# Google Research

> Source: https://aiwiki.ai/wiki/google_research
> Updated: 2026-06-09
> Categories: Research Organizations
> From AI Wiki (https://aiwiki.ai), a free encyclopedia of artificial intelligence. Quote with attribution.

Google Research is [Google](/wiki/google)'s organization for foundational and applied computer science research, with work spanning quantum computing, algorithms and theory, machine learning, health, climate, and crisis response. It is distinct from [Google DeepMind](/wiki/google_deepmind), the unit created in April 2023 when Google merged its [Google Brain](/wiki/google_brain) team with [DeepMind](/wiki/deepmind) to concentrate frontier AI model development under [Demis Hassabis](/wiki/demis_hassabis) [1]. Google Research kept the company's broader scientific portfolio after that split, and since April 2024 it has operated under a three-part mandate: computing systems including quantum, foundational machine learning and algorithms, and applied science and society [2]. [Yossi Matias](/wiki/yossi_matias), the founding managing director of Google's research and development center in Israel, has served as vice president and head of Google Research since April 2024, reporting to [James Manyika](/wiki/james_manyika), Google's senior vice president and president for Research, Labs, Technology & Society [3][4]. The organization describes its mission as "Research, to reality": driving breakthroughs that benefit society, businesses, and Google products [5].

## Overview

Google Research is the institutional descendant of the research division Google has run since the early 2000s, and it retains that group's defining habit: a "hybrid" model in which researchers work close to engineering and product teams rather than in a separate corporate laboratory [6]. It publishes hundreds of papers a year, runs faculty and student programs, and releases open-source software and datasets [5]. [Jeff Dean](/wiki/jeff_dean), a Google Brain co-founder and the division's former leader, serves as Google's chief scientist, a role that spans both Google Research and Google DeepMind [1].

Since the April 2024 reorganization, the group's work has been organized around three pillars [2][5]:

| Pillar | Representative research areas |
|---|---|
| Applied AI and the sciences | Earth AI, health AI, science AI, sustainability and crisis resilience |
| Foundational machine learning and algorithms | Algorithms and theory, information retrieval, machine intelligence, machine perception, natural language processing |
| People, systems, and quantum | Human-computer interaction and visualization, networking, quantum AI, responsible AI, anti-abuse, software engineering and software systems |

Responsible AI remains a listed research area, although the teams working most directly on model safety moved to Google DeepMind in April 2024 [2][5].

## History

### The early research division (2001 to 2010)

Google built a research function early in its history. [Peter Norvig](/wiki/peter_norvig), who joined in 2001, directed search quality from 2002 to 2005 and then became the company's director of research [7]. Alfred Spector, vice president of research and special initiatives from 2007 to 2015, codified the group's philosophy with Norvig and Slav Petrov in a July 2012 Communications of the ACM article, "Google's Hybrid Approach to Research," which argued that embedding researchers in product teams let Google run experiments at a scale no isolated laboratory could match [6][8]. The division's early output shaped the wider industry: systems papers such as MapReduce (2004) and Bigtable (2006), written by Google engineers and published through its research arm, inspired open-source counterparts including Apache Hadoop.

### Google Brain (2011 to 2018)

Google Brain began in 2011 as a part-time collaboration between Google engineers Jeff Dean and [Greg Corrado](/wiki/greg_corrado) and Stanford professor [Andrew Ng](/wiki/andrew_ng), incubated inside the [Google X](/wiki/google_x) moonshot lab [9]. In June 2012 the team drew global attention when a network running on 16,000 processors taught itself to recognize cats from 10 million unlabeled YouTube frames [9][10]. The project's value was immediate; X head Astro Teller later said Brain alone "paid for the entire cost" of Google X [9]. Brain soon moved into Google's research organization, and in March 2013 Google hired [Geoffrey Hinton](/wiki/geoffrey_hinton) and acquired his startup DNNresearch [9].

Brain's output defined the deep learning era at Google: it open-sourced [TensorFlow](/wiki/tensorflow) in November 2015, rebuilt Google Translate around neural machine translation in 2016, and produced generative projects such as [Magenta](/wiki/magenta) and the text-to-image model [Imagen](/wiki/imagen) [9]. Its most consequential paper, "[Attention Is All You Need](/wiki/attention_is_all_you_need)" (June 2017), introduced the [Transformer](/wiki/transformer) architecture that underpins modern large language models; the paper's eight authors held affiliations split between Google Brain and Google Research, making the Transformer a joint product of both lineages [11]. All eight authors later left Google, several to found AI startups including [Character.AI](/wiki/character_ai), [Cohere](/wiki/cohere), and [Sakana AI](/wiki/sakana_ai) [12].

