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Elite Justice Force Squad Book Series Vlogs

7  Followers

Welcome to my Rumble channel! This platform will be my mainstay for video content containing updates from Michael J. Beasley, the author/creator of the Elite Justice Force Squad series. I will post videos periodically and semi-regularly to update my fans and followers on upcoming titles and book release news. I am also on Goodreads, where I post monthly blogs on my profile. If you want to find more information about my literature, please check out my social media links.

The Eighth Square

2  Followers

I’m a former intersectional feminist and social justice leftist. Currently I’m best described as a libertarian, free speech absolutist, and personal responsibility advocate. For those obsessed with identity labels, I’m bisexual, latina, poly...yeah, I’m not your typical anti-feminist. In my videos, I critique feminism and the modern left, review feminist books, and give non-feminist perspective on media. I also share interesting experiences from my ongoing intellectual journey. I’m quite a reasonable human, and welcome constructive criticism and civil disagreement.

Squatch Watch Official | Bigfoot & Yeti Encounters, Vlogs, and Stories

4  Followers

👣 Welcome to Squatch Watch Official — your all-access pass to the wild world of Bigfoot, Yeti, and legendary cryptids! From AI-generated Bigfoot vlogs to real-life sighting stories, trail cam footage, mysterious forest encounters, and fun cryptid content — we cover everything Sasquatch. 🧠 Whether you're a believer, a skeptic, or just love a good mystery, this is where fact meets folklore and the forest comes alive. 🎥 Expect: Real and AI-created Bigfoot & Yeti videos Witness accounts and sighting breakdowns Paranormal encounters and forest legends Original stories, shorts, and skits Wild speculation… and even wilder creatures! 🔔 Subscribe and follow — because when you’re watching the woods... something might be watching back.

Users can generate videos up to 1080p resolution, up to 20 sec long, and in widescreen, vertical or square aspect ratios. You can bring your own assets to extend, remix, and blend, or generate entirely new content from text.

12  Followers

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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