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Make Your Own Destiny

16  Followers

Welcome to 'Make Your Own Destiny Talk,' the podcast that empowers you to take control of your life and create the future you desire. Join us each week as we explore the stories and strategies of successful entrepreneurs, thought leaders, and visionaries who have taken charge of their destinies and achieved their dreams. Through in-depth interviews and expert insights, we'll share practical tips and inspiration to help you navigate challenges, overcome obstacles, and chart your own course to success. Whether you're starting a business, pursuing a passion, or seeking personal growth, 'Make Your Own Destiny' is your guide to creating the life you truly want. Follow us on Instagram/TikTok/Twitter @MYODPodcast

Be Your Own Boss

6  Followers

Welcome to Be Your Own Boss , the ultimate destination for aspiring and seasoned entrepreneurs alike! This channel is your go-to resource for actionable insights, inspiring stories, and practical strategies to help you take control of your career, create financial freedom, and design a life on your terms. Whether you're launching a startup, scaling your business, or simply dreaming of independence, we’re here to guide you every step of the way. From mindset shifts and productivity hacks to marketing tips and leadership lessons, Be Your Own Boss equips you with the tools to overcome challenges, seize opportunities, and unlock your full potential. Join our community of bold thinkers and changemakers as we explore what it truly means to be unstoppable in business—and in life. Subscribe now and start your journey toward becoming the CEO of your dreams! 💼🚀 #BeYourOwnBoss

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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.