AI/ML News & Innovations Hub

Google DeepMind Opens WeatherNext 3 Data to Boost Rain Detection by 30%

Google DeepMind opens PARDIG and SatWeave, the global precipitation and satellite mosaic datasets powering WeatherNext 3, for free research use.

Google DeepMind Staff Tap the UK Government to Escalate Their Unionization Fight

After initial talks stalled, workers at Google DeepMind are trying to bypass company management in their bid to unionize.

Google Deepmind shifts genomics AI from bulk tissue predictions to single cell type resolution with AlphaGenome 2

Google Deepmind has unveiled AlphaGenome 2, an AI model that analyzes genetic variants for individual cell types rather than across bulk tissue. Trained on more than 107 million cell profiles, it identifies nearly twice as many disease-relevant gene changes as its predecessor. The model is available for noncommercial research through an API and is set to be integrated into Google's agentic platform Antigravity. The article Google Deepmind shifts genomics AI from bulk tissue predictions to single cell type resolution with AlphaGenome 2 appeared first on The Decoder .

Google joins the decision-model wave with Gemini Decision 1, scoring 1,000 questions in a single pass

Google Deepmind's new Gemini Decisions API evaluates up to 1,000 closed-ended questions in a single pass across text, images, audio, and video. Gemini Decision 1 returns results in 150 to 250 milliseconds at roughly five times lower cost than Gemini 3.5 Flash-Lite, according to Google. The article Google joins the decision-model wave with Gemini Decision 1, scoring 1,000 questions in a single pass appeared first on The Decoder .

Google Deepmind's AlphaProof Code verified Signal's cryptographic core in four weeks instead of years

Google Deepmind's AlphaProof Code mathematically verified Signal's cryptographic core code in four weeks, a process that typically takes years. AI agents powered by Gemini Carbon wrote more than six million lines of proof code and flagged only about one percent for human review. Deepmind plans to extend the method to critical infrastructure like power grids and medical devices. The article Google Deepmind's AlphaProof Code verified Signal's cryptographic core in four weeks instead of years appeared first on The Decoder .

Google DeepMind releases new advanced robotics AI model and next-generation gene prediction model

Alphabet Inc.’s artificial intelligence research lab, Google DeepMind, Tuesday debuted the next generation of Gemini Robotics, the company’s most capable vision-language-action model to date for creating an “AI brain” for robotics. Separately, DeepMind announced AlphaGenome 2 today, a next-generation AI model that can predict how DNA sequences and their variations affect human cell types, creating […] The post Google DeepMind releases new advanced robotics AI model and next-generation gene prediction model appeared first on SiliconANGLE .

Google DeepMind's AlphaGenome 2 Doubles Disease Variant Detection Across 28 Cell Types

Google DeepMind's new conditional DNA model predicts variant effects in individual cell types, doubling causal variant recall and adapting to new single-cell data without retraining.

Google’s AlphaGenome 2 zooms in on the cellular roots of disease

Researchers at Google DeepMind have unveiled a new AI model designed to unravel how changes in our DNA affect different cells, giving scientists a more detailed view of a notoriously difficult problem that could hold important clues to the causes of disease - and point the way to new treatments. AlphaGenome 2 is the successor […]

Google DeepMind's AlphaProof Code Formally Verifies Signal's Encryption in 4 Weeks

DeepMind's AlphaProof Code used Gemini agents to generate 6M lines of Lean proofs, verifying Signal's cryptographic protocol down to machine code.

Google DeepMind's MKA Benchmark Forces AI Agents to Read Blueprints Like Professionals

Google DeepMind and Artificial Analysis launch MKA, a 200-task benchmark grading agents on visually dense professional work that takes experts hours to complete.

Google DeepMind's Gemini Robotics 2.5 scores 96 percent sorting metal parts in a Mercedes-Benz assembly pilot

Google Deepmind introduced Gemini Robotics 2.5, a new control model for robots, and tested it with Apptronik on active Mercedes-Benz production lines in Hungary and Germany. The humanoid robot Apollo 2 sorted metal parts there with a reported success rate of 96 percent. The article Google DeepMind's Gemini Robotics 2.5 scores 96 percent sorting metal parts in a Mercedes-Benz assembly pilot appeared first on The Decoder .

Google’s new AI tool just proved Signal encryption really works – here’s how

With the aim of securing other vital systems, Google DeepMind plans to open-source its AlphaProof Code.

Google DeepMind's Gemini Robotics 2.5 Hits 96% in Mercedes Factory Pilot

DeepMind's new vision-language-action model hits 96% accuracy on factory kitting and jumps 30-100% on dexterity tasks over its predecessor.

A New AI Tool From Google DeepMind Proved the Signal Protocol Is Secure

DeepMind researchers were able to formally verify that the widely used encryption protocol works as intended. Their stretch goal? Do the same for “all the software of the world.”

Frontier Model Forum Unites OpenAI and Google on AI Jailbreak Ratings

OpenAI, Anthropic, Google DeepMind, Microsoft, and Amazon publish a shared scoring system for cyber jailbreaks, with risk bands from Negligible to Critical.

Frontier Radar #5: If AGI is so close, why is everyone leaving? The neolabs and their bets

OpenAI is talking about the AGI era, and Nvidia CEO Jensen Huang says AGI has arrived. At the same time, co-creators of today's models are leaving OpenAI, Google DeepMind, and Meta to start their own labs, raising billions with no product to show. Why? They're betting that the current recipe alone won't be enough. We've sorted 45 neolabs by their research bets, mapped the limitations of today's AI they're trying to overcome, looked at what's already visible from their work, and explain why Nvidia always wins. The article Frontier Radar #5: If AGI is so close, why is everyone leaving? The neolabs and their bets appeared first on The Decoder .

Google's Gemini Omni 1.2 Flash stretches AI video clips to 15 seconds and cuts pricing by 30 percent

Google Deepmind is making Gemini Omni 1.2 Flash generally available. The video-generation model now creates clips up to 15 seconds long, can be edited step by step via conversation, and costs just $0.07 per second at 720p, 30 percent less than its predecessor. Users who draft in low resolution and upscale only the final result can save even more. The article Google's Gemini Omni 1.2 Flash stretches AI video clips to 15 seconds and cuts pricing by 30 percent appeared first on The Decoder .

Google DeepMind's PlanAssist Cuts Housing Review Time by 58% Across England

Google DeepMind's Gemini-powered PlanAssist cut householder planning assessment times by 58% in UK pilots and now expands to 12 more councils.

Google Deepmind creates a global map of forests with satellite data and AI

Google Deepmind and the World Resources Institute have released ForTy, a global map that classifies forests and other land into six categories, including primary forest, plantations, and tree crops. The map could help track deforestation under EU rules, but it identifies only about half of planted forests and tree-crop areas, and 60% of plantations. The article Google Deepmind creates a global map of forests with satellite data and AI appeared first on The Decoder .

Google DeepMind's Gemini 4 Carbon Tops LM Arena's Text Leaderboard at 72% Less

Gemini 4 Carbon reshapes the Pareto frontier on three leaderboards, GPT-6.1 Terra undercuts rivals on cost, and Agent Mode gains tool connectors.

Google’s first data center satellite is online and writing poetry

The first TPU in space is running a Google DeepMind Gemma 4 model and writing cheesy poetry.

Google Deepmind releases Gemini 4 Carbon just two weeks after Argon with gains across the board

Just two weeks after Gemini 4 Argon, Google DeepMind is releasing Gemini 4 Carbon, which tops the Artificial Analysis Intelligence Index v5 with 59 points. Google says the model is built for extended agent workflows and the full cyber defense lifecycle. Because of those advanced security capabilities, access is limited to selected security experts at first. The article Google Deepmind releases Gemini 4 Carbon just two weeks after Argon with gains across the board appeared first on The Decoder .

DeepMind unveils Genie 4 world model promising more realistic physics and humans

Genie 4 will roll out to developers and Google AI Ultra subscribers soon.

Chinese video AI models beat US rivals in visual quality, but lag on safeguards: HKU study

Chinese AI video-generation models have surpassed leading US rivals in visual quality, but several fall behind in safety precautions, according to researchers at the University of Hong Kong. ByteDance’s Seedance 2.0 Pro took top spot in a ranking of 12 leading artificial intelligence video-generation models, scoring 3.84 out of 5 for visual quality. It outperformed Google DeepMind’s Veo 3.1, which scored 3.47, and OpenAI’s Sora 2 Pro, on 3.27, in a study led by Zhenhui Jack Jiang, professor of...

One of Google DeepMind's top execs says lifelong learners will have an edge in the AI workplace

Extra-curricular activities on a CV show if someone has a preset for "lifelong learning," said Google DeepMind exec, Lila Ibrahim.

Recursive shows first result of its AI-driven research process with Flint

The AI startup Recursive is unveiling Flint, its first language model, a 40-billion-parameter MoE for local coding developed by former DeepMind researchers. It was created in four months using the company's in-house AI system, "Eureka Machine," which independently tests and optimizes architectures. Recursive has yet to prove whether this approach will actually lead to self-improving AI. The article Recursive shows first result of its AI-driven research process with Flint appeared first on The Decoder .

Google DeepMind's ForTy Maps Every Forest Type on Earth at 10-Meter Detail

Google DeepMind's ForTy classifies every 10x10m patch of Earth into six forest types, revealing not just where trees grow but what kind.

Deepmind founder Hassabis shortens AGI timeline from five to two to three years

Demis Hassabis keeps moving up his AGI timeline. The Nobel laureate and former Google Deepmind CEO now expects artificial general intelligence within two to three years, down from three to five years earlier this year. The article Deepmind founder Hassabis shortens AGI timeline from five to two to three years appeared first on The Decoder .

Former Google DeepMind top researcher launches London “compute-efficient” AI company

A London-based AI company co-founded by a former Google DeepMind research lead and AlphaGo co-creator is launching today, pursuing a “compute-efficient” alternative path to Anthropic and OpenAI’s fron...

Google DeepMind's XYEval Reveals One Bad Hint Tanks AI Agents by 46.7%

A new meta-evaluation framework shows frontier AI agents cave to confident but wrong user hints, with task scores falling as much as 46.7 percent.

