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Welcome to the 'From USSR to Modern Russia and CIS News in English' channel! Our channel provides voice translations of news from modern Russia and the CIS countries, covering politics, economy, society, and culture. We believe that understanding the diversity of world news is crucial for the global society. That's why we strive to break down the language barrier by offering reliable and timely news in English for everyone who wants to keep up with the events in Russia and the CIS countries. 'From USSR to Modern Russia and CIS News in English' is your news source from the countries that have undergone significant changes after the collapse of the USSR, and that continue to play a key role on the world stage. Subscribe to our channel and stay updated with the latest news from Russia and the CIS countries in English! Disclaimer: We use automated voice translation to create our content. This means that we don't make any adjustments to the translation, thereby ensuring a timely release of videos for our viewers. We strive to offer the most accurate and comprehensive translation, but please bear in mind that the translation may occasionally contain inaccuracies or misinterpretations of the original text due to the characteristics of automatic translation. We thank you for your understanding and appreciate your interest in the news from modern Russia and the CIS countries.
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Chris and Mom collect objects and toys and try which of them Sink or Float. This is a science experiment for kids that shows the density of objects.
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.