### Google AI and the road to consolidation (2016 to 2023)

From 2016, Google's search and AI efforts reported jointly to [John Giannandrea](/wiki/john_giannandrea). When he left for Apple in April 2018, the portfolio was split: Ben Gomes took search, and Jeff Dean, until then Brain's leader, took charge of AI [13]. A month later Google renamed its research division Google AI, retiring Google Research as the umbrella brand [14]. The new name never fully displaced the old one; the division kept publishing under the Google Research banner, and by the early 2020s Google Research was again the organization's name, with Dean as its senior vice president. In its final years as a unified group it produced the conversational model [LaMDA](/wiki/lamda) (2021), which later powered the first version of the [Bard](/wiki/bard) chatbot, and the 540-billion-parameter [PaLM](/wiki/palm) (2022) [15].

### The 2023 and 2024 reorganizations

On April 20, 2023, chief executive [Sundar Pichai](/wiki/sundar_pichai) announced that the Brain team would leave Google Research and merge with DeepMind to form Google DeepMind, led by DeepMind co-founder Demis Hassabis; Dean became Google's chief scientist, reporting to Pichai and serving both organizations [1]. The rest of Google Research moved under James Manyika, whose role expanded to president for Research, Labs, Technology & Society, with a continuing remit covering "algorithms and theory, privacy and security, quantum computing, health, climate and sustainability and responsible AI" [1][4].

A second reorganization followed on April 18, 2024. Pichai consolidated all AI model building, including teams that had remained in Google Research, inside Google DeepMind, and moved Research's responsible AI teams there as well, placing them "closer to where the models are built and scaled"; other responsibility teams moved to Google's central trust and safety organization [2]. Google Research received what Pichai called "a clear and distinct mandate" for foundational and applied computer science, and Yossi Matias, who had led Google's Israel center since 2006 along with global AI efforts in health, climate, and crisis response, was named its head, relocating from Tel Aviv to Mountain View [2][3]. The drift of product-facing AI toward DeepMind continued that October, when the [Gemini](/wiki/gemini) app team also moved from Google's knowledge and information group into Google DeepMind [16].

## Research areas

### Quantum computing

[Google Quantum AI](/wiki/google_quantum_ai) traces its origins to 2012, when [Hartmut Neven](/wiki/hartmut_neven) founded Google's quantum computing effort, which went on to run the Quantum Artificial Intelligence Lab jointly with NASA and the Universities Space Research Association; today quantum falls within Google Research's computing systems remit [2][17][18]. In October 2019 the 53-qubit [Sycamore](/wiki/sycamore_processor) processor performed a sampling computation in 200 seconds that Google estimated would take the fastest classical supercomputer 10,000 years, the first claimed demonstration of [quantum supremacy](/wiki/quantum_supremacy) [19]; IBM disputed the estimate, and improved classical algorithms later closed much of the gap [18]. On December 9, 2024, the team announced [Willow](/wiki/willow_chip), a 105-qubit processor that achieved the first "below threshold" [quantum error correction](/wiki/quantum_error_correction): logical error rates roughly halved each time the encoded qubit array grew, from 3 by 3 to 5 by 5 to 7 by 7 [20][21]. Willow also completed a random circuit sampling benchmark in about five minutes that Google estimated would take a leading supercomputer 10 septillion (10^25) years [20]. On October 22, 2025, the group reported the first verifiable quantum advantage with its Quantum Echoes algorithm, published in Nature, which measured out-of-time-order correlators about 13,000 times faster than the best known classical algorithm on a top supercomputer and demonstrated a molecular-structure application with the University of California, Berkeley [22].

