>>About the 33rd largest supercomputer in the world right now is the US Air Force Research Laboratory's (AFRL) newest system, which has a core made of 1,760 Sony PlayStation 3 (PS3) consoles. In addition to its large capacity, the so-called "Condor Cluster" is capable of performing 500 trillion floating point operations per second (TFLOPS), making it the fastest interactive computer in the entire US Defense Department.
>>It will be used by Air Force centers across the country for tasks such as radar enhancement, pattern recognition, satellite imagery processing, and artificial intelligence research.
A super majority is a specified proportion that is in excess of a simple majority (e.g., where a simple majority would be 51%, a super majority might be defined as 60%). Per your link (which is admittedly interesting), military/defense only accounts for about for 770k of 2.2M full time federal employees. That is not a super majority, that is a plurality (the single largest group but not a majority).
The supermajority is in reference to both military/defense, though I could see how that could be very confusing wording with the "military-defense" category also on that site. The difference being non-military level defense numbers were not included in your calculation of the pularality. Non military defense also at least includes Department of Homeland Security, for example. Between those two you've already left plurality for majority. To get to the supermajority you need to bring in the department of veterans affairs. Note these 3 compose the top 3 offices in terms of employment.
Now I suppose you could argue the latter as bureaucracy as it's not direct defense but even then it's sole purpose is to provide lifelong healthcare support and benefits for the military personnel figure, not just random bureaucratic growth. Since that site didn't actually categorize the types of office, this reference includes similar defense categories as I listed above (though it also includes the Department of Justice, so gets a slightly higher percentage than my mind at the low 60% area): pdf warning https://ourpublicservice.org/wp-content/uploads/2022/03/FedF...
Actually, I believe what you want is 3mm, which I believe they use in accounting. Lowercase m in this instance would stand for milli-, as in thousand. So 3mm would be 3 thousand thousand. 3M is technically correct, though confusing in this specific case. Capital M would indicate Mega, as in the progression from kilobit to megabit to gigabit.
To chime in with the others, my Chase card offers this service automatically as well. I think most major credit card companies are starting to offer these services because they want a piece of the pie that companies like Intuit (through Mint, now Credit Karma) are grabbing by just siphoning up financial data.
So after re-reading your comment a few times, I am left with this thought: either you don't understand what k-means clustering is, or I don't understand what k-means clustering is. I wouldn't describe myself as a machine learning expert, but I have taken some grad level classes in statistics, analytics, and methods like this/related to this.
Not GP, but I understood their question as follows.
Assume you collect some kindergartners and top NBA players into a room and collect their heights. Now say you pass these to two hapless grad students and ask them to
perform K-means clustering.
Suppose one of the grad students knew the composition of the people you measured and can guess these height should clump into 2 nice clusters. The other student who doesn't know the composition of the class - what should they guess K to be?
I understood the GP's comment to refer to the state of the second grad student. How useful is K-means clustering without knowing K in advance?
>I understood the GP's comment to refer to the state of the second grad student. How useful is K-means clustering without knowing K in advance?
There are several heuristics for this. Googling I see that the elbow method, average sillhouette method and gap statistic method is the most used.
I think you could play around with your own heuristics as well. Simple KDE plots showing the amount of peaks. Maybe, say the variance between clusters should be greater than the variance inside any cluster could maybe work. (Edit: this seems to be the main point of the average sillhouette method).
The first problem is picking k. The second problem is the definition of distance or, equivalently, the uniformity of the space. Naively using the Euclidean distance in an embedding where similarity is non-uniform leads to bad outcomes. This problem is solved by learning a uniform embedding, and this is much harder than running k-means.
k-means assumes these hard parts of the problem are taken care of and offers a trivial solution to the rest. Thanks for the help, I'll cluster it myself.
You have to choose the number of clusters, before using k-means.
Imagine that you have a dataset, where you think there are likely meaningful clusters, but you don't know how many, especially where it's many-dimensioned.
If you pick a k that is too small, you lump unrelated points together.
If k is too large, your meaningful clusters will be fragmented/overfitted.
There are some algorithms that try to estimate the number of clusters or try to find the k with the best fit to the data to make up for this.
Couldn’t you make some educated guesses and then stop when you arrive at a K that gives you meaningful clusters that are neither too high level nor too atomized.
>>Companies want to dump all their Excels in it and get insights that no human could produce in any reasonable amount of time.
>>Companies want to dump a zillion help desk tickets into and gain meaningful insights from it.
>>Companies want to dump all their Sharepoints and Wikis into it that currently nobody can even find or manage, and finally have functioning knowledge search.
Mature organizations already have solutions for all of these things. If you can't mine your own data competently, you've got bigger problems than not having AI doing it for you. It means you don't have humans who understand what's going on. AI is not the answer to everything.
Well, they're still there, they just don't get a platform and the creators get doxxed and hounded off the internet. A fairly recent example was a(nother) school shooter game.
They're trolling, they're trying to create a logical knot by pitting different values they perceive people to hold against each other to try and create contradiction and confusion. But it's easily untied by recognizing they don't have any interesting point to make and we can just ignore them.
>>Distal limb injection is recommended to facilitate amputation with 5 cm margins in two fascial planes in the case of injection-site sarcoma.
>>Tail vaccination has also been reported as well tolerated and elicited acceptable serological responses to vaccination in the distal limbs.
>>The 2013 AAFP Feline Vaccination Advisory Panel Report included recommendations for specific vaccine antigens to be administered at specific anatomical locations in the distal limbs. This technique has helped facilitate the identification of the vaccine antigen used if a sarcoma developed subsequently at the injection site. Since this technique has been widely adopted, these injection-site recommendations have also led to a shift in the site of tumor formation to the distal limbs, thus facilitating potentially life-saving surgery for patients suffering from these invasive tumors.
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If you have better information, please provide it. This has nothing to do with the concept that vaccines are bad, and everything to do with the concept that evidence-based (vet) medicine saves (animal) lives.
