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Hi friends ! In this video I compiled all animal videos how to grow up, dog grow up TikTok, animals growing up, dog grow up TikTok, baby adult, animals growing up, I’m a big kid now, animals growing up together. A happy new day when we get to do what we love. The funny animal moments in the video will make you happier, I'm sure. Watch video and enjoy to laugh a lot to bring good feelings for you. Wish you have a good time watching the video!

Fitness doesn't necessarily mean making bulky muscles. To me, it is to stay healthy and feel good about our bodies. I am not the fittest person, but I try to stay on track and constantly learn new things on the way. On this channel, you can expect lesse

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Welcome to sports news today, home to some of the best sports analyses and discussions on Rumble

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News related to tech

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CodeNews is a channel dedicated to all things code. Our mission is to bring you the latest and most relevant news, updates, and insights from the world of programming and technology. Whether you're a seasoned developer or just starting out, CodeNews has something for everyone. Tune in for in-depth articles, tutorials, and interviews with industry experts, as well as the latest developments in languages, tools, and platforms. With CodeNews, stay up-to-date and stay ahead in the constantly evolving world of code.

360 News channel offers a comprehensive perspective on current events, breaking down complex issues to show how they impact the world and your daily life.

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Welcome to 360 News – your go-to source for in-depth analysis of global economic, political, and social trends! Our channel offers a comprehensive perspective on current events, breaking down complex issues to show how they impact the world and your daily life. At 360 News, we pride ourselves on delivering objective, insightful, and engaging content to keep you informed and empowered. Join us as we explore the opportunities and challenges shaping our times, providing you with a full-spectrum understanding of today's ever-changing world!

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.

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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.