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Aug 27Thu
  1. Google DeepMind54

    Piloting the world's first double-blind AI evaluations

    Google DeepMind launched what it says is the world's first double-blind evaluation for proprietary frontier models, confining external evaluations to cryptographically secured isolated environments to prevent benchmark contamination from models seeing test questions in advance. In partnership with the Singapore AI Safety Institute, OpenMined, AVERI and MLCommons, it tested a Gemini Flash Lite model with confidential benchmarks to improve the credibility and integrity of evaluations.

  2. Google Research47

    GlucoFM: Foundation model for continuous glucose monitoring

    Google unveils GlucoFM, a self-supervised foundation model with a two-stream design that separates slow glucose baseline trends from short-term deviations. Across four cohorts, seven clinical prediction tasks and 14 evaluations, its mean PR-AUC improves from 54.7 to 58.8 over the strongest baseline CGM baseline, leading on all diabetes risk and beta cell dysfunction evaluations, and achieves the lowest MAE in postprandial glucose response prediction.

  3. Google DeepMind64

    Intelligent transcription with Gemini 3.5 Transcribe

    Google DeepMind released Gemini 3.5 Transcribe, positioned as a more accurate real-time speech transcription model with intelligent interaction. The model converts raw audio directly into formatted text, automatically removes filler words and self-corrections, supports custom vocabularies, over 85 languages, up to three speaker diarization, and can call other Gemini models via function calling.

    Why it matters: The official release details the model's capabilities in noisy environments, custom vocabularies and multi-speaker scenarios, plus API access, letting readers evaluate whether to migrate their transcription workflows.

Aug 26Wed
Aug 22Sat
  1. Google Research50

    An AI tool for prioritizing candidate biomarkers from wearable sensor data

    Google Research introduces the Biomarker Discovery Framework, a multi-agent system that structures candidate biomarker prioritization into an iterative, human-supervised research pipeline combining hypothesis generation, statistical analysis, model training and adversarial validation. Across three cohorts totaling 9,279 person-observations, the system autonomously identified 41 candidate digital biomarkers for mental health and 25 metabolic candidates, and built novel composite features such as sleep-duration variability.

Aug 21Fri
Aug 17Mon
Aug 14Fri
  1. Google DeepMind72

    Introducing Gemini 3.7 Flash

    Google DeepMind released Gemini 3.7 Flash, positioned as its flagship model for coding and agents, just three weeks after Gemini 3.6 Flash.

    Why it matters: The official announcement includes multiple benchmark scores and the half-price pricing details, so readers can decide whether to switch to 3.7 Flash.

Aug 12Wed
  1. Google DeepMind74

    Putting sign language AI into users’ hands

    Google DeepMind released a large-scale multilingual sign language-to-text (SL2T) translation model, bringing new sign language input features to deaf and hard-of-hearing users, first supporting American Sign Language (ASL) to English in Gboard and Live Transcribe on Pixel 11, with more devices and languages to follow.

    Why it matters: The article gives concrete details on training scale, benchmark scores, and privacy design, letting readers understand the real technical path to deploying sign language translation.

  3. Google Research65

    Advancing AMIE towards expert-level audio-visual clinical consultations

    Google Research has launched AMIE (Video), built on Gemini and Project Astra, which can conduct real-time synchronized clinical video consultations, perceive non-verbal cues, and guide virtual physical exams.

    Why it matters: The article covers the architecture, evaluation scale, and quantitative comparisons against primary care physicians (PCPs), while candidly noting limitations like simulated scenarios, helping readers understand the practical boundaries of audio-visual clinical AI.

Aug 6Thu
  1. Google DeepMind79

    WeatherNext: AI model achieves breakthrough in forecasting cyclones

    Google DeepMind published a Nature paper and open-sourced WeatherNext 2, WeatherNext Cyclones, and WeatherNext 2-mini; a single AI model can simultaneously predict a tropical cyclone's track, intensity, and wind structure, offering on average about 24 hours more lead time than previous models — roughly a decade's worth of meteorological progress over the past 20 years.

    Why it matters: Google detailed the roughly one additional day of lead time in cyclone forecasting and released the code and weights, so researchers can directly reproduce or improve the models.

Jul 31Fri
Jul 30Thu
  1. Google DeepMind67

    Gemini Robotics ER 2: powering robotics with video understanding, task orchestration, and multi-robot collaboration

    Google DeepMind released Gemini Robotics ER 2, positioned as a high-level embodied reasoning model for robots that can orchestrate low-level control interfaces such as VLA models and call tools like Google Search.

    Why it matters: Official benchmark numbers on progress classification and temporal grounding, plus multi-robot collaboration demos, let readers evaluate how usable this embodied reasoning model is for real-world robot orchestration.

  2. Google DeepMind46

    We’re launching Lyria 3.5 in Google Flow Music, with advances across musicality, lyrics, vocals, and creative control

    Google DeepMind has launched its latest music generation model, Lyria 3.5, available now in Google Flow Music. The model improves on musicality, lyrics, and vocals: it generates more natural and complex melodic structures, lyrics that better match prompts with structural awareness, and more expressive vocals with clearer articulation. Users also get easier control over the tempo and duration of the generated music.

