The engine room of @Google. Building AI safely and responsibly to solve the world’s most complex problems. Join us: https://xitter.twitterapi.xyz/t.co/jUHQA27iBL
London, UK
Joined January 2016
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Google DeepMind retweeted
Our Gemini team @GoogleDeepMind is making tremendous progress.
Gemini 4 Carbon’s coding and scientific reasoning capabilities are a step change and have accelerated our own science workflows.
Carbon has already helped our team:
- Debug complex experimental flows. In one complex case, it traced anomalous assay results back to a faulty liquid handler and reagent carryover between high-throughput experimental runs.
- Develop strategies to mine massive datasets, making models like AlphaFold 3 even more effective.
- Compress computational workflows like cheminformatics tasks from weeks down to hours.
I am happy to share that Gemini 4 Carbon is now generally available. Together with our updated Antigravity Science plugin, Carbon can greatly assist the work of scientists.
I am really looking forward to seeing scientists across the world use it for their work.
Google DeepMind retweeted
Announcing a major milestone achieved by my team @GoogleDeepMind: we have proven key security properties of the machine code underlying the Signal messaging protocol. This is the first time the actual production code of a widely deployed protocol has been formally verified – by any method, with or without AI – and at this depth.
More than a research breakthrough, it’s a prime example of how frontier AI can be a force multiplier to counter the speed and scale of modern cyber threats.
Formal verification offers a higher standard of security; however, it’s extremely complex and time consuming, which is why in many cases it’s considered to be impractical. Similar efforts could take teams decades of combined effort. We did it in a month’s time.
Using AlphaProof Code, Google DeepMind's new agentic framework for formal verification that runs on LLMs including Gemini Carbon, we verified the Signal protocol down to the actual code running on devices. This milestone lays the groundwork for a future in which vital systems, from medical device firmware to power grid software, ship with mathematical proof that their protections work.
Reaching that future will take continued research from our community. In that spirit, we're open-sourcing our formal specifications and verification artifacts so others can build on this work.
On a personal note, this moment is of deep significance to me. I started my own research journey more than two decades ago as an intern working on checking the consistency of complex software systems. Seeing this grand challenge made practical with modern AI is extraordinary.
I'm extremely proud of my team members: Swarat Chaudhuri, Andrew Ferraiuolo, Anton Kovsharov, Tej Chajed, Sam Schlesinger, Karn Seth, Jonathan Katz, and Charles Sutton for taking on this grand challenge and delivering, and grateful to the Signal team for their partnership.
deepmind.google/blog/formal-…
Google DeepMind retweeted
introducing gemini omni 1.2 flash, now available in ai studio and the gemini api
what's new:
- fast & 30% lower cost
- longer clips
- edit in conversation
- more natural speech
- more controllable shots
Google DeepMind retweeted
Voice Remixing for Gemini 3.8 Flash TTS is now live in @GoogleAIStudio and the Gemini API.
Start with a preset from our library of 2,000+ voices or a custom-designed voice, and use prompts to fine-tune timbre, pitch, pace, and accent, like "add a subtle Southern U.S. accent" or "make it sound like a fire-breathing dragon." 🐉
Google DeepMind retweeted
While most jack-o-lanterns just sit on porches, the @GoogleDeepMind team decided to let AI agents design, simulate, and bring one to life. 🎃🤖
We built a physical, dancing 12 degrees of freedom robot directly from a single plain-English prompt, with no manual CAD required!
Google DeepMind retweeted
Delighted to see the launch of the UK’s new AI Biosecurity Lab, which @GoogleDeepMind will partner with and support.
As life sciences enter a transformational era, it is essential for researchers, government, and industry to collectively strengthen our biological defences and guide these new capabilities responsibly.
Congratulations to @KanishkaNarayan and the entire team that has worked on making this a reality.
2/7 We're launching a £165m National AI Biosecurity Initiative, including £100m for a world-first AI Biosecurity Lab.
This will harness British scientific expertise and latest AI to detect biological threats and protect people from emerging risks.
As with our @AISecurityInst, I want the new AIBL to lead the world in capability.
Google DeepMind retweeted
Springer Nature and @GoogleDeepMind are partnering to co-design an AI peer review assistant, exploring how AI can help reviewers navigate manuscripts more effectively and focus their time on specialist scientific judgement.
Learn more: spklr.io/6017EVgrh
ALT A quote from Saskia Steinacker, Chief Product Officer, stating: \"AI has the potential to support reviewers... For us, innovation and trust have to go hand in hand.\" She is pictured smiling below the text, with the Springer Nature logo at the bottom.
