Sing Along Tunes - Audio Lyrics - Lyric Video's - Songs with Lyrics - Karaoke - Music Video's - Music with Lyrics - Sing Along Songs-

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300+ of my favorite Sing Along Tunes - Audio Lyric Video's, to sing around a campfire or after a dinner party, or just after a few drinks with friends, I know I'm showing my age but these songs never grow old, It's also interesting how many words I have been singing wrong over the years, who doesn't love Karaoke please follow, like & share these awesome Audio Lyric Video's .... enjoy!

ALL TYPE SHORT LONG VIDEOS & STATUS (@darklove5525)

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Welcome to my channel! Here, you'll find a collection of short video statuses that capture the essence of different emotions, moods, and occasions. From funny and lighthearted to heartfelt and emotional, my videos are designed to evoke feelings and make you smile. Whether you're looking for a quick pick-me-up during a busy day, a way to express your feelings to someone special, or just want to share a moment with friends and family, my video statuses are perfect for any occasion. So sit back, relax, and enjoy the ride as we take a journey through the ups and downs of life through the magic of video statuses. Don't forget to hit that subscribe button to stay up-to-date with my latest uploads!

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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Weā€™ve discovered neurons in CLIP that respond to the same concept whether presented literally, symbolically, or conceptually. This may explain CLIPā€™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 ā€œHalle Berryā€ neuron, a neuron featured in both Scientific Americanā (opens in a new window) and The New York Timesā (opens in a new window), that responds to photographs, sketches, and the text ā€œHalle Berryā€ (but not other names). Two months ago, OpenAI announced CLIPā , 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 modelā€™s robustness to not recognizing not just simple distortions or changes in lighting or pose, but also to complete abstraction and reconstructionā€”sketches, cartoons, and even statues of the objects. Now, weā€™re releasing our discovery of the presence of multimodal neurons in CLIP. One such neuron, for example, is a ā€œSpider-Manā€ neuron (bearing a remarkable resemblance to the ā€œHalle Berryā€ neuron) that responds to an image of a spider, an image of the text ā€œspider,ā€ and the comic book character ā€œSpider-Manā€ 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 systemsā€”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 modelā€™s versatility and the representationā€™s compactness.