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Squaring The Circle, A Randall Carlson Podcast Verified

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Welcome to the Randall Carlson channel! Squaring The Circle Podcast is produced by Randall Carlson Media as a vehicle for Randall to explore and present unique, original and powerful content on a wide variety of subjects without limitation. ❤️ ADD FREE EPISODES: Watch full episodes of Squaring The Circle AD FREE on howtube.com with a subscription -- LINK: https://www.howtube.com/channels/SquaringTheCircle#tab_video_series For those familiar with Randall's work, you can of course expect this podcast to feature his extensive knowledge in all areas of his expertise, but that's not all. Randall will also play host to some of the finest minds of our time in exclusive interviews, where he and his guests will tackle the most complex and controversial issues facing our world today. Join Randall in ‘Squaring The Circle’; an endeavor to "reconcile the irreconcilable" using reason, rationale and critical thinking. 🔥 NEED A GREAT CBD SUPPLIER? OUR CBD SPONSOR OFFERS MY VIEWERS FREE SHIPPING FOR LIFE: 🌿 Use Code-- RCSHIPSFREE https://cbdfromthegods.com For everything Randall, visit RANDALLCARLSON.COM

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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.