Google has released a zero-trust security design for private analytics that combines a new cryptographic aggregation protocol with trusted execution environment (TEE) hardware protections, provably ensuring Google only receives anonymized, aggregate group insights.
Mistral has acquired Emmi AI and launched a physics AI foundation model for engineering. Traditional CFD/FEM simulations take hours to weeks per design variant, while physics AI predicts physical fields with second-level forward inference on a single GPU, without replacing first-principles solvers. The capability is already used by partners including ASML, Airbus, Safran and Siemens Energy, spanning product design, tooling process design acceleration and real-time digital twins.
Mistral acquired Emmi AI, positioning Physics AI for engineering R&D in industries such as aerospace, automotive, semiconductors, and energy, aiming to let engineers build next-generation products faster and continuously improve performance in large-scale operations.
Import AI issue 458 features the author's 2026 Cosmos HAI Lab talk, hosted with the Institute for Human-Centered AI (HAI Lab) at Oxford University and the Cosmos Institute, exploring how to respond to the success of AI technology and posing the choice between 'exploring the future or retreating to the present.'
Mistral AI announced a definitive agreement to acquire Physics AI company Emmi AI, strengthening its position in AI transformation services for industrial enterprises. Headquartered in Austria with more than 30 researchers and engineers, Emmi AI's large engineering models accelerate engineering processes and product design cycles, replace multi-day computations with real-time simulation, and build digital twins; its team will join Mistral AI's Science and Applied AI teams in May.
Mistral released the 128B open-weight flagship model Mistral Medium 3.5 (public preview), combining instruction following, reasoning and coding, with a 256k context window and a SWE-Bench Verified score of 77.6%. API pricing is $1.5 per million input tokens and $7.5 per million output tokens.
Why it matters: The original provides model specs, benchmark scores, pricing and the open-weight access point, useful for evaluating it for self-hosting and asynchronous coding use cases.
Mistral released Connectors in public preview for Studio; both built-in connectors and custom MCPs can be used via API/SDK with all models and agent calls. New features include direct tool calls, human-in-the-loop approval flows, and programmatic management of connectors. Once centrally registered, connectors can be reused in LeChat and AI Studio, making it easy to connect to enterprise systems like CRMs and knowledge bases.
Google DeepMind launched the first cohort of its Accelerator program in Asia Pacific, themed “AI for the Planet.” The three-month program is open to regional startups, research teams, and nonprofits using frontier AI to tackle environmental problems in nature, climate, agriculture, and energy. Selected organizations receive expert mentoring and support integrating frontier and science AI models; it kicks off with an in-person bootcamp in Singapore and applications are now open.
Arista Networks announced it has been positioned as a Leader in the 2026 Gartner Magic Quadrant for Enterprise Wired and Wireless LAN.
Google's research tool ERA uses Gemini to write and optimize scientific code, and the related paper was published in Nature today. ERA uses tree search to consider thousands of approaches and reaches expert-level performance on benchmarks in genomics, public health, satellite imagery, neuroscience, time series forecasting, and mathematics.
Why it matters: The article gives ERA's performance on benchmarks across multiple disciplines and eight application cases, letting readers understand how AI-assisted scientific computing is actually being deployed.
The labs of biologists Omar Abudayyeh and Jonathan Gootenberg use Google DeepMind's Co-Scientist to accelerate aging research. After scanning tens of thousands of papers, the system proposed over 20 novel candidate genetic factors, some experimentally verified to drive cells into a younger, better-functioning state. Large-scale screening analysis that once took up to 6 months can now be done in days.
SentinelOne researchers dissected a virus from roughly 20 years ago, fast16.sys, which tampered with in-memory computation results of high-precision engineering software (such as LS-DYNA 970, PKPM, and MOHID), potentially enabling sabotage related to weapons development — reminiscent of the sophon interference in The Three-Body Problem.
Google DeepMind added a Street View grounding capability to Project Genie, letting Google AI Ultra users generate interactive worlds from real US locations in styles like "Ocean World" and "B&W film", powered by Maps Imagery Grounding.
Google DeepMind launched Gemini Omni Flash, the first model in the Gemini Omni family, which can combine image, audio, video, and text inputs to generate videos, with multi-turn editing via natural language and support for character consistency and physical plausibility.
Why it matters: The official introduction of the Omni family details the capability boundaries of conversational video editing, multi-input composition, and physical realism.
Google DeepMind released Gemini for Science, launching three experimental prototypes in Google Labs, including Hypothesis Generation based on Co-Scientist.
On May 19, Google announced an expansion of its content transparency and verification tools, covering Search, Gemini, Chrome, Pixel, and Cloud.
Why it matters: The original maps out how SynthID watermarking and C2PA Content Credentials verification are being extended across Search, Gemini, Chrome, and Pixel, along with rollout timing, to show where content provenance tools stand.
Google DeepMind announced a new National Partnerships for AI initiative in Singapore, focused on healthcare, science, and education. The collaboration includes exploring an AI co-clinician "trio of care" with public healthcare clusters, using AlphaFold to accelerate outbreak research, developing a Gemma-powered running assistant for visually impaired runners, and providing Gemini for Education to all teachers from primary school through junior college.
University of Cambridge professor Clare Bryant uses Google DeepMind's Co-Scientist to study the molecular switches behind how pathogens crossing species (such as avian influenza to humans) trigger severe illness like sepsis.
