1. For DS3 I'm planning to do a torrent, or if researches approach me directly, I can give them one-time access to my download directly from my home systems. DS1/DS2 will likely remain small enough to download directly.
2. At the moment the priority is the updated client and DS4, in the order I mentioned in the other forum post (CPU first, then GPU). As for other features, the highest priority is getting NaN recovery working. That requires someone to change the way the training class is coded in C++ to both swap out the current model and create a new optimizer object.. which isn't possible from the main loop the way I coded it up originally. It's not rocket science, but would take some careful thinking. DS4 support is already in the new client, so all that is left there is to update the WU generation/validation/assimilation scripts to handle it, which shouldn't take long. That's not a complete list, but its a start.
3. I don't think we need more examples of the same things we have. I think DS4 with CNNs will be a big help. For DS5, I'm thinking of maybe varying the shape and size parameters for DS1/2/3/4. Right now, we only vary the weights.. this made the analysis easy, and showed the good results we already have. It would be nice if we could show the same clustering even if we vary the shape of the network (different numbers of hidden nodes, different number of layers, etc..). DS5 may do that, but I haven't decided yet.
4. I've decided I'm going to swing for the fences, and beef up the paper to submit to one of the big ML conferences, AAAI. It's a stretch and hyper competitive, but even if its only a poster, that should get some people's interest. The poster would be more about MLC@Home itself as a research platform. The paper mentions it too.
5. As for Discord, I find I'm personally on discord a lot, and checking this forum a lot less. I've hooked the forum into the RSS feed on discord, but even then I seem to work better on Discord. Still, for conversations that need to exist long term, like longer term discussions, the forum is probably better. But for tech support and actually getting my attention faster, Discord is a better bet.
6. The server computation-wise if fine. Since the upgrade to a 6c/12t processor (thanks Ryzen!) its barely breaking a sweat. Disk space is more of a concern, but I can mitigate that more easily by moving the DS3 archive off of that onto a torrent. Now, network bandwidth hasn't been a huge issue, but I am still running the system off my home network, as my university is just now allowing people back onto campus post pandemic. That means I can move the server onto their network. However, it also means if I need to access it, it'll be 30 minutes away by car instead of under my desk. So, I think we're good for now.
7. At the moment, it's only me. Once it grows beyond me, I'd like to set up a small governance committee, but for now all you have is my word on that. I will say that I will keep to the tenets laid out on our homepage, that the resulting data needs to be made publicaly available as soon as possible.
8. Of course, any new applications will be separate BOINC application queues, so you can opt in/out as you wish.
9. It's difficult, but I'm open to anything. I've learned early on if you want something you create to thrive, you have to be open to other's ideas on how to use it. In general, I feel ML on the BOINC platform is more suited to trying out a lot of small things in parallel, rather than trying to build one bigger network to target a single problem. That said, one can look at, trying a whole bunch of parameters on a network at once to see which ones perform better, etc. Another problem with real-world data is privacy. First, dealing with medical data is a potential whole can of worms in the US, as the data could be personally identifying, so hosting the data on something like MLC@Home could potentially open us up to some liability there. On the other end of the spectrum, there's so many businesses looking to make money on ML, they tend to jealously guard their data. So getting real world data is often a problem. Keeping things at the fundamental ML level tends to avoid all those problems and I think has a broader impact on the field as a whole. But like I said, we'd absolutely listen to any honest researcher with an idea!
10. Heck, I'm still learning BOINC peculiarities, so while I've had some informal discussions with other researchers and mentioned some of the caveats, I don't have anything formal.
11. So the funny thing at the moment is that MLC@Home is completely unfunded. The new server was purchased with some grant money, but that's it. I'm able to work on it because I'm essentially self-funded and work another full-time job (I'm a part-time student). The main issue I've had talking with other researchers is that they need funding to continue as a grad student, and working on MLC won't get them any. I've been looking at potential funding opportunities and collaborations, but one particular one I had high hopes for didn't pan out. So at the moment it's advertise and hope to get noticed. Attending conferences like AAAI might help.

Whew, I think that covered most things, I hope that was useful information. Thanks again for being such a supportive community.

• Received contributions from over 2500+ volunteers and 9200+ hosts
  
• Processed over 3.4 million BOINC workunits
  
• Trained over 1.1 million neural networks for analysis over 3 different datasets, the largest datasets of their kind
  
• Generated over 4.3TB of data for analysis
  
• Published one academic paper (more coming..)
  
• Presented at the 2021 BOINC Workshop
  
• Released 47 client versions targeting 3 different CPU architectures, 2 GPU architectures, and multiple versions of Windows and Liunx.
  
• Outgrew the initial server within the first few months!
  

I'm overwhelmed by our community and what we've accomplished together. We've already shown that networks trained with the same data cluster together in weight space, despite the randomness associated with neural network training. We've also shown we can use this clustering to detect networks trained with poisoned data versus clean data, a significant finding in the field.

But there's still soo much more to do! So while we want to acknowledge and celebrate what we've jointly accomplished so far, let's also look forward and set some loose goals for the next year of MLC@Home:

• MLDS will continue near term!
  DS4 is (almost) ready and expands the dataset to include CNN network types as well as RNNs used in DS1-3. DS5 will likely vary the shape and size of each network slightly to see if clustering still happens when shape is varies. Future MLDS work beyond DS5 is TBD, but we expect there to be plenty DS4/DS5 WUs for many months to come. We expect to update the paper with the latest runs over the next month.
  
  
• We'd like to expand beyond MLDS!
  We are the first project to do ML on a BOINC-sized scale. We would like to expand to supporting other areas of research, and want to commit to bringing at least one other ML project online within the next year. Please contact us if you are a researcher who is interested in working with the platform!
  
  
• We need to improve the technical side of the project
  From the client supporting AMD GPUs and OSX to optimizing utilization of graphics cards to a better validation process for WUs, there's a laundry list of technical issues we'd like to address, and have not done so effectively in the past three months. We're also hitting some corner-cases of the BOINC software stack that are tricky to work around. If you are a developer and want to help, we'd welcome the support.
  
  
• We'd like to improve outreach
  To get more people involved, we'd like to produce a few short videos about the project, what we've found and how others can help. These should be short, easily accessible, and easy to share. We'd like to produce at least one of these within the next 6 months.
  

These are loose goals but should give you an idea where we're concentrating our efforts for the next year. If you have further insights, please share them below or on Discord.

Thanks again for supporting MLC@Home, and here to many more years of successful, important research in an important field.

Other News

• DS3 is all but complete (just a last few 130+ trickling in!). I consider DS3 to be the most important dataset and can't wait to run our analysis on the whole thing!
  
• From now on we'll be blasting DS1 (then DS2) WUs into both the GPU and CPU queues until that completes and/or until DS4 is ready. We'll try to get those over the hump ASAP.
  
• Some fun news! MLC Discord user Tankbuster has updated our banner graphic! See the updated banner on project and home pages!
  
• Even more exciting, Tankbuster built a prototype graphics app for MLC@Home! You can see mockups and videos and follow the discussion at the MLC Discord server (link at the bottom). Screenshot:
  
• Reminder: the MLC client is open source, and has an issues list at gitlab. If you're a programmer or data scientist and want to help, feel free to look over the issues and submit a pull request.
  

Project status snapshot:
(note these numbers are approximations)






Last month's TMIM Notes: Jun 8 2021

Thanks again to all our volunteers!

-- The MLC@Home Admins(s)
Homepage: https://www.mlcathome.org/
Discord invite: https://discord.gg/BdE4PGpX2y
Twitter: @MLCHome2