Google and DeepMind just open-sourced VaultGemma, a 1B-parameter model trained from scratch with differential privacy. By mathematically injecting noise into training, it ensures the model never memorizes sensitive sequences. While privacy constraints pull its benchmark performance back to roughly GPT-2 levels, it provides the first empirical scaling laws for provably private AI.
Google just released TxGemma, an open family of models fine-tuned from Gemma 2 to predict molecular properties and analyze therapeutics data. Available in 2B, 9B, and 27B sizes, the models include specialized predict and chat variants that explain reasoning. The 27B predict model matches or beats previous SOTA on 64 out of 66 clinical and chemical tasks.
Google just launched PaliGemma 2, its next-gen vision-language model family built on Gemma 2. It offers drop-in replacement upgrades across multiple model sizes without major code changes, making it significantly easier for developers to fine-tune visual AI for custom datasets and vision tasks.
Google just released Gemma Scope, a massive open-source suite of over 400 sparse autoencoders to help researchers decipher Gemma 2's inner workings. By isolating more than 30 million learned features across layers, the tool functions like a microscope for mechanistic interpretability. Why it matters: Cracking open neural network black boxes is key to catching hallucinations, deception, and safety vulnerabilities before they cause harm.
Google just officially released Gemma 2, introducing ultra-efficient 9B and 27B open models for developers. The 27B variant punches above its weight by matching models twice its size while running full precision on a single Nvidia H100, and the 9B edges out Llama 3 8B. It is available now under a commercial-friendly license.
Google just opened up Gemini 1.5 Pro's 2-million-token context window to all developers, removing the waitlist. Both 1.5 Pro and Flash now also feature built-in Python code execution in an offline sandbox to tackle math and data reasoning, alongside Gemma 2 landing in Google AI Studio. Why it matters: developers can now feed entire codebases into Gemini while letting the model verify its own math by running code.
