PeopleofSuperintelligence

Researcher

Eric Noland

Eric Noland is a researcher whose work spans video action datasets, language-model training and multimodal AI. He coauthored DeepMind’s Kinetics-600 and Kinetics-700 expansions in 2018 and 2019, then a 2022 compute-optimal language-model study that received a NeurIPS Outstanding Paper award. His Gemini and Gemma reports cover multimodal systems and open models from Gemini in 2023 through Gemma 4 in July 2026. As of October 2026, he is a software engineer at DeepMind.

Timeline

  1. 2018–2019

    Kinetics-600 and Kinetics-700

    In 2018, he coauthors *A Short Note about Kinetics-600*, extending DeepMind’s human-action dataset from 400 to 600 classes. In 2019, he coauthors *A Short Note on the Kinetics-700 Human Action Dataset*, extending the collection from 600 to 700 action classes.

  2. 2022

    Compute-optimal training

    In 2022, he coauthors *Training Compute-Optimal Large Language Models*, which finds that model size and training tokens should scale equally. Its 70B Chinchilla model uses the same compute budget as Gopher while consuming four times more data, and it outperforms Gopher, GPT-3, Jurassic-1 and Megatron-Turing NLG. NeurIPS gives the study an Outstanding Paper award under the title *An empirical analysis of compute-optimal large language model training*.

  3. 2023

    Gemini multimodal models

    In 2023, he coauthors *Gemini: A Family of Highly Capable Multimodal Models*. The report presents models for image, audio, video and text understanding.

  4. March 2024

    Gemma open models

    In March 2024, he coauthors *Gemma: Open Models Based on Gemini Research and Technology*. The report introduces lightweight open models built from the research and technology used to create Gemini.

  5. July 2024

    Gemma 2 open models

    In July 2024, he coauthors *Gemma 2: Improving Open Language Models at a Practical Size*. The report introduces models ranging from 2 to 27 billion parameters.

  6. March 2025

    Gemma 3 technical report

    In March 2025, he coauthors the Gemma 3 technical report, which adds vision understanding to the lightweight open-model family and ranges from 1 to 27 billion parameters.

  7. July 2026

    Gemma 4 technical report

    In July 2026, he coauthors the Gemma 4 technical report, which introduces a new generation of open-weight, natively multimodal language models in the Gemma family.

  8. As of October 2026

    Software engineer at DeepMind

    As of October 2026, he works as a software engineer at DeepMind.

  1. Google had the most people shaping AI every year from 2014 to 2025Includes Google. Our estimates of which organizations employed the most people shaping AI each year from 1955 to 2025, tracing the shift from university labs to companies.
  2. Google has the highest combined h-index among AI organizationsIncludes Google. We estimate that Google holds the highest combined h-index at 8,016, while universities account for 73% of the deduplicated total across AI organizations.
  3. 20% of people at companies who shape AI also hold a university role, led by Google and Stanford UniversityIncludes Google. We estimate that 539 of the 6,766 people with a known current organization (8%) hold both a university role and a company role, including 20% of people at companies and 13% of people at universities.
  4. Google alumni have founded or led 133 other organizations, more than any other AI labIncludes Google. We estimate that alumni of 17 major AI labs went on to found or lead 350 other organizations, including 226 they founded, and we trace 472 career moves directly between 23 labs since 2010.

Sources and credits

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  1. [1808.01340] A Short Note about Kinetics-600 arxiv.org. Checked 22 October 2026.
  2. A Short Note about Kinetics-600 arxiv.org. Checked 22 October 2026.
  3. [1907.06987] A Short Note on the Kinetics-700 Human Action Dataset arxiv.org. Checked 22 October 2026.
  4. A Short Note on the Kinetics-700 Human Action Dataset arxiv.org. Checked 22 October 2026.
  5. [2203.15556v1] Training Compute-Optimal Large Language Models arxiv.org. Checked 22 October 2026.
  6. Training Compute-Optimal Large Language Models arxiv.org. Checked 22 October 2026.
  7. [2203.15556] Training Compute-Optimal Large Language Models arxiv.org. Checked 22 October 2026.
  8. NeurIPS 2022 Awards neurips.cc. Checked 22 October 2026.
  9. [2312.11805] Gemini: A Family of Highly Capable Multimodal Models arxiv.org. Checked 22 October 2026.
  10. Gemini: A Family of Highly Capable Multimodal Models arxiv.org. Checked 22 October 2026.
  11. [2403.08295] Gemma: Open Models Based on Gemini Research and Technology arxiv.org. Checked 22 October 2026.
  12. Gemma: Open Models Based on Gemini Research and Technology arxiv.org. Checked 22 October 2026.
  13. [2408.00118] Gemma 2: Improving Open Language Models at a Practical Size arxiv.org. Checked 22 October 2026.
  14. [2408.00118v1] Gemma 2: Improving Open Language Models at a Practical Size arxiv.org. Checked 22 October 2026.
  15. Gemma 2: Improving Open Language Models at a Practical Size arxiv.org. Checked 22 October 2026.
  16. Gemma 3 Technical Report | European Laboratory for Learning and Intelligent Systems ellis.eu. Checked 22 October 2026.
  17. Gemma 3 Technical Report arxiv.org. Checked 22 October 2026.
  18. [2607.02770] Gemma 4 Technical Report arxiv.org. Checked 22 October 2026.
  19. Gemma 4 Technical Report arxiv.org. Checked 22 October 2026.
  20. Eric Noland - LinkedIn me.sh. Checked 22 October 2026.