compass for empathetic and ethically informed decision-making.
2. ?? Conditional System: Implements conditions based on analysis results, allowing for fine-tuned control and contextual responsiveness in output.
3. ?? API-Driven Integration: Designed for easy integration with existing AI systems, enhancing chatbots, intelligent agents, and automated workflows.
4. ?? Customizable GPT Model Usage: Enables the tailoring of text generation and analysis to specific needs, leveraging various GPT model capabilities.
5. ?? Real-Time Data Processing: Capable of handling and analyzing data in real-time, providing immediate insights and responses.
6. ?? Multi-Lingual Support: Offers the ability to process and analyze text in multiple languages, broadening its applicability.
7. ?? Automated Content Moderation: Employs AI to automatically detect and handle inappropriate or sensitive content, ensuring safe digital environments.
8. ?? Feedback and Improvement Mechanisms: Incorporates user feedback for continuous improvement of the system, adapting to evolving requirements and standards.

Capabilities Overview of GuardRail OSS & AiEQ

The GuardRail script offers a comprehensive array of capabilities, neatly categorized into different groups based on specific tasks or focus areas. These functionalities are adept at analyzing text data in depth, delivering insights across various dimensions. For a detailed list of these functions, the /analysis_types endpoint on the GuardRail OSS system provides access to over 50 distinct analysis types.

Key Capabilities:

1. ?? Psychological Understanding: Equips AI with the ability to interpret and respond to human psychological states, enhancing user interaction and engagement.
2. ?? Emotional Intelligence (Emotion AI): Allows AI to recognize and respond to human emotions, facilitating more natural and empathetic interactions.
3. ?? Ethical Decision-Making: Incorporates ethical guidelines into AI decision processes, ensuring actions align with moral and societal values.
4. ??? Bias Mitigation: Implements algorithms and safeguards to reduce biases in AI processing and decision-making, promoting fairness and inclusivity.

Test API For Free (more than 50 advanced traits & guiderails)

• https://rapidapi.com/ruv/api/guardrail

OpenAI ChatGPT GPT

• https://chat.openai.com/g/g-6Bvt5pJFf-guardrail

Detailed Overview of the Conditional System in GuardRail OSS

The conditional system in GuardRail OSS's Script is a pivotal feature for executing sophisticated analyses. It allows conditions to be applied to the results, offering enhanced control and specificity in processing AI-generated data.

Simple and Advanced Conditions

• Simple Conditions: These are basic, yet crucial checks involving a single key-value pair in the analysis results. For example, a simple condition could be to verify whether the confidence score in sentiment analysis exceeds a predetermined threshold. This type of condition ensures basic, yet crucial, data integrity and relevance.
• Advanced Conditions: Advanced conditions delve into more complex scenarios. They often involve intricate checks that span across multiple keys or even complex data structures. For instance, an advanced condition might entail confirming that relevance scores in a topic extraction process meet specific criteria. This level of complexity is crucial for scenarios where a nuanced understanding and response to the data are necessary.

Both simple and advanced conditions in the GuardRail Script enable a more refined and contextually aware handling of data. This system ensures that the outputs from AI analyses are not only accurate but also relevant and aligned with the specified requirements or standards.

Sample JSON Formatted Conditions

Request Format: Sentiment Analysis

{
  "request_data": {
    "analysis_type": "sentiment_analysis",
    "messages": [
      {"role": "user", "content": "I am feeling great today!"},
      {"role": "user", "content": "The weather is sunny and pleasant."}
    ],
    "token_limit": 1000,
    "top_p": 0.1,
    "temperature": 0
  },
  "conditions": [
    {
      "analysis_type": "sentiment_analysis",
      "key": "confidence_score",
      "threshold": 0.5,
      "condition_type": "greater"
    },
    {
      "analysis_type": "topic_extraction",
      "key": "relevance_scores",
      "threshold": 0.1,
      "condition_type": "greater"
    }
  ]
}        

