coronavirus VS hivemind

How difficult world it be to set up Folding@home on these? https://foldingathome.org

My personal recommendation would be Folding@Home[1]. Protein folding is an insanely complex thing life does and they use distributed computing to try to solve the large problems.

1. https://foldingathome.org/?lng=en

Context: Banano team is the top contributor to Folding@Home which is a philanthropic project that uses computing power to help study complex proteins involved in disease research, seeking preventions and cures! https://bananominer.com/ https://foldingathome.org/

A major and essential part of the Banano culture is helping fight various diseases through the use of Folding at Home. You can join the team here at Banano miner and earn Banano whilst contributing to science. For almost two years Banano has been the top contributor other than the default team. For more information on this check out Folding at Home statistics.

foldingathome.org try this op

Others could be used for folding@home. If you want to try it

https://github.com/learning-at-home/hivemind is also relevant

I really hope you'll join me, for the Petals support, at least! A single docker-compose.yml file is all we need, for now. If we are able to find enough people willing to host some smaller models, perhaps we could expand into the Hivemind, and create our own, custom foundation model one day?

Decentralized deep learning: https://github.com/learning-at-home/hivemind

https://github.com/learning-at-home/hivemind - same people behind it, was made before petals I think.

not if we rise to meet that challenge. here's a few tools that facilitate AI research in the face of an advanced persistent threat: Hivemind- a distributed Pytorch framework

yeah, there's Hivemind. and there's research wrt how to chunk out training workload so it can be scaled up. not sure why there's commentary that latency issues would limit this sort of enterprise, the architecture typically isn't designed for liveness. other subfields of distributed training/inference include zero-knowledge machine learning. besides all of that, there's also adversarial computation like SafetyNets and refereed delegation of computation.

We already have the software for it. There are some projects, but the one I'm most familiar with is https://github.com/learning-at-home/hivemind for training and it's sister project https://petals.ml/ for running large models distributed.

I'm not entirely how the approach they're using works [0], but I study federated learning and one of the highly-cited survey papers has several chapters (5 and 6 in particular) addressing potential attacks, failure modes, and bias [1].

0: https://github.com/learning-at-home/hivemind

1: https://arxiv.org/abs/1912.04977

The Hivemind project is just that

https://github.com/learning-at-home/hivemind