Robust Intelligence

What is Robust Intelligence?

Robust Intelligence is a platform designed to provide security and validation for AI models and data by offering real-time protection against security threats, operational threats, and ethical threats. It includes features such as an AI Firewall for securing production models, Continuous Validation for regular validation of models, and automated red teaming to surface weaknesses in AI systems. This platform aims to prevent potential adverse outcomes resulting from machine learning model failures and ensure the secure and accurate operation of AI models.

Who created Robust Intelligence?

Robust Intelligence was co-founded by Professor Yaron Singer and Kojin Oshiba in 2019. Professor Yaron Singer, the CEO, has a background in computer science, applied math, and significant experience in machine learning and optimization. Kojin Oshiba, the other co-founder, has a BA in Computer Science from Harvard and has published multiple papers on AI security. The company was established to bridge the gap between AI potential and practical use, focusing on AI security by developing the industry's first AI Firewall.

What is Robust Intelligence used for?

• Real-time protection of models
• Continuous Validation feature
• Automated red teaming
• Ethical threat management
• Operational threat protection
• Standards and regulatory compliance
• Secures against attacks
• Offers undesirable response protection
• Tracks adherence to standards
• Saves time-to-market for production

Who is Robust Intelligence for?

• Technology professionals
• Financial services professionals
• Insurance professionals
• Government & Defense professionals
• HR Management professionals

How to use Robust Intelligence?

To use Robust Intelligence effectively, follow these step-by-step guidelines:

1. Set Clear Objectives: Define your goals and what you aim to achieve with Robust Intelligence to guide your usage effectively.

2. Data Collection: Gather relevant data from diverse sources to enhance the robustness of your analysis and insights.

3. Preprocessing Data: Clean and preprocess the data by removing noise, handling missing values, and standardizing formats for accurate results.

4. Selecting Algorithms: Choose appropriate algorithms based on the nature of your data and the insights you want to extract.

5. Model Training: Train your selected model on the preprocessed data to make predictions or derive meaningful conclusions.

6. Testing and Evaluation: Evaluate the performance of the model using test data to ensure it meets your defined objectives.

7. Iterate and Refine: Continuously refine your model based on evaluation results and insights gained from the data.

8. Interpret Results: Analyze the outcomes of the model to draw actionable conclusions and insights.

9. Implement Findings: Implement the findings from the model into your decision-making processes or operational activities.

10. Monitor and Update: Continuously monitor the performance of your model and update it as needed to maintain its effectiveness over time.

By following these steps, you can leverage Robust Intelligence to extract valuable insights, make informed decisions, and drive positive outcomes in various applications.