Google DeepMind retweeted
SynthID timeline over the last few weeks has been momentous – from opening SynthID Detector to the public, to launching SynthID Bio to watermark functional, AI-designed proteins.
Each moment brought us a step closer to our broader vision on provenance, or helping verify what is AI-generated and what isn’t.
Recently, Dartmouth Professor Hany Farid and I discussed why solving provenance is vital to @GoogleDeepMind’s broader mission to develop AI to benefit humanity.
In short: solving provenance is a highly complex, multi-disciplinary effort with watermarking technology at its core. It requires a collective mix of frontier research, interoperable open standards, and direct collaboration across platforms and developers.
Read our full conversation on Google's Public Policy blog: publicpolicy.google/article/…
Google DeepMind retweeted
Wiz Research discovered PHP2Shell - CVSS 9.2 vulnerabilities in core PHP affecting 70% of the web. Pleased to see the important role that Gemini 4 Argon played here, finding the vulnerability, developing the complex exploit chain, creating attack detectors and validating the patch, giving defenders an advantage. Read more here: wiz.io/blog/php2shell-php-rc…
Google DeepMind retweeted
Over the past year, the Discovery team at @GoogleDeepMind, in a project led by student researcher @dsantosmarco, has made new discoveries in one of the most beautiful open problems at the edge of computation: Finding the 6th Busy Beaver (BB) number.
While most AI math systems solve human-generated problems, like theorems, this work tackles a different aspect of mathematical creativity: Discovering hidden problems.
For years, the BB Community has pursued one of theoretical computer science’s most captivating collaborative quests: finding the simplest programs that run the longest before halting. Because determining whether these programs ever stop is uncomputable, no universal algorithm can solve it, meaning new mathematical techniques need to be found and invented to make progress.
In this way, the BB challenge serves as an open ended domain for mathematical creativity and, in particular, a generator of hard math problems called Cryptids. These are tiny programs whose halting behavior is governed by and equivalent to deep, unsolved math problems in number theory, similar to the Collatz or Goldbach conjectures. Historically, the BB community had recognized ~20 Cryptids in BB(6), and the best problems were selected for the Busy Math Olympiad (BMO).
Building on their work, Marco built a pipeline powered by Gemini 4 Argon that:
1️⃣ Simulates raw Turing machine traces to compress recurring patterns.
2️⃣ Conjectures mathematical transition rules & runs synthetic experiments to test edge cases.
3️⃣ Proves the equivalence formally in Lean 4.
Using this pipeline, Marco and the team discovered 20 new potential Cryptids and verified 33 more machines previously studied in the BB community. All the proofs were open sourced and shared in the community discord channel and we hope that they will help the community to make progress towards resolving BB(6). We also contributed related halting/non-halting proofs directly to the community GitHub repository.
Finding these Cryptids shows hidden links between simple programs and number theory, and helps mathematicians map the limits of computation. I'm also excited about Gemini 4 Argon's ability to help researchers both solve hard problems and accelerate scientific discoveries, of which Busy Beaver is just one example.
Work led by our student researcher @dsantosmarco with paper contributions from @vivek_veeriah, @Dahoas1 , Andrew Bolt, @zuza777 , Satinder Singh, and myself with additional contributions from Hussain Masoom, @maxencefaldor , @MoritzFirsching and Petar Sirkovic.
Read more about it in our paper: arxiv.org/abs/2610.22603
Google DeepMind retweeted
We did a lot to understand Gemini 4's capabilities and propensities, and to improve our mitigations for this launch -- you can get a tl;dr here blog.google/innovation-and-a… and dive into the report storage.googleapis.com/deepm…
Google DeepMind retweeted
Last week, Hurricane Isaias made landfall in the Florida Panhandle. Our thoughts are with everyone affected and with the recovery efforts still underway. Earlier and more accurate forecasting can help protect communities by giving them more time to prepare, and we are grateful that Google’s WeatherNext model was able to assist the @NWSNHC 's expert forecasters.
Based on initial evaluations, here's how our model supported the response to #Isaias:
- Tracking the Hurricane’s Path: WeatherNext was the most accurate model on the storm’s track. While traditional physics models predicted landfall closer to New Orleans, WeatherNext correctly predicted a path further east. This allowed NHC forecasters to highlight the potential for wind and storm surge impacts in the Florida Panhandle from their very first forecast.
- More Time to Prepare: Up to three days before landfall, our model consistently predicted its eventual location in the Florida Panhandle to within 60 km or less, supporting the NHC as they honed in on a track toward the Florida Panhandle and issued hurricane watches and warnings to give officials in Florida, Alabama, and Mississippi more time to prepare.
