ClaimsDemis Hassabis

“It’s not every day that one gets to celebrate two papers published in the same issue of @Nature!”

Demis Hassabis, DeepMind CEO, on X 15 January 2020 Post by @demishassabis on X, 2020-01-15 (X)

Factual Science and research methods

Two DeepMind papers were published in the same issue of Nature in January 2020.

Unresolvable The public record could not settle it.

Factual: Statements about the present or the past, checked against the record: did they hold up?

The second judge would not resolve this claim; the verdict shown is the first judge's.

Said for an organisation they lead or work for; it counts on both records.

What would count as true or false

True: two papers by DeepMind authors appeared in the same issue of Nature around January 2020 (for example the AlphaFold/protein structure paper and the dopamine/distributional RL paper in the 16 January 2020 issue, vol. 577). Mostly true: two DeepMind papers did appear in one issue, but the issue date is slightly off from January 2020. Mostly false: the two papers appeared in Nature at about the same time but in different issues (online only versus print). False: Nature did not publish two DeepMind papers in any single issue at that time.

Set before the research began.

Evidence (4)

  1. E1 Nature - Mapping the future Nature (Springer Nature) · 30 January 2020

    “Volume 577 Issue 7792, 30 January 2020”
  2. E2 Nature - Mapping the future Nature (Springer Nature) · 30 January 2020

    “In this issue, Will Dabney and his colleagues suggest that the picture is more complex.”
  3. E3 Improved protein structure prediction using potentials from deep learning - Nature Nature (Springer Nature) · 15 January 2020

    “Senior, A.W., Evans, R., Jumper, J. et al. Improved protein structure prediction using potentials from deep learning. Nature 577, 706–710 (2020).”
  4. E4 A distributional code for value in dopamine-based reinforcement learning - Nature Nature (Springer Nature) · 15 January 2020

    “Dabney, W., Kurth-Nelson, Z., Uchida, N. et al. A distributional code for value in dopamine-based reinforcement learning. Nature 577, 671–675 (2020).”

Reasoning

The evidence shows that issue 7792 of volume 577 (30 January 2020) includes work by Will Dabney and colleagues [E1][E2], and that both papers are in volume 577 at pages 671–675 and 706–710 [E3][E4]. No evidence item shows that the Senior et al. protein structure paper was in the same issue, so the evidence does not settle whether the two papers shared one issue.

Cite this claim

Demis Hassabis, “Two DeepMind papers were published in the same issue of Nature in January 2020.” (said 15 January 2020). Verdict: unresolvable. Reliability API, claim c_3016eac6c0a4, method 0.1.0+c876030c, dataset release v1.1.2. https://epistacklabs.com/claims/c_3016eac6c0a4

This claim is in dataset release v1.1.2, with the same verdict.

Reply to this claim

For Demis Hassabis, or someone speaking for them: a correction or counter-evidence. Replies are read before anything is shown; an approved reply shows your name, role, date, statement and link. Your email address is never shown and is deleted 30 days after the decision (privacy).

More factual claims by Demis Hassabis

  1. Mostly true Factual 10 April 2026
    “what’s important about them is their 3D structure.”

    A protein's 3D structure is what determines, or partially determines, its function.

  2. Resolved true Factual 10 April 2026
    “So over 3 million scientists are now using AlphaFold.”

    Over 3 million scientists are now using AlphaFold.

  3. Resolved true Factual 10 April 2026
    “Only about 10% of drugs actually get through all the clinical stages.”

    Only about 10% of drugs get through all the clinical stages.

  4. Mostly true Factual 25 November 2025
    “The AlphaFold Server is empowering non-commercial researchers globally to harness this technology, accelerating their ability to formulate and test new hypotheses.”

    The AlphaFold Server is available to non-commercial researchers globally for predicting structures and interactions.

  5. Mostly true Factual 25 November 2025
    “Our AlphaProteo model can design novel, high-strength protein binders that target diverse molecules – including those associated with cancer and diabetes.”

    AlphaProteo can design novel, high-strength protein binders targeting diverse molecules, including ones associated with cancer and diabetes.

All of them

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