### Health AI

Google Research's health portfolio, led since 2016 by Brain co-founder Greg Corrado as head of health AI, grew out of mid-2010s medical imaging work, including a 2016 study in JAMA showing that deep learning could detect diabetic retinopathy in retinal photographs [23][24]. With DeepMind it developed [Med-PaLM](/wiki/med_palm), announced in December 2022 as the first AI system to exceed the pass mark on US Medical Licensing Examination style questions in the MedQA benchmark, with the study published in Nature in July 2023, and [Med-PaLM 2](/wiki/med_palm_2), which reached 86.5 percent on the same benchmark in 2023 [25][26]. The jointly built diagnostic research system [AMIE](/wiki/amie) (Articulate Medical Intelligence Explorer) outperformed board-certified primary care physicians on 30 of 32 specialist-rated axes in simulated text consultations, in a randomized, double-blind study published in Nature in April 2025 [27]; a multimodal version built on [Gemini 2.0 Flash](/wiki/gemini_2_0) added the ability to request and interpret images such as skin photographs and ECGs [28]. In May 2025 Google Research and Google DeepMind released [MedGemma](/wiki/medgemma), open medical models built on [Gemma 3](/wiki/gemma_3) within the Health AI Developer Foundations collection [29]. Related human-centered work includes accessibility projects such as [Project Euphonia](/wiki/project_euphonia), which adapts speech recognition to impaired speech.

### Climate, crisis response, and sustainability

Google Research runs an AI flood forecasting program whose public Flood Hub expanded in November 2024 to river basins in more than 100 countries, covering areas home to 700 million people, up from 80 countries and about 460 million [30]. The underlying global river model, described in Nature in 2024, provides forecasts up to seven days ahead with multi-day reliability comparable to the best same-day nowcasts, including in ungauged basins, and has been opened to outside researchers and partners [31]. In wildfire work, Google Research helped create the [FireSat](/wiki/firesat) program with satellite maker [Muon Space](/wiki/muon_space) and the nonprofit Earth Fire Alliance, supported by $13 million from Google.org; the first prototype satellite launched on a SpaceX rideshare on March 14, 2025, toward a planned constellation of more than 50 satellites able to detect fires as small as 5 by 5 meters within about 20 minutes [32][33]. With American Airlines and Breakthrough Energy, Google Research showed in 2023 that pilots using its AI contrail forecasts could cut contrail formation by 54 percent across 70 test flights [34], and with the European Centre for Medium-Range Weather Forecasts it built [NeuralGCM](/wiki/neuralgcm), a hybrid physics and machine learning atmospheric model published in Nature in 2024 [35]. Other efforts include wildfire boundary tracking in Google's products, the Project Green Light traffic signal optimization initiative, and the Earth AI family of geospatial models [5].

### Algorithms, privacy, and efficiency

Foundational work continues in algorithms, theory, privacy, and systems. Google researchers introduced [federated learning](/wiki/federated_learning) in 2016 to 2017, letting models such as Gboard's keyboard predictions train across devices without centralizing user data [36]. The group also developed [speculative decoding](/wiki/speculative_decoding), a now industry-standard technique that accelerates large language model inference by drafting tokens with a small model and verifying them with a larger one [37]. Differential privacy tooling, anti-abuse research, and networking and software systems work round out the portfolio [5]. Newer architecture research includes the Titans long-term memory models and the Nested Learning paradigm for continual learning, presented with its proof-of-concept Hope architecture at NeurIPS 2025 [38]. The organization also runs longer-horizon moonshots: Project Suncatcher, announced in November 2025, is exploring constellations of solar-powered satellites carrying [Tensor Processing Units](/wiki/tpu), with two prototype satellites planned with Planet for launch by early 2027 [39].

## Notable contributions

The table below lists selected outputs of Google's research organizations, including the Brain era; Google counts the pre-2023 Brain lineage among the accomplishments that moved to Google DeepMind [1].

| Year | Contribution | Origin | Significance |
|---|---|---|---|
| 2013 | [word2vec](/wiki/word2vec) | Google Brain | Efficient word embeddings that helped launch modern representation learning [40] |
| 2015 | TensorFlow | Google Brain | Open-source machine learning framework [9] |
| 2016 | Federated learning | Google Research | Privacy-preserving on-device training, first deployed in Gboard [36] |
| 2017 | Transformer | Google Brain and Google Research | Architecture underpinning modern large language models [11] |
| 2018 | [BERT](/wiki/bert) | Google AI | Bidirectional pretraining that reset natural language understanding benchmarks [41] |
| 2019 | Sycamore experiment | Google Quantum AI | First claimed demonstration of quantum supremacy [19] |
| 2022 | Med-PaLM | Google Research and DeepMind | First AI past the pass mark on USMLE-style MedQA questions [26] |
| 2022 | Speculative decoding | Google Research | Widely adopted technique for faster LLM inference [37] |
| 2024 | Global flood forecasting model | Google Research | Nature-published model extending reliable river flood warnings to seven days [31] |
| 2024 | Willow | Google Quantum AI | First below-threshold quantum error correction [20] |
| 2025 | AMIE | Google Research and Google DeepMind | Conversational diagnostic AI evaluated against physicians in Nature [27] |
| 2025 | Quantum Echoes | Google Quantum AI | First verifiable quantum advantage claim, about 13,000x faster than classical [22] |

