Announcing Concrete ML v0.6

Zama has released a new version of Concrete-ML, incorporating features like fast, high-precision linear models and support for 16-bit accumulators, enhancing the performance of both built-in and custom neural networks. The update allows encrypted values to significantly boost the accuracy of neural networks, particularly in complex computer vision tasks, as demonstrated by tutorials and use-case examples on datasets like CIFAR10 and CIFAR100. The release optimizes Concrete-ML for performing only linear computations on encrypted data, achieving high precision and low latency. Concrete-ML also introduces better simulation and debugging features, allowing users to simulate the performance of models using Fully Homomorphic Encryption (FHE) without running time-consuming computations. Additionally, a live demo on Hugging Face Space illustrates the real-time application of FHE in analyzing encrypted texts for sentiment inference, supported by detailed documentation and community engagement channels.