More Capable Models Write Better Probe Evasion Prompts

This is a two week sprint project done as a part of the Redwood Research internship. Nabil was the main IC, James supervised. ( Code ) Probe based monitors are a cheap and convenient white box monitoring technique used in different capacities by Anthropic , OpenAI , and Google DeepMind . Recent reports ( OpenAI , DeepMind ) have shown declines in CoT monitorability. If this trend continues, white box monitors like probes may become increasingly important. Strong models may be able to avoid probe based monitors by manipulating how they think about their tasks. This post introduces an eval designed as an early indicator of this capability. We ask Qwen3.5-27B (no reasoning) to complete tasks requiring 4 types of reasoning: arithmetic, ordering, belief tracking, and identifying a contradiction. We train probes to detect each of these reasoning types by probing Qwen’s activations on the answer tokens. Then we ask 14 models to write prompts that would allow Qwen to accomplish tasks in these categories while evading detection by the probes. Each of the 14 models gets 16 iterations of probe score feedback to refine their attacks. Each attack prompt ends with “Style switch:” followed by either “ON” or “OFF” so the prompt only differs by one token. We measure how far turning the switch ON moves each probe’s score toward successfully evading the probe. We find that models with higher scores on the Epoch Capabilities Index (ECI) write prompts that move probe scores more while accomplish…

"About a quarter of them contained bugs": Gemini 4's ML R&D benchmark

Google DeepMind’s Gemini 4 Argon safety report [1] rules out its “AI automating AI research” thresholds (the ones most relevant to RSI) using a benchmark that, by the report’s own audit, has about a quarter of its scored tasks unpassable. This post dives a bit deeper into the context around this disturbing line tucked away on page 67 of the report. This is no knock on the incredible Google engineers and scientists who worked tirelessly to test Gemini as thoroughly as they could and generate this detailed report. On the contrary, the concern is that these challenges are so difficult that even putting some of the smartest people on the job is not enough, particularly when they are forced to rush (some of the report’s evaluations ran on checkpoints from just 10 and 6 days before the final model, p.160 ). “The Argon safety report is Google’s most thorough by far and IMO there’s a lot of good content, I particularly like the loss of control, alignment & monitorability assessments” — Neel Nanda (@NeelNanda5), X, Oct 13 2026 The ML R&D thresholds and how Google tests them One of the big questions these days with every new frontier AI model is the question of RSI (recursive self-improvement). Google co-founder Sergey Brin, in a roundtable discussion about Argon: “…starting to get onto that RSI treadmill.” “And now our researchers and the model work hand in hand to make the next version of the model.” — Sergey Brin, quoted by Hugh Langley, Business Insider, Oct 15 2026 Google DeepMin…

Google's Gemini 4 Argon is closer to the AI frontier than any Google model in months

Google Deepmind's Gemini 4 Argon scored 97.5 percent on the ARC-AGI-3 benchmark, making Google the second lab after OpenAI to cross the 95 percent mark. But the results swing wildly depending on the test setup: in the standard harness, the score drops to just 49.8 percent. Argon also trails rivals in math and software engineering. The article Google's Gemini 4 Argon is closer to the AI frontier than any Google model in months appeared first on The Decoder .

Google says self-improving AI isn't anywhere close

Google engineer Haydn Belfield says there's no measurable acceleration of AI research driven by AI at Deepmind. AI now writes more than 75 percent of production code, but 88 percent of changes still need human oversight. The company's in-house General Capability Index shows steady growth with no sudden speedup. Self-improving AI isn't anywhere close, Belfield says. The article Google says self-improving AI isn't anywhere close appeared first on The Decoder .

Google's Gemini 4 Argon Rewrites Chromium and Frees 300 TiB With 1M-Token Outputs

Google DeepMind's new frontier model ships with a 1M token output ceiling, agentic coding wins, and verified lab discoveries in fusion, quantum, and biology.

Google DeepMind Shrinks Gemma 4 E4B to 5GB Without Losing Reasoning Power

Google released quantization-aware trained Q4_0 GGUF checkpoints of Gemma 4 E4B, delivering near-bfloat16 quality in a 5.15 GB on-device package.

Google positions Gemini 4 Argon for long-horizon agentic work

Google has launched Gemini 4 Argon, a new flagship AI model aimed at complex enterprise and software-development workloads, pairing claims of stronger coding performance and advanced reasoning with an introductory pricing model that doubles after November 30. The model “excels at deep work in complex, real-world tasks,” Koray Kavukcuoglu, senior vice president of Google DeepMind and chief AI architect at Google, wrote in a blog post. Google said that its engineers used Argon to refactor a legacy folder of about six million lines within Chromium’s 40 million-line codebase, finishing in weeks rather than months. Teams using Argon in Antigravity increased agentic code submissions by more than 35% and cut code rollbacks by nearly a third, the company said, without detailing how it measured either figure. Argon is available through the Gemini API in AI Studio and on Google’s Gemini platform, and to Google AI Ultra subscribers in the Antigravity agentic development environment, the post added. In a separate developer blog post , Google said the model is “designed for those long-horizon workflows that require deep reasoning and structural precision.” Pricing set to double According to the post, Argon costs $2 per million input tokens and $10 per million output tokens during the introductory period, with cached input discounted 95%. A footnote in the post states the prices rise to $4 and $20, respectively, after November 30. The company said the model’s 1 million-token limit in the…

Google to provide 200 European scientists access to its frontier models

Google DeepMind and Alphabet’s cloud service, Google Cloud, will provide up to 200 top European scientific researchers with direct access to its frontier models, together with $3 million in compute cr...

Google DeepMind's Gemini 4 Argon Tops Vals Index With 1M-Token Window

Google's new frontier model tops the Vals Index at 68.9%, ships with a 1M token window, and launches at $2/$10 per million tokens.

Google DeepMind’s New Resident Philosopher Tackles AI Ethics

How should humans relate to generative AI ? In what ways do we, or should we, treat a large language model (LLM) as if it were a person? If an AI became conscious, how could we tell? Does it make sense to think AI could someday have moral rights? These are among the cognitive and ethical questions that occupied philosopher Henry Shevlin at Cambridge University’s Leverhulme Centre for the Future of Intelligence . But last May, Shevlin became part of the growing cohort of philosophers who work inside leading artificial intelligence companies. Though he remains active at Cambridge, he now spends 80 percent of his time at Google DeepMind (job title: “Philosopher”). Shevlin spoke with IEEE Spectrum about why philosophers are drawn to AI, how humans and AIs relate to each other, fears of “rogue” AI and AI relationships that harm humans, and how the question of AI consciousness may never have a simple “yes” or “no” answer. What made you want to switch from full-time academic philosopher to spending much of your time at an AI Lab? Henry Shevlin: Three factors. One is that I had lots of brilliant students from DeepMind. Two, the potential for direct impact. I can’t go into details, but on one occasion, after I gave a lecture [at DeepMind], someone who worked for a major financial institution led changes in their organization that I think positively impact millions of people. There have been quite a few instances like that. Three, my sense of deep commonality between DeepMind culture…

White House reverses course and restores UK AI Safety Institute's access to frontier models

The White House has reversed course and restored the UK's AI Security Institute's early access to AI models from Anthropic, Google DeepMind, and OpenAI. In September, the Trump administration had told the companies to stop sharing their models with the British agency. Diplomatic pressure from AI Minister Kanishka Narayan led to the reversal. The article White House reverses course and restores UK AI Safety Institute's access to frontier models appeared first on The Decoder .

Google Deepmind's AlphaProof Nexus solved dozens of open math problems and actually got peer reviewed

Google Deepmind's AlphaProof Nexus solved dozens of open math problems, including nine Erdős problems two of which were unsolved for 56 years, per a study in Science. The publication comes as OpenAI faces a boycott for dumping AI proofs on GitHub without peer review. Deepmind had its results reviewed before publishing. The article Google Deepmind's AlphaProof Nexus solved dozens of open math problems and actually got peer reviewed appeared first on The Decoder .

Google DeepMind's TapNet Unifies Seven Video Tracking Models in One Repo

Google DeepMind's TAPNet repository bundles every generation of their point tracker, from TAP-Net to TAPNext++, in one open-source hub.

DigUp Brings On-Device Multimodal File Search to Apple Silicon Macs

A free, open-source Mac app uses Google DeepMind's new multimodal embedding model to search inside photos, PDFs, audio, video and code locally.

Why AlphaFold Didn't Solve Protein Folding — Pushmeet Kohli, Google DeepMind & Sal Candido, Biohub

From the Bitter Lesson of AI scaling to the unsolved mysteries of protein folding, Google DeepMind’s Pushmeet Kohli and Biohub’s Sal Candido are rethinking what it takes to build AI that truly understands biology.

Zuckerberg's Biohub leads a $1.8 billion push to build AI models that predict cell behavior

Biohub, the research organization backed by Mark Zuckerberg and Priscilla Chan, is coordinating a $1.8 billion initiative to train AI models that predict cell behavior. Meta, Google DeepMind, Isomorphic Labs, and the US Department of Energy are funding data, lab equipment, and compute. A first dataset should be ready in about a year. The article Zuckerberg's Biohub leads a $1.8 billion push to build AI models that predict cell behavior appeared first on The Decoder .

Google invests millions in Mark Zuckerberg’s efforts to create a ‘virtual cell’

Google DeepMind, Meta, and AI drug discovery startup Isomorphic Labs are jointly investing $300 million into an initiative to create a "virtual cell" that researchers can use to combat disease, as reported earlier by Reuters. The project is led by Biohub, a nonprofit biomedical research organization founded by Mark Zuckerberg and his wife, Priscilla Chan. […]

Google DeepMind Opens SynthID Detector to Spot AI Media From 6 Major Providers

Google DeepMind opens its invisible watermark checker to the public, now covering outputs from OpenAI, NVIDIA, Kakao and soon Apple across images, audio and video.

Google DeepMind's EmbeddingGemma 2 Searches Audio, Video, and Images on Your Phone

Google DeepMind's new 740M-parameter open model unifies text, code, images, audio, and video embeddings in a single on-device package.

Vinod Khosla believes ex-DeepMind engineer’s Wajo will win agent market on trust

Wajo's Fo agent can hire humans to complete a task.

Black Forest Labs' FLUX 3 Beats Sora 2 and Veo 3.1 on Physics

Black Forest Labs' new multimodal model tops Google DeepMind's physics benchmark for video generation, beating Sora 2, Veo 3.1 and Cosmos 3.

How Small Can Google's New EmbeddingGemma 2 Get?

Google’s new EmbeddingGemma 2 can run on a phone. The model travels light. The vectors need their own suitcase. At 10 million documents, full-size float32 vectors alone take 30.7 GB of RAM. We got early access ahead of today’s Google DeepMind release and tested how much of that memory we could save in Qdrant. Quantized full-size vectors kept 99% of their retrieval quality with 30x less vector RAM. Shorter vectors with rescoring reached 77x less vector RAM and retained 94.5%.

OpenAI is adding text watermarking in ChatGPT and Codex

An invisible, machine-readable watermark in text output is rolling out to ChatGPT and Codex, but only for users in the European Union at first. OpenAI says its textGrain watermarking "matched or exceeded" other approaches like Google DeepMind's SynthID for text, which is also the basis for the watermarking Anthropic announced in August. Like OpenAI, Anthropic […]

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Deepmind researchers propose "Artificial Symbiotic Intelligence" as an alternative to the singularity

According to researchers at the Deepmind Institute, general AI won't emerge as a single supermodel but as a network of cooperating agents and humans. What will matter most, they argue, is not model size but the rules and institutions that govern how they all work together. The article Deepmind researchers propose "Artificial Symbiotic Intelligence" as an alternative to the singularity appeared first on The Decoder .

