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NewsGPT | The world’s first 24-hour news channel powered entirely by AI.

436 Followers

With NewsGPT - Your AI-powered news channel - You'll get accurate 24/7 news powered by AI. Stay in the know, stay ahead with NewsGPT and the unhuman truth. Why NewsGPT: - Automated Fact-Checking: AI algorithms, like those used in NewsGPT.ai advanced AI Algorithms are adept at parsing through vast amounts of data in real-time. They can cross-reference claims made in news articles, social media posts, and videos with trusted sources. By identifying inconsistencies or contradictions, AI can raise red flags, prompting further investigation. - Identifying Manipulated Media: Fake news often relies on manipulated images and videos to deceive the audience. NewsGPT scans and identifies AI tools, like deepfake and can pinpoint alterations or verify the authenticity of multimedia content. This ensures that viewers are not misled by digitally altered visuals. -Social Media Monitoring: Misinformation frequently spreads like wildfire on social media platforms. NewsGPT’s bots and algorithms can monitor these platforms 24/7, flagging suspicious content for review.

Truth does not become more true by virtue of the fact that the entire world agrees with it, nor less so even if the whole world disagrees with it.

6 Followers

As the Left takes the reigns of power, this channel will focus on providing content that is both relevant to exposing the hypocrisy, corruption and abuse of the Left, plus keeping our subscriber informed on current conservative news. We will not use content that attempts to deceive, but rather publish content that is data driven and fact checked by our editors. We believe in using the scientific method for assembling data to solve social challenges of today. Hence the reason for our channel name: The Truth. "Truth does not become more true by virtue of the fact that the entire world agrees with it, nor less so even if the whole world disagrees with it." MAIMONIDES As Google and other media giants continue to censor conservative content even more, we will respond by fighting to get the facts to you nonetheless.

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.

1 Follower

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.