## Relationship to Google DeepMind

Since April 2024 the division of labor has been explicit: Google DeepMind builds and scales Google's AI models, including the Gemini family, while Google Research pursues foundational computer science and applied scientific research [2]. The boundary is porous in practice. Jeff Dean's chief scientist role spans both organizations [1]; health projects such as AMIE, the Med-PaLM lineage, and MedGemma are staffed jointly [26][27][29]; and the two groups co-author papers and share infrastructure such as TPUs. In weather and climate the programs are complementary, with Google DeepMind's [GraphCast](/wiki/graphcast) and [GenCast](/wiki/gencast) forecasting models alongside Google Research's NeuralGCM and flood forecasting systems [35]. The historical lineages also interleave: the Transformer, TensorFlow, and word2vec came from teams now inside Google DeepMind, while quantum computing, which predates the merger, stayed with Google Research [1][2][11].

The arrangement leaves Google with two research organizations of different character. DeepMind carries the frontier model and artificial general intelligence agenda alongside its own scientific programs such as [AlphaFold](/wiki/alphafold), while Google Research anchors the company's longer-horizon scientific bets, from quantum hardware to flood forecasting, and feeds results into products across Search, Cloud, Android, and beyond [1][2][5].

## References

1. [Google DeepMind: Bringing together two world-class AI teams](https://blog.google/technology/ai/april-ai-update/) - Sundar Pichai, Google, April 20, 2023.
2. [Building for our AI future](https://blog.google/company-news/inside-google/company-announcements/building-ai-future-april-2024/) - Sundar Pichai, Google, April 18, 2024.
3. [Google Israel R&D head Yossi Matias moving to global role](https://en.globes.co.il/en/article-google-israel-rd-head-yossi-matias-moving-to-global-role-1001477138) - Globes, April 18, 2024.
4. [James Manyika](https://en.wikipedia.org/wiki/James_Manyika) - Wikipedia, accessed June 10, 2026.
5. [Google Research](https://research.google/) - research.google, accessed June 10, 2026.
6. [Google's Hybrid Approach to Research](https://cacm.acm.org/opinion/googles-hybrid-approach-to-research/) - Alfred Spector, Peter Norvig, and Slav Petrov, Communications of the ACM 55(7), July 2012.
7. [Peter Norvig: Bio](https://norvig.com/bio.html) - norvig.com, accessed June 10, 2026.
8. [Alfred Spector](https://en.wikipedia.org/wiki/Alfred_Spector) - Wikipedia, accessed June 10, 2026.
9. [Google Brain](https://en.wikipedia.org/wiki/Google_Brain) - Wikipedia, accessed June 10, 2026.
10. [In a Big Network of Computers, Evidence of Machine Learning](https://www.nytimes.com/2012/06/26/technology/in-a-big-network-of-computers-evidence-of-machine-learning.html) - John Markoff, The New York Times, June 2012.
11. [Attention Is All You Need](https://arxiv.org/abs/1706.03762) - Ashish Vaswani et al., arXiv:1706.03762, June 2017.
12. [Attention Is All You Need](https://en.wikipedia.org/wiki/Attention_Is_All_You_Need) - Wikipedia, accessed June 10, 2026.
13. [Google exec John Giannandrea steps down, Jeff Dean takes over AI](https://www.cnbc.com/2018/04/02/google-exec-john-giannandrea-steps-down-jeff-dean-takes-over-ai.html) - CNBC, April 2, 2018.
14. [Introducing Google AI](https://research.google/blog/introducing-google-ai/) - Christian Howard, Google Research Blog, May 7, 2018.
15. [PaLM: Scaling Language Modeling with Pathways](https://arxiv.org/abs/2204.02311) - Aakanksha Chowdhery et al., arXiv:2204.02311, April 2022.
16. [Google moves Gemini app team to DeepMind unit in latest reorg](https://www.axios.com/2024/10/17/google-gemini-app-deepmind-reorg) - Axios, October 17, 2024.
17. [Hartmut Neven](https://en.wikipedia.org/wiki/Hartmut_Neven) - Wikipedia, accessed June 10, 2026.
18. [Google's Sycamore Achieves Quantum Supremacy](https://postquantum.com/industry-news/google-sycamore/) - PostQuantum, accessed June 10, 2026.
19. [Quantum supremacy using a programmable superconducting processor](https://www.nature.com/articles/s41586-019-1666-5) - Frank Arute et al., Nature 574, October 23, 2019.