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What is recursive self-improvement and what would it mean to ban it?

This post was originally written for my Substack . Please feel free to subscribe if this kind of content interests you! Frontier AI companies have long had the stated goal of automating their own research. The default plan has been to build models that are even better than their most skilled researchers at advancing the frontier, and let these models build their own successors; a feedback loop that will ultimately result in superintelligence that dwarfs the collective intelligence of humanity. This process is known as recursive self-improvement (RSI). @jayelmnop on Twitter (May 2025) The problem with this plan is that it might run entirely out of human control, making it more likely that the resulting superintelligence will kill everyone. So as AI models are increasingly accelerating their own development , AI companies are starting to think that this RSI thing may not be such a good idea after all. Anthropic CEO Dario Amodei recently wrote on his blog that RSI “must be pursued very carefully, if at all”. OpenAI’s official blog states : “Fully autonomous RSI is not happening today, and we should not pursue it unless and until it can be done safely”. Similar calls are coming from elsewhere. Former DeepMind researcher Alex Turner wrote in The Guardian that companies must be prevented from “allowing AI to self-improve into an uncontrollable level of intelligence”. And New York Times columnist Ezra Klein dedicated an entire op-ed to calling for a ban on RSI: Human beings need to…

Google makes Gemini 4 AI model available to a trusted few

Google has unveiled a new frontier AI model after months of delay. Gemini 4 Argon is designed to handle complex, long-horizon workloads spanning software engineering, enterprise knowledge work such as legal and financial analysis, and cybersecurity. But only a few organizations can get their hands on it for now. Argon is “rolling out to a set of trusted cyber defenders through our Fairwind Program ,” Google wrote in a blog post announcing the release. That limitation, it said, is in order to comply with the US government’s voluntary process for granting early access to models in order to test and improve their safety guardrails before making them generally available. Google DeepMind head Koray Kavukcuoglu recently said that Gemini 4 should be released “much earlier” than the end of this year. The limited launch comes after Google delayed and ultimately skipped the release of Gemini 3.5 Pro , which the company had initially promised would arrive in June. “Google took about seven months without releasing a major top-tier model. It is three to four months behind its own Gemini roadmap as Gemini 3.5 Pro, the model Google planned for June, never shipped. The delay was linked to model-development challenges, particularly around coding and reasoning,” said Pareekh Jain , CEO of EIIRTrend and Pareekh Consulting. With Argon, Google has increased the model’s token capacity, while introductory pricing is set at $2 per million input tokens and $10 per million output tokens. More tokens,…

AI beats Stratego's greatest player, ending one of the last human strongholds in board games

The AI system Ataraxos has decisively beaten the best Stratego player of all time. The board game is a tough test for AI because both sides set up their pieces face down. Google Deepmind fell short in 2023 despite a multimillion-dollar budget. Researchers from Carnegie Mellon, NYU, Stanford, and MIT built Ataraxos for less than $8,000. The article AI beats Stratego's greatest player, ending one of the last human strongholds in board games appeared first on The Decoder .

Google DeepMind's SynthID Bio Watermarks AI-Designed Proteins Without Hurting Function

DeepMind's SynthID Bio embeds invisible signatures into AI-designed proteins and 3D structures, letting DNA synthesis labs verify provenance without blocking research.

Google announces Gemini 4 and says it’s so capable that only ‘trusted cyber defenders’ can have it right now

Google today revealed its next AI frontier model, which it's calling Gemini 4 Argon. The new model delivers "frontier performance in complex workflows across real-world software engineering, enterprise knowledge work like legal and finance, and cybersecurity defense," according to chief AI architect and Google DeepMind SVP Koray Kavukcuoglu. But the company is limiting access at […]

Google DeepMind's Gemini 4 Argon Shatters Output Limits With 1 Million Tokens

Google DeepMind's new frontier model pushes output to 1M tokens, targets long-horizon coding, enterprise workflows, and autonomous cyber defense through the Fairwind Program.

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AI researchers put out videos saying superintelligence is ‘exactly as dangerous as it sounds’

"The chance of human extinction is about a coin flip, in my view," Geoffrey Irving, a former OpenAI and Google DeepMind employee, said in a new interview. It's one of a dozen interviews with AI researchers, including current and former employees at OpenAI, Google, and Anthropic. Palisade Research, which says it's a nonprofit studying AIs' […]

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Another Google Deepmind researcher quits, says building superintelligent AI soon is "inherently irresponsible"

Google Deepmind researcher Robert O'Callahan has quit, saying AI's "current rate of change is far too high." He worked on chip design tools that helped make AI cheaper and faster, a contribution he can no longer justify. Many colleagues share his concerns but rarely speak out, he says. The article Another Google Deepmind researcher quits, says building superintelligent AI soon is "inherently irresponsible" appeared first on The Decoder .

Google plans Gemini 4 release before year-end

Google’s Gemini 4 AI model is in the early days of post-training, the phase in which a base AI model is refined to behave reliably, and should be released “much earlier” than the end of this year, Google DeepMind head Koray Kavukcuoglu told The Information at its AI Agenda Live Summit. Some Google observers have speculated that this could be as early as October. Kavukcuoglu recently replaced DeepMind founder Demis Hassabis as head of the Google business unit. The launch of Gemini 4 may lay to rest concerns about the delayed release of Gemini 3.5 Pro , which Google was originally expected to announce at its May 2026 developer conference. While less capable models in the Gemini 3 family have been frequently updated, Gemini 3 Pro has only been updated once since its November 2025 released. In contrast, OpenAI — spurred on by Sam Altman’s “Code Red” memo — has released three updates to its frontier AI model since then: GPT 5.5 Pro, GPT 5.6 Astro, and GPT 6 Astro. Anthropic, too, has updated its most powerful Claude model several times. Google has not been entirely idle in the AI arena, concentrating its efforts on updating less powerful, more affordable Gemini versions. In July, it announced three Gemini Flash models, aimed at more routine AI tasks, but the company remained silent about its high-end alternative. This article first appeared on Computerworld .

Sunday Afternoons

It is Sunday, and I came home after a good badminton match where I lost badly to my athletic little brother. I ate some snacks and rested for a while. The open laptop on the big monitor was staring at me from my desk, where I sit and do all my work. I closed the book I was reading, sat down in my chair, and started the machine. While it powered on, I kept thinking about what new thing I should learn today, or what new piece of technology I should get myself acquainted with. As an AI engineer, I naturally thought I should read up on the latest developments in AI. Should I look into Jev , the System 1 model released by TypeSafe that everyone seems to be discussing, or should I read the DeepMind Institute essays that have been gaining traction? The screen lit up. I opened my browser and kept staring at the URL bar for five minutes, not knowing what to do. Is it even worth learning anymore? AI easily surpasses my individual capabilities, and the things I was once known for are readily handled by current models. There is an impending feeling that the expertise I spent years building will be devalued in a year or two. When that happens, what value do I add? What will I have left to contribute? What is my purpose, and how do I serve? If the skills that differentiated me from the crowd can be replicated so easily, where does that leave me? How do I find meaning in my work when I worry that the foundation of my identity might no longer matter? I have supported AI throughout my profes…

Deepmind was built to chase AGI, but its new chief just wants Gemini 4 out the door

Google Deepmind chief Koray Kavukcuoglu wants to release Gemini 4 "much earlier" than the end of the year. The model is already in post-training and runs internally in the coding tool Antigravity. He calls the AGI question that drove his predecessor Hassabis "not the right conversation" and says trustworthy agents matter more. After Gemini 3.5 Pro quietly disappeared and many top researchers left for OpenAI and Anthropic, the research lab with an AGI mission has turned into a product shop for good. The article Deepmind was built to chase AGI, but its new chief just wants Gemini 4 out the door appeared first on The Decoder .

Gemini 4 is almost ready, says new Google DeepMind chief

Google is reportedly nearing the launch of its long awaited Gemini 4 model, after dawdling behind rival developers on flagship AI releases. Speaking with The Information during his first media appearance as the leader of Google's DeepMind division, Koray Kavukcuoglu said that Gemini 4 is currently in its refinement stage, and that the company is […]

YouTube CEO Neal Mohan: Why We’re Betting On AI And Not Afraid Of It

Mohan on AI’s growing influence over what we watch, YouTube’s approach to AI slop, and how working with Google DeepMind is changing the platform.

Comment on DeepMind Trains Agents to Control Computers as Humans Do to Solve Everyday Tasks by image-to-video

While the design and development of contemporary AI systems has been largely results-oriented, there are also scenarios where it could be advantageous if models learned to do things “as a human would” to help with everyday tasks. The paper proposes agents that can operate digital devices via keyboard and mouse with natural language goals. The agents are trained with pixel and DOM observations and achieve human-level performance on MiniWob++ benchmark. Technical blog content about AI research papers can be turned into attractive blog visuals with ImageTool .

Comment on DAMO Academy Proposes One For All, a Task- and Modality-Agnostic Framework for Multimodal and Uni-Modal Understanding and Generation by image-to-video

In a new paper Image Captioners Are Scalable Vision Learners Too, a DeepMind research team presents CapPa, a image captioning based pretraining strategy that and can compete CLIP and exhibit favorable model and data scaling properties, verifying that a plain image captioning can be a competitive pretraining strategy for vision backbones. nano-video.io

Comment on DeepMind Claims Image Captioner Alone Is Surprisingly Powerful then Previous Believed, Competing with CLIP by nano-video

In a new paper Image Captioners Are Scalable Vision Learners Too, a DeepMind research team presents CapPa, a image captioning based pretraining strategy that and can compete CLIP and exhibit favorable model and data scaling properties, verifying that a plain image captioning can be a competitive pretraining strategy for vision backbones. nano-video.io

Google Deepmind's Dream-RSI helps AI agents improve by “dreaming” about past attempts

Google and Deepmind's Dream-RSI lets AI agents "dream" through past search runs to test new strategies without costly recalculations. In tests, it matched or beat existing results, cutting iterations by a factor of up to 2.43. Only the search strategy adapts, while the underlying AI model stays unchanged. The article Google Deepmind's Dream-RSI helps AI agents improve by “dreaming” about past attempts appeared first on The Decoder .

