JKjka·Feb 24, 2022Submission
DeepMind Research – code to accompany DeepMind publications
2 points · Open discussiongoogle-deepmind
deepmind-research
Jupyter Notebook2020–2022
google-deepmind / deepmind-research
deepmind-research
This repository contains implementations and illustrative code to accompany DeepMind publications
13Distinct people
14Mentions
0Explicit recommenders
15.3KGitHub stars
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What it is
This repository contains implementations and illustrative code to accompany DeepMind publications
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Where it is used
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This week on Hacker News
Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 3:03:16 PM UTC.
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Mentions over time
HN mentions by month14 dated mentions
Observed mentions: 2020-01-20 13:14:40 UTC to 2022-02-24 18:53:01 UTC.
Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.
View mention counts and date ranges
| Period | Start (UTC) | End (exclusive, UTC) | Mentions | Submissions | Comments |
|---|---|---|---|---|---|
| Jan 2020 | 2020-01-01 00:00:00 UTC | 2020-02-01 00:00:00 UTC | 3 | 0 | 3 |
| Feb 2020 | 2020-02-01 00:00:00 UTC | 2020-03-01 00:00:00 UTC | 0 | 0 | 0 |
| Mar 2020 | 2020-03-01 00:00:00 UTC | 2020-04-01 00:00:00 UTC | 0 | 0 | 0 |
| Apr 2020 | 2020-04-01 00:00:00 UTC | 2020-05-01 00:00:00 UTC | 0 | 0 | 0 |
| May 2020 | 2020-05-01 00:00:00 UTC | 2020-06-01 00:00:00 UTC | 0 | 0 | 0 |
| Jun 2020 | 2020-06-01 00:00:00 UTC | 2020-07-01 00:00:00 UTC | 0 | 0 | 0 |
| Jul 2020 | 2020-07-01 00:00:00 UTC | 2020-08-01 00:00:00 UTC | 1 | 1 | 0 |
| Aug 2020 | 2020-08-01 00:00:00 UTC | 2020-09-01 00:00:00 UTC | 0 | 0 | 0 |
| Sep 2020 | 2020-09-01 00:00:00 UTC | 2020-10-01 00:00:00 UTC | 0 | 0 | 0 |
| Oct 2020 | 2020-10-01 00:00:00 UTC | 2020-11-01 00:00:00 UTC | 0 | 0 | 0 |
| Nov 2020 | 2020-11-01 00:00:00 UTC | 2020-12-01 00:00:00 UTC | 1 | 0 | 1 |
| Dec 2020 | 2020-12-01 00:00:00 UTC | 2021-01-01 00:00:00 UTC | 1 | 0 | 1 |
| Jan 2021 | 2021-01-01 00:00:00 UTC | 2021-02-01 00:00:00 UTC | 0 | 0 | 0 |
| Feb 2021 | 2021-02-01 00:00:00 UTC | 2021-03-01 00:00:00 UTC | 1 | 0 | 1 |
| Mar 2021 | 2021-03-01 00:00:00 UTC | 2021-04-01 00:00:00 UTC | 0 | 0 | 0 |
| Apr 2021 | 2021-04-01 00:00:00 UTC | 2021-05-01 00:00:00 UTC | 0 | 0 | 0 |
| May 2021 | 2021-05-01 00:00:00 UTC | 2021-06-01 00:00:00 UTC | 0 | 0 | 0 |
| Jun 2021 | 2021-06-01 00:00:00 UTC | 2021-07-01 00:00:00 UTC | 0 | 0 | 0 |
| Jul 2021 | 2021-07-01 00:00:00 UTC | 2021-08-01 00:00:00 UTC | 1 | 0 | 1 |
| Aug 2021 | 2021-08-01 00:00:00 UTC | 2021-09-01 00:00:00 UTC | 1 | 1 | 0 |
| Sep 2021 | 2021-09-01 00:00:00 UTC | 2021-10-01 00:00:00 UTC | 1 | 1 | 0 |
| Oct 2021 | 2021-10-01 00:00:00 UTC | 2021-11-01 00:00:00 UTC | 1 | 1 | 0 |
| Nov 2021 | 2021-11-01 00:00:00 UTC | 2021-12-01 00:00:00 UTC | 1 | 0 | 1 |
| Dec 2021 | 2021-12-01 00:00:00 UTC | 2022-01-01 00:00:00 UTC | 1 | 0 | 1 |
| Jan 2022 | 2022-01-01 00:00:00 UTC | 2022-02-01 00:00:00 UTC | 0 | 0 | 0 |
| Feb 2022 | 2022-02-01 00:00:00 UTC | 2022-03-01 00:00:00 UTC | 1 | 1 | 0 |
What Hacker News says
14 sourcesPaper: https://www.science.org/doi/10.1126/science.abj6511
Code: https://github.com/deepmind/deepmind-research/tree/master/de...
