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ALL-IN-ONE Fleet Management, ELD & Asset Tracking Solution

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Ezlogz is a fleet management company that specializes in electronic logging devices (ELD). Easy-to-use ELD app includes an electronic logbook (elog), truck GPS navigation, Hours of Service (HOS) violation alerts, trip planner, chat, etc. It is considered to be the first provider of blockchain-based electronic logging devices in the US. Our mission is to make the truckers' life on the road comfortable and easy. Subscribe to our channel to keep truck drivers & fleet managers informed about: * latest news from freight industry; * FMCSA, Hours of Service (HOS) rules; * ELD devices with unique and useful features; * dashcam for trucks with the latest AI technologies; * training how to use the Ezlogz ELD app & dashboard; * new features of our products (external API for third-party integrations, etc.); * our new products like load board to book better loads & earn more with Ezloadz. Subscribe to our channel and reach your business goals with Ezlogz!

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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.