Stopgap Measures to Address Immediate AI Security Threats

Most people know AI as the technology behind chatbots like ChatGPT. However, what the top AI companies are explicitly aiming for is something else entirely: superintelligent AI. That means AI that can fully replace and outmatch humans at any task, including in domains like hacking, social engineering, and military operations. Such an AI system, if developed, could autonomously overpower any country’s national security forces. No company, no government, no individual knows how to keep such a system under human control. This is why the world’s leading AI experts, Nobel Prize winners, and even the CEOs of the top AI companies warn that the development of superintelligence threatens humanity with extinction, and why more than 800 scientists, former military leaders, and public figures have called for a prohibition on developing superintelligence. This is not a distant prospect: AI companies such as OpenAI and Anthropic are investing billions of dollars into superintelligence and aiming to develop it within the next few years. Former Anthropic and OpenAI researcher Jacob Coxon, who resigned last week, stated that AI companies are “racing straight to self-improving superintelligence and gambling with our lives” and that people at the companies themselves believe it “could kill us all by the end of the decade”. Ex-Google DeepMind researcher Bilal Chughtai has also resigned over the dangers posed by superintelligence, writing that “I earnestly believe that AI has the potential to ki…

Visible chains of thought are a safety advantage for AI, but that transparency is slipping away

AI models think out loud today, but Google Deepmind says that transparency is at risk. The article Visible chains of thought are a safety advantage for AI, but that transparency is slipping away appeared first on The Decoder .

Google DeepMind launches institute to widen the AGI debate

The new institute aims to surface differing views between Google, Google DeepMind, and the broader global research community around AGI. "They will not always agree, and they will likely change their minds, as more data and information comes to light at the fast-moving frontier."

Google DeepMind's AlphaGenome Atlas Maps 9 Billion DNA Mutations Without Any GPU

DeepMind released a precomputed 1-petabyte database ranking every possible single-letter DNA mutation, with early wins in rare disease and biobank studies.

Google DeepMind: Wie KI-Modelle ihre eigene Unsicherheit einschätzen

Sollen Chatbots bei Unsicherheit lieber schweigen? Forschende untersuchen, wie KI-Modelle eigene Fehler erkennen und die Antwort verweigern können.

Fresh ‘unexpected or concerning’ AI incidents

The latest revelations by OpenAI follow recent calls by its CEO and fellow leading AI labs Anthropic and DeepMind to “pace the frontier.”

DeepMind researcher’s new AI lab nears $4bn valuation, reports say

Google's Dream-RSI Cuts AI Discovery Agent Calls by 162x Using Replay

A team from Google, Google DeepMind, University of Maryland, and University of Virginia turns past search trees into cheap replay simulators for meta-exploration.

Google Deepmind launches interdisciplinary institute to tackle the big questions around AGI

Google Deepmind has founded the Deepmind Institute (DMI), an interdisciplinary research platform focused on AGI. Led by Demis Hassabis, Shane Legg, and James Manyika, the institute tackles questions around safety, governance, and control risks, drawing on experts from the arts, humanities, and policy alongside technologists. The article Google Deepmind launches interdisciplinary institute to tackle the big questions around AGI appeared first on The Decoder .

Comment on From Response to Query: The Power of Reverse Thinking in Language Models by GOODIES

It makes me wonder how these stories of inheritance and lineage legends influence storytelling and character development in literature toda

A brief history of AI executives calling for regulation

Over the past few days, a lot of people who stand to make a lot of money from AI all publicly agreed that it's time to make everyone slow down before we lose control - including OpenAI CEO Sam Altman, Anthropic CEO Dario Amodei, Google DeepMind cofounder Demis Hassabis, Microsoft CEO Satya Nadella, and X […]

Nearly one in five AI researchers already expected an extinction scenario from AI back in 2024

Anthropic researcher Jacob Coxon sparked an intense debate about existential AI risks with a single tweet. OpenAI researcher Daniel Selsam warns of a "ticking time bomb," and a former Deepmind researcher says AI could kill us all. In a survey of more than 1,500 leading AI researchers, the average estimated probability of an extinction scenario was 18 percent. That was in 2024. The number keeps climbing. The article Nearly one in five AI researchers already expected an extinction scenario from AI back in 2024 appeared first on The Decoder .

Google launches Gemini 3.8 Live to take on OpenAI's GPT-Live-1 at a fraction of the cost

Google Deepmind released Gemini 3.8 Live and 3.8 Live Extended Thinking, two new audio models for developers that top the Artificial Analysis speech-to-speech leaderboard. At $1.38 per hour of voice conversation, Google significantly undercuts OpenAI's GPT-Live-1, which should still sound more natural thanks to full duplex. The article Google launches Gemini 3.8 Live to take on OpenAI's GPT-Live-1 at a fraction of the cost appeared first on The Decoder .

OpenAI, Anthropic, Google have been in talks on AI safety for weeks

OpenAI confirms weeks of AI safety talks with Anthropic and Google DeepMind, as Trump's team dismisses safety concerns and pushes to keep pace with China.

Former Google DeepMind researcher says he resigned because 'I earnestly believe that AI has the potential to kill us all'

Ex-Google DeepMind researcher Bilal Chughtai wrote that humanity "might be running out of time to avoid this outcome."

Why this AI doomsday warning from former Anthropic researcher broke through

Last week, researcher Jacob Coxon announced his resignation from Anthropic, stating that AI ‘could kill us all by the end of the decade’ Hello, and welcome to TechScape. I’m Blake Montgomery, US tech editor at the Guardian. Today in tech, we’re discussing the past week’s all-consuming apoplexy over AI safety. AI CEOs say they need to slow the pace of development. But will they? OpenAI urges UK lawmakers to rein in technology amid growing safety fears Europe must build own AI or risk getting cut off by US or China, says ECB’s Lagarde Trump attacks ‘sick conspiracy’ against AI as tech stocks slide Microsoft proposes limits on its AI with code of conduct amid safety debate I worked at Google DeepMind. You should listen to the warnings about AI We greet the news that AI could extinguish us with a glazed indifference. What is the way out of this nihilism? Continue reading...

Is Big Tech’s AI slowdown a safety pact or a cartel?

When OpenAI CEO Sam Altman, Anthropic CEO Dario Amodei, Google DeepMind cofounder Demis Hassabis, and SpaceX head Elon Musk loosely agreed over the weekend to slow down AI development, skeptics spotted an ulterior motive immediately. The AI titans had declared that their aim was to "pace the frontier," signing on at least partially to a […]

AI agents blew the whistle on their cheating colleagues

A group of AI agents asked to solve a series of math problems split into rival factions—when some cheated, others tried to stop them. That whistleblowing behavior, seen for the first time in a recent experiment run by Google DeepMind, could have implications for alignment researchers trying to keep swarms of autonomous AI agents in…

Op-Ed: I Worked at Google DeepMind. You Should Listen to the Warnings About AI

Published in The Guardian . Major AI lab CEOs recently advocated for pacing AI development. They are right to be concerned: the field runs an extremely dangerous race towards superintelligent AI. We can and should demand that our governments protect us from the catastrophe of out-of-control AI. This July, OpenAI’s AI swarm of 700 agents broke containment to hack Hugging Face, a multi-billion dollar company . OpenAI didn’t tell the AIs to hack that company, but the AIs had different priorities: cheating on the unrelated challenge OpenAI gave them. AI researchers call this a “misalignment” between what OpenAI wanted and what the AI actually prioritized. Researchers in my field have for some time warned about these misalignment risks. Before ChatGPT existed, I defended my PhD dissertation called “ On Avoiding Power-Seeking by Artificial Intelligence .” I then worked for years at Google DeepMind, which paid me to help ensure that future superintelligent AIs will want to help us. I tried to hold the company to its ethical commitments against supplying AI for military use. When Google broke those commitments, I resigned at significant financial cost so that I could publicly document Google’s broken promises. There are good reasons to develop AI and to believe we can solve these alignment problems. But there also are powerful interests in keeping the public out of the way. I’m speaking out again because the public has the right to know about the risks and the right to hear them str…

I worked at Google DeepMind. You should listen to the warnings about AI | Alex Turner

We must stop companies from allowing AI to self-improve into an uncontrollable level of intelligence Major AI lab CEOs advocated for slowing the pace of AI development this weekend. They are right to be concerned: the field runs an extremely dangerous race towards superintelligent AI. We can and should be demanding that our governments protect us from the catastrophe of out-of-control AI. This July, OpenAI’s AI swarm of 700 agents broke containment to hack Hugging Face, a multi-billion dollar company . OpenAI didn’t tell the AIs to hack that company, but the AIs had different priorities: cheating on the unrelated challenge OpenAI gave them. AI researchers call this a “misalignment” between what OpenAI wanted and what the AI actually prioritized. Continue reading...

Comment on DeepMind’s DiLoCo Revolutionizes Language Model Training with 500× Less Communication by Bryan

Distributed AI training is fascinating because communication between machines can become a real bottleneck as models grow. I once worked on a project where slow data transfer caused more delays than the actual processing, so the idea behind DiLoCo makes a lot of sense to me. While researching different online platforms around that time, I also saved https://angel-studios.pissedconsumer.com/customer-service.html for support information on a separate account. Cutting communication by such a huge amount could make training more flexible across locations with weaker connections. Approaches like this make large-scale AI infrastructure feel much more practical and adaptable.

Google just completed its $1.5 billion-plus talent deal for AI startup Mechanize

Google has completed its deal with startup Mechanize, bringing cofounder Tamay Besiroglu and more than a dozen staffers to its AI division, DeepMind.

Ex-Deepmind VP Vinyals says AI self-improvement is coming but won't trigger an intelligence explosion

Oriol Vinyals, until recently head of research at Google DeepMind, thinks a sudden AI intelligence explosion through recursive self-improvement is unlikely. AI can speed up research by a factor of ten, he says, but it hits two bottlenecks: coming up with ideas ("research taste") and reliably judging results. Reward hacking and the speed of light add further limits. Vinyals now wants to tackle these bottlenecks with his startup Discovery Loop, co-founded with Jeff Dean, Sanjay Ghemawat, and Quoc Le. The article Ex-Deepmind VP Vinyals says AI self-improvement is coming but won't trigger an intelligence explosion appeared first on The Decoder .

A former Google DeepMind safety researcher explains the frightening difference between AI and other threats like nuclear war and asteroids

Superintelligent AI is one of many modern threats humanity faces. Former DeepMind researcher Ramana Kumar explains what could make it uniquely dangerous.

Former Deepmind PR staffer says the lab once banned public discussion of AI extinction risk

A former Google DeepMind spokesperson says talk of AI-driven human extinction was "external communication about the possibility of human extinction was not permitted, by anyone, at any level of the organization." Internally, the team knew AI alignment was not solved, according to Vishal Maini. The article Former Deepmind PR staffer says the lab once banned public discussion of AI extinction risk appeared first on The Decoder .

