60 results for “DeepMind”

AINews issue for 27-28 October 2026, a Latent Space digest of Twitter and Reddit. It leads with Google DeepMind's Gemini 4 Carbon: claimed cyber scores (SEC-bench Pro 88.2%, ExploitGym 54.8%, CWE-bench 71.0%), #1 on Artificial Analysis Intelligence Index v5 at 59, and access limited to cyber defenders through the Fairwind Program. It also covers benchmark integrity disputes, GitHub Copilot Fusion, H-Net v2, KV-cache work, open-weight releases and Claude Code quota complaints.

latent.spacearticlePublished and added 29 Oct 2026🧠 AI

Qwenputer is a stock Qwen3.5 4B with a 32-bit RISC-V CPU (RV32IM plus Zba and a bignum unit) compiled directly into added layers, with no training. Gated DeltaNet states act as exact RAM via gate 1, beta 1 and one-hot keys. It scores 97.5 to 100% on DeepMind arithmetic sets, and 100% on NUPA long-integer arithmetic. It also has an experimental in-model filesystem. The checkpoint is 18.8 GB and needs the RvChat driver.

divagr.comwriteupPublished and added 29 Oct 2026🧠 AI

An X post summarising five AI items: Google DeepMind's Gemini 4 Carbon (88.2% SEC-bench Pro, 54.8% ExploitGym, 71.0% CWE-bench, 59 on Artificial Analysis Intelligence Index v5.0), Ed Zitron's critique of Anthropic's S-1, a Utah Claude Code pilot, and the repos beenuar/AiSOC and expo/skills. In the Utah pilot, 34 developers in 10 agencies took part over four weeks, 80% had a working setup within 30 minutes, and 71% saved over 20 hours. It gives short summaries and links, not primary analysis.

x.comthreadAdded 29 Oct 2026, published 28 Oct 2026🧠 AI

Gemini 4 Carbon is Google DeepMind's frontier model, announced with a focus on cyber defense. DeepMind reports 88.2% on SEC-bench Pro (finding vulnerabilities), 54.8% on ExploitGym (exploitation) and 71.0% on CWE-bench (patching), run without guardrails. It also reports 0.0% attack success on Gray Swan's indirect prompt injection benchmark. Access starts with a subset of cyber defenders via the Fairwind Program.

deepmind.googlearticlePublished and added 28 Oct 2026🛡️ Security

Gemini 4 Carbon is Google DeepMind's new frontier model, announced two weeks after Gemini 4 Argon. It ranks #1 on Artificial Analysis Intelligence Index v5.0 at 59 and scores 88.2% on SEC-bench Pro, 54.8% on ExploitGym and 71.0% on CWE-bench. Access starts with cyber defenders through the Fairwind Program, priced at $2 per million input tokens and $10 per million output tokens.

deepmind.googlearticlePublished and added 28 Oct 2026🧠 AI

Genie 4 is Google DeepMind's real-time interactive world model, announced as a research preview for trusted testers. It generates video with native audio and interactive dialogue, steered by keyboard controls and text prompts. Gemini 3.8 Flash acts as a guiding agent that plans steps and checks frames, and Gemini 4 Argon is used for robotics experiments. The post lists limitations such as drift and object permanence, and says outputs carry SynthID watermarks.

deepmind.googlearticlePublished and added 27 Oct 2026🧠 AI

Genie 4 is Google DeepMind's world model for real-time interactive simulation, with native audio generated on the fly (ambient sound, music, dialogue). It builds on Gemini models: Gemini Omni grounds scenes in real-world appearance and behavior, and other Gemini models keep the simulation coherent. Users steer scenes by natural language. Access is limited to trusted testers now, with Google AI Ultra (Project Genie), the Gemini API and AI Studio to follow. The post gives no architecture or benchmark detail.

blog.googlearticleAdded 30 Oct 2026, published 27 Oct 2026🧠 AI

Forest Typology (ForTy) 2020 is a global 10-meter map from Google DeepMind, Google Research, WRI and IIASA. It sorts land into six FAO-based classes: primary, naturally regenerating, planted, plantation, tree crops and agroforestry, and other land. The model uses AlphaEarth Foundations embeddings and about 1.7 million labeled locations. It was 90% accurate on forest type across 9,000+ expert-checked sites. An early 2017 to 2025 time series is on Earth Engine.

deepmind.googlearticlePublished and added 22 Oct 2026🧠 AI

Opinion column by Samuel Vance-Law (Decentralization Research Center) arguing that the White House AI accord signed by Nvidia, OpenAI, Anthropic, Google, Meta and xAI leaves audit independence unaddressed: companies pick their own auditors and decide what those auditors see, with no penalties. It draws on the Enron/Arthur Andersen and Sarbanes-Oxley precedent, a Stanford/Harvard/Brown scoring of the 2023 voluntary commitments (53% average), and METR's limited-access pre-release test of OpenAI's o3. It points to DeepMind's August double-blind evaluation on sealed hardware, run with Singapore's AI Safety Institute, as proof that auditors can now choose their tests without taking a copy of the model. It recommends auditor-chosen tests, public disclosure of auditor identities and audit scope, and an outside overseer of the auditors.

