Shills complain and moan about the amount of disinformation that Q has put out. Most of us do not understand (or care) about game theory and its usage in AI. Facebook/Google have developed very sophisticated and powerful Generative Adversarial Networks (GANS) that the cabal has been using to suppress dissent and keep us under control. The Military has been pushed out of this technology, but under POTUS, the Q team has been unleashed to not just win the battle, but destroy them forever using their own tech. Ever wonder why POTUS makes so many, seemingly, erratic moves. Saying something then coming back a couple days later and reversing himself. Same with missed deadlines and erratic Q team behavior. It is all Game Theory. Discriminator in the enemy GAN gets completely confused as what is real and what isn't. It therefore, cannot predict Q teams next move. Meanwhile, Q team is able to make moves and perfect its prediction of counter moves using disinformation and headfakes. We are stopping more and more of their mass shootings and are able to cut the legs out from under their media hype with ease. Eventually the battle will not just be over, but the enemy will be toothless and neutered. Destruction is the desired outcome.
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20437377? ago
I've felt like I innately just have a gut feeling that this is true, or at least very close to the truth.
I don't know why, except to say that I do have somewhat of a decent idea of how far AI has come (we use it in digital marketing like crazy anymore in a variety of ways) and I have to assume that the government (who invented the internet to begin with) uses the most advanced version(s) of it for all sorts of shit, not the least of which being something so big as waging war/controlling people.
Just curious, where (if anywhere at all) are you getting this info, OP?
20438784? ago
Keep in mind the "government" is not separate from FB/Google, these things were born out of the CIA with taxpayer funds. The deep state is, or has been until now, the de-facto "government". The Old Guard.
Q is the spiritual forces at the next level above this. Angels. "We have everything."
I don't think it's AI vs AI.
It's AI vs II (infinite intelligence). II = God. God wins.
20445830? ago
Pardon me while I semi-hijack this high-ranked post. Downvoat if you must, but as someone who knows more than a little something about this field, I'd like to point something out. The "adversarial" in GAN specifically refers to 2 competing networks only within the scope of training the discriminator network used to then predict how likely seen features are, given some assumption of the post (in this case). The generator network creates data and the discriminator network then evaluates the data and likelihood of it being in the training dataset. Think of it as network co-evolution. This adversarial training all happens in one scope (i.e. the deep state, google, facebook, whichever organization or group is building the network).
OP's post is still correct no matter what kind of training method used to build a network. You can simply substitute "ML network" or "AI network" for "GAN" and the info in the post still stands. Do not get hyper focused on GANs and do not mistake the "adversarial" name as being inherently bad.
Again, sorry for the hijack, but this needed to be seen and not lost at the bottom of ~150 posts.
20448199? ago
Shit in Shit out.
20450169? ago
100% true. Bad training data makes bad models.
20446643? ago
Fren, you need to define some of these terms and give us a little more of an introduction... bridge the gap for the laypeople.
I mean, even if I'm a pretty experienced software guy, I have no experience in the machine-learning domain so I can't follow what you're saying.
20446934? ago
Apologies.
Machine learning is how to get computers to classify or predict things that our brains take for granted, because it's so easy for us. What is built is a model, and in many cases you can think about a model as a black box that takes many inputs (the object has 4 mag wheels, a steering wheel, manual transmission, 2 seats, 8 cyl engine) and classifies or predicts what that object is: a sports car. This task is (used to be) complex for a computer, but easy for our brain. Advances in machine learning libraries (such as scikit-learn, keras and others) make these sorts of classifications easy to program now.
There's a branch of machine learning called "deep learning" and it involves synthetic neural networks made up of "neurons." The neurons have different properties. Some amplify a signal. Some have a short term memory. There are many others. The key part of these neural networks are the pieces and how they are put together. Initially, researchers put them together by hand, but very shortly they started building programs that would build neural networks. Then came the neural networks that would build neural networks. It's tools like these that underlie things like generative adversarial networks (GANs).