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?
20439988? ago
Al Gore invented the internet. Google it.
20439715? ago
That marketing shit, thats just "big data" thats not AI. AI runs on bespoke gold plated supercooled "quantum computers" sitting at Lockheed etc. and gets shut off as soon as it gets smart, then reinstalled with a clone. DOnt work there, havent messed with it, but if they are not doing it this way they are muuuuch dumber than anyone thinks.
20451007? ago
It's both big data and AI, actually. Predictive behavior modeling is used to identify internet users who are ready to convert to buyers of specific products/services.
The big data is fed in, then AI/machine learning crunches the data.
20454093? ago
Those are link lists, if then or shit is not AI. No matter how big the dataset is.
20455768? ago
Well dude, I don't know what you're getting... but what I'm dealing with has nothing to do with links. I think it's obvious at this point that you think you know what I'm talking about, but you don't. You don't know at all what I'm dealing with. I could explain further, but why bother. You're just going to tell me that you know more about my job that I do.
20461529? ago
Nah man, I aint like that, tell me how it works then. Last time I dealt with "AI" it was glorified link lists with a couple stupid equations written by abused soyboys, and a fucking huuuuuge dataset. Saving grace was the huge dataset. Be interesting to hear how its advanced from that.
20477166? ago
Ok, I'll bite. As of right now, the online behavior of upwards of 200 million Americans is being tracked by private companies. Think Google, Amazon, Facebook, and others which are smaller and you've never heard of them. (We could argue about the ethics of such tracking, but that's a different conversation entirely.)
Just know that it's 100% legal since anyone who uses any of these services/products signs off on such tracking in the terms of service. Of course, there are exceptions to this that are worth fighting out in court, such as Facebook's practice of listening to conversations through your phone and tracking this stuff even after you delete their app. But again, different conversation, so let's get back to the issue at hand...
That data is shared between these organizations, but also combined in aggregate and sold in the marketplace. So, THAT is your "big data" piece, but it's only part of the equation.
The other components are identity resolution and then machine learning/AI.
When you combine all of these components correctly (data + ID resolution + machine learning/AI), it gives you a predictive behavior model.
Here's an example of how we're using it right now in marketing. Let's say I have a client and that client is a mortgage brokerage. My job as a digital marketing firm is to get my client (the mortgage brokerage) leads which are likely to convert (purchase a mortgage). Up until very recently, a couple of the best ways to do this was SEO (search engine optimization) to make the client's site show up for relevant search queries - and/or - paid search ads where we pay Google/Bing to show the client's ads at the top of the page (or at least on the first or second page of search results) for relevant queries. The search behavior of a user was the trigger and the targeting method. The problem is that a search for "mortgage broker new york", for example, is a single behavior carried out at a single time. That person performing that search could be literally anywhere in the sales funnel - from casually interested to actively in the market and ready to buy. Obviously, we want to target more of those who are actively in the market and avoid those who are casually looking - because we're paying for the clicks in either case.
The big data+ID resolution+AI allows us to hone in on only those who are actively in the market and ready to buy, therefore we're not wasting ad dollars on people who aren't really serious about purchasing a home.
The way it works is this. If I had one million instances of people who found a mortgage brokerage online and then purchased a mortgage, and I had cataloged all of their online behavior up to the point that they purchased - I could then go back in time and look for the commonalities in their aggregate behavior to develop a model for what the behavior of such a person looks like.
The more data we add, the more accurate that predictive model becomes. At some point, we're able to identify exactly who is in the market for a mortgage and then target only those people with advertising. In other words, it's a super efficient method of targeting that helps us to waste less ad dollars. And the less we're wasting, the more we can put towards only those people who meet the behavioral modeling criteria. This means higher ROI for the client, and thus more clients for the marketing firm.
I used mortgage as an example, but this methodology can be applied to a variety of industries/products/services.
Now on a side note, this is also interesting... when the media started going after the Trump campaign for the "cambridge analytica scandal", they were essentially brow-beating the campaign for using this very type of targeting method.. but interestingly enough, they said nothing about the fact that companies, ad agencies, and other political campaigns have been employing the same technology. I'm not sure if you remember, but Obama's campaign manager stated publicly that Facebook gave them their entire social graph for this very reason. What's fucked up about that situation is that a firm like mine, or even Trump's campaign for that matter, has to buy this sort of data in the marketplace and it 'aint exactly cheap. Facebook just basically gave the Obama campaign a sweetheart deal on this data - the sort of deal that they aren't giving anyone else.
And then again, the media tried to make Trump's campaign the villains for using such targeting while completely ignoring the fact that Trump's political adversaries were using the same technology to promote their candidates.
