5 ms·

>Most radio SETI projects process data in near real-time using special purpose analyzers at the telescope. SETI@home takes a different approach. It records digi

>Most radio SETI projects process data in near real-time using special purpose analyzers at the telescope. SETI@home takes a different approach. It records digital time-domain (also called baseband) data, and distributes it over the internet to large numbers of computers that process the data, using both CPUs and GPUs. Definetly something going on here I'm not following. >SETI@home is in hiberation. We are no longer distributing tasks. [0] Is this paper really old or something? I would love to turn on my clients again :D [0 ]https://setiathome.berkeley.edu/ https://setiathome.berkeley.edu/

elicash 8mo ago
They went into hibernation, in terms of accepting new inputs, several years ago. They had more data than they could handle and switched to just analyzing existing data and final reports.
drb493 8mo ago
The distributed compute part of the project has turned off but data analysis continues. I know what you mean these types of projects inspired me to contribute as a young citizen scientist. A different domain, but https://foldingathome.org/ https://foldingathome.org/ is still running. Using distributed compute to study protein folding.
- washedDeveloper 8mo ago
  If you are looking for a good list of these types of projects: https://boinc.berkeley.edu/projects.php https://boinc.berkeley.edu/projects.php
- alexpotato 8mo ago
  Wasn't this largely solved by DeepMind's AlphaFold? https://alphafold.ebi.ac.uk/ https://alphafold.ebi.ac.uk/
  - drb493 8mo ago
    I'd discourage claiming any biological process is "solved." But to your point: No--AlphaFold is an amazing machine learning approach to predicting protein structure but Folding@Home is still immensely useful for simulating how proteins fold up over a timescale. They are/will be complimentary methods. https://pmc.ncbi.nlm.nih.gov/articles/PMC11892350/ https://pmc.ncbi.nlm.nih.gov/articles/PMC11892350/
  - dekhn 8mo ago
    no, alphafold is basically just a static structure predictor. folding@home explicitly models the folding process (the journey, not just the destination).

elicash 8mo ago

They went into hibernation, in terms of accepting new inputs, several years ago. They had more data than they could handle and switched to just analyzing existing data and final reports.

drb493 8mo ago

The distributed compute part of the project has turned off but data analysis continues. I know what you mean these types of projects inspired me to contribute as a young citizen scientist. A different domain, but https://foldingathome.org/ https://foldingathome.org/ is still running. Using distributed compute to study protein folding.

washedDeveloper 8mo ago
If you are looking for a good list of these types of projects: https://boinc.berkeley.edu/projects.php https://boinc.berkeley.edu/projects.php
alexpotato 8mo ago
Wasn't this largely solved by DeepMind's AlphaFold? https://alphafold.ebi.ac.uk/ https://alphafold.ebi.ac.uk/
- drb493 8mo ago
  I'd discourage claiming any biological process is "solved." But to your point: No--AlphaFold is an amazing machine learning approach to predicting protein structure but Folding@Home is still immensely useful for simulating how proteins fold up over a timescale. They are/will be complimentary methods. https://pmc.ncbi.nlm.nih.gov/articles/PMC11892350/ https://pmc.ncbi.nlm.nih.gov/articles/PMC11892350/
- dekhn 8mo ago
  no, alphafold is basically just a static structure predictor. folding@home explicitly models the folding process (the journey, not just the destination).