🚄HapticAI

Haptic is an innovative feedback layer designed for training and subsequently upgrading Large Language Models (LLMs) and various AI networks. Utilizing its decentralized human feedback infrastructure, Haptic acts as a bridge, seamlessly connecting AI training process and human cognition. We aim to revolutionize the way AI training and optimization are carried out by focusing on a decentralized rewards redistribution model to gather the highest quality of feedback and training data.

As builders in the realm of decentralized AI retraining, we at Haptic are utilizing the cutting edge of reinforcement learning with human feedback. Recognizing the unique capabilities of human cognition, we are developing a distributed feedback infrastructure that harnesses these capabilities to their full potential and ensure human contributions are rewarded appropriately. We are committed to providing the nuanced human input that is crucial for refining LLMs and enhancing AI models. This unique synergy between human feedback and AI is the key to unlocking new avenues for accuracy, efficiency, and innovation in the development of future AI models.

Our initial focus is on the LLM sector, a field in which our team has a wealth of experience and expertise. This focus will provide us with a strong foundation from which to expand into other AI domains. As we move forward, we plan to integrate upcoming innovative AI fine-tuning methods beyond reinforcement learning into our process. This will allow us to remain at the cutting edge of AI training, ensuring that we are always ready to meet the evolving needs of the AI industry.

Several domains can benefit from Reinforcement Learning from Human Feedback (RLHF). These will include but are not limited to the following

1. AI Bias Mitigation: RLHF could be used to train AI models to better understand and respect ethical guidelines and avoid biases. This could be especially useful in domains like AI moderation tools, personal assistants, and recommendation systems, ensuring they align more closely with human values. Bias introduced via poor dataset generation can be easily tackled through RLHF retraining.

2. AI Art/Creativity: Generative AI tools, such as those used for generating art, music or creative writing, could be improved with RLHF by providing better alignment with human aesthetic preferences and understanding of creative norms. Artists working in conjunction with AI tools have already started training custom AI model to their art style and preferences.

3. Emotional AI: Affective computing involves teaching machines to recognize and respond to human emotions. RLHF could help these models to better interpret human emotional feedback and respond in a more empathetic manner and has far-reaching implications in robotics.

4. Medical AI: In medical diagnostics or treatment recommendation systems, RLHF could help train AI to better align with the feedback from medical professionals and too some extent even patients, improving the accuracy and usefulness of these systems.

5. AI for Accessibility: RLHF could be used to improve AI tools designed for people with disabilities. For instance, feedback from users could help improve the functionality of AI-driven prosthetics, accessibility software, and other assistive technologies.