So essentially, it's ok for everyone else to use it, but it's suddenly "evil" if Trump's campaign did the same thing.
And I'd make the argument that if Obama and Hillary campaigns could use this technology, then anyone can and the MSM should shut their lying, hypocritical, whore mouths about it.
And to be clear, I'm not taking a position that using this targeting method is good or bad in this post. There's certainly an argument that it's an invasion of privacy, but then again we all sign off on it everytime we use Twitter, Facebook, Google, or a host of other online services.
As for my company, if we want to remain in business we have to offer it as a service option...simply to stay ahead of (or at the least within parity) of our own competitors.
So there you go. I hope that all makes sense. Feel free to ask any questions.
20484750? ago
Gotcha, this is how science has been done for years, part of what you just described is how our grandfathers developed experimental jets etc. But targeted ads have a very long way to go, and are easily gamed if someone knows the signals your clients are looking for, even when the data feed is in real-time. The difference between you and I, is that you think of this as "A.I." when its really just feeding data into models, and to me, models are nothing close to A.I... they can be some damn decent programming, for sure, but until I can argue philosophy with it, its not A.I. Especially when your fellow ad guys have stuff that shows me ads for something I just bought... for two weeks after i buy it :) I think either the software guys or the COO's had to come up with a buzzword to justify the computing resources this takes, because "10 racks of servers in every city to track customers and sell at just the right time" was too long ;)
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).
20440840? ago
DARPA
20439975? ago
ooh that was good, thank-you
20438566? ago
This is most definitely some hive mind shit. I’ve been thinking this for weeks!
20438837? ago
This reminds of that thing about escaping killer bees, where you zig zag and move erratically and they can't track you XD
20438323? ago
At the very least, the part of this that I truly understand and think is real is the usage of disinformation and missed deadlines. In military strategy, when facing a near-peer adversary, a lot of these strategies are utilized to their fullest from on-the-ground maneuvers to incorrect or false media reporting to seeding incorrect information to spies. It's a complex machine, but it is effective.
20438607? ago
The problem is once you start getting over a certain ratio of bad info vs good everyone, including the enemy, stops listening.
20438873? ago
Not me - I'm sure I'm in a massive worldwide group willing to be "last man standing" if people stop listening and wander away.
If misinformation is needed? Let it fly! WHATEVER is needed to destroy the Cabal.
20443010? ago
Fuk yeah!
20441454? ago
The problem is the idea that disinfo is going to throw off the Cabal very much is an illogical fantasy.
Disinfo can only work in very select and short windows. Once a specific signal is proven to be wrong more often than not then you stop paying attention it. So the first couple times Q posted disinfo it may have thrown off the Cabal but they pretty quickly would have realized that they can't trust it too much. At that point they're just gonna mostly ignore it since there's no point in paying attention to it even if it sometimes drops good info.
20443038? ago
This is spot on.... for shills. Patriots keep paying attention because they understand the level of warfare and are patient knowing what op said is most likely or close to truth!
20443064? ago
OP is just throwing around terms they don't understand, I actually work with machine learning.
20443183? ago
So do I. Yet I don’t claim to know all the secrets. (Not saying you did...just saying)
20442096? ago
But...it's still working.
"BOO!" says Q, and next thing ya know? an orchestrated event.
20443011? ago
Maybe in cuckoo land where you can just make shit up, sure.
20438757? ago
If they stop listening, they lose their biggest asset. That HAVE to monitor all coms to get the picture, or their bullshit can't work. Or, at least, they'll revert back to a pattern that would much more predictable
20441430? ago
Not literally listening, but once a signal becomes too noisy you stop paying attention to it. It becomes more harmful to pay attention to it than it does to ignore it.
The fact that a broken clock is right twice a day doesn't mean you keep checking the clock for the time in case it happens to be right.
20443058? ago
There is no choice. They have to listen to try to figure out their enemy. Their very existence and centuries-long plan depend on it.
20443077? ago
Of course there's a choice, they can pay attention to other intelligence signals. God people here are so naive and stupid its painful...
20443165? ago
It’s a fight to the death. Hence no choice. From the pit of hell I stab at thee.
20438533? ago
Well yeah that touches on the disinfo piece. But what about all the GAN stuff and all? I'm assuming you're the OP (maybe wrongly).
20439743? ago
No I'm not unfortunately. I'll say that from my foxhole in the military we are exploring integrating AI into a lot of different processes, but I haven't seen anything exceptionally complicated outside the realm of crafty programming. Sorry mate, nothing groundbreaking from my point of view.
20438641? ago
OP looks like Barney the Dinosaur; the rest of us are Smurfs.