Raze Systems

company
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AI & ML interests

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Raze the old, raise the machines

Originating at the Center for Advanced Studies in Digital Democracy in the Federal University of Bahia, our Research Laboratory is dedicated to advancing Digital Methods in social sciences, with a significant focus on data, AI, and Machine Learning. We specialize in developing innovative solutions and conducting comprehensive research to further the integration of these technologies in social science disciplines. Our team possesses a wide range of skills, from synthetic data generation to model training and evaluation for diverse applications, including complex conversational systems.

We believe that the advancements we make in AI must be guided by principles of safety, privacy, and a strong sense of humanity. Our aim is not only to create cutting-edge technologies but also to empower individuals to realize their dreams and aspirations. We envision a future where AI systems are deeply aligned with the needs of people, fostering collaboration, and driving progress towards a better reality for all.

Commitments:

  1. Ethical Innovation: We are committed to prioritizing ethical considerations in our pursuit of AI advancements. Our work will adhere to strict standards of fairness, accountability, and transparency, ensuring our innovations serve the greater good.
  2. Privacy and Security: We will place a strong emphasis on safeguarding personal data and protecting user privacy. Our systems will be designed with robust security measures, giving people peace of mind as they engage with our AI technologies.
  3. Human-Centric Approach: We will focus on creating AI systems that cater to human needs and aspirations, fostering a symbiotic relationship between technology and individuals. Our aim is to empower people and enable them to achieve their goals.
  4. Open Source and Open Weights: We are dedicated to contributing to the growth and development of the AI community by supporting open-source initiatives and promoting transparency through open weights. This commitment enables collaboration, encourages learning, and ensures that our work remains accessible to all who share our vision for a better future.

models

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datasets

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