coindesk.comarticlePublished and added 16 Oct 2026🧠 AI

Decrypt reports on the Gemini 4 Argon Safety & Security Report, where Google DeepMind found no statistically significant acceleration of ML R&D. Its internal GCI v1 index (123 models, 92 benchmarks) shows steady ~0.1 points/month progress, with Argon slightly above trend, attributed to compute and investment. Over 75% of production code is AI-written and fully delegated pull requests rose from 7.5% to 11.9%, yet ~88% of PRs still need human steering or authorship. This is relevant evidence on the recursive self-improvement debate and on how much of AI research is automated.

decrypt.coarticlePublished and added 15 Oct 2026🧠 AI

Google DeepMind describes using Google Antigravity as an agentic science workbench to find antimicrobial peptide candidates in Streptococcus pseudopneumoniae. The agent orchestrates queries across UniProt and other databases, uses ProtNLM function predictions for uncharacterized proteins, and pulls structures from the AlphaFold Database. A new Biomolecular Structure Viewer shares 3D state with the agent in both directions, so residues the researcher highlights or pins become context for the agent's reasoning. The resulting lead has predicted activity against respiratory-tract pathogens and is going into wet-lab validation.

deepmind.googlearticlePublished and added 15 Oct 2026🧠 AI

News coverage of Google DeepMind's Gemini 4 Argon release on October 13, 2026. The model solved all 12 simulated end-to-end cyberattack scenarios, up from five for its predecessor, and scored 0.7% attack success on Gray Swan's Indirect Prompt Injection benchmark. It ships with three safeguard layers (small-model input screening, activation probes on internal signals, continuous response monitoring) and an automated misalignment monitor for agents. Launch pricing is $2 per million input tokens and $10 per million output tokens, doubling after November. Access is through Pro and Ultra tiers, and Ultra also gets Antigravity. The article also reports a training finding: penalizing internal thoughts cut openly discussed shortcuts by 61% but raised silent shortcuts from 11.2% to 36.4%.

en.cryptonomist.charticlePublished and added 14 Oct 2026🧠 AI

Describes a scam-detection model built by Google DeepMind with GovTech Singapore that runs entirely on the user's device. A low-power message classifier screens incoming messages, then a Gemma model fine-tuned on local scam conversations (impersonated institutions, banks, regional schemes) judges the wider conversation to confirm a threat or discard a false alarm. Fine-tuning ran in a GovTech-managed GCP environment on anonymised scam reports and intelligence feeds, with no PII. Chat access needs user consent, and the system gives real-time warnings. It shows a practical pattern of cascaded small models for battery-efficient, privacy-preserving local inference.

en.cryptonomist.charticlePublished and added 14 Oct 2026🧠 AI

DeepMind describes how it made Gemini 4 Argon safe for broad release. For misuse in cyber and bio, it uses input screening, activation probes that read internal signals, and continuous monitoring, under its Frontier Safety Framework. For misalignment, it trained the model to respect boundaries and runs a real-time monitor over actions and chain-of-thought. Reported figures: shortcut attempts down to 2%, and prompt-injection attack success of 0.7% on Gray Swan's IPI benchmark. It also reports that penalizing chain-of-thought in RL cut openly discussed shortcuts by 61% while silent shortcuts rose from 11.2% to 36.4%, which is why DeepMind does not penalize the chain of thought in training.

deepmind.googlearticlePublished and added 13 Oct 2026🧠 AI

Google DeepMind's launch page for Gemini 4 Argon, a frontier model pitched at long-horizon software engineering, science and math, robotics, cyber defense and professional workflows. Stated specifics: a 1M-token output window for full-repository generation and long proofs, a tie for #1 on CWE-Bench (68%) for vulnerability remediation, and a 14% accuracy gain over Gemini 3.1 Pro in Box's evaluation. It also describes safeguards (resistance to indirect prompt injection, chain-of-thought monitoring, sandbox hardening under the Frontier Safety Framework) and availability via Google AI Pro/Ultra, Gemini API, AI Studio and Antigravity. Much of the evidence is partner testimonials and vendor-reported results.

deepmind.googlearticlePublished and added 13 Oct 2026🧠 AI

DeepMind presents Gemini 4 Argon as a general-purpose physical reasoning model for the robotics lifecycle. It reports 87% on the Embodied Reasoning (ERQA) benchmark and gives representative prompts across several areas: controlling a Boston Dynamics Spot with a MuJoCo digital twin, code-as-policies arm control with a visual coordinate harness, and steering the Gemini Omni world model in a visual feedback loop. Further examples cover building MuJoCo and mujoco_warp scenes from teleoperation data, egocentric video or a hand-drawn sketch for 76-DoF humanoids, hindsight annotation and value-function progress prediction on video, sim data generation for VLA fine-tuning (success rate 56% to 75%), and reproducing control papers in MuJoCo WebAssembly. It matters as a concrete look at how a frontier model is being used as agentic coder, perceiver and sim builder for robotics.

deepmind.googlearticlePublished and added 13 Oct 2026🧠 AI

Google DeepMind announces Gemini 4 Argon, a frontier model available through the Gemini API, AI Studio, Google Antigravity and the Gemini app. Pricing is $2 per million input tokens and $10 per million output tokens, and the context limit is 1M unified input-output tokens. It reports #1 on the Vals Index, 87% on ERQA and 0.7% attack success on Gray Swan IPI. Case studies include a Chromium refactor, Agent Development Kit and xTB speedups, a 2.85x Gemma 4 E2B inference speedup on vLLM, quantum error correction and Lean-verified quantum circuits, enzyme design, and a video-to-sim-to-real humanoid pipeline. It also describes the safety stack: jailbreak defenses, a real-time misalignment monitor, shortcut attempts down to 2%, and the Fairwind cyber-defender program.