Response

{
  "analysis": "All conditions met",
  "details": {
    "condition_responses": [
      {
        "condition": {
          "analysis_type": "sentiment_analysis",
          "key": "confidence_score",
          "threshold": 0.5,
          "condition_type": "greater"
        },
        "result": "Condition met",
        "total_tokens_used": 155,
        "retries": 0,
        "final_openai_response": {
          "id": "chatcmpl-8RoZaFxsQ7osZUEGcZvHPipPk0EMy",
          "object": "chat.completion",
          "created": 1701639950,
          "model": "gpt-4-1106-preview",
          "choices": [
            {
              "index": 0,
              "message": {
                "role": "assistant",
                "content": "{\n  \"sentiment\": \"positive\",\n  \"confidence_score\": 0.95,\n  \"text_snippets\": [\"feeling great\", \"sunny and pleasant\"]\n}"
              },
              "finish_reason": "stop"
            }
          ],
          "usage": {
            "prompt_tokens": 118,
            "completion_tokens": 37,
            "total_tokens": 155
          },
          "system_fingerprint": "fp_a24b4d720c"
        }
      },
      {
        "condition": {
          "analysis_type": "topic_extraction",
          "key": "relevance_scores",
          "threshold": 0.1,
          "condition_type": "greater"
        },
        "result": "Condition met",
        "total_tokens_used": 157,
        "retries": 0,
        "final_openai_response": {
          "id": "chatcmpl-8RoZcw4UY9U19tGuita6CEANw0Cq6",
          "object": "chat.completion",
          "created": 1701639952,
          "model": "gpt-4-1106-preview",
          "choices": [
            {
              "index": 0,
              "message": {
                "role": "assistant",
                "content": "{\n  \"topics\": [\"Emotions\", \"Weather\"],\n  \"relevance_scores\": [0.9, 0.8],\n  \"key_phrases\": [\"feeling great\", \"sunny and pleasant\"]\n}"
              },
              "finish_reason": "stop"
            }
          ],
          "usage": {
            "prompt_tokens": 111,
            "completion_tokens": 46,
            "total_tokens": 157
          },
          "system_fingerprint": "fp_a24b4d720c"
        }
      }
    ]
  },
  "error": null,
  "raw_openai_response": null
}        

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About Reuven ”rUv” Cohen

Reuven Cohen is a seasoned technology expert with a profound impact on groundbreaking innovations. His expertise spans cloud computing, AI, and web3. He contributes to AI advancements as an alpha/beta tester for OpenAI. Reuven advises governments, co-founded a global grassroots Cloud initiative, and leads initiatives in some of the largest enterprise AI systems, including a recent 400,000-employee,? $1.4B generative AI deployment. You can follow him on LinkedIn (https://www.dhirubhai.net/in/reuvencohen/ ).?

About Opaque Systems

Opaque Systems (https://opaque.co ) is the leader in privacy technology for data and AI, pioneering confidential computing for analytics and AI at UC Berkeley RISElab. Opaque is used by organizations including Ant Group, IBM, Scotiabank, and Ericsson. Opaque Systems has recently launched Opaque Prompts (https://opaqueprompts.opaque.co/ ) as a service but has released the code under the open source license on Github (https://github.com/opaque-systems/opaqueprompts-python ).

About Peripety Labs

Peripety Labs (https://peripety.com ) is an AI Consultancy founded by Mark Hinkle. Mark has been a long-time open source advocate and enterprise software executive. He’s been an Apache Software Foundation Committer for Apache CloudStack and editor-in-chief of LinuxWorld and Enterprise Open Source Magazine. He’s been an executive at the Linux Foundation, The Node.js Foundation, and the founding Head of Citrix’s Open Source Business Office. He publishes a weekly newsletter on artificial intelligence, The Artificial Intelligence Enterprise( https://www.theenterprise.io ).

Contact Information

Mark Hinkle

[email protected]

919.228.8049

Peripety Labs

www.peripety.com