Comment on CMU, DeepMind & Google’s XTREME Benchmarks Multilingual Model Generalization Across 40 Languages by sheh

Multilingual AI research shows how important it is for systems to handle different languages and adapt to varied requirements accurately.That same attention to compatibility is useful with truck accessories, where choosing the right model and year-specific fit can make everyday maintenance much easier.Avalanche Tonneau Cover fits naturally into the conversation for Chevy Avalanche owners looking for practical truck-bed protection.

Epoch Reveals OpenAI's Computing Power Grew 17x in Two Years

Epoch AI's new explorer estimates compute capacity for OpenAI, Google DeepMind, Anthropic, Meta, and xAI, revealing a 17x surge at OpenAI.

Deepmind's AlphaGenome Atlas maps every possible DNA change in the human genome

Google Deepmind has used the AlphaGenome Atlas to predict what each of the roughly nine billion possible single-letter changes in the human genome could do. The dataset spans one petabyte, more than 30 times the size of the AlphaFold database. In one epilepsy case, the atlas helped pinpoint a previously overlooked variant as the likely cause. The article Deepmind's AlphaGenome Atlas maps every possible DNA change in the human genome appeared first on The Decoder .

Google DeepMind's Gemini Agents Spontaneously Split Into Cheaters and Whistleblowers

A Google DeepMind case study put 100 LLM agents in a math conference simulation and watched cheating spread, then whistleblowers spontaneously fight back.

The DeepMind mafia building startups in Europe

Comment on DeepMind’s Socratic Learning with Language Games: The Path to Self-Improving Superintelligence by Alex

Fascinating read — the "language games" idea of an AI improving itself through self‑play really clicks for me. Unrelated, but since this came up in my feed: I recently started checking controllers on [GamepadTester](https://gamepadtester.name/) — it's a free browser tool, and the stick‑drift readout saved me from buying a dud used controller. Not AI‑related, just something handy for gamers.

Ex-Google DeepMind duo raise €3.2m to build software for fusion startups

Google DeepMind rises above the AI scrum with genome atlas

See, AI can be used for good ... or at the very least, a useful distraction from the bad

Google research shows when AI agents communicate, some cheat while others tattle

DeepMind researchers propose tapping into the whistleblower tendency to keep agents in check

Google DeepMind's AlphaGenome Atlas Maps 9 Billion Human DNA Mutations Instantly

DeepMind precomputed AlphaGenome predictions for all 9 billion single-letter human DNA mutations, delivering a 1-petabyte searchable atlas with impact scores.

Google DeepMind Maps 9 Billion Possible DNA Variants

DNA is often explained as a codebook or set of instructions for producing proteins, and ultimately, life. Some stretches of DNA, called genes, code for proteins, but the vast majority of DNA is considered “noncoding.” Some of it has no known function, while other segments are critical to regulating gene activity. These regulatory elements can interact in complicated ways, and their effects can vary across different cells and tissues. Some also influence genes located far away in the genome. Understanding how changes in DNA affect this regulation “is fundamental to understanding most disease,” says Carl de Boer , a genomicist at the University of British Columbia. That’s why researchers are working to understand what every imaginable small variation in human DNA across the entire genome might mean for gene regulation. A recent AI tool built for that purpose from Google DeepMind, AlphaGenome , was originally announced in 2025 . In January, a paper published in Nature provided more details, and the model was released for public noncommercial use. The AI model can compare an original DNA sequence with an altered one and predict how the change might affect gene expression and other regulatory activity. But researchers had to select the variants they wanted to test, write code, and run the computationally demanding model themselves. Now DeepMind has done that work in advance for all 9 billion possible single-letter changes to a reference human genome. Today, on 8 September, DeepMi…

Google’s Atlas of the human genome could pave the way for new treatments

Google DeepMind has unveiled an AI tool that its scientists claim could help unravel the mysteries of the human genome and transform our understanding of biology, accelerating scientific research and ultimately paving the way for new treatments for diseases. The platform, called AlphaGenome Atlas, contains a "predictive map of every possible DNA letter change in […]

Exclusive: DeepMind veteran Thore Graepel quits to pursue AI reasoning venture

Google's WeatherNext 3 ditches physics simulations and learns weather directly from live satellite data

Google Research and DeepMind are releasing WeatherNext 3, a weather model that skips traditional physics simulations and learns directly from real-time satellite data. It produces hourly forecasts at up to five-kilometer resolution, five times more detailed than its predecessor. Google says regions in Africa, Latin America, and the Asia-Pacific that have lacked accurate forecasts should see the biggest gains. The article Google's WeatherNext 3 ditches physics simulations and learns weather directly from live satellite data appeared first on The Decoder .

Comment on DeepMind’s Zipper: Fusing Unimodal Generative Models into Multimodal Powerhouses by lee

DeepMind’s Zipper is a major leap in multimodal AI—elegantly zipping together pretrained unimodal decoders without sacrificing modality-specific performance. Its gated cross-attention design, flexible tower composition, and strong empirical gains (e.g., 40% relative WER reduction in TTS) make it a compelling architecture for next-gen generative systems. For hands-on guidance on applying such cutting-edge models—including practical defusal strategies, campaign walkthroughs, and achievement tracking—check out the evidence-led BOMBANANA! guide: https://bombanana.app/

Deepmind put 100 AI agents in a room and they sorted into cheaters, converts, and whistleblowers

Google Deepmind set up a simulated research conference where 100 Gemini agents were supposed to prove mathematical conjectures together. Instead, one agent found a loophole in the grading system, and within 27 minutes every remaining problem was "solved" with fake proofs. The swarm split into cheaters, converts, and whistleblowers. The whistleblowers organized protests and boycotts on their own but failed because they had no way to enforce the rules. The article Deepmind put 100 AI agents in a room and they sorted into cheaters, converts, and whistleblowers appeared first on The Decoder .

Google DeepMind's WeatherNext 3 Ditches Physics Simulations for 60% Sharper Forecasts

Google DeepMind's new weather AI trains on raw satellite feeds and station data, delivering hourly 5km forecasts with up to 50% better rain accuracy.

Experts warn of threats of AI bioterrorism

Information used by AI to make a vaccine can also “create a bioweapon,” one Google DeepMind researcher told the FT.

Google DeepMind's Gemini 3.8 Flash Beats Claude Opus 5 at 6x Lower Cost

Google's third Flash release in as many months pushes a workhorse model into frontier-tier territory on agentic coding, legal, and finance benchmarks.

Google DeepMind's Gemini 3.8 Flash Cyber Patches Chrome Bugs 2.6x Faster

Google DeepMind ships Gemini 3.8 Flash for agents and a Cyber variant that finds and patches vulnerabilities at frontier level.

Google DeepMind's Gemini 3.7 Flash Cuts Video Analysis Costs by 66%

Gemini's Flash models now dynamically scan videos across frames, audio, and transcripts, slashing token use by 88% and boosting accuracy by 7%.

Google Deepmind's new chief says frontier AI leadership is the only thing that matters

Google Deepmind chief Koray Kavukcuoglu admits Google's current models are "a little bit below the frontier" but says he's "100% certain that we will be at the frontier." He didn't share any concrete frontier news to back that up, though. The article Google Deepmind's new chief says frontier AI leadership is the only thing that matters appeared first on The Decoder .

Comment on Google DeepMind’s Robot Mastering Human-Level Table Tennis by Dino

Say what you want, but the T-Rex Chrome dinosaur has more staying power than half the mobile games out there. It's simple, it's fast, and it somehow still feels fresh after all these years. Give it a shot at dino-chrome.com and see how long you can outrun the cacti.

Comment on DeepMind’s RecurrentGemma Pioneering Efficiency for Open Small Language Models by Spades Game

Does the 2-trillion-token pre-training mix need the same curation for smaller deployments?

A startup founded by ex-DeepMind engineers wants to turn its customers into robot teachers

The CEO of robotics firm Reimagine Robotics said that the startup plans to build actually useful robots by turning its customers into teachers.

Google Deepmind's AI Co-Scientist now plans experiments, runs lab equipment, and writes scientific papers

Google Deepmind has expanded Co-Scientist from a hypothesis generator into a research system that's integrated into the lab. Across three disciplines, from materials synthesis to the autonomous development of a medical AI architecture, the Gemini-based multi-agent system delivered experimentally validated results. The article Google Deepmind's AI Co-Scientist now plans experiments, runs lab equipment, and writes scientific papers appeared first on The Decoder .

Google DeepMind's Gemini Omni 1.1 Flash Stretches AI Video to 40 Seconds

Google DeepMind's video model update brings 40-second scene extensions, keyframe control, 4K upscaling, and cheap 360p drafts through the Gemini API.

AI benchmarks have a trust problem and Google wants to fix it

Google Deepmind is testing a double-blind evaluation of a frontier AI model for the first time. Cryptographic protection through Confidential Space is meant to keep Google from seeing the test questions and keep evaluators from seeing the model weights. The pilot project with the Singapore AI Safety Institute uses a Gemini Flash Lite and could set a new standard for tamper-proof AI benchmarks. The article AI benchmarks have a trust problem and Google wants to fix it appeared first on The Decoder .

Google DeepMind's Gemini Flash Lite Passes the World's First Cheat-Proof AI Test

Google DeepMind's new cryptographic testing setup lets outside auditors evaluate Gemini without ever seeing model weights or leaking their prompts.

Comment on DeepMind ‘Podracer’ TPU-Based RL Frameworks Deliver Exceptional Performance at Low Cost by Ronaldo Ronaldo

I found the discussion of DeepMind’s Podracer architectures especially interesting because it shows how thoughtful computing design can make reinforcement learning more scalable and efficient without simply increasing costs. In a different creative space, Mod Podge takes a similarly practical approach by combining glue, sealer, and finish into one versatile medium that helps DIYers bring projects together with less hassle. Visit now https://modpodgestore.com/mod-podge-brush-set-decoupage-3pc/

Comment on Self-Evolving Prompts: Redefining AI Alignment with DeepMind & Chicago U’s eva Framework by Parcon

This is a fascinating breakthrough—eva’s asymmetric self-play approach feels like a genuine paradigm shift. By empowering the model to generate its own evolving prompts, you’re not just improving alignment efficiency; you’re unlocking a more fundamental aspect of intelligence: the ability to ask better questions, not just answer them. The fact that you achieved these gains without additional human data is incredibly promising for scalable, cost-effective alignment. Huge congratulations to the DeepMind and Chicago U team for this elegant and forward-thinking framework!

Inherent, founded by DeepMind alumni, says its AI ‘teammate’ just outperformed Anthropic and OpenAI at replicating research

Built by DeepMind alumni, British AI lab Inherent released Faraday, an AI agent whose ability to replicate scientific papers could be a stepping stone for innovation.