Matt Onsum, AI/ML lead, and principal scientist Katherine Labbé at Calico Life Sciences use Google DeepMind's Co-Scientist to connect scattered research findings in aging biology and generate hypotheses worth testing.
University of Edinburgh bioengineer Filippo Menolascina used Co-Scientist to study metabolic dysfunction-associated steatohepatitis (MASH), screening candidate combination therapies from a vast body of literature. Addressing why the drug resmetirom works for only some MASH patients, the system proposed the hypothesis that the NLRP3 inflammasome is a molecular bridge linking inflammation and metabolism — a hypothesis later validated experimentally, pointing toward dual-target therapies.
Google DeepMind's Co-Scientist is connecting the labs of MIT mechanical engineer Ritu Raman and Boston Children's Hospital chemical biologist Ryan Flynn to study ALS together.
A team led by Stanford School of Medicine geneticist Gary Peltz published research in Advanced Science using Google DeepMind's Co-Scientist to screen existing drug literature for candidates that can be repurposed to treat liver fibrosis.
Google DeepMind's AI weather model WeatherNext predicted 5 days in advance, with 80% confidence, that Hurricane Melissa would make landfall in Jamaica at Category 5 intensity; 3 days out, confidence rose to nearly 100%.
Google DeepMind has released the Gemini 3.5 model family, starting with 3.5 Flash, positioned for agents and coding. It outperforms Gemini 3.1 Pro on Terminal-Bench 2.1 (76.2%), GDPval-AA (1656 Elo), MCP Atlas (83.6%), and CharXiv Reasoning (84.2%), with output speed 4x that of other frontier models.
Why it matters: The official release shares 3.5 Flash's benchmark scores, availability channels, and plans for a Pro follow-up, letting readers evaluate its trade-offs for agent and coding workloads.
Google DeepMind has published Co-Scientist in Nature, a Gemini-based multi-agent system that generates, debates, and evolves hypotheses to accelerate scientific research. It is being opened to individual researchers through the experimental Hypothesis Generation tool, rolling out within weeks, with sign-up at labs.google/science.
Why it matters: The official release provides the system architecture, existing experimental results, and an open registration portal, letting readers assess its fit for research workflows.
Import AI issue 456 focuses on three topics. Law & AI researchers propose a "radical optionality" approach to AI regulation: governments should avoid overregulating now and instead invest in capabilities like information gathering, whistleblower protection, model weight security, and talent to prepare for future transformative AI.
Berkeley AI Research publishes a research survey on adaptive parallel reasoning, exploring the idea of letting reasoning models autonomously decide when to decompose tasks, how many threads to generate in parallel, and how to coordinate them.
Google DeepMind has released a one-year impact report on AlphaEvolve, the Gemini-powered coding agent that has expanded from pilots to multiple research and commercial domains.
Why it matters: The official report details AlphaEvolve's concrete results and figures across research and commercial settings over the past year, showing the real-world reach of this kind of algorithm-discovery agent.
Arista Networks released its first-quarter 2026 financial results. The original announcement page failed to load due to loading issues, so specific figures such as revenue and profit could not yet be confirmed.
In Import AI 455, Jack Clark judges there is more than a 60% probability of fully automated AI R&D (models autonomously training their own successors) by the end of 2028, and 30% for 2027.
Google Research outlines its open science practices: over a decade, open-sourced tools and datasets such as DeepVariant, Neuroglancer, NeuralGCM, SpeciesNet, and HAI-DEF (including MedGemma) have served more than 250,000 researchers and developers worldwide.
Google DeepMind has announced the AI co-clinician research program, exploring medical AI that assists both doctors and patients under physician supervision.
Why it matters: The official announcement includes multiple sets of clinical evaluation figures and a dual-agent safety architecture, letting readers understand the capability boundaries and actual role of AI co-diagnosis assistants.
Google Research describes real-world progress in four scientific fields from Empirical Research Assistance (ERA), which it released in September 2025.
Mistral AI released Workflows in public preview, positioned as an enterprise AI orchestration layer offering durable execution, observability, fault tolerance, and human-in-the-loop approvals. Customers including ASML, ABANCA, CMA-CGM, France Travail, and La Banque Postale are already using it to automate critical processes.
Google DeepMind announced a partnership with Korea's Ministry of Science and ICT (MSIT) as part of its National Partnerships for AI program, including an AI Campus in Seoul.
Google announced that a 3D scene understanding-based photo recomposition method is now rolling out to the Auto frame feature in Google Photos.
Google DeepMind released the Decoupled DiLoCo distributed training architecture, which splits large model training into asynchronously communicating compute islands, isolating hardware failures and sharply reducing bandwidth requirements.
Google Research has published the ICLR paper ReasoningBank, proposing a framework that distills high-level reasoning memory from an agent's successful and failed experiences, distinguishing it from Synapse and AWM, which only record trajectories or summarize successful workflows.
Google DeepMind announced partnerships with Accenture, Bain & Company, BCG, Deloitte and McKinsey to help enterprises adopt frontier AI at scale.
Berkeley AI Research proposes GRASP, a gradient-based world model planner that makes long-horizon planning more practical. Three core improvements: lifting trajectories into virtual states so optimization runs in parallel over time, injecting randomness directly into state iterations to support exploration, and reshaping gradients to avoid traversing fragile state-input gradients of high-dimensional vision models.