- Early Indications about Intensity: Early on, WeatherNext accurately predicted the intensification to a major hurricane. Alongside the regional hurricane models HWRF and HMON, WeatherNext’s intensity forecasts helped NHC forecasters to predict that the Isaias would approach the coast as a category 2 hurricane from the very first advisory.
Giving communities more time and more accurate warnings is critical for protecting lives and livelihoods. While model performance varies from storm to storm, Isaias reflects our broader findings. In our recent @Nature paper co-authored with NHC forecasters, evaluating 200+ global tropical cyclones, we found our model provides ~1 day of additional lead time for both track and intensity.
It’s been an honor for the @GoogleDeepMind and @GoogleResearch teams to work alongside NHC forecasters. By combining AI, physics models, satellite data, and the crucial expertise of meteorologists, we're excited to keep pushing the boundaries of forecasting to help protect communities.
Google DeepMind retweeted
Explore the latest advances in native audio models and the challenges of building better real-time voice experiences with @GoogleDeepMind + @SierraPlatform.
Learn about conversational latency, multilingual code-switching, agentic voice tasks, and more →goo.gle/4hKV9B5
Google DeepMind retweeted
New essay for the @GoogleDeepMind Institute with James Manyika on AI x Science, "Bending the Curve of Discovery".
While accelerating existing scientific practices is certainly useful, the real promise of AI is its potential to act as an invention of a method of invention (IMI) a la Griliches--e.g., the microscope or statistical inference--which would unlock questions and whole modes of discovery that were previously beyond human reach.
Today, LLMs and specialized models like AlphaFold act as economic complements. LLMs handle analysis, coding, and writing, while specialized tools handle domain-specific predictions. Most handoffs between them run through the scientist.
What may the future of science look like? LLMs orchestrating those handoffs automatically--prompting specialized models, auditing outputs, and looping until either the question is answered or a non-automated stage is reached (e.g., wet lab testing). The scientist's role shifts from running each step to designing this loop. We saw a glimpse of this workflow with Anthropic’s enzyme discovery a few weeks ago, and we're seeing this in our own work too.
This raises foundational epistemic questions:
1. What will scientific understanding look like when discoveries are made by black-box models whose output is increasingly difficult to interpret?
2. How do we extract underlying mechanisms, not just outputs, as science becomes more automated? What will theory look like?
3. If AI automates the writing and junior lab work, how do we train the next generation of scientists to push the frontier?
4. What will motivate scientists in this epistemic reality?
There's an economics angle too. Hypothesis generation gets cheap, so bottlenecks move downstream to verification and to choosing which questions are worth asking. Realizing AI’s full potential will take investment in infrastructure and institutional reform--making this as much an organizational challenge as a technical one
We don't have the answers yet, and I'd love to hear what others think.
Paper here: bit.ly/ai-in-science
Google DeepMind retweeted
Building predictive biology models is one of the most important scientific challenges of our time, but solving this requires new experimental biological data at an unprecedented scale.
I’m pleased to announce that @GoogleDeepmind is partnering with @ChanZuckerberg’s Virtual Biology Initiative to create the multi-modal datasets needed for improved predictive biology models.
I wrote recently about the need to discover how life works at many different scales, from DNA and individual proteins to living cells and whole-body health. As we learnt from AlphaFold and the Protein Data Bank, solving grand biological challenges will not be possible without high-quality open data and efforts like the @czbiohub initiative.
Read the announcement here: biohub.org/news/virtual-biol…
And my previous essay here: linkedin.com/pulse/understan…
@AllenInstitute, @BroadInstitute, @GladstoneInst, @humancellatlas, @ProteinAtlas, @sangerinstitute, @IsomorphicLabs, @Meta
Google DeepMind retweeted
Supported by @GoogleDeepMind , our new 3-year programme will develop the methods and standards needed to evaluate artificial intelligence for antimicrobial resistance , so that we can know when an AI system is accurate, reliable and ready to use.
fleminginitiative.org/post/n…
Google DeepMind retweeted
Power your agents: Gemini 3.8 Live with Live Avatar is now generally available in Gemini Enterprise.
Key capabilities include: video avatars, fluid dialogue, tool calling, and more.
Start building today ↓ goo.gle/4y8lf6A
Google DeepMind retweeted
My journey to develop AGI spans 25 yrs, including 10+ yrs thinking about technical & societal perspectives at Google DeepMind.
AGI is on the horizon - we need deeper understanding of its implications. To help, we've created the DeepMind Institute.