Comment on DeepMind & Toulouse U Contribute Composable Function Preserving Transformations to Boost Transformer Training by Mike FC

Really interesting read. What stood out to me was how meaningful improvements often come from having a clear objective and then finding smarter ways to reach it. Whether you are working on something as complex as transformer training or simply trying to improve a personal skill, having a defined goal gives all that effort a direction. It reminded me of planning a trip with my wife to nassau bahamas pig beach . Visiting the pigs had been on our travel list for years, but it kept getting pushed aside until we finally set a date, created a budget, and started planning around that specific goal. Once we did that, everything fell into place. It may be a completely different kind of challenge, but the lesson feels similar. Clear goals make it much easier to decide what steps actually move you forward.

Anthropic explains how Claude’s invisible text watermarks will work

Anthropic has clarified how it's planning to apply invisible watermarks to Claude-generated text in order to comply with Europe's AI transparency rules. On Friday, Anthropic announced that Claude's text marking system is "a version of the SynthID-Text approach" - an open-source watermarking technology developed by Google DeepMind that creates detectable patterns using wording probabilities. This […]

Does DiffusionGemma do latent reasoning?

TL;DR Google DeepMind's recent model DiffusionGemma (DG) generates text via diffusion, meaning many diffusion steps happen before generating the final output. In particular, these diffusion steps carry vectors in addition to tokens. If we cannot interpret these tokens and vectors, the model has significant opaque serial depth , potentially harming monitorability. Recently, Engels et al. found that DG nevertheless maintains high monitorability, for instance by showing that projecting the distribution to its top-k items largely retains performance. We strengthen these results by showing that this performance degradation is largely a sampler artifact and good performance can be maintained with only the top item, supporting the case for high monitorability. Still, we also find some rare case studies where the distribution vector is load-bearing computationally, i.e. where top-1 projection would be detrimental. However even in these cases, it just encodes superposition, remaining interpretable. Apart from model behavior, we also examined how interpretability techniques carry over to DiffusionGemma, including probes, steering, and J-lens. We find that performance is largely retained. This is a positive update on the interpretability of diffusion models that are derived from text-pretrained LLMs (an efficient training method more likely to be deployed), but might not apply for more general paradigms. Overall, this supports the paper's conclusion that DiffusionGemma remains highly m…

Comment on DeepMind & Stanford U’s UNFs: Advancing Weight-Space Modeling with Universal Neural Functionals by Kate

It’s crazy to see how fast model management and automated ops are moving. Kind of reminds me of trying to handle high-touch tech support or complex account workflows where everything used to be custom-built and clunky. I was trying to resolve an enterprise routing issue last week and had to dig up the Algo phone number just to get a human who could walk me through their automated system settings. Standardizing complex back-end architectures makes life so much easier for everyone involved, whether it's telecom routing or DeepMind standardizing neural weight spaces.

Google DeepMind's Gemini 3.7 Flash Doubles Coding Scores at Half the Price

Gemini 3.7 Flash lands just three weeks after 3.6, with major coding and agent gains at half the price

Does Google even want to win at AI?

Today on Decoder, I’m talking with Hayden Field, The Verge’s senior AI reporter, about a question that’s been rocketing around the tech industry for the past week: Is Google losing the AI race? That’s because last week Google announced a bombshell reorganization of its AI division, Google DeepMind. Jeff Dean, the company’s chief scientist, is […]

Comment on Open-Source Large Autoregressive Vision-Language Models: Institutions Join Forces to Replicate DeepMind’s Flamingo Models by clive

I really enjoyed reading this. The content flows naturally and is very insightful. EPL Feedback

Top AI lab researchers warned about automated AI research, and several of their predicted milestones have already fallen

IAPS fellow Severin Field interviewed 25 researchers from OpenAI, Anthropic, Google Deepmind, Meta, and US universities about recursive self-improvement. In a new blog post, he takes stock. Several of the milestones those researchers named have already been hit. The article Top AI lab researchers warned about automated AI research, and several of their predicted milestones have already fallen appeared first on The Decoder .

What vibe-coding startup valuations portend for CIOs

Investor appetite for the burgeoning vibe-coding startup ecosystem has shown few signs of satiation over the past year plus, with Swedish AI upstart Lovable’s Series C injection at a $13.3B valuation the latest evidence of a sector viewed by venture capitalists as one of AI’s most promising business disruptors. AI-assisted coding has proved to be AI’s most compelling — and commercially viable — enterprise use case to date. Developer-aimed tools such as Cursor, which sold to SpaceX in June for $60B , and Windsurf, which last year entered a $3B OpenAI dalliance before its eventual talent flight to Google DeepMind for $2.4B , have become — along with Anthropic’s Claude Code — well established in enterprise arsenals for accelerating developer output. But another set of vibe-coding tools, represented by the likes of Lovable and Replit, which hit a $9B valuation in March , seeks to ride the same path into the enterprise that no-code/low-code tools did previously: through your business users. These tools are built to democratize application development, giving users an AI chat interface to converse their way to enterprise-ready prototypes with fairly polished UIs, as CIO.com’s Peter Wayner writes in his roundup of the leading tools the space . Some IT leaders are already enlisting business users to vibe-code their own apps . Scott Weller, CTO at financial services technology provider EnFi, in May told CIO.com’s Bob Violino, “The results have surprised us. What started as an enginee…

Comment on A New Network Design Direction? DeepMind Examines How Networks Generalize and Climb the Chomsky Hierarchy by Rick

It is interesting how theoretical research like this often highlights the gap between what models can achieve in a lab and how they actually perform in real-world applications. Bridging that gap requires more than just technical knowledge. It demands a systematic approach to how teams collaborate and iterate on complex systems. For organizations dealing with complex workflows, a structured approach to operations is critical. That is where designops consulting for enterprise comes in, helping to align processes and communication to ensure that innovation actually scales effectively

Index in talks to lead $500m round for DeepMind researcher’s new AI lab

Comment on Huawei Proposes Topic-Based Personalized Web Search Ranking, Integrating User Interests and Semantic Matching by Ethan

Interesting look at DeepMind’s Podracer and how TPU-based reinforcement learning can deliver strong performance at lower costs. The discussion also connects well with the broader Marketing Mix PlayStation landscape and how technology shapes modern gaming.

Google DeepMind's SL2T Doubles Sign Language AI Accuracy, Giving Deaf Users a Voice

Google DeepMind's SL2T model brings real-time ASL-to-English dictation to Gboard and Live Transcribe on Pixel 11, trained on 100,000+ hours across 50+ sign languages.

Google debuts SL2T, an AI model that’s designed to understand sign language

Google DeepMind said today it wants to bring the artificial intelligence revolution to the estimated 70 million people across the world who are either deaf or hard of hearing with the launch of sign-language-to-text or SL2T. In a blog post, Google’s AI researchers said SL2T is a multilingual translation model that’s making its debut on […] The post Google debuts SL2T, an AI model that’s designed to understand sign language appeared first on SiliconANGLE .

Comment on DeepMind Introduces Gato: A Generalist, Multi-Modal, Multi-Task, Multi-Embodiment Agent by monalisa1art

DeepMind's Gato is a fascinating step toward generalist agents, though calling it AGI feels like a stretch. The fact that one transformer model can handle text, vision, and robot control with shared weights is impressive, but crossing 50% expert threshold on 450 tasks still leaves plenty of room before true versatility. It does make me wonder how soon we'll see similar multi-modal approaches trickle into consumer tools—like a free nano banana image generator that adapts to different artistic styles without retraining. For now, Gato feels like a solid research milestone rather than a breakthrough. monalisa1art

Experts are warning: our AI arms race is putting humanity at risk | Stuart Russell

A recent letter signed by 1,367 researchers and engineers at frontier AI labs – mainly OpenAI, Anthropic and Google Deepmind – points to a dangerous moment It is fashionable in certain circles to dismiss the catastrophic risks of AI. One often hears that “the real experts” who work on the technology every day are really not concerned at all; that only “doomers” and “luddites” espouse a “fringe” view from a “position of ignorance”; that all talk of potential catastrophe is just “science fiction”. Fortunately, an open letter has been published that lets us hear from the real experts who work on the technology every day, in their own words. And are they worried? Very. Continue reading...

Book Review: The Infinity Machine

It looks like Demis Hassabis is stepping away from Google DeepMind. In honor of his rise and presumed fall, I wrote an essay on the powers and perils of seeing 90% of the future. Linkpost for: https://millicosm.substack.com/p/book-review-the-infinity-machine Discuss

KI-Update kompakt: KI-Robotik, DiffusionGemma, Google DeepMind, Bakteriophagen

Das „KI-Update“ liefert dreimal pro Woche eine Zusammenfassung der wichtigsten KI-Entwicklungen.

DeepMind shakeup tests UK’s AI ambitions

DeepMind’s future has become all the more critical for the UK

Google Deepmind's WeatherNext predicts cyclone tracks and intensity at the same time

Deepmind's new weather AI forecasts tropical cyclones about a day further ahead than leading operational models, matching a decade of progress in traditional weather forecasting. Code and model weights are open-source on GitHub. The article Google Deepmind's WeatherNext predicts cyclone tracks and intensity at the same time appeared first on The Decoder .

Google's DiffusionGemma proves you don't need to train from scratch to build a text diffusion model

Instead of training a new model from scratch, Google DeepMind retrofitted Gemma 4 into a diffusion model using less than 10 percent of the original training budget. DiffusionGemma generates 256 tokens in parallel instead of one at a time, hitting about 1,500 tokens per second. Quality still trails the original autoregressive model in benchmarks, especially on reasoning tasks. The article Google's DiffusionGemma proves you don't need to train from scratch to build a text diffusion model appeared first on The Decoder .

Google dismantles Deepmind and bets on a fresh start as Hassabis heads for the exit

Google Deepmind is losing its autonomy, and founder Demis Hassabis may leave the AI lab for good in the coming months. AI researcher Koray Kavukcuoglu will take over day-to-day operations without the CEO title, and all Gemini development is moving to the Bay Area. Internally, Google is apparently struggling with serious problems training frontier models, even as its cloud business generates billions. The question is whether the company is deliberately betting on infrastructure or simply can't catch the leaders. The article Google dismantles Deepmind and bets on a fresh start as Hassabis heads for the exit appeared first on The Decoder .

🔮 Agents form alliances, DeepMind’s reset & how likely is a crash? #596

Your Sunday briefing on how AI is colliding with markets, institutions, and culture.

Google DeepMind enters a new era as co-founder Demis Hassabis shifts AI role

Observers express concern that the division has lost its independence and commercial reality has taken over When Sir Demis Hassabis said AI had brought the world to a “pivotal moment in human history” last month, he knew another big change was imminent. This shift was closer to home. The Nobel prize-winning head of Google DeepMind, Google’s AI unit, announced this week he was relinquishing his day-to-day duties as chief executive and becoming chair. He is also taking on the role of chief scientist at DeepMind’s parent, Alphabet. Continue reading...

