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SUBSCRIBE FOR A COOKIE! Accomplishments: - Raised $20,000,000 To Plant 20,000,000 Trees

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SUBSCRIBE FOR A COOKIE! Accomplishments: - Raised $20,000,000 To Plant 20,000,000 Trees - Removed 30,000,000 pounds of trash from the ocean - Built wells in Africa - helped 1,000 blind people see - helped 1,000 deaf people hear - Given millions to charity - Started my own snack company Feastables - Donated over 100 cars lol - Gave away a private island (twice) - Gave away 1 million dollars in one video - Counted to 100k - Read the Dictionary - Read Bee Movie Script - Read Longest English Word - Watched Paint Dry - Ubering Across America - Watched It's Every Day Bro For 10 Hours - Ran a marathon in the world's largest shoes - Adopted every dog in a shelter - Bought $1,000,000 in lottery tickets - Sold houses for $1 - I got buried alive - Recreated Squid Game in real life - Gave away a chocolate factory - Gave away private jet - Survived 50 hours in Antarctica You get the point ha

Baby dogs _ cut and funny dogs videos compilation #20

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Hi all friends! Welcome to Baby Awesome, where you can find the best and the funniest baby videos in the world. Our channel shares the content of hilarious videos compilations. Enjoy the video, and comment what you like the most. Follow and subscribe my channel! Baby Awesome love you!Watching funny baby dogs is the hardest try not to laugh challenge. Baby dogs are amazing creature because they are the cutest and most funny. This is the cutest and best video ever. It is funny and cute! Hope you like our funny compilation and don\'t forget to SUBSCRIBE us and share with your friends!Watching funny baby dogs is the hardest try not to laugh challenge. Baby dogs are amazing creature because they are the cutest and most funny. This is the cutest and best video ever. It is funny and cute! Hope you like our funny compilation and don\'t forget to SUBSCRIBE us and share with your friends!all is well thinks see my channel videos.

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.