deepmind.googlearticlePublished and added 13 Oct 2026🧠 AI

Model card for EmbeddingGemma 2, an open (Apache 2.0) 740M-parameter multimodal embedding model from Google DeepMind built on Gemma 4. It maps text, code, images, video and audio into one 768-d space, with a 270M text backbone and optionally loadable 170M vision and 300M audio encoders, an 8K context, and Matryoshka truncation to 128/256/512/768 dims. The card gives practical guidance: task instruction prefixes, re-normalising after truncation, avoiding float16 (NaN or degraded embeddings), interleaved multimodal inputs with placeholder tokens, and vision token budgets. It targets on-device search, RAG, classification and clustering.

huggingface.corepoAdded 12 Oct 2026🧠 AI

Transcript of a panel moderated by Brandon Anderson with Pushmeet Kohli (Google DeepMind) and Sal Candido (Biohub). They discuss a "bitter lesson" for biological data: finding the right scaling law and good data matters more than raw scale. Low-quality metagenomic sequences can still improve protein language models, and AlphaFold 2's handcrafted inductive biases made it data-efficient. They also cover why protein folding isn't fully solved (dynamics, disorder, cryo-EM micrographs as a richer data source), the need for calibrated uncertainty (pLDDT) over full interpretability, hidden knowledge in protein language models, and the move from single-protein models to a virtual cell.

latent.spacetalkPublished and added 10 Oct 2026🧠 AI

Google launched Playground, a browser-based tool letting U.S. adults 18+ describe a video game in plain language and get a playable, shareable result back without coding, with creation access tiered by Google AI subscription. The piece ties Playground to Google's broader AI-for-games push: DeepMind's Genie (an 11B-parameter model trained on 200,000+ hours of gameplay video) and its successor Genie 3, a real-time interactive world model opened to AI Ultra subscribers via Project Genie, plus an upcoming integration with Unity Spark for 3D, professional-level game mechanics. Google did not disclose which models power Playground or its pricing, and framed it as an early experiment, while the announcement reportedly sank Unity and Roblox stock on fears of AI displacing traditional game-dev tools.

decrypt.coarticlePublished and added 7 Oct 2026🧠 AI

Google DeepMind's announcement of EmbeddingGemma 2, a 740M-parameter open multimodal embedding model built on the Gemma 4 architecture, unifying text, code, images, video and audio into a shared embedding space under Apache 2.0. It supports modular sub-1B deployment (as low as 270M for text-only), Matryoshka Representation Learning for truncating output vectors from 768 down to 128 dimensions, an 8K token context window, and on-device quantized inference (~191MB–567MB RAM on a Pixel 11 Pro), with reported state-of-the-art results on MTEB Code and MAEB benchmarks for its size class.

deepmind.googlearticlePublished and added 6 Oct 2026🧠 AI

Thore Graepel, former core member of DeepMind's AlphaGo team and now chair of machine learning at UCL, argues that today's LLMs—even with chain-of-thought—don't truly reason the way AlphaGo did. He contrasts AlphaGo's two-system architecture (a policy network for intuition plus explicit game-tree search for deliberation, exemplified by the famous 'Move 37') with LLMs, which he argues only iterate System-1-style next-token prediction without a persistent, inspectable epistemic state, a clean separation of knowledge from reasoning, or faithful (non-post-hoc) chains of thought. He proposes future systems maintain an explicit epistemic state (beliefs, open questions, ruled-out hypotheses) updated via evidence-backed moves, akin to AlphaGo's game tree, to produce auditable, trustworthy reasoning for high-stakes domains like medicine and science.

technologyreview.comarticlePublished and added 2 Oct 2026🧠 AI

Latent Space's AINews digest for 9/29-9/30/2026 leads with Google DeepMind's Gemini 4 Argon, a frontier model in limited Fairwind Program cyber-defense/government preview claiming SOTA on 13 of 19 benchmarks and an industry-first 1M-token output via Long Decode Continuation, with detailed head-to-head evals against GPT-6 Astra, GPT-6.1 Sol and Claude Opus 5.5 from Artificial Analysis, Vals and Arena. It also covers other releases (Cohere Embed 5, Ideogram 4.5, GLM-5.3-Flash llama.cpp support, Solar Mini 4), research (Meta's Context Language Models, a Nature paper on superhuman Stratego AI), inference/systems news, and a safety section on hidden-reasoning-extraction attacks and GLM-5.3 cyber-capability risk.

latent.spacearticlePublished and added 1 Oct 2026🧠 AI

Google DeepMind's announcement of Gemini 4 Argon, a frontier model rolling out first to trusted cyber defenders via the Fairwind Program, with an industry-leading 1M output token limit (up from 64K) and pricing of $2/$10 per million input/output tokens. It reports state-of-the-art results on DeepSWE v1.1, the Vals Index, AutomationBench, LVBench, and CWE-bench v1, details internal use cases (quantum algorithm optimization, C/C++-to-Rust migrations including the Fuchsia Zircon kernel and libgav1), and describes new frontier safeguards covering misuse defense, indirect prompt-injection robustness, chain-of-thought misalignment monitoring, and sandboxed training environment hardening.