In Simulating matter on the quantum scale with AIPaper - https://www.nature.com/articles/s41592-021-01252-x
Model - https://github.com/deepmind/deepmind-research/tree/master/en...
In Predicting Gene Expression with AISkillful Precipitation Nowcasting Using Deep Generative Models of Radar
1 points · Open discussionSkillful Precipitation Nowcasting Using Deep Generative Models of Radar
2 points · Open discussionPerceiver and Perceiver IO (Deepmind)
1 points · Open discussionwhich is basically admission that they will API it, not release the models... again.
The original version on github can only compute the specific systems in the paper.
https://github.com/deepmind/deepmind-research/tree/master/al...
I don't know why scientific publications keep doing PR work for them.
In What DeepMind’s AlphaFold 2 really achieved (2020)>if it’s good enough to do protein folding for Google and video classification for Tesla, it’s good enough for me.
That's funny, since protein folding uses the methods I described to achieve it's results. Multiple stages in their work [1] uses physical modeling, not NNs, to do protein folding, most likely because the problem becomes currently intractable to solve with simple Python + TF. NNs are a step to adjust the physical model, exactly like I described above.
Simply read their paper and note all the physics models incorporated at about every step of the process to enforce physical constraints - this means vastly less parameters, less training time, less training data, faster evolution of the process, etc.
Here's their repo [2]. They did that work in Python and TF, likely because it was started years ago. As Julia becomes a much faster
Read the rest
develop tool for this type of work I expect this will change. They also used TF 1.14 - showing the age of their development. They did not release the feature generation code which is a significant component; the released code only works on the specific dataset they provide. This is likely because this component is not simply simple python they wrote, but an amalgam of things written to make the physics parts of the chain fast enough. But they don't clearly state either way.
Also, by your argument, since it's possible to solve any NN problem with a network only 3 layers deep, why not just claim that's all one needs? Because it's also not computationally feasible.
The point is that by adding outside knowledge, such as physics models, you can have vastly smaller networks, require less training data, train and infer faster, with the end result of being able to solve a much larger class of problems efficiently.
So yes, you can do it in python, or any language, if you want to waste orders of magnitude more effort and resources to do it, effectively limiting the things you can practically do.
This is why Python incurs a unnecessary cost for such development.
By willfully ignoring learning about these methods and being ignorant about even the results you cited you will miss out on extremely useful knowledge.
[1] https://www.nature.com/articles/s41586-019-1923-7.epdf?autho...
[2] https://github.com/deepmind/deepmind-research/tree/master/al...
does anyone know where the latest code for this is ?
i can see CASP 13 is here - https://github.com/deepmind/deepmind-research/tree/master/al...
In No, DeepMind has not solved protein foldingThe method relies on multiple-sequence-alignment (MSA) of homologous proteins. This cannot fold arbitrary proteins, only biologically relevant ones that have high quality MSAs available. It's also worth pointing out that the gold-standard for validating MSAs relies on PDBs of folded proteins. This is exciting work that will assist NMR and XRay crystallographers, but it's not a panacea of protein folding.
https://github.com/deepmind/deepmind-research/issues/18
In AlphaFold: a solution to a 50-year-old grand challenge in biologyRL Unplugged: Benchmarks for Offline Reinforcement Learning
1 points · Open discussionThe fact that the software in question is not publicly available or runnable certainly makes it a "black box", regardless of whether the underlying models are human-interpretable. EDIT: my apologies, apparently it is available: https://github.com/deepmind/deepmind-research/tree/master/al... I must have gotten mixed this up with a different black-box-advertised-in-scientific-journal story.
In A watershed moment for protein structure predictionThey provide code and data for full verification. https://github.com/deepmind/deepmind-research/blob/master/al...
In A watershed moment for protein structure predictionCode and neural network weights for AlphaFold: https://github.com/deepmind/deepmind-research/tree/master/al...
In A watershed moment for protein structure prediction