Data Privacy
Generating Synthetic data is a problem that has existed since the early days of deep learning [1]. Good data has always been a bottleneck for good quality machine learning models that can work well in unseen real-world data. However, since the generative AI revolution boom, after the incoming ChatGPT and
MoEs
Just yesterday, Mistral AI announced their new model Mixtral 8x7B. It is a high-quality Sparse Mixture of Expert models (SMoEs). Its weights are open-source and licensed under Apache 2.0 magnet:?xt=urn:btih:5546272da9065eddeb6fcd7ffddeef5b75be79a7&dn=mixtral-8x7b-32kseqlen&tr=udp%3A%2F%https://t.co/uV4WVdtpwZ%3A6969%2Fannounce&
RAGs
With the boom of Generative AI and Large Language Models, RAG or Retrieval Augmented Generations are playing a major role in question answering on private documents. A huge number of practitioners are turning to hosted vector database services (like PineCone, Weaviate, Milvus, etc) to execute embedding searches at scale. For