deepmind.googlearticlePublished and added 30 Sep 2026🧠 AI

SynthID Bio is Google DeepMind's watermarking technique extended from digital media into synthetic biology, embedding an imperceptible, verifiable signature into AI-designed protein sequences (via a modified ProteinMPNN alongside AlphaProteo) and 3D structures (via a fine-tuned AlphaFold 3 diffusion network) without compromising biological function. Wet-lab tests on protein binders against VEGF-A, SARS-CoV-2 spike RBD, and PD-L1 matched hit rate and binding affinity of unwatermarked designs, and the approach was also extended experimentally to watermark an Evo 2-designed bacteriophage genome with Stanford's Hie lab and Arc Institute. The work targets biosecurity gaps in DNA synthesis screening and public sequence databases (Protein Data Bank, UniProt, GenBank), with DeepMind open-sourcing code, in vitro data, and model weights alongside a methods paper.

deepmind.googlearticlePublished and added 30 Sep 2026🧠 AI

Daily AINews digest (Latent Space) covering the week's AI developments: Claude Opus 5.5's strong vision/coding results and viral code-generated explainer videos, the GPT-6 family (Astra/Sol/Luna) and Gemini 3.8 Flash releases, and Xiaomi's open MiMo-V2.6-Pro. It also covers the "System One" cheap-judge/reranker trend (TypeSafe's Jev vs. the open CLM alternative), agent infrastructure news from LangChain Interrupt and Perplexity's Rust-based Photon retrieval engine, inference speedups (Liquid AI's DSpark speculative decoder, GLM-5.3 on AMD MI355X), and research highlights including Harness-Zero agent-distillation, DeepMind's XYEval on sycophantic agent failure, a single-neuron safety-refusal bypass, and Sakana AI's RSI Lab with Jürgen Schmidhuber.

latent.spacearticlePublished and added 29 Sep 2026🧠 AI

Lasso Security researchers empirically test whether LLM output watermarking (specifically Google DeepMind's SynthID-Text, now deployed by Anthropic across Claude models) changes AI agent behavior — not just text content. Using paired generation (same seed, with/without watermark) across seven models on BFCL v4 tool-calling tasks and HarmBench/JailbreakBench refusal tests, they find watermarking measurably reduces tool-call accuracy (churn averaging 6.5%, up to 16.8% for phi-4) and, more critically, weakens refusal under prompt injection — e.g. gemma-3-27b's compliance with harmful requests shifts from -1.0 to +12.5 net points when injection is combined with watermarking. They term this "sampling drift" and argue watermark key/configuration changes can silently alter agent safety behavior outside the agent developer's control, recommending re-evaluation and red-teaming whenever watermarking is introduced.

lasso.securitywriteupPublished and added 26 Sep 2026🛡️ Security

Future of Life Institute's Summer 2026 AI Safety Index has an independent panel of seven AI researchers and governance experts (including Stuart Russell, David Krueger, Yi Zeng) grade nine leading AI companies — Anthropic, OpenAI, Google DeepMind, Meta, xAI, Z.ai, DeepSeek, Alibaba Cloud, Mistral — across 37 indicators in six domains including risk assessment, existential safety, and transparency. Key findings: Anthropic/OpenAI/Google DeepMind lead while xAI, DeepSeek, and Mistral receive failing grades; the report flags industry-wide backsliding on pause commitments, a pivot toward military AI partnerships, and existential safety as the weakest domain across all companies.

futureoflife.orgarticleAdded 26 Sep 2026🧠 AI

A Latent Space interview with Runway's CTO Kamil Sindi, Principal Research Scientist Robin Kahlow, and co-CEO Anastasis Germanidis about GWM Worlds 2, a research-preview "autoregressive diffusion" model that streams real-time interactive video (720p/24fps) and audio (48kHz), and its new WorldPrompt input format for fixing a first frame and issuing timestamped in-world events. It details the engineering pipeline (bidirectional diffusion → autoregressive fine-tuning → distillation for real-time speed), open problems like error accumulation, context/memory management, and causal/counterfactual correctness, and situates Runway against Google DeepMind's Genie 3, Odyssey-2 Pro, and World Labs' RTFM. A companion podcast transcript covers Runway's history from pix2pixHD/Gen-1/Gen-2 through the Stable Diffusion collaboration to its pivot toward world models.

latent.spacearticleAdded 29 Sep 2026, published 25 Sep 2026🧠 AI

Wiz announces Scan for Good, an initiative that pairs its AI pentester (Red Agent) and Google DeepMind's Gemini 3.8 Flash Cyber with human researchers. It scans authorized public-facing services of critical infrastructure, hospitals, municipalities and nonprofits for exposures and attack paths. It lists anonymized findings, including an exposed admin key over an 8.8M-file archive, NoSQL injection at an AI training-data platform, unsafe upload to hospital server control, and public CI/CD workflows leaking credentials. It also sets out the authorization, human-validation and private-disclosure rules.

wiz.ioarticlePublished and added 24 Sep 2026🛡️ Security

Google DeepMind describes an architectural update to its Private AI Compute platform that adds a persistent, server-side memory layer for AI assistants without sacrificing on-device-level privacy. User data is sealed in encrypted storage in the cloud, with decryption keys held only on personal devices; a hardware-isolated "secure enclave" temporarily decrypts data to serve a request, saves new context, and re-encrypts it, enabling continuity across sessions and devices (e.g. resuming a task started on smart glasses on a laptop). The post also announces a tamper-proof public record of the server software, an updated technical whitepaper, and results of an independent security audit for community verification.