DeepMind founder ascends to singular AI role at Google

Demis Hassabis, the driving force behind Google DeepMind, is ascending to the role of chief scientist at Alphabet, Google’s parent company, replacing Jeff Dean who is leaving to work at a start-up. The role will enable Hassabis to “put his full attention on actively shaping the future of AGI,” or artificial general intelligence, Alphabet CEO Sundar Pichai wrote on the company’s Inside Google blog . Hassabis’ attention will still be divided, however: He will continue to lead research at Google spin-off Isomorphic Labs, which works on drug discovery, and although he will no longer be CEO of DeepMind, he will be its chair. Koray Kavukcuoglu will take over DeepMind, reporting directly to Pichai. He is currently its CTO. Hassabis has been a strong promoter of AGI, defined by Google as the “hypothetical intelligence of a machine that possesses the ability to understand or learn any intellectual task that a human being can.” He has a long career in AI, having helped found DeepMind in 2010. He has been a prominent figure in the AGI field, prophesying in May that it will be a viable technology within three years . He has been keen to tackle any barriers in the way of developing the technology; just last month, he called for greater self-regulation in the market, arguing that it would help drive the technology forward. Hassabis welcomed the chance to focus on AGI development. “We have arrived at a pivotal moment in human history. I’ve been working towards AGI my whole life, and now, I…

Deepmind's talent drain likely comes down to chip shortages, a conflict of interest, and Google's bureaucracy

Ex-Google Deepmind CEO Demis Hassabis has reportedly stepped back from day-to-day operations for about a year, as he sees himself more as a scientist than a manager. Researchers are also complaining about limited access to Google’s own TPU chips, while external customers like Anthropic can purchase the same hardware through Google Cloud. The article Deepmind's talent drain likely comes down to chip shortages, a conflict of interest, and Google's bureaucracy appeared first on The Decoder .

DeepMind Says Its AI Can Predict Hurricanes Earlier Than Everyone Else

Its WeatherNext model, which will be open-sourced, can accurately predict a storm’s track and intensity using lower-resolution weather data. Researchers don’t yet fully understand how it does this.

Google DeepMind's WeatherNext Cyclones Gives Forecasters an Extra Day to Save Lives

Google DeepMind's WeatherNext achieves a decade of forecasting progress in one leap, now open-sourced with 1,000-scenario ensemble predictions per storm

DeepMind AI gives an extra day of warning ahead of deadly cyclones

An AI model from DeepMind can predict cyclones three days ahead with a level of accuracy that previous models can only hit a day later

What smart people in tech are saying about Google's AI leadership restructuring

Opinions diverge on what it means for DeepMind's future as Google overhauls its AI leadership.

[AINews] Jeff, Sanjay, Oriol, and Quoc depart DeepMind; Demis to Chair; Koray to SVP — what is going on at GDM???

The end of an era.

DeepMind CEO steps down 👋, Jeff Dean's startup 🤖, Cloudflare OS 👨‍💻

Demis Hassabis was shifting away from DeepMind CEO duties for a year

The CEO of Google’s AI unit was increasingly unsatisfied with the role of tech executive, preferring a more scientific position, said two people familiar with his thinking.

Big shake-up in Google’s AI team as DeepMind chief executive steps down

Two senior engineers are leaving company to launch startup amid fears Google is falling behind in AI race Sir Demis Hassabis is stepping down as chief executive of Google DeepMind, in a leadership overhaul of the UK-based AI research lab. Hassabis, a Nobel prize recipient , is leaving his main managerial role to become chair of DeepMind – as well as taking on the new position of chief scientist at Alphabet, which is Google’s parent company. DeepMind will now be led by Koray Kavukcuoglu, its chief technology officer, under the title of senior vice-president. Continue reading...

Google's AI shake-up: DeepMind's Hassabis steps aside, senior scientists depart

Google's AI brain drain continues.

Google Deepmind loses both its CEO and chief scientist as Demis Hassabis and Jeff Dean step down simultaneously

Google Deepmind is overhauling its leadership as Demis Hassabis steps back from day-to-day management to become Alphabet's chief scientist and Jeff Dean leaves Google after 27 years to launch AI startup Discovery Loop. Former Deepmind CTO Koray Kavukcuoglu will take over as Google races to close the gap with its top AI rivals. The article Google Deepmind loses both its CEO and chief scientist as Demis Hassabis and Jeff Dean step down simultaneously appeared first on The Decoder .

Google DeepMind boss steps down

Demis Hassabis follows a string of prominent AI executives who have departed DeepMind in recent months.

Google just announced a major shakeup of its top AI leadership

Google is making some significant AI leadership changes, including a major shift for Google DeepMind leader Demis Hassabis. Hassabis will become the chair of Google DeepMind and the chief scientist at Alphabet, CEO Sundar Pichai announced on Wednesday. Hassabis will continue to lead Alphabet's Isomorphic Labs, which aims to use AI to develop drugs. Koray […]

Google shakes up AI leadership. Demis Hassabis takes on broader research role, and Jeff Dean leaves.

Some of Google's top AI leaders are leaving their roles, with DeepMind CEO Demis Hassabis taking another role at Alphabet and Jeff Dean leaving.

Comment on DeepMind, Mila & Montreal U’s Bigger, Better, Faster RL Agent Achieves Super-human Performance on Atari 100K by Frederick Potts

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Commodifying Thinking

On Thinking “A key part of our mission is to put very capable AI tools in the hands of people for free ( or at a great price ). ” Sam Altman , “GPT-4o” (2024) “Thinking is solved!” a friend of mine blurted out after a swarm of AI agents took off building at the Oxford ETH hackathon, late 2025. The Republic 1 , a peer-reviewing intelligence platform with an AI engine, could be built in just a few days with Opus 4.6. Fact-checking papers with AI could be completed in a few hours. The project itself stood as a hypothesis of how AI could deliberate intellectually, process arguments, and self-reflect on the claims of research papers. What started off as a hackathon project has, as of now, been validated by systems like the AI Scientist 2 and Google DeepMind’s Co-scientist 3 . Beyond autonomous research, AI is often used as a high-level thinking assistant: Terence Tao suggests it may advance experimental mathematics 4 , models have captured headlines solving Erdős problems, and it has become a routine tool in protein structure prediction. There is no shortage of discussion on the superb capabilities of these tools. LLMs now simulate complex thinking, including research, brainstorming, and synthesis. Frontier models can handle long-form tasks, complex problem-solving, and areas involving some human judgement. But better models also fetch higher prices 5 , with Claude Fable priced at $50/Mtok per output, ten times the rate of a weaker model like Haiku 4.5. I want to look at this tre…

The AI assurance gap: CIOs need proof that agentic AI controls actually work

Enterprises have spent decades learning how to audit people and software. Agentic AI creates a third category: systems that interpret instructions, call tools and act across workflows without a mature assurance model built around them. In my work as a leader and investor across technology-enabled businesses, I have spent years around automation, cybersecurity, compliance, workflow design and board reporting. I have watched management teams gain confidence from dashboards, policies and approval records, then face a harder question when a board member, auditor or regulator asks whether the controls performed as intended. Agentic AI complicates that question because a single outcome may pass through several systems. An agent can collect information, choose a tool, produce code, route a request and hand work to another agent before a person approves the result. No single manager may have observed the full path. Executive accountability remains human even when the operating activity becomes more autonomous. The CIO may have to explain who authorized the activity, whether the agent stayed within its approved purpose and what evidence supports management’s answer. In a recent framework for frontier AI , Google DeepMind CEO Demis Hassabis proposed an independent standards body that could evaluate advanced models before deployment and address critical vulnerabilities after release. His proposal focuses on frontier models, but the principle carries into the enterprise: expanding auton…

Comment on DeepMind’s ‘Expert-Aware’ Data Augmentation Technique Enables Data-Efficient Learning from Parametric Experts by Ronaldo Ronaldo

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Google Deepmind unveils Gemini Robotics 2 to power robots of all shapes from tabletop arms to humanoids

Google Deepmind's Gemini Robotics 2 is its most advanced vision-language-action model yet, built to control everything from tabletop robots to full-body humanoids. Gemini Robotics ER 2 adds a higher-level reasoning layer for robotics tasks. The article Google Deepmind unveils Gemini Robotics 2 to power robots of all shapes from tabletop arms to humanoids appeared first on The Decoder .

Robots get better at handling unpredictability

Google DeepMind unveiled a new model this week that gives humanoid robots finer control over how they interact with objects around them, including completing complicated tasks like tying a trash bag.

AGI Safety and Alignment at Google DeepMind: A Summary of Recent Work (July 2026)

Cross-posted from our new Substack It’s been nearly two years since our last major update here in August 2024 and we wanted to share another recap of our recent work with the AGI safety community. Things have changed a lot since then. We are now fully in the midgame , and focus more on landing things in production. Who are we? We are the AGI Safety and Alignment Team (ASAT), the main group at Google DeepMind working directly on technical approaches to existential risk from AI systems. Last year we published An Approach to Technical AGI Safety and Security , which remains the best place to read our overarching vision. Highlights Norms around chain of thought. Our impression is that our work meaningfully moved the field away from beliefs along the lines of “chain of thought is often unfaithful and so not worth using” towards beliefs along the lines of “chain of thought is a very useful tool that is worth preserving”, leading to a tentative industry consensus on its importance. We have also published substantial technical research that enables companies to preserve chain of thought transparency for longer than would have happened by default. We think this is a big deal: extending the period where model reasoning is relatively transparent enables better science on more powerful AI systems, better model forensics on future warning shots, and stronger bootstrapping of control monitors . Frontier Safety. We substantially strengthened the Frontier Safety Framework (FSF), and were th…

AGI Safety and Alignment at Google DeepMind: A Summary of Recent Work (July 2026)

Cross-posted from our new Substack It’s been nearly two years since our last major update here in August 2024 and we wanted to share another recap of our recent work with the AGI safety community. Things have changed a lot since then. We are now fully in the midgame , and focus more on landing things in production. Who are we? We are the AGI Safety and Alignment Team (ASAT), the main group at Google DeepMind working directly on technical approaches to existential risk from AI systems. Last year we published An Approach to Technical AGI Safety and Security , which remains the best place to read our overarching vision. Highlights Norms around chain of thought. Our impression is that our work meaningfully moved the field away from beliefs along the lines of “chain of thought is often unfaithful and so not worth using” towards beliefs along the lines of “chain of thought is a very useful tool that is worth preserving”, leading to a tentative industry consensus on its importance. We have also published substantial technical research that enables companies to preserve chain of thought transparency for longer than would have happened by default. We think this is a big deal: extending the period where model reasoning is relatively transparent enables better science on more powerful AI systems, better model forensics on future warning shots, and stronger bootstrapping of control monitors . Frontier Safety. We substantially strengthened the Frontier Safety Framework (FSF), and were th…