Jul 28Tue
  1. Google DeepMind65

    Gemini Robotics 2 brings whole body intelligence to robots

    Google DeepMind released Gemini Robotics 2, comprising three models — a VLA model, the Gemini Robotics ER 2 embodied reasoning model, and On-Device 2 that runs locally — enabling whole-body control, dexterous manipulation, and multi-robot collaboration for humanoid robots.

    Why it matters: The official release explains the division of roles among the three models, the data cost of adapting to new robots, and safety benchmarks, letting readers judge the path to real-world deployment of robotics AI.

Jul 23Thu
  1. Google Research64

    SymptomAI: Towards a conversational AI agent for everyday symptom assessment

    Google Research has published the SymptomAI study, using five Gemini Flash 2.0-based conversational prototype agents to conduct end-to-end symptom intake and differential diagnosis (DDx) with 13,917 participants as a research benchmark.

    Why it matters: Based on a randomized real-population study of 13,917 people, it reports how SymptomAI's differential diagnosis performs against a clinician baseline and compares transferable intake strategies.

  2. Google Research49

    Towards a quantum computer that learns from its errors

    Google Research published a reinforcement learning-based quantum error correction control framework in Nature, letting an agent continuously tune thousands of control parameters using error detection events to counter drift during computation. In experiments injecting artificial drift on the Willow superconducting processor, RL control improved logical stability by 3.5x and further reduced the logical error rate by 20% on top of expert calibration.

Jul 22Wed
Jul 21Tue
  1. Google DeepMind76

    Introducing Gemini 3.6 Flash, 3.5 Flash-Lite, and 3.5 Flash Cyber

    Google DeepMind released three Gemini models: Gemini 3.6 Flash cuts output token usage by 17% versus 3.5 Flash on the Artificial Analysis Index, priced at $1.50/1M input and $7.50/1M output, with DeepSWE 49% vs 37% and OSWorld-Verified 83.0% vs 78.4%.

    Why it matters: The official article gives concrete pricing, token efficiency, and multiple benchmark numbers for the three new models, and explains the deployment scenarios each targets.

Jul 17Fri
Jul 16Thu
Jul 13Mon
Jul 9Thu
Jul 8Wed
Jul 3Fri
  1. Google DeepMind49

    Google DeepMind and A24 announce first-of-its-kind research partnership

    Google DeepMind and A24 have announced a first-of-its-kind research partnership pairing a frontier AI research lab with a film studio to help artists develop new workflows and techniques. The two will collaborate on long-term R&D across multiple projects: A24's filmmakers can help shape new technologies, while Google DeepMind gets guidance and feedback from working artists. Google has also invested in A24, and the partnership's specific goals and technical outputs will evolve over time.

Jul 1Wed
Jun 30Tue
  1. Google Research61

    Introducing TabFM: A zero-shot foundation model for tabular data

    Google Research has launched TabFM, a foundation model for tabular data classification and regression that frames tabular prediction as an in-context learning (ICL) problem, producing predictions in a single forward pass without manual training, hyperparameter tuning, or feature engineering.

    Why it matters: The article explains TabFM's architecture, synthetic-data training approach, and TabArena benchmark results, letting readers weigh it against traditional tuning workflows.

Jun 27Sat
Jun 25Thu
  1. Google Research47

    Optimizing cloud economics with linear elastic caching

    Google Research's linear elastic caching approach, published at CIDR, models page eviction as a ski rental problem, using a lightweight decision tree model to predict TTLs for cached pages and dynamically resize caches. In Spanner production, it cut memory usage by 15.5% versus fixed-size caches while cache misses rose only 5.5%, lowering TCO by about 5% with only a 0.5% impact on actual I/O costs.

  3. Google DeepMind69

    Introducing computer use in Gemini 3.5 Flash

    Google DeepMind announced that computer use is now a built-in tool in Gemini 3.5 Flash, whereas the capability was previously available only as a standalone Gemini 2.5 computer use model.

    Why it matters: The article explains that computer use has moved from a standalone model to being built into Gemini 3.5 Flash, and gives an API entry point and enterprise-grade safeguards, making it easier to evaluate automation scenarios.

Jun 22Mon
  1. Chrome Developers · AI and Browser68

    A developer toolkit to make your website agent-ready

    Chrome released an agent-ready toolkit for developers, and Lighthouse gains a new Agentic browsing audit category from M150, checking three metrics: accessibility tree, CLS visual stability, and WebMCP integration.

    Why it matters: The article details the specific checks in Lighthouse's new Agentic browsing audit and Chrome DevTools debugging methods, so developers can directly improve how agent-friendly their sites are.

Jun 18Thu
Jun 17Wed
  2. Google DeepMind49

    Unlocking UK house-building with AI-accelerated planning

    Google DeepMind is partnering with the UK government to develop an AI planning prototype tool built on Gemini, aiming to help planning officers cut decision time on residential planning applications by 50%. The tool is being developed with local planning authorities such as Barnet, Camden and Dorset, and can consolidate data, identify relevant policies, summarize feedback and draft assessment reports, though officers remain the final decision-makers.