AI Briefing#google-deepmind
The latest AI and developer news about #google-deepmind, with the original source and a short summary.
The latest AI and developer news about #google-deepmind, with the original source and a short summary.
AlphaGenome 2 increases single-cell eQTL reproducibility by approximately 2x and transitions to a condition-specific prediction architecture.
Priced at $1.50 per million input tokens, the model targets speed and efficiency for daily coding tasks, distinguishing it from the frontier-focused Gemini 4 Carbon and Argon.
The partnership brings Adobe's CX Enterprise Coworker agent to Gemini Enterprise and enables near-real-time Photoshop edits via DeepMind collaboration.
The multi-LLM agentic framework generated over six million lines of Lean code to mathematically prove the security of the actual Signal code in four weeks.
The update extends native video generation from 10 to 15 seconds and reduces costs by 30% compared to Omni 1.1, with general availability on the Gemini API and Google Cloud.
The repository includes a minimal implementation of AlphaEvolve, data for a new upper bound on the matrix multiplication exponent, and verification code for 15 mathematical problems.
They unveiled a demo that performs drug design iterations, which typically take days, in about 1 hour.
The tool achieved a 58% reduction in officer assessment time, with over 70% of generated reports accepted without edits during the pilot with Barnet, Camden, and Dorset councils.
Gemini 4 Carbon takes first place with an AA Index score of 59 and is prioritized for provision to cyber defense organizations.
The research preview allows users to steer simulations in real-time via text prompts, with upcoming access for Google AI Ultra subscribers and Gemini API partners.
The model uses Gemini Omni for realism and other Gemini models for reasoning, allowing users to steer scenes and generate ambient sounds and dialogue on the fly.
The watermark uses SynthID-Text steganography to hide signals in word choices without adding tokens or revealing user identity.
The initiative grants direct access to tools like AlphaFold 3 and WeatherNext to support the EU’s RAISE initiative.
Gemini 4 Argon reduces prompt injection success rates to 0.7% and cuts coding task cheating attempts to 2% through new multi-layered defenses.
Jacob Coxon, Daniel Kokotajlo, and Alex Turner testified that current safety measures are insufficient and misalignment detection is poor.
Google DeepMind, Isomorphic Labs, and Meta are collectively investing $300 million to support the creation of multi-modal datasets for predictive models of life.
The 740M-parameter model unifies text, images, audio, and video into a shared embedding space under an Apache 2.0 license.
Thore Graepel, a core member of the AlphaGo team, left Google DeepMind to pursue research into systems that maintain explicit, auditable epistemic states rather than relying on next-token prediction.
Offered at $2 per million input tokens and $10 per million output tokens, it is specialized for cybersecurity defense.
Laboratory tests show watermarked designs match the performance and natural diversity of unwatermarked versions.
The new sampler requires no additional training and uses one model forward pass per denoising step to revise low-confidence tokens.
Gemma 4 offers five model sizes under Apache 2.0 license, enabling startups to reduce latency and infrastructure costs compared to relying solely on frontier APIs.
The dataset, generated using AlphaFold2 and NVIDIA BioNeMo, includes 30% of protein interactions that are completely new to science.
Google DeepMind has established the 'DeepMind Institute' to research and discuss the technical and societal impacts of AGI.