Coming up with ideas and thinking about the future is a lot easier than always finishing things. Mountains of ideas are hard to keep up with and the time commitment to close out something that only took an instant to create can be extreme to finish. Right now I can sketch out the chapters of a book on applied machine learning on a sheet of paper. Actually taking the time to write that book would be a large multiple month commitment of my time. So many people are probably writing that book that it does not seem like a reasonable use of my time at the moment, but it is an example of where a few minutes of work would translate to months of effort to close that effort out. That is why always finishing things is harder than it sounds. You will quickly find your list of potential things to do quickly faces a situation with over-supplied ideas and undersupplied time. That is one of those things that is always the hallmark of having to make choices about our time. We stare out at the perfect possible future and know that only so much can be done in one lifetime. Inherently that is what makes contributions to the academy of academic thought so compelling. You get the chance to contribute to a body of work with intergenerational continuity. Permanence is an interesting thing to associate with an idea, but it is a rather powerful one. Great ideas are sold, stolen, and reused based on their merit. The best ones keep on moving along from thinker to thinker.
My time is always something that needs to be better accounted for. It is a scarce resource and applying my time to problems should be done based on some reasonable heuristic. Always finishing things is very time consuming. It is done without any filtering heuristic. Always doing anything is potentially problematic. You have to account for the situation and think about what is actually being done. For the next week or so I’m going to take stock of the things I’m spending my time doing by using some note cards to keep track of the larger blocks of things that are going to be consuming my time. Yesterday I had decided to spend a block of my time reading 2-3 peer reviewed machine learning literature reviews a day until that exercise was exhausted. I’m expecting that the literature review reading will show up on the note cards, but the times when it does not will be insightful as well. It will give me a look at what needed to happen and what happened instead based on how I’m committing my time.
Some time later… I went out to Google Scholar and started a search for, “machine learning literature review.” During the search autocomplete suggested that I might be interested in a search for, “machine learning literature survey.” Apparently, both searches are pretty common and I’ll keep that “survey” term in my back pocket for after I read a few literature reviews. After completing that first search the Google search algorithm had a few related searches to suggest that included: machine learning algorithms, pattern recognition machine learning, machine learning mitchell, supervised machine learning, machine learning classification, uci machine learning, machine learning intrusion, and machine learning repository. I can tell the web of ideas that will spill out from reading machine learning literature reviews is going to include a lot of spokes into very specific lines of inquiry. To try to stay as current as possible I applied a filter to only show me articles since 2020. I’ll have to use the more recent publications to work my way back to the more foundational scholarly work on the subject.
The first 3 articles I encountered based on relevance to the search for articles since 2020 were as follows:
1. https://arxiv.org/abs/2007.11354v4 This one was free and easy to download.
2. https://www.sciencedirect.com/science/article/abs/pii/S0305054820300435 This one cost $39.95 to read and was abandoned.
3. https://www.sciencedirect.com/science/article/pii/S0952197619302672 This one was free to download.
For two consecutive weeks I have tried to order one of the new NVIDIA 30 series graphics cards. First, the RTX 3080 sold out in seconds last week. Second, the RTX 3090 sold out almost instantly this week. At this point, NVIDIA would have been better off just listing the tiny supply of graphics cards they have on eBay and allowing the market to take care of things. Whatever they did as a course of business failed miserably. I still do not understand why they did not just set up a method for preorder that accepted payment and put people on a waiting list for delivery. The people that really wanted one of the first graphics cards in this new series would have just waited in line to get the card. Demand was that strong on this one. That would have been the best and most fair way to share this graphics card with the world. At this point, I can say the launch process has been rotten. On October 15, 2020 the last NVIDIA 30 series card will go on sale. I’m not expecting the sale process for the RTX 370 to go any better based on the previous two examples.