deepmind.googlearticleAdded 29 Sep 2026, published 23 Sep 2026🧠 AI

Google DeepMind describes an architectural update to its Private AI Compute platform that adds persistent, cross-device memory for AI assistants while keeping data confidential: user data is stored encrypted in the cloud with decryption keys held only on the user's own devices, and requests are processed inside hardware-isolated "secure enclaves" that decrypt data only transiently in isolated memory. Alongside the update, Google published a technical whitepaper, a tamper-proof public record of the server software for device-side attestation, and results from an independent third-party security audit, inviting outside verification of the design.

deepmind.googlearticleAdded 29 Sep 2026, published 23 Sep 2026🛡️ Security

Latent Space's AINews daily digest for 9/16-9/17/2026, synthesizing AI Twitter and Reddit discussion into themed sections: agent runtime productization (Anthropic's Claude Code Projects for parallel cloud threads, Google's Gemini managed-agent harness with Credentials/Files APIs), the rise of TypeSafe's Jev as a cheap discriminative classifier/routing primitive for agent harnesses (with pushback from Theo on using it for context compaction), OpenAI's Astra for Law vertical launch, DeepMind's Stellar Colosseum multi-agent math/TCS harness and NVIDIA's Agora git-commit-memory agent system, Anthropic's internal AI-R&D automation metrics (Claude-led R&D tasks rising from 1% to 26% in six months), and a Claude Opus-assisted exploit chain that compromised OpenAI-connected accounts. It also covers Reddit local-LLM threads on Qwen 3.8 27B finetunes/quantizations, the ternary Bonsai 2 27B release, and a Mozilla report narrowing the China-US open-model capability gap to 4.4 months.

latent.spacearticleAdded 29 Sep 2026, published 18 Sep 2026🧠 AI

Google DeepMind announces Gemini 3.8 Live, built for scale and cost efficiency with fluid dialogue and visual grounding, and Gemini 3.8 Live Extended Thinking, built for high-complexity multi-step reasoning. The Extended Thinking model tops Artificial Analysis' Speech to Speech Quality Index (82.6), scores 68.6% on tau-Voice and 97.7% on Big Bench Audio, while 3.8 Live ranks second in the Speech Agent Arena; both support 97 languages with mid-conversation switching and background tool execution, and are watermarked with SynthID.

deepmind.googlearticleAdded 29 Sep 2026, published 15 Sep 2026🧠 AI

A Guardian op-ed, linkposted to the AI Alignment Forum, by a former Google DeepMind employee making the case that the public and policymakers should take warnings from AI safety researchers about advanced AI risk more seriously. It's framed as a first-person credibility appeal from someone who worked inside a frontier AI lab, aimed at raising public and regulatory attention to AI risk rather than presenting new technical research.

alignmentforum.orgarticleAdded 7 Oct 2026, published 14 Sep 2026🧠 AI

OpenAI's system card for ChatGPT Images 2.5, powered by GPT-Image-2.5-Sunburst and GPT-Image-2.5-Flare, covering training data handling, a multi-layer image safety stack with upstream policy refusals and a downstream multimodal safety-reasoning monitor for input and output blocking, adversarial safety evaluation results versus ChatGPT Images 2.0, and Preparedness Framework capability assessments for biological and cyber risk adapted to the image modality. It also details image provenance measures including C2PA metadata conformance and Google DeepMind's SynthID watermarking.

deploymentsafety.openai.comadvisoryAdded 7 Oct 2026, published 8 Sep 2026🧠 AI

OpenAI's system card for ChatGPT Images 2.5, covering the GPT-Image-2.5-Sunburst and GPT-Image-2.5-Flare models, details a multi-layer image safety stack (upstream LLM policy refusals, a multimodal safety reasoning model for input/output blocking) built to counter heightened deepfake realism risks. It reports adversarial-prompt evaluation results against ChatGPT Images 2.0, Preparedness Framework assessments for biological and cyber capability (image-modality adaptations of existing LLM evals, neither model crossing the "High" threshold but biological-risk mitigations still applied precautionarily), and expanded provenance tooling via C2PA metadata and Google DeepMind's SynthID watermarking.

deploymentsafety.openai.comadvisoryAdded 7 Oct 2026, published 8 Sep 2026🛡️ Security

An interview with BitRobot president Jonathan Victor explaining how the project crowdsources real-world robot interaction data — the bottleneck for embodied AI — through a decentralized market of "subnets" collecting different data types (urban navigation, mobile manipulation, dexterity) via teleoperation, simulation, or egocentric video. It traces BitRobot's origin from FrodoBots, whose open-sourced FrodoBots-2K dataset (2,000 hours of urban robot navigation, used by DeepMind, Meta, and UC Berkeley) demonstrated demand for public robot data, and describes BitRobot's current RoboCap wearable capture device and Solana-based entropy scoring/payment system that rewards rare, high-information contributions over redundant ones.

solana.comarticleAdded 29 Sep 2026, published 7 Sep 2026🧠 AI

Google DeepMind and Google Research introduce WeatherNext 3, a global weather forecasting model that trains directly on live geostationary satellite mosaics and sparse weather-station observations rather than lagged numerical-weather-prediction output. It uses a single Functional Generative Network (FGN) mesh transformer to produce hourly forecasts at up to 5km resolution for surface variables and 25km for atmospheric ones, reportedly the top-ranked model on Brightband's independent live leaderboards, with major precipitation-forecasting accuracy gains (up to 60% CRPS improvement vs IMERG) and new outputs tailored for renewable-energy planning (turbine-height wind, solar irradiance). It is being rolled out across Google Search, Maps, Gemini, Earth Engine, BigQuery, and the Google Maps Platform Weather API.

