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Anote
Learn how Anote actively improves AI models like GPT-4 by learning from human feedback and domain experts, overcoming limitations like hallucination and unreliability for tailored predictions.
The Anote platform, pioneered by human centered AI, actively learns from human feedback to make AI algorithms like GPT-4, Bard and Claude and techniques like RLHF, Fine-Tuning and RAG, perform better for specific use cases over time. Over time, we are able to provide more tailored answers to questions, category predictions and entities found because our platform enables AI models to actively learn and rapidly improve from the knowledge of domain specific subject matter experts.
On the Anote platform, users can upload unstructured data like PDFs, TXTs, DOCXs, PPTXs, scrape HTML files from websites, or upload structured data like CSVs. Users can connect to any dataset on the hugging face dataset hub, or integrate with external data sources like Reddit, S3, Notion, Asana, Github, Snowflake, Twitter. After the user customizes their requirements (adding the questions, categories or entities they care about), the AI model is able to actively learn from people with just a few interventions to improve model performance (think hundreds of rows labeled, rather than millions required for re-training LLMs). Users can download the resulting CSV of actual and predicted results, as well as export the updated AI model to make real-time, improved model inference and predictions via the call of an API.
Platform fine-tunes LLMs via human-centered data labeling and model inference.
Human-Centered AI platform for LLM fine-tuning, RAG, and autonomous agent deployment.
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