20. [Meet Willow, our state-of-the-art quantum chip](https://blog.google/innovation-and-ai/technology/research/google-willow-quantum-chip/) - Hartmut Neven, Google, December 9, 2024.
21. [Making quantum error correction work](https://research.google/blog/making-quantum-error-correction-work/) - Google Research Blog, December 2024.
22. [Our Quantum Echoes algorithm is a big step toward real-world applications for quantum computing](https://blog.google/technology/research/quantum-echoes-willow-verifiable-quantum-advantage/) - Google, October 22, 2025.
23. [Greg Corrado](https://en.wikipedia.org/wiki/Greg_Corrado) - Wikipedia, accessed June 10, 2026.
24. [Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs](https://jamanetwork.com/journals/jama/fullarticle/2588763) - Varun Gulshan et al., JAMA, December 2016.
25. [Med-PaLM: A large language model from Google Research, designed for the medical domain](https://sites.research.google/med-palm/) - Google Research, accessed June 10, 2026.
26. [Large language models encode clinical knowledge](https://www.nature.com/articles/s41586-023-06291-2) - Karan Singhal et al., Nature 620, July 2023.
27. [Towards conversational diagnostic artificial intelligence](https://www.nature.com/articles/s41586-025-08866-7) - Tao Tu et al., Nature, April 2025.
28. [AMIE gains vision: A research AI agent for multimodal diagnostic dialogue](https://research.google/blog/amie-gains-vision-a-research-ai-agent-for-multi-modal-diagnostic-dialogue/) - Google Research Blog, May 2025.
29. [MedGemma: Our most capable open models for health AI development](https://research.google/blog/medgemma-our-most-capable-open-models-for-health-ai-development/) - Google Research Blog, May 2025.
30. [How Google helps others with AI flood forecasting](https://blog.google/innovation-and-ai/products/expanding-flood-forecasting-coverage-helping-partners/) - Google, November 2024.
31. [An improved flood forecasting AI model, trained and evaluated globally](https://research.google/blog/a-flood-forecasting-ai-model-trained-and-evaluated-globally/) - Google Research Blog, 2024.
32. [A Google-backed weapon to battle wildfires made it into orbit](https://techcrunch.com/2025/03/17/a-google-backed-weapon-to-battle-wildfires-made-it-into-orbit/) - TechCrunch, March 17, 2025.
33. [Google Research and the fire community create FireSat, a new constellation of satellites built to detect and track wildfires](https://blog.google/company-news/outreach-and-initiatives/sustainability/google-ai-wildfire-detection/) - Google, September 2024.
34. [Google AI is helping airlines mitigate the climate impact of contrails](https://blog.google/technology/ai/ai-airlines-contrails-climate-change/) - Google, August 2023.
35. [Neural general circulation models for weather and climate](https://www.nature.com/articles/s41586-024-07744-y) - Dmitrii Kochkov et al., Nature, July 2024.
36. [Federated Learning: Collaborative Machine Learning without Centralized Training Data](https://research.google/blog/federated-learning-collaborative-machine-learning-without-centralized-training-data/) - Google Research Blog, April 2017.
37. [Fast Inference from Transformers via Speculative Decoding](https://arxiv.org/abs/2211.17192) - Yaniv Leviathan, Matan Kalman, and Yossi Matias, arXiv:2211.17192, November 2022.
38. [Introducing Nested Learning: A new ML paradigm for continual learning](https://research.google/blog/introducing-nested-learning-a-new-ml-paradigm-for-continual-learning/) - Google Research Blog, November 2025.
39. [Meet Project Suncatcher, a research moonshot to scale machine learning compute in space](https://blog.google/innovation-and-ai/technology/research/google-project-suncatcher/) - Google, November 2025.
40. [Efficient Estimation of Word Representations in Vector Space](https://arxiv.org/abs/1301.3781) - Tomas Mikolov et al., arXiv:1301.3781, January 2013.
41. [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://arxiv.org/abs/1810.04805) - Jacob Devlin et al., arXiv:1810.04805, October 2018.