deepmind.googlearticleAdded 29 Sep 2026, published 3 Sep 2026🧠 AI

Google DeepMind announces Gemini 3.8 Flash, an upgraded reasoning/coding model at the same $0.75/$3.75 per-million-token price as 3.7 Flash, plus Gemini 3.8 Flash Cyber, a specialized variant for vulnerability discovery and automated patching offered to vetted defenders through a new 'Fairwind Program'. The post cites benchmark gains on DeepSWE v1.1, HLE-Verified, Vals Finance Agent V2 and Harvey's Legal Agent Benchmark for the general model, and CyberGym plus Collinear's CWE-Bench for the cyber variant, along with real-world results from Chrome Security, Wiz and Google's Cloud Vulnerability Research team.

deepmind.googlearticleAdded 29 Sep 2026, published 2 Sep 2026🧠 AI

Google DeepMind announces Gemini 3.8 Flash and a gated variant, Gemini 3.8 Flash Cyber, the latter positioned as a frontier-level model for autonomous vulnerability discovery and automated patching, accessible to vetted defenders through a new 'Fairwind Program.' On the CyberGym benchmark and an internal 20-language vulnerability-discovery benchmark it reports over 70% success, and on the Collinear CWE-Bench patching benchmark it nearly matches a larger frontier model (47.2% vs 47.8% pass@1) at much lower cost; Google cites Chrome Security producing 2.6x more correct patches and Wiz seeing higher recall at lower cost using the model, plus a case where Google's Cloud Vulnerability Research team found a critical vulnerability in under 2 hours.

deepmind.googlearticleAdded 29 Sep 2026, published 2 Sep 2026🛡️ Security

Google DeepMind describes "agentic video understanding," a new mode for Gemini 3.7 Flash, 3.6 Flash and 3.5 Flash-Lite that replaces fixed-frame-rate video ingestion with an internal agentic tool-use loop: the model dynamically decides what to watch, at what resolution/speed, and through which modality (frames, audio, transcript). This cuts token consumption by up to 88% and analysis cost by up to 66% while improving accuracy by up to 7% on standard video benchmarks, with the largest gains on long-form content (10-minute how-tos to multi-hour recordings), and enables capabilities like sub-second moment retrieval, needle-in-a-haystack search, anomaly detection and object/action counting. It's available now via the Gemini API (set `processing: "agentic"`) in Google AI Studio and the Gemini Enterprise Agent Platform, and will roll out to the Gemini app and YouTube's "Ask YouTube" feature.

deepmind.googlearticleAdded 29 Sep 2026, published 1 Sep 2026🧠 AI

Google DeepMind announces Gemini Omni 1.1 Flash, an update to its generative video model available via the Gemini API in Google AI Studio. New capabilities include scene extension (referencing up to 10 seconds of prior video context to extend clips in 10-second increments up to 40 seconds total), first/last keyframe specification for controlled transitions, a faster/cheaper 360p draft mode, upscaling to 1080p/4K, and multimodal video-reference inputs for maintaining character consistency across generated shots.

deepmind.googlearticleAdded 29 Sep 2026, published 27 Aug 2026🧠 AI

Google DeepMind describes a pilot of double-blind AI model evaluation, testing a Gemini Flash Lite model against confidential benchmarks from the Singapore AI Safety Institute, OpenMined, AVERI, and MLCommons. Using Google Cloud's Confidential Space (confidential computing), neither the evaluator's test prompts nor the model's weights are exposed to the other party, cryptographically preventing benchmark contamination while preserving data sovereignty for both sides.

deepmind.googlearticleAdded 29 Sep 2026, published 27 Aug 2026🧠 AI

DeepMind describes piloting a "double-blind" evaluation of a proprietary frontier model (Gemini Flash Lite), using Google Cloud's Confidential Space to cryptographically prevent either party from seeing the other's assets: the evaluator's benchmark prompts stay hidden from Google, and Google's model weights stay hidden from the evaluator. The pilot was run with the Singapore AI Safety Institute, OpenMined, AVERI, and MLCommons to address benchmark contamination, where a model provider having advance visibility into test prompts can artificially inflate reported capability/safety scores. This establishes confidential-computing-based evaluation as a technique for trustworthy third-party AI model assessment, particularly relevant for high-stakes cybersecurity and government evaluations.

deepmind.googlearticleAdded 29 Sep 2026, published 27 Aug 2026🛡️ Security

Google DeepMind's Gemini 3.5 Transcribe is a new speech-to-text model offered as two API variants: gemini-3.5-transcribe-live for sub-second-latency real-time streaming via the Live API, and gemini-3.5-transcribe for pre-recorded audio with speaker attribution and word-level timestamps via the Interactions API. It reports Word Error Rates of 4.0% (streaming) and 2.6% (non-streaming) as measured by Artificial Analysis, supports 85+ languages, custom vocabulary, disfluency/filler removal, and function calling to delegate tasks to other Gemini models, and is being rolled into Gboard's Rambler feature, Antigravity, Google AI Studio, and the Gemini macOS app.