6:00 AM (Denver, Colorado): Here in about 55 minutes I’m going to try to buy one of the NVIDIA RTX 3090 graphics cards when they go on sale. What might be a concerning sign about that is that NVIDIA has already apologized for the online sale that happens in 55 minutes. Apparently, they have a very small supply and things are not going to go well…
7:00 AM (Denver, Colorado): I tried to buy one of these cards using the website this morning. At just after 7:00 AM mountain time it looked like the website changed and then went almost directly to an “OUT OF STOCK” message in all extremely disappointing capital letters.
The team over at NVIDIA will put the brand new GeForce RTX 3090 aka the “BFGPU” on sale in the morning on Thursday September 24, 2020. Apparently, the team over at NVIDIA has publicly apologized for the frustration caused by the 3080 launch last week. It was a terrible online product sale launch. Simply terrible. The attention of people worldwide was focused on it and things did not go well. Even thinking back on it is frustrating and disappointing. I’m probably going to try to buy the 3090 this week during the launch window. My bet is that I won’t actually be able to buy one and it will be an exercise in trying to do something that should be simple and failing miserably at it while knowing the entire time that is the most likely outcome. I’m not sure an exact word exists to express that emotion, but if the team over at NVIDIA is not careful it is entirely plausible that their brand name will become synonymous with that feeling. It has to be a word that conveys a certain type of exasperated emotion that is somehow worse than futility.
My Corsair Air 740 cube computer case is already ready for the installation of the new graphics card that has not even been purchased at this time. I have the special 8-pin Corsair cables on my desk and a special support bracket for GPUs that came with the ASUS TUF X299 Mark 1 motherboard. None of that will help one of these new 30 series graphics cards show up. I’m not entirely sure exactly what I want to do with this new graphics card. That is one of those things that need to be considered. I’m probably going to spin up some type of TensorFlow instance on this computer and do some things with it. Generally, I could just use the Google Colab notebooks to do that type of thing and it would be a lot less expensive, but at the same time it is a lot less fun. That second part cannot be understood when it comes to buying a new GPU. The excitement part of the whole thing has a certain appeal. Being ready for the inevitable 8K monitor that I’m going to buy is also a big plus, but the 3090 is probably overkill for that type of effort alone.
Last night I finished listening to Walter Isaacson’s, “The Innovators,” audiobook (2014). Instead of listening to books from the Dune franchise I have been listening to non-fiction efforts recently before going to bed.
My internet browsing has been rather aimless this weekend. I keep checking a variety of news sites hoping for better news. This effort seems misguided, but it was oddly reactionary to a year of ineffable debacles.
My next effort for the day is going to be to plugin this new USB to IDE cable to see if my classic Zip 250 drive from my Lian Li cube computer case will work. This is all part of an adventure to see what is on the five Zip 250 disks sitting on my desk. Not knowing what was on the disks has been bothering me since they arrived on my desk. Dealing with legacy storage media is increasingly challenging. Now that things are going to the cloud and physical storage is generally a problem for cloud providers to deal with that older storage media is increasingly becoming harder and harder to manage.
Cable used: StarTech USB3SSATAIDE USB to SATA IDE Adapter
Note: This adapter came with a stand alone power supply for the drive being connected. Keep in mind that this was extremely helpful to plug in the Zip 250 drive with power.
Zip 250 disk 1: It had files from 2008 and only two of them failed to transfer. Given that it has been about 12 years since the disk has been in a drive that is not bad.
Zip 100 disk 2: This was just some garbage software backups from 2002. This disk was totally useless, but the data did transfer off of it without any issues. That was surprising given that it was 6 years older than the disk with two file failures.
Zip 250 disk 3: The only file on this disk was a 4kb readme.txt from 1999. I mean obviously it had to be opened. It contained the, “Getting the Most out of Your Zip Disks,” instructions.
Zip 100 disk 4: This was just a bunch of 2002 documents related to coursework
Zip 250 disk 5: Strangely enough this disk was a different batch of 2002 and 2003 coursework documents.
Documents were backed up and then I ran the “Permanently erase with Webroot” command.