AI at Google and DeepMind: the Gemini models, the Veo video models, research results and the product family.
Google DeepMind announced the world’s first double-blind evaluation of proprietary frontier AI models, restricting external evaluations to cryptographically isolated environments to prevent models seeing test questions in advance. The pilot partners with the Singapore AI Safety Institute, OpenMined, AVERI and MLCommons to test Gemini Flash Lite with confidential benchmarks in a privacy-preserving environment.
Google DeepMind released Gemini 3.5 Transcribe, calling it the most accurate speech-to-text model available, converting raw audio directly into accurate, formatted text.
Google Research proposed PhotoScan, a deep learning framework that estimates body fat percentage, A/G ratio and V/S ratio from ordinary 2D phone photos to predict insulin resistance.
Google DeepMind released Gemini 3.7 Flash, positioning it as its strongest workhorse model for coding and agents, just three weeks after Gemini 3.6 Flash.
Google DeepMind released SL2T, a multilingual sign-language-to-text model, bringing the capability to consumer products for the first time. Gboard and Live Transcribe on Pixel 11 support ASL-to-English dictation, with more devices and languages to follow.
Google Research released AMIE (Video), built on Gemini and Project Astra, for real-time video clinical consultations. It can perceive non-verbal cues and guide virtual physical examinations.
Google DeepMind released the WeatherNext AI model, achieving state-of-the-art cyclone track, intensity and wind-field structure forecasts and adding an average extra day of warning, equivalent to roughly a decade of meteorological progress.
Google Research released the Chain-of-Evidence (CoE) verifiability framework, implemented in a Science One Framework prototype, with automated CoE Audit metrics.
Google DeepMind released Gemini Robotics ER 2 as a high-level brain for robots, supporting video understanding, multi-step task orchestration and multi-robot collaboration, while delegating action execution to lower-level VLA models.
Google DeepMind released its next-generation music model Lyria 3.5 on Google Flow Music. It claims improvements in musicality, lyrics, vocals and creative control, including more complex and natural melodic structures, lyrics with better prompt adherence and structural awareness, more expressive vocals with clearer pronunciation, and easier control of rhythm and duration.
Google DeepMind released Gemini Robotics 2, a next-generation robot intelligence layer achieving whole-body control of a complete humanoid robot for the first time, with fine manipulation using both hands and grippers.
Google Research published SymptomAI research, using five Gemini Flash 2.0 agents with different questioning strategies for symptom interviews and differential diagnosis, involving 13,917 participants.
Google Research published a study in Nature proposing a reinforcement learning framework in which agents continuously learn from quantum error-correction detection events, dynamically adjusting thousands of control parameters during computation to counter drift.
Google DeepMind released three models: Gemini 3.6 Flash, 3.5 Flash-Lite and cybersecurity-focused 3.5 Flash Cyber.
Google DeepMind released Gemini 3.5 Flash Cyber, a lightweight cybersecurity model fine-tuned from 3.5 Flash for rapid vulnerability discovery, validation and patching. Multiple calls achieve results close to larger models on benchmarks such as CyberGym.
Google Research and Google DeepMind proposed SensorFM, a large sensor foundation model learning directly from unlabelled wearable data. Pretraining uses over 1 trillion minutes of multimodal sensor signals from 5 million consenting participants across more than 100 countries and over 20 Fitbit and Pixel Watch devices.
Hugging Face and Cerebras jointly demonstrated a real-time speech-to-speech pipeline, accelerating Gemma 4 31B inference with Cerebras and combining Nvidia Parakeet speech recognition with Alibaba Qwen3TTS synthesis.
Google DeepMind released Nano Banana 2 Lite (gemini-3.1-flash-lite-image) and Gemini Omni Flash (gemini-omni-flash-preview).
Google Research released TabFM for tabular classification and regression. It reframes table prediction as in-context learning, producing predictions in one forward pass without manual training, hyperparameter tuning or feature engineering.
Google Research published an architecture attaching multi-token prediction (MTP) heads to frozen Gemini Nano v3 models, accelerating on-device inference without changing backbone weights. It has shipped with the Pixel 9 and 10 series.