Infowars - BANNED.video
258,241 FollowersThe most banned network in the world! INFOWARS.COM & BANNED.VIDEO
The most banned network in the world! INFOWARS.COM & BANNED.VIDEO
This channel contains a list of noteworthy videos going against the grain of Big Tech, Big Pharma and Main Stream Media establishments. Like many of you who have grown tired of been force fed the so-called "narrative", "Banned Youtube Videos" attempts to fill this void that the global elitists deliberately keeps from you, the viewing public. If this channel can better inform you, then this channel is serving its purpose. Any tips on videos that you would like to be shown on "Banned Youtube Videos" are most welcome.
Check out the truth the radical left don't want you to know Visit Our Site: https://christiansmatters.com It's Free Christians' sermons and posts you will enjoy I'm sure.
Welcome from Ken Owen. Here you’ll find clips from my career (I was a news anchor/reporter) as well as odds and ends that are designed to be enjoyed with nostalgia in mind. All copyrights acknowledged. All videos are uploaded for historical and educational purposes only and can be removed if needed or may be updated. All material is of varying quality (audio and video) based on original source material.
These series of videos were originally posted to YouTube by Anonymous Charity, also known as The Collective. They were, of course, censored. Other users preserved their hardwork and message. This is a repository of that effort.
These are my videos that Youtube banned
I Can Show You The Random Animals Video
This is for Kent Hovind's videos. Please go check out his YouTube channel. This account is just reserved for him.
A collection of stories
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government corruption exposed
BannedVideos
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Here is the most Banned Videos of MHFM (vaticancatholic.com).
Occasional witty thoughts and occasional looks at video games…
These are videos to wake us up to reality and truth. Some serious and some funny!
Collection of Banned Videos
Videos of events at the Derby Radio Control club taken by members or Ken of Kansas Digital Images of Kansas.
Due to the possibility of being censored on certain social media platforms I will be posting anything that will be removed due to TikTok's Guidelines over here.
What [THEY] don't want YOU to SEE
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International Trump’s fan club. In 2020, YouTube banned Donald Trump's account. We have restored this account! Watch the video! Glory to Trump! Nothing else matters!
California photographer and videographer with years of creative experience. I turn precious moments into lifelong memories - creating timeless, heartfelt, and exceptional content that will last a lifetime. My greatest pride and joy in life is watching your stories come to life.
We’ve discovered neurons in CLIP that respond to the same concept whether presented literally, symbolically, or conceptually. This may explain CLIP’s accuracy in classifying surprising visual renditions of concepts, and is also an important step toward understanding the associations and biases that CLIP and similar models learn. Fifteen years ago, Quiroga et al.1 discovered that the human brain possesses multimodal neurons. These neurons respond to clusters of abstract concepts centered around a common high-level theme, rather than any specific visual feature. The most famous of these was the “Halle Berry” neuron, a neuron featured in both Scientific American(opens in a new window) and The New York Times(opens in a new window), that responds to photographs, sketches, and the text “Halle Berry” (but not other names). Two months ago, OpenAI announced CLIP, a general-purpose vision system that matches the performance of a ResNet-50,2 but outperforms existing vision systems on some of the most challenging datasets. Each of these challenge datasets, ObjectNet, ImageNet Rendition, and ImageNet Sketch, stress tests the model’s robustness to not recognizing not just simple distortions or changes in lighting or pose, but also to complete abstraction and reconstruction—sketches, cartoons, and even statues of the objects. Now, we’re releasing our discovery of the presence of multimodal neurons in CLIP. One such neuron, for example, is a “Spider-Man” neuron (bearing a remarkable resemblance to the “Halle Berry” neuron) that responds to an image of a spider, an image of the text “spider,” and the comic book character “Spider-Man” either in costume or illustrated. Our discovery of multimodal neurons in CLIP gives us a clue as to what may be a common mechanism of both synthetic and natural vision systems—abstraction. We discover that the highest layers of CLIP organize images as a loose semantic collection of ideas, providing a simple explanation for both the model’s versatility and the representation’s compactness.
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