PeopleofSuperintelligence

Researcher

Malcolm Reynolds

Malcolm Reynolds is a co-author of AI papers from 2016 to 2025, with work that runs from neural memory to meta-reinforcement learning and the Gemini model family. He co-wrote a 2016 Nature paper with Google DeepMind colleagues on a neural network with dynamic external memory. In 2021 he co-authored Alchemy, a benchmark and analysis toolkit for meta-reinforcement learning agents. In 2022 he was one of the authors of Flamingo, a family of visual language models for few-shot learning. In 2023 he was co-lead for codebase and parallelism on Gemini 1.0, and he appeared among the contributors to Gemini 1.5 in 2024 and to Gemini 2.5 in 2025.

Timeline

  1. 2016

    A neural network with external memory

    A Nature paper, 'Hybrid computing using a neural network with dynamic external memory,' appears in 2016 with him among its co-authors from Google DeepMind.

  2. 2021

    A meta-reinforcement learning benchmark

    He co-authors Alchemy, a benchmark and analysis toolkit for meta-reinforcement learning agents, a NeurIPS Datasets and Benchmarks paper from 2021.

  3. 2022

    Flamingo visual language models

    Flamingo, a family of visual language models for few-shot learning, appears at NeurIPS in 2022 with him among its authors.

  4. 2023

    Co-lead for codebase and parallelism

    In 2023 he is listed as co-lead for codebase and parallelism on Gemini 1.0.

  5. 2024

    A contributor to Gemini 1.5

    Gemini 1.5 lists him among its core contributors, in a report version dated December 2024.

  6. 2025

    A contributor to Gemini 2.5

    Gemini 2.5 lists him among its contributors, in a report version dated December 2025.

Sources and credits

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  1. Hybrid computing using a neural network with dynamic external memory | Nature nature.com. Checked 15 October 2026.
  2. Alchemy: A benchmark and analysis toolkit for meta-reinforcement learning agents datasets-benchmarks-proceedings.neurips.cc. Checked 15 October 2026.
  3. Flamingo: A Visual Language Model for Few-Shot Learning | ML Anthology mlanthology.org. Checked 15 October 2026.
  4. Gemini: A Family of Highly Capable Multimodal Models arxiv.org. Checked 15 October 2026.
  5. Gemini - A Family of Highly Capable Multimodal Models: Contributions and Acknowledgments hackernoon.com. Checked 15 October 2026.
  6. Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context arxiv.org. Checked 15 October 2026.
  7. Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities arxiv.org. Checked 15 October 2026.