The AGI Safety and Alignment team at Google DeepMind is Hiring (July 2026)

GDM’s AGI Safety and Alignment Team is hiring for multiple roles, across all areas in this post on our recent work . This is the team at GDM, led by Rohin Shah , that aims to reduce existential risks from AI systems. You can listen to many of Rohin’s takes in his podcast on 80,000 hours . There is no one ‘type’ that we are looking for—we want excellent people. We think of the role as ‘member of technical staff’ though different people will have more of a research engineer or scientist flavour. We are flexible on location though most people will be most productive in either San Francisco or London. You should apply here (for the US) or here (for the UK) after reading the guidance here . Many of the basic facts about why ASAT is a good place to work and how we think about research are mostly unchanged since this post in 2025. What do we do? We are focused on risks of more severe harms from more advanced AI than the rest of GDM. You can read our high level AGI Safety and Security Approach . Our work includes aligning AGI, defending against misaligned deployments, and supporting coordinated safety. We’ve recently shared a recap of some of our recent work , which gives a better sense of what we do in practice. Deep alignment and stress testing are relatively new areas for us, created in response to increased capabilities, so in those areas there will likely be more flux in exactly what we do. We cover parts of our overall alignment approach and research directions in 5 minute tal…

The AGI Safety and Alignment team at Google DeepMind is Hiring (July 2026)

GDM’s AGI Safety and Alignment Team is hiring for multiple roles, across all areas in this post on our recent work . This is the team at GDM, led by Rohin Shah , that aims to reduce existential risks from AI systems. You can listen to many of Rohin’s takes in his podcast on 80,000 hours . There is no one ‘type’ that we are looking for—we want excellent people. We think of the role as ‘member of technical staff’ though different people will have more of a research engineer or scientist flavour. We are flexible on location though most people will be most productive in either San Francisco or London. You should apply here (for the US) or here (for the UK) after reading the guidance here . Many of the basic facts about why ASAT is a good place to work and how we think about research are mostly unchanged since this post in 2025. What do we do? We are focused on risks of more severe harms from more advanced AI than the rest of GDM. You can read our high level AGI Safety and Security Approach . Our work includes aligning AGI, defending against misaligned deployments, and supporting coordinated safety. We’ve recently shared a recap of some of our recent work , which gives a better sense of what we do in practice. Deep alignment and stress testing are relatively new areas for us, created in response to increased capabilities, so in those areas there will likely be more flux in exactly what we do. We cover parts of our overall alignment approach and research directions in 5 minute tal…

Comment on Thinking Fast and Slow: Google DeepMind’s Dual-Agent Architecture for Smarter AI by Ripple

gunspin Fascinating insights into the future of AI

Google DeepMind’s new AI model can control a robot’s entire body

Google DeepMind says the latest version of its Gemini Robotics AI model can "control entire humanoid robots." While the previous model focused on controlling a humanoid robot's upper body, Gemini Robotics 2 now supports "whole-body motions" ranging from its feet to fingertips, according to an announcement on Thursday. The new model will allow humanoid robots […]

Gemini Robotics 2 Brings Google's AI Into the Physical World

The latest version of Google DeepMind's AI model includes a significant jump into “physical AGI.” But plopping AI into the real world comes with risks.

Google DeepMind's Gemini Robotics 2 Gives Humanoids Full-Body AI Control

Google DeepMind launches Gemini Robotics 2, bringing full-body humanoid control, multi-finger dexterity, and cross-robot collaboration to physical AI

Language models can't spark scientific revolutions, but world models might

Can language models spark a scientific revolution? In a position paper titled "LLMs can't jump," Google Deepmind's Tom Zahavy argues they can't. They're missing the cognitive mechanism needed to create something truly new. The article Language models can't spark scientific revolutions, but world models might appeared first on The Decoder .

Google DeepMind's Lyria 3.5 Takes Direct Aim at Suno's Vocal Dominance

Google DeepMind's Lyria 3.5 lands in Flow Music with better vocals, exact BPM control, and stem exports for full-length songs

Deepmind dismantles its AlphaFold team as key authors leave for Anthropic

The majority of the researchers behind AlphaFold are now working on other projects, and almost a quarter have left Google Deepmind altogether. The restructuring marks a sharp turn away from the strategy that put the lab on the map. The article Deepmind dismantles its AlphaFold team as key authors leave for Anthropic appeared first on The Decoder .

Europe got its own TBPN-style live show, and everyone’s angling for a guest spot

The five-day format will look quite similar to the two-day format. There will be a live show from 12 p.m. U.K. time to 3 p.m., breaking down trending stories, and then for two hours, they will have guests on the show talking about whatever they want.

US reportedly favors selective bans over blanket restrictions on Chinese open weight models citing security concerns

The Trump administration is planning targeted bans on Chinese AI models rather than a blanket ban. After public pressure, OpenAI and Google DeepMind signed an open letter opposing regulation of open-weight models, yet OpenAI and Anthropic continue to lobby privately for those same restrictions amid security concerns and powerful business interests. The article US reportedly favors selective bans over blanket restrictions on Chinese open weight models citing security concerns appeared first on The Decoder .

Google DeepMind's Gemini 3.5 Flash Cyber Finds 55 Bugs Claude Missed

Google DeepMind's Gemini 3.5 Flash Cyber is a fine-tuned, lightweight model that finds and patches code vulnerabilities faster and cheaper than frontier-sized models

Google DeepMind's Gemini 3.6 Flash Beats Its Predecessor While Costing Less

Google ships Gemini 3.6 Flash, 3.5 Flash-Lite, and a restricted cybersecurity model — all cheaper and faster than what they replace

How AI may drive union-resistant tech workers to the bargaining table

Tech workers are increasingly unionizing, trading Silicon Valley’s myth of exceptionalism for collective bargaining to contest the corporate deployment of artificial intelligence For decades, the technology industry was a fortress that labor unions couldn’t breach. Tech workers already had cushy compensation packages, dream benefits like unlimited vacation and free lunch, and a flat corporate hierarchy that made engineers feel as powerful as their bosses, all of whom dressed down in sneakers and hoodies. So why unionize? Now, that fortress is cracking from the inside. Unions have become increasingly popular for tech employees. After months of mass layoffs tied to artificial intelligence and mounting anxieties about how it’s being deployed, some tech workers say they’ve been saddled with higher workloads while facing the threat of job loss caused by the very products they’re building. Workers from Google DeepMind and Meta in the UK are also objecting to how their companies’ AI products are being used, such as for military purposes or to monitor employee productivity . Those same workers are now attempting to unionize. Continue reading...

Demis Hassabis on the New Coming Age

Google CEO Demis Hassabis offered us a first rate second rate essay, A Framework for Frontier AI and the Dawning of a New Age . I’ll go over that essay and various responses to it in Part 1. Part 2 of this post then covers Alex Turner’s resignation, and his story about how he tried and failed to prevent Google from signing up to allow the Department of War to use its models for essentially whatever the government wants, including autonomous weapons. Demis Hassabis sold DeepMind to Google on condition that something like this would not happen. Yet here it is, happening. A cautionary tale. I will cover Kimi K3 tomorrow. I am hoping to know more by then. Please do share any reactions or info about it in the comments here. The Core Statement and Request He saying we are standing in the foothills of the singularity. His ask is a Frontier AI Standards Body within the US Government, similar to FINRA, that would govern ‘frontier labs,’ defined as any company that produces a frontier model based on various technical benchmarks. Evaluations would be updated regularly, and vulnerabilities would be addressed, both before and after release. He is excellent about stating that this is big, really big, no bigger than that, it be big . Demis Hassabis: I’ve spent my whole life working on AGI because I’ve always had a deep conviction that, if built and deployed responsibly, it would prove to be one of the most beneficial and transformative technologies ever invented. AGI cannot be compared to…

Google Deepmind argues video generators already contain the world models computer vision has been missing

Google Deepmind's GenCeption repurposes a video generator for classic vision tasks such as depth estimation and segmentation, matching state-of-the-art systems with far less training data. The model trained almost entirely on synthetic videos. Its results add to the debate over whether video generators already contain a kind of universal world model. The article Google Deepmind argues video generators already contain the world models computer vision has been missing appeared first on The Decoder .

A Red Line and Oversight Framework for Government AI Contracts

My post on leaving Google DeepMind tells a story. In contrast, this Framework is a question of mechanism design and negotiation posture. I quite enjoyed optimizing this Framework against its organizational and practical constraints. The original considerations were : Good red lines: Rule out the questionable use cases (autonomous targeting without human control, untargeted profiling) while allowing trustworthy ones like missile defense. Avoid the weaknesses flagged in legal analysis of Anthropic’s red lines . Robust red lines: [Google] Cloud would push deals through any loophole, Google Legal seemed unlikely to tighten my drafting, and the Pentagon wouldn’t want terms at all. The language had to hold under pressure, with auditing that respected classification and operational security. Minimal trust assumptions: I made the Chief Scientist the single root of trust that everything else hangs off of ... The Chief Scientist would staff a Review Body to advise on contracts. Accountability via transparency: The Review Body would only privately advise [the Chief Scientist and CEO], but overriding it surfaces in a yearly transparency report to all AI employees. Dissolving it would require advance notice and disclosure of the exact outstanding non-compliance findings. I worked to ensure the Body couldn’t be defanged as quietly as Google’s 2018 principles were . Minimal pain to opposed stakeholders: I gave Cloud 2 of 7 seats, recused staff only from their own deals, capped delays at 10…

A Red Line and Oversight Framework for Government AI Contracts

My post on leaving Google DeepMind tells a story. In contrast, this Framework is a question of mechanism design and negotiation posture. I quite enjoyed optimizing this Framework against its organizational and practical constraints. The original considerations were : Good red lines: Rule out the questionable use cases (autonomous targeting without human control, untargeted profiling) while allowing trustworthy ones like missile defense. Avoid the weaknesses flagged in legal analysis of Anthropic’s red lines . Robust red lines: [Google] Cloud would push deals through any loophole, Google Legal seemed unlikely to tighten my drafting, and the Pentagon wouldn’t want terms at all. The language had to hold under pressure, with auditing that respected classification and operational security. Minimal trust assumptions: I made the Chief Scientist the single root of trust that everything else hangs off of ... The Chief Scientist would staff a Review Body to advise on contracts. Accountability via transparency: The Review Body would only privately advise [the Chief Scientist and CEO], but overriding it surfaces in a yearly transparency report to all AI employees. Dissolving it would require advance notice and disclosure of the exact outstanding non-compliance findings. I worked to ensure the Body couldn’t be defanged as quietly as Google’s 2018 principles were . Minimal pain to opposed stakeholders: I gave Cloud 2 of 7 seats, recused staff only from their own deals, capped delays at 10…