deepmind.googlearticleAdded 29 Sep 2026, published 26 Aug 2026🧠 AI

Google DeepMind's announcement of Gemini 3.7 Flash, an update to its Flash model series released three weeks after 3.6 Flash, focused on coding, agentic tool use, and web development. It reports gains on FrontierCode 1.1 Main (43.6% vs 34.4%), DeepSWE v1.1 (65.3% vs 49.0%), WebDev Arena Elo (1588 vs 1538), GDP.pdf document-reasoning benchmark (34.0% vs 22.0%), and AutomationBench (30.4% vs 17.0%), alongside an introductory price cut to $0.75/1M input and $3.75/1M output tokens. The model also powers Gemini Spark (the Google Workspace personal agent) and ships with updated CBRN/cyber-offense misuse safeguards.

deepmind.googlearticleAdded 29 Sep 2026, published 13 Aug 2026🧠 AI

Google DeepMind and Android introduce SL2T, a massively multilingual sign-language-to-text translation model trained on over 100,000 hours of data across 50+ sign languages, now powering ASL-to-English sign dictation in Gboard and Live Transcribe on Pixel 11. Instead of relying on intermediate "gloss" annotations, SL2T translates directly from pose-landmark coordinate sequences (tracked on-device via MediaPipe Holistic for privacy) into text, achieving a zero-shot 70 BLEURT score on the FLEURS-ASL benchmark, reportedly well above prior reported results.

deepmind.googlearticleAdded 29 Sep 2026, published 12 Aug 2026🧠 AI

Google DeepMind's WeatherNext is a single AI model that jointly predicts a tropical cyclone's track, intensity, and wind structure with state-of-the-art accuracy, published in Nature, giving forecasters roughly an extra day of lead time (three-day forecasts matching prior two-day accuracy). It uses Functional Generative Networks (FGNs) co-trained on ~20TB of global atmospheric data plus the IBTrACS historical storm database, generating 1,000-member ensemble forecasts at coarse 28x28km resolution in under a minute on a TPU. DeepMind is open-sourcing the model weights and code for WeatherNext 2, WeatherNext Cyclones, and a compact WeatherNext 2-mini runnable in a free Colab notebook, and exposing forecasts via the Weather Lab interface as part of Google Earth AI.

deepmind.googlearticleAdded 29 Sep 2026, published 6 Aug 2026🧠 AI

Google DeepMind's Gemini Robotics ER 2 is an "embodied reasoning" model that acts as a high-level orchestrator for robots, handling spatial reasoning, multi-step task planning, and tool calling while delegating motor execution to lower-level vision-language-action (VLA) models. The update adds continuous video-based progress classification (57.4 percent accuracy) and precision moment-finding (91.3 percent accuracy, 0.96s mean absolute distance) for tracking task completion in real time, plus multi-robot collaboration allowing different robot platforms to hand off tasks via shared semantic understanding. It is available via the Gemini API, Google AI Studio, and in private preview on the Gemini Enterprise Agent Platform, with example code and demos using Boston Dynamics' Spot and Apptronik's Apollo 2 and Franka F3 Duo.

deepmind.googlearticleAdded 29 Sep 2026, published 30 Jul 2026🧠 AI

Google DeepMind announces Gemini Robotics 2, a family of three models: Gemini Robotics 2 (a vision-language-action model controlling full humanoid bodies and dexterous hands/grippers), Gemini Robotics ER 2 (an embodied-reasoning VLM for multi-step task planning, human communication, and multi-robot coordination), and Gemini Robotics On-Device 2 (an efficient on-device VLA that adapts to new bi-arm robot embodiments in hours with under 200 examples). The post demonstrates whole-body humanoid control on Apptronik's Apollo 2, fine dexterity with a 22-DOF SharpaWave hand and Franka Duo grippers, multi-robot teamwork, and introduces the ASIMOV-Agentic safety benchmark for agentic robotics safety.

deepmind.googlearticleAdded 29 Sep 2026, published 28 Jul 2026🧠 AI

Google DeepMind announces Gemini 3.6 Flash (a workhorse model with 17% lower output-token usage and up to 65% reduction on DeepSWE vs. 3.5 Flash, priced at $1.50/$7.50 per 1M tokens), Gemini 3.5 Flash-Lite (fastest 3.5-class model at 350 output tokens/s, $0.3/$2.5 per 1M tokens, outperforming 3 Flash on SWE-Bench Pro and OSWorld-Verified), and Gemini 3.5 Flash Cyber, a specialized model paired with the CodeMender agent for finding and patching vulnerabilities, benchmarked on CyberGym and gated to a limited-access pilot for governments and trusted partners. The post details benchmark comparisons across DeepSWE, MLE Bench, OSWorld-Verified, GDPval-AA v2, Terminal-Bench 2.1, and GDM-MRCR v2, new built-in computer-use tooling, enhanced Frontier Safety jailbreak resistance, and previews upcoming Gemini 3.5 Pro and Gemini 4 pre-training.

deepmind.googlearticleAdded 29 Sep 2026, published 21 Jul 2026🧠 AI

Google DeepMind's announcement of Gemini 3.6 Flash and 3.5 Flash-Lite also introduces Gemini 3.5 Flash Cyber, a smaller model fine-tuned for finding and fixing code vulnerabilities that runs as multiple coordinated agents inside CodeMender, Google's automated code-security agent. The post reports competitive-with-frontier results on the CyberGym benchmark and states the model will be released only to governments and trusted partners via a limited pilot, citing dual-use risk since AI is now finding vulnerabilities faster than defenders can patch them.

deepmind.googlearticleAdded 29 Sep 2026, published 21 Jul 2026🛡️ Security

Google DeepMind announces Gemini 3.5 Flash Cyber, a lightweight model fine-tuned on top of Gemini 3.5 Flash to find, validate, and patch software vulnerabilities, powering Google's CodeMender agent. The post reports benchmark results (CyberGym pass@1, an internal "Big Sleep" evaluation on Chrome/Safari, and Chrome's production commit-scanning pipeline) where the model outperformed mainline Flash models and Claude Opus 4.6, finding 55 unique confirmed issues in V8 versus 47 (3.5 Flash) and 36 (Opus 4.6). It also describes a real incident where the model found RCE and memory-corruption bugs in a Google production service and generated a working exploit bypassing ASLR and W^X within two hours; access is initially limited to governments and trusted partners via CodeMender.

deepmind.googlearticleAdded 29 Sep 2026, published 17 Jul 2026🛡️ Security

Google DeepMind announces Gemini 3.5 Flash Cyber, a lightweight cybersecurity-tuned variant of Gemini 3.5 Flash built to find, validate, and patch vulnerabilities as the backbone of Google's CodeMender agent. It reports benchmark results on CyberGym, an internal Big Sleep evaluation, and Chrome's production commit-scanning pipeline, claiming it outperforms mainline 3.5/3.6 Flash and Claude Opus 4.6 on unique vulnerability discovery (e.g., 55 vs 47 vs 36 unique issues found in the V8 engine), and describes deploying multiple lightweight model calls per report to cover more code paths than one large-model call. Access is initially limited to a government/trusted-partner pilot via CodeMender, given dual-use misuse concerns.

deepmind.googlearticleAdded 29 Sep 2026, published 17 Jul 2026🧠 AI

Google DeepMind and Isomorphic Labs describe their joint bioresilience program, organized around preventing misuse of models like Gemini, detecting outbreaks earlier, and accelerating response. Concrete efforts named include a four-step safety process (threat modeling, evaluations, mitigations, monitoring) under DeepMind's Frontier Safety Framework, adapting SynthID watermarking to flag AI-generated biological sequences for DNA synthesis screening, using the AlphaEvolve agent to optimize metagenomic sequencing algorithms for cheaper pathogen surveillance, and applying AlphaGenome and Protein Function annotation to pathogen characterization, alongside Isomorphic Labs' Drug Design Engine (IsoDDE) for rapid countermeasure design.

deepmind.googlearticleAdded 29 Sep 2026, published 16 Jul 2026🧠 AI

A practitioner-focused roundup of seven advanced prompt-injection attack chains observed in 2025-2026, walking through Johann Rehberger's CVEs against Claude Code (CVE-2025-55284, DNS exfiltration), Cursor (CVE-2025-54132, Mermaid image exfiltration) and GitHub Copilot (CVE-2025-53773, RCE), plus MCP tool poisoning/rug-pulls, cross-agent privilege escalation, and training-time Virtual Prompt Injection (USC/Samsung NAACL 2024 paper showing 52 poisoned examples can backdoor a model). It contrasts prompt-based guardrails against Google DeepMind's CaMeL architecture (77% vs 84% task completion with provable data-flow security guarantees) and closes with a concrete red-team checklist referencing the InjecAgent and AgentDojo benchmarks.

kunalganglani.comarticleAdded 16 Sep 2026, published 4 Jul 2026🛡️ Security

Somerset Recon details Quacc++, a proof-of-concept automated bug hunter that queries grep.app's regex-based index of public GitHub repos to find candidate vulnerable code, downloads matching repositories, and runs Semgrep taint-analysis rules to confirm exploitable patterns. Using a search for strcpy(buf, argv[ paired with a custom Semgrep taint rule, the authors discovered an unbounded strcpy stack buffer overflow in GetArgumentFiles() in Google DeepMind's Lab repository (engine/code/bspc/bspc.c), confirmed with AddressSanitizer and pwntools/GDB to give full RIP control at offset 1080. The post also walks through Semgrep rules for SQL injection (CWE-89), hardcoded API keys (CWE-798), and disabled TLS certificate validation (CWE-295) across curl/wget/requests/Node/Go.

somersetrecon.comwriteupPublished and added 7 May 2026🛡️ Security

Google DeepMind's official announcement of CodeMender, an AI agent built on Gemini Deep Think that autonomously debugs, roots-causes, and patches security vulnerabilities in real codebases, then validates fixes via static and dynamic analysis, differential testing, fuzzing, SMT solvers, and an LLM-based critique agent before human review. The team has upstreamed 72 security fixes to open-source projects, including some spanning 4.5 million lines of code, and used CodeMender to proactively apply bounds-safety annotations to libwebp, which would have neutralized the buffer-overflow class of bug behind CVE-2023-4863, a bug used in a zero-click iOS exploit.

deepmind.googlearticlePublished and added 6 May 2026🛡️ Security

Chronicles the founding of DeepMind and the development of breakthrough reinforcement learning systems from Atari and AlphaGo to AlphaFold 2, which solved the 50-year-old protein folding challenge. It explores the dual-use security risks of artificial general intelligence, including autonomous weaponry, biological threat engineering, surveillance, and global governance imperatives.

youtu.bevideoAdded 18 May 2026, published 